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hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/spectrogram_diffusion/pipeline_spectrogram_diffusion.py | # Copyright 2022 The Music Spectrogram Diffusion Authors.
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import T5FilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
TARGET_FEATURE_LENGTH = 256
class SpectrogramDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline for unconditional audio generation.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
notes_encoder ([`SpectrogramNotesEncoder`]):
continuous_encoder ([`SpectrogramContEncoder`]):
decoder ([`T5FilmDecoder`]):
A [`T5FilmDecoder`] to denoise the encoded audio latents.
scheduler ([`DDPMScheduler`]):
A scheduler to be used in combination with `decoder` to denoise the encoded audio latents.
melgan ([`OnnxRuntimeModel`]):
"""
_optional_components = ["melgan"]
def __init__(
self,
notes_encoder: SpectrogramNotesEncoder,
continuous_encoder: SpectrogramContEncoder,
decoder: T5FilmDecoder,
scheduler: DDPMScheduler,
melgan: OnnxRuntimeModel if is_onnx_available() else Any,
) -> None:
super().__init__()
# From MELGAN
self.min_value = math.log(1e-5) # Matches MelGAN training.
self.max_value = 4.0 # Largest value for most examples
self.n_dims = 128
self.register_modules(
notes_encoder=notes_encoder,
continuous_encoder=continuous_encoder,
decoder=decoder,
scheduler=scheduler,
melgan=melgan,
)
def scale_features(self, features, output_range=(-1.0, 1.0), clip=False):
"""Linearly scale features to network outputs range."""
min_out, max_out = output_range
if clip:
features = torch.clip(features, self.min_value, self.max_value)
# Scale to [0, 1].
zero_one = (features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def scale_to_features(self, outputs, input_range=(-1.0, 1.0), clip=False):
"""Invert by linearly scaling network outputs to features range."""
min_out, max_out = input_range
outputs = torch.clip(outputs, min_out, max_out) if clip else outputs
# Scale to [0, 1].
zero_one = (outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def encode(self, input_tokens, continuous_inputs, continuous_mask):
tokens_mask = input_tokens > 0
tokens_encoded, tokens_mask = self.notes_encoder(
encoder_input_tokens=input_tokens, encoder_inputs_mask=tokens_mask
)
continuous_encoded, continuous_mask = self.continuous_encoder(
encoder_inputs=continuous_inputs, encoder_inputs_mask=continuous_mask
)
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def decode(self, encodings_and_masks, input_tokens, noise_time):
timesteps = noise_time
if not torch.is_tensor(timesteps):
timesteps = torch.tensor([timesteps], dtype=torch.long, device=input_tokens.device)
elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
timesteps = timesteps[None].to(input_tokens.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timesteps = timesteps * torch.ones(input_tokens.shape[0], dtype=timesteps.dtype, device=timesteps.device)
logits = self.decoder(
encodings_and_masks=encodings_and_masks, decoder_input_tokens=input_tokens, decoder_noise_time=timesteps
)
return logits
@torch.no_grad()
def __call__(
self,
input_tokens: List[List[int]],
generator: Optional[torch.Generator] = None,
num_inference_steps: int = 100,
return_dict: bool = True,
output_type: str = "numpy",
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
) -> Union[AudioPipelineOutput, Tuple]:
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
r"""
The call function to the pipeline for generation.
Args:
input_tokens (`List[List[int]]`):
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lead to a higher quality audio at the
expense of slower inference.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.AudioPipelineOutput`] instead of a plain tuple.
output_type (`str`, *optional*, defaults to `"numpy"`):
The output format of the generated audio.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
Example:
```py
>>> from diffusers import SpectrogramDiffusionPipeline, MidiProcessor
>>> pipe = SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion")
>>> pipe = pipe.to("cuda")
>>> processor = MidiProcessor()
>>> # Download MIDI from: wget http://www.piano-midi.de/midis/beethoven/beethoven_hammerklavier_2.mid
>>> output = pipe(processor("beethoven_hammerklavier_2.mid"))
>>> audio = output.audios[0]
```
Returns:
[`pipelines.AudioPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`pipelines.AudioPipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated audio.
"""
pred_mel = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims], dtype=np.float32)
full_pred_mel = np.zeros([1, 0, self.n_dims], np.float32)
ones = torch.ones((1, TARGET_FEATURE_LENGTH), dtype=bool, device=self.device)
for i, encoder_input_tokens in enumerate(input_tokens):
if i == 0:
encoder_continuous_inputs = torch.from_numpy(pred_mel[:1].copy()).to(
device=self.device, dtype=self.decoder.dtype
)
# The first chunk has no previous context.
encoder_continuous_mask = torch.zeros((1, TARGET_FEATURE_LENGTH), dtype=bool, device=self.device)
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
encoder_continuous_mask = ones
encoder_continuous_inputs = self.scale_features(
encoder_continuous_inputs, output_range=[-1.0, 1.0], clip=True
)
encodings_and_masks = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens]).to(device=self.device),
continuous_inputs=encoder_continuous_inputs,
continuous_mask=encoder_continuous_mask,
)
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
x = randn_tensor(
shape=encoder_continuous_inputs.shape,
generator=generator,
device=self.device,
dtype=self.decoder.dtype,
)
# set step values
self.scheduler.set_timesteps(num_inference_steps)
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
output = self.decode(
encodings_and_masks=encodings_and_masks,
input_tokens=x,
noise_time=t / self.scheduler.config.num_train_timesteps, # rescale to [0, 1)
)
# Compute previous output: x_t -> x_t-1
x = self.scheduler.step(output, t, x, generator=generator).prev_sample
mel = self.scale_to_features(x, input_range=[-1.0, 1.0])
encoder_continuous_inputs = mel[:1]
pred_mel = mel.cpu().float().numpy()
full_pred_mel = np.concatenate([full_pred_mel, pred_mel[:1]], axis=1)
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, full_pred_mel)
logger.info("Generated segment", i)
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
"Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'."
)
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
"Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'."
)
if output_type == "numpy":
output = self.melgan(input_features=full_pred_mel.astype(np.float32))
else:
output = full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=output)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/spectrogram_diffusion/notes_encoder.py | # Copyright 2022 The Music Spectrogram Diffusion Authors.
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.t5.modeling_t5 import T5Block, T5Config, T5LayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SpectrogramNotesEncoder(ModelMixin, ConfigMixin, ModuleUtilsMixin):
@register_to_config
def __init__(
self,
max_length: int,
vocab_size: int,
d_model: int,
dropout_rate: float,
num_layers: int,
num_heads: int,
d_kv: int,
d_ff: int,
feed_forward_proj: str,
is_decoder: bool = False,
):
super().__init__()
self.token_embedder = nn.Embedding(vocab_size, d_model)
self.position_encoding = nn.Embedding(max_length, d_model)
self.position_encoding.weight.requires_grad = False
self.dropout_pre = nn.Dropout(p=dropout_rate)
t5config = T5Config(
vocab_size=vocab_size,
d_model=d_model,
num_heads=num_heads,
d_kv=d_kv,
d_ff=d_ff,
dropout_rate=dropout_rate,
feed_forward_proj=feed_forward_proj,
is_decoder=is_decoder,
is_encoder_decoder=False,
)
self.encoders = nn.ModuleList()
for lyr_num in range(num_layers):
lyr = T5Block(t5config)
self.encoders.append(lyr)
self.layer_norm = T5LayerNorm(d_model)
self.dropout_post = nn.Dropout(p=dropout_rate)
def forward(self, encoder_input_tokens, encoder_inputs_mask):
x = self.token_embedder(encoder_input_tokens)
seq_length = encoder_input_tokens.shape[1]
inputs_positions = torch.arange(seq_length, device=encoder_input_tokens.device)
x += self.position_encoding(inputs_positions)
x = self.dropout_pre(x)
# inverted the attention mask
input_shape = encoder_input_tokens.size()
extended_attention_mask = self.get_extended_attention_mask(encoder_inputs_mask, input_shape)
for lyr in self.encoders:
x = lyr(x, extended_attention_mask)[0]
x = self.layer_norm(x)
return self.dropout_post(x), encoder_inputs_mask
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/spectrogram_diffusion/midi_utils.py | # Copyright 2022 The Music Spectrogram Diffusion Authors.
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import dataclasses
import math
import os
from typing import Any, Callable, List, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from ...utils import is_note_seq_available
from .pipeline_spectrogram_diffusion import TARGET_FEATURE_LENGTH
if is_note_seq_available():
import note_seq
else:
raise ImportError("Please install note-seq via `pip install note-seq`")
INPUT_FEATURE_LENGTH = 2048
SAMPLE_RATE = 16000
HOP_SIZE = 320
FRAME_RATE = int(SAMPLE_RATE // HOP_SIZE)
DEFAULT_STEPS_PER_SECOND = 100
DEFAULT_MAX_SHIFT_SECONDS = 10
DEFAULT_NUM_VELOCITY_BINS = 1
SLAKH_CLASS_PROGRAMS = {
"Acoustic Piano": 0,
"Electric Piano": 4,
"Chromatic Percussion": 8,
"Organ": 16,
"Acoustic Guitar": 24,
"Clean Electric Guitar": 26,
"Distorted Electric Guitar": 29,
"Acoustic Bass": 32,
"Electric Bass": 33,
"Violin": 40,
"Viola": 41,
"Cello": 42,
"Contrabass": 43,
"Orchestral Harp": 46,
"Timpani": 47,
"String Ensemble": 48,
"Synth Strings": 50,
"Choir and Voice": 52,
"Orchestral Hit": 55,
"Trumpet": 56,
"Trombone": 57,
"Tuba": 58,
"French Horn": 60,
"Brass Section": 61,
"Soprano/Alto Sax": 64,
"Tenor Sax": 66,
"Baritone Sax": 67,
"Oboe": 68,
"English Horn": 69,
"Bassoon": 70,
"Clarinet": 71,
"Pipe": 73,
"Synth Lead": 80,
"Synth Pad": 88,
}
@dataclasses.dataclass
class NoteRepresentationConfig:
"""Configuration note representations."""
onsets_only: bool
include_ties: bool
@dataclasses.dataclass
class NoteEventData:
pitch: int
velocity: Optional[int] = None
program: Optional[int] = None
is_drum: Optional[bool] = None
instrument: Optional[int] = None
@dataclasses.dataclass
class NoteEncodingState:
"""Encoding state for note transcription, keeping track of active pitches."""
# velocity bin for active pitches and programs
active_pitches: MutableMapping[Tuple[int, int], int] = dataclasses.field(default_factory=dict)
@dataclasses.dataclass
class EventRange:
type: str
min_value: int
max_value: int
@dataclasses.dataclass
class Event:
type: str
value: int
class Tokenizer:
def __init__(self, regular_ids: int):
# The special tokens: 0=PAD, 1=EOS, and 2=UNK
self._num_special_tokens = 3
self._num_regular_tokens = regular_ids
def encode(self, token_ids):
encoded = []
for token_id in token_ids:
if not 0 <= token_id < self._num_regular_tokens:
raise ValueError(
f"token_id {token_id} does not fall within valid range of [0, {self._num_regular_tokens})"
)
encoded.append(token_id + self._num_special_tokens)
# Add EOS token
encoded.append(1)
# Pad to till INPUT_FEATURE_LENGTH
encoded = encoded + [0] * (INPUT_FEATURE_LENGTH - len(encoded))
return encoded
class Codec:
"""Encode and decode events.
Useful for declaring what certain ranges of a vocabulary should be used for. This is intended to be used from
Python before encoding or after decoding with GenericTokenVocabulary. This class is more lightweight and does not
include things like EOS or UNK token handling.
To ensure that 'shift' events are always the first block of the vocab and start at 0, that event type is required
and specified separately.
"""
def __init__(self, max_shift_steps: int, steps_per_second: float, event_ranges: List[EventRange]):
"""Define Codec.
Args:
max_shift_steps: Maximum number of shift steps that can be encoded.
steps_per_second: Shift steps will be interpreted as having a duration of
1 / steps_per_second.
event_ranges: Other supported event types and their ranges.
"""
self.steps_per_second = steps_per_second
self._shift_range = EventRange(type="shift", min_value=0, max_value=max_shift_steps)
self._event_ranges = [self._shift_range] + event_ranges
# Ensure all event types have unique names.
assert len(self._event_ranges) == len({er.type for er in self._event_ranges})
@property
def num_classes(self) -> int:
return sum(er.max_value - er.min_value + 1 for er in self._event_ranges)
# The next couple methods are simplified special case methods just for shift
# events that are intended to be used from within autograph functions.
def is_shift_event_index(self, index: int) -> bool:
return (self._shift_range.min_value <= index) and (index <= self._shift_range.max_value)
@property
def max_shift_steps(self) -> int:
return self._shift_range.max_value
def encode_event(self, event: Event) -> int:
"""Encode an event to an index."""
offset = 0
for er in self._event_ranges:
if event.type == er.type:
if not er.min_value <= event.value <= er.max_value:
raise ValueError(
f"Event value {event.value} is not within valid range "
f"[{er.min_value}, {er.max_value}] for type {event.type}"
)
return offset + event.value - er.min_value
offset += er.max_value - er.min_value + 1
raise ValueError(f"Unknown event type: {event.type}")
def event_type_range(self, event_type: str) -> Tuple[int, int]:
"""Return [min_id, max_id] for an event type."""
offset = 0
for er in self._event_ranges:
if event_type == er.type:
return offset, offset + (er.max_value - er.min_value)
offset += er.max_value - er.min_value + 1
raise ValueError(f"Unknown event type: {event_type}")
def decode_event_index(self, index: int) -> Event:
"""Decode an event index to an Event."""
offset = 0
for er in self._event_ranges:
if offset <= index <= offset + er.max_value - er.min_value:
return Event(type=er.type, value=er.min_value + index - offset)
offset += er.max_value - er.min_value + 1
raise ValueError(f"Unknown event index: {index}")
@dataclasses.dataclass
class ProgramGranularity:
# both tokens_map_fn and program_map_fn should be idempotent
tokens_map_fn: Callable[[Sequence[int], Codec], Sequence[int]]
program_map_fn: Callable[[int], int]
def drop_programs(tokens, codec: Codec):
"""Drops program change events from a token sequence."""
min_program_id, max_program_id = codec.event_type_range("program")
return tokens[(tokens < min_program_id) | (tokens > max_program_id)]
def programs_to_midi_classes(tokens, codec):
"""Modifies program events to be the first program in the MIDI class."""
min_program_id, max_program_id = codec.event_type_range("program")
is_program = (tokens >= min_program_id) & (tokens <= max_program_id)
return np.where(is_program, min_program_id + 8 * ((tokens - min_program_id) // 8), tokens)
PROGRAM_GRANULARITIES = {
# "flat" granularity; drop program change tokens and set NoteSequence
# programs to zero
"flat": ProgramGranularity(tokens_map_fn=drop_programs, program_map_fn=lambda program: 0),
# map each program to the first program in its MIDI class
"midi_class": ProgramGranularity(
tokens_map_fn=programs_to_midi_classes, program_map_fn=lambda program: 8 * (program // 8)
),
# leave programs as is
"full": ProgramGranularity(tokens_map_fn=lambda tokens, codec: tokens, program_map_fn=lambda program: program),
}
def frame(signal, frame_length, frame_step, pad_end=False, pad_value=0, axis=-1):
"""
equivalent of tf.signal.frame
"""
signal_length = signal.shape[axis]
if pad_end:
frames_overlap = frame_length - frame_step
rest_samples = np.abs(signal_length - frames_overlap) % np.abs(frame_length - frames_overlap)
pad_size = int(frame_length - rest_samples)
if pad_size != 0:
pad_axis = [0] * signal.ndim
pad_axis[axis] = pad_size
signal = F.pad(signal, pad_axis, "constant", pad_value)
frames = signal.unfold(axis, frame_length, frame_step)
return frames
def program_to_slakh_program(program):
# this is done very hackily, probably should use a custom mapping
for slakh_program in sorted(SLAKH_CLASS_PROGRAMS.values(), reverse=True):
if program >= slakh_program:
return slakh_program
def audio_to_frames(
samples,
hop_size: int,
frame_rate: int,
) -> Tuple[Sequence[Sequence[int]], torch.Tensor]:
"""Convert audio samples to non-overlapping frames and frame times."""
frame_size = hop_size
samples = np.pad(samples, [0, frame_size - len(samples) % frame_size], mode="constant")
# Split audio into frames.
frames = frame(
torch.Tensor(samples).unsqueeze(0),
frame_length=frame_size,
frame_step=frame_size,
pad_end=False, # TODO check why its off by 1 here when True
)
num_frames = len(samples) // frame_size
times = np.arange(num_frames) / frame_rate
return frames, times
def note_sequence_to_onsets_and_offsets_and_programs(
ns: note_seq.NoteSequence,
) -> Tuple[Sequence[float], Sequence[NoteEventData]]:
"""Extract onset & offset times and pitches & programs from a NoteSequence.
The onset & offset times will not necessarily be in sorted order.
Args:
ns: NoteSequence from which to extract onsets and offsets.
Returns:
times: A list of note onset and offset times. values: A list of NoteEventData objects where velocity is zero for
note
offsets.
"""
# Sort by program and pitch and put offsets before onsets as a tiebreaker for
# subsequent stable sort.
notes = sorted(ns.notes, key=lambda note: (note.is_drum, note.program, note.pitch))
times = [note.end_time for note in notes if not note.is_drum] + [note.start_time for note in notes]
values = [
NoteEventData(pitch=note.pitch, velocity=0, program=note.program, is_drum=False)
for note in notes
if not note.is_drum
] + [
NoteEventData(pitch=note.pitch, velocity=note.velocity, program=note.program, is_drum=note.is_drum)
for note in notes
]
return times, values
def num_velocity_bins_from_codec(codec: Codec):
"""Get number of velocity bins from event codec."""
lo, hi = codec.event_type_range("velocity")
return hi - lo
# segment an array into segments of length n
def segment(a, n):
return [a[i : i + n] for i in range(0, len(a), n)]
def velocity_to_bin(velocity, num_velocity_bins):
if velocity == 0:
return 0
else:
return math.ceil(num_velocity_bins * velocity / note_seq.MAX_MIDI_VELOCITY)
def note_event_data_to_events(
state: Optional[NoteEncodingState],
value: NoteEventData,
codec: Codec,
) -> Sequence[Event]:
"""Convert note event data to a sequence of events."""
if value.velocity is None:
# onsets only, no program or velocity
return [Event("pitch", value.pitch)]
else:
num_velocity_bins = num_velocity_bins_from_codec(codec)
velocity_bin = velocity_to_bin(value.velocity, num_velocity_bins)
if value.program is None:
# onsets + offsets + velocities only, no programs
if state is not None:
state.active_pitches[(value.pitch, 0)] = velocity_bin
return [Event("velocity", velocity_bin), Event("pitch", value.pitch)]
else:
if value.is_drum:
# drum events use a separate vocabulary
return [Event("velocity", velocity_bin), Event("drum", value.pitch)]
else:
# program + velocity + pitch
if state is not None:
state.active_pitches[(value.pitch, value.program)] = velocity_bin
return [
Event("program", value.program),
Event("velocity", velocity_bin),
Event("pitch", value.pitch),
]
def note_encoding_state_to_events(state: NoteEncodingState) -> Sequence[Event]:
"""Output program and pitch events for active notes plus a final tie event."""
events = []
for pitch, program in sorted(state.active_pitches.keys(), key=lambda k: k[::-1]):
if state.active_pitches[(pitch, program)]:
events += [Event("program", program), Event("pitch", pitch)]
events.append(Event("tie", 0))
return events
def encode_and_index_events(
state, event_times, event_values, codec, frame_times, encode_event_fn, encoding_state_to_events_fn=None
):
"""Encode a sequence of timed events and index to audio frame times.
Encodes time shifts as repeated single step shifts for later run length encoding.
Optionally, also encodes a sequence of "state events", keeping track of the current encoding state at each audio
frame. This can be used e.g. to prepend events representing the current state to a targets segment.
Args:
state: Initial event encoding state.
event_times: Sequence of event times.
event_values: Sequence of event values.
encode_event_fn: Function that transforms event value into a sequence of one
or more Event objects.
codec: An Codec object that maps Event objects to indices.
frame_times: Time for every audio frame.
encoding_state_to_events_fn: Function that transforms encoding state into a
sequence of one or more Event objects.
Returns:
events: Encoded events and shifts. event_start_indices: Corresponding start event index for every audio frame.
Note: one event can correspond to multiple audio indices due to sampling rate differences. This makes
splitting sequences tricky because the same event can appear at the end of one sequence and the beginning of
another.
event_end_indices: Corresponding end event index for every audio frame. Used
to ensure when slicing that one chunk ends where the next begins. Should always be true that
event_end_indices[i] = event_start_indices[i + 1].
state_events: Encoded "state" events representing the encoding state before
each event.
state_event_indices: Corresponding state event index for every audio frame.
"""
indices = np.argsort(event_times, kind="stable")
event_steps = [round(event_times[i] * codec.steps_per_second) for i in indices]
event_values = [event_values[i] for i in indices]
events = []
state_events = []
event_start_indices = []
state_event_indices = []
cur_step = 0
cur_event_idx = 0
cur_state_event_idx = 0
def fill_event_start_indices_to_cur_step():
while (
len(event_start_indices) < len(frame_times)
and frame_times[len(event_start_indices)] < cur_step / codec.steps_per_second
):
event_start_indices.append(cur_event_idx)
state_event_indices.append(cur_state_event_idx)
for event_step, event_value in zip(event_steps, event_values):
while event_step > cur_step:
events.append(codec.encode_event(Event(type="shift", value=1)))
cur_step += 1
fill_event_start_indices_to_cur_step()
cur_event_idx = len(events)
cur_state_event_idx = len(state_events)
if encoding_state_to_events_fn:
# Dump state to state events *before* processing the next event, because
# we want to capture the state prior to the occurrence of the event.
for e in encoding_state_to_events_fn(state):
state_events.append(codec.encode_event(e))
for e in encode_event_fn(state, event_value, codec):
events.append(codec.encode_event(e))
# After the last event, continue filling out the event_start_indices array.
# The inequality is not strict because if our current step lines up exactly
# with (the start of) an audio frame, we need to add an additional shift event
# to "cover" that frame.
while cur_step / codec.steps_per_second <= frame_times[-1]:
events.append(codec.encode_event(Event(type="shift", value=1)))
cur_step += 1
fill_event_start_indices_to_cur_step()
cur_event_idx = len(events)
# Now fill in event_end_indices. We need this extra array to make sure that
# when we slice events, each slice ends exactly where the subsequent slice
# begins.
event_end_indices = event_start_indices[1:] + [len(events)]
events = np.array(events).astype(np.int32)
state_events = np.array(state_events).astype(np.int32)
event_start_indices = segment(np.array(event_start_indices).astype(np.int32), TARGET_FEATURE_LENGTH)
event_end_indices = segment(np.array(event_end_indices).astype(np.int32), TARGET_FEATURE_LENGTH)
state_event_indices = segment(np.array(state_event_indices).astype(np.int32), TARGET_FEATURE_LENGTH)
outputs = []
for start_indices, end_indices, event_indices in zip(event_start_indices, event_end_indices, state_event_indices):
outputs.append(
{
"inputs": events,
"event_start_indices": start_indices,
"event_end_indices": end_indices,
"state_events": state_events,
"state_event_indices": event_indices,
}
)
return outputs
def extract_sequence_with_indices(features, state_events_end_token=None, feature_key="inputs"):
"""Extract target sequence corresponding to audio token segment."""
features = features.copy()
start_idx = features["event_start_indices"][0]
end_idx = features["event_end_indices"][-1]
features[feature_key] = features[feature_key][start_idx:end_idx]
if state_events_end_token is not None:
# Extract the state events corresponding to the audio start token, and
# prepend them to the targets array.
state_event_start_idx = features["state_event_indices"][0]
state_event_end_idx = state_event_start_idx + 1
while features["state_events"][state_event_end_idx - 1] != state_events_end_token:
state_event_end_idx += 1
features[feature_key] = np.concatenate(
[
features["state_events"][state_event_start_idx:state_event_end_idx],
features[feature_key],
],
axis=0,
)
return features
def map_midi_programs(
feature, codec: Codec, granularity_type: str = "full", feature_key: str = "inputs"
) -> Mapping[str, Any]:
"""Apply MIDI program map to token sequences."""
granularity = PROGRAM_GRANULARITIES[granularity_type]
feature[feature_key] = granularity.tokens_map_fn(feature[feature_key], codec)
return feature
def run_length_encode_shifts_fn(
features,
codec: Codec,
feature_key: str = "inputs",
state_change_event_types: Sequence[str] = (),
) -> Callable[[Mapping[str, Any]], Mapping[str, Any]]:
"""Return a function that run-length encodes shifts for a given codec.
Args:
codec: The Codec to use for shift events.
feature_key: The feature key for which to run-length encode shifts.
state_change_event_types: A list of event types that represent state
changes; tokens corresponding to these event types will be interpreted as state changes and redundant ones
will be removed.
Returns:
A preprocessing function that run-length encodes single-step shifts.
"""
state_change_event_ranges = [codec.event_type_range(event_type) for event_type in state_change_event_types]
def run_length_encode_shifts(features: MutableMapping[str, Any]) -> Mapping[str, Any]:
"""Combine leading/interior shifts, trim trailing shifts.
Args:
features: Dict of features to process.
Returns:
A dict of features.
"""
events = features[feature_key]
shift_steps = 0
total_shift_steps = 0
output = np.array([], dtype=np.int32)
current_state = np.zeros(len(state_change_event_ranges), dtype=np.int32)
for event in events:
if codec.is_shift_event_index(event):
shift_steps += 1
total_shift_steps += 1
else:
# If this event is a state change and has the same value as the current
# state, we can skip it entirely.
is_redundant = False
for i, (min_index, max_index) in enumerate(state_change_event_ranges):
if (min_index <= event) and (event <= max_index):
if current_state[i] == event:
is_redundant = True
current_state[i] = event
if is_redundant:
continue
# Once we've reached a non-shift event, RLE all previous shift events
# before outputting the non-shift event.
if shift_steps > 0:
shift_steps = total_shift_steps
while shift_steps > 0:
output_steps = np.minimum(codec.max_shift_steps, shift_steps)
output = np.concatenate([output, [output_steps]], axis=0)
shift_steps -= output_steps
output = np.concatenate([output, [event]], axis=0)
features[feature_key] = output
return features
return run_length_encode_shifts(features)
def note_representation_processor_chain(features, codec: Codec, note_representation_config: NoteRepresentationConfig):
tie_token = codec.encode_event(Event("tie", 0))
state_events_end_token = tie_token if note_representation_config.include_ties else None
features = extract_sequence_with_indices(
features, state_events_end_token=state_events_end_token, feature_key="inputs"
)
features = map_midi_programs(features, codec)
features = run_length_encode_shifts_fn(features, codec, state_change_event_types=["velocity", "program"])
return features
class MidiProcessor:
def __init__(self):
self.codec = Codec(
max_shift_steps=DEFAULT_MAX_SHIFT_SECONDS * DEFAULT_STEPS_PER_SECOND,
steps_per_second=DEFAULT_STEPS_PER_SECOND,
event_ranges=[
EventRange("pitch", note_seq.MIN_MIDI_PITCH, note_seq.MAX_MIDI_PITCH),
EventRange("velocity", 0, DEFAULT_NUM_VELOCITY_BINS),
EventRange("tie", 0, 0),
EventRange("program", note_seq.MIN_MIDI_PROGRAM, note_seq.MAX_MIDI_PROGRAM),
EventRange("drum", note_seq.MIN_MIDI_PITCH, note_seq.MAX_MIDI_PITCH),
],
)
self.tokenizer = Tokenizer(self.codec.num_classes)
self.note_representation_config = NoteRepresentationConfig(onsets_only=False, include_ties=True)
def __call__(self, midi: Union[bytes, os.PathLike, str]):
if not isinstance(midi, bytes):
with open(midi, "rb") as f:
midi = f.read()
ns = note_seq.midi_to_note_sequence(midi)
ns_sus = note_seq.apply_sustain_control_changes(ns)
for note in ns_sus.notes:
if not note.is_drum:
note.program = program_to_slakh_program(note.program)
samples = np.zeros(int(ns_sus.total_time * SAMPLE_RATE))
_, frame_times = audio_to_frames(samples, HOP_SIZE, FRAME_RATE)
times, values = note_sequence_to_onsets_and_offsets_and_programs(ns_sus)
events = encode_and_index_events(
state=NoteEncodingState(),
event_times=times,
event_values=values,
frame_times=frame_times,
codec=self.codec,
encode_event_fn=note_event_data_to_events,
encoding_state_to_events_fn=note_encoding_state_to_events,
)
events = [
note_representation_processor_chain(event, self.codec, self.note_representation_config) for event in events
]
input_tokens = [self.tokenizer.encode(event["inputs"]) for event in events]
return input_tokens
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/spectrogram_diffusion/continous_encoder.py | # Copyright 2022 The Music Spectrogram Diffusion Authors.
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.t5.modeling_t5 import (
T5Block,
T5Config,
T5LayerNorm,
)
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SpectrogramContEncoder(ModelMixin, ConfigMixin, ModuleUtilsMixin):
@register_to_config
def __init__(
self,
input_dims: int,
targets_context_length: int,
d_model: int,
dropout_rate: float,
num_layers: int,
num_heads: int,
d_kv: int,
d_ff: int,
feed_forward_proj: str,
is_decoder: bool = False,
):
super().__init__()
self.input_proj = nn.Linear(input_dims, d_model, bias=False)
self.position_encoding = nn.Embedding(targets_context_length, d_model)
self.position_encoding.weight.requires_grad = False
self.dropout_pre = nn.Dropout(p=dropout_rate)
t5config = T5Config(
d_model=d_model,
num_heads=num_heads,
d_kv=d_kv,
d_ff=d_ff,
feed_forward_proj=feed_forward_proj,
dropout_rate=dropout_rate,
is_decoder=is_decoder,
is_encoder_decoder=False,
)
self.encoders = nn.ModuleList()
for lyr_num in range(num_layers):
lyr = T5Block(t5config)
self.encoders.append(lyr)
self.layer_norm = T5LayerNorm(d_model)
self.dropout_post = nn.Dropout(p=dropout_rate)
def forward(self, encoder_inputs, encoder_inputs_mask):
x = self.input_proj(encoder_inputs)
# terminal relative positional encodings
max_positions = encoder_inputs.shape[1]
input_positions = torch.arange(max_positions, device=encoder_inputs.device)
seq_lens = encoder_inputs_mask.sum(-1)
input_positions = torch.roll(input_positions.unsqueeze(0), tuple(seq_lens.tolist()), dims=0)
x += self.position_encoding(input_positions)
x = self.dropout_pre(x)
# inverted the attention mask
input_shape = encoder_inputs.size()
extended_attention_mask = self.get_extended_attention_mask(encoder_inputs_mask, input_shape)
for lyr in self.encoders:
x = lyr(x, extended_attention_mask)[0]
x = self.layer_norm(x)
return self.dropout_post(x), encoder_inputs_mask
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/latent_diffusion_uncond/__init__.py | from .pipeline_latent_diffusion_uncond import LDMPipeline
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNet2DModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class LDMPipeline(DiffusionPipeline):
r"""
Pipeline for unconditional image generation using latent diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Parameters:
vqvae ([`VQModel`]):
Vector-quantized (VQ) model to encode and decode images to and from latent representations.
unet ([`UNet2DModel`]):
A `UNet2DModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
[`DDIMScheduler`] is used in combination with `unet` to denoise the encoded image latents.
"""
def __init__(self, vqvae: VQModel, unet: UNet2DModel, scheduler: DDIMScheduler):
super().__init__()
self.register_modules(vqvae=vqvae, unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
eta: float = 0.0,
num_inference_steps: int = 50,
output_type: Optional[str] = "pil",
return_dict: bool = True,
**kwargs,
) -> Union[Tuple, ImagePipelineOutput]:
r"""
The call function to the pipeline for generation.
Args:
batch_size (`int`, *optional*, defaults to 1):
Number of images to generate.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
Example:
```py
>>> from diffusers import LDMPipeline
>>> # load model and scheduler
>>> pipe = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256")
>>> # run pipeline in inference (sample random noise and denoise)
>>> image = pipe().images[0]
```
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images
"""
latents = randn_tensor(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),
generator=generator,
)
latents = latents.to(self.device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(num_inference_steps)
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_kwargs = {}
if accepts_eta:
extra_kwargs["eta"] = eta
for t in self.progress_bar(self.scheduler.timesteps):
latent_model_input = self.scheduler.scale_model_input(latents, t)
# predict the noise residual
noise_prediction = self.unet(latent_model_input, t).sample
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_prediction, t, latents, **extra_kwargs).prev_sample
# decode the image latents with the VAE
image = self.vqvae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/paint_by_example/__init__.py | from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
from .image_encoder import PaintByExampleImageEncoder
from .pipeline_paint_by_example import PaintByExamplePipeline
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/paint_by_example/image_encoder.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class PaintByExampleImageEncoder(CLIPPreTrainedModel):
def __init__(self, config, proj_size=768):
super().__init__(config)
self.proj_size = proj_size
self.model = CLIPVisionModel(config)
self.mapper = PaintByExampleMapper(config)
self.final_layer_norm = nn.LayerNorm(config.hidden_size)
self.proj_out = nn.Linear(config.hidden_size, self.proj_size)
# uncondition for scaling
self.uncond_vector = nn.Parameter(torch.randn((1, 1, self.proj_size)))
def forward(self, pixel_values, return_uncond_vector=False):
clip_output = self.model(pixel_values=pixel_values)
latent_states = clip_output.pooler_output
latent_states = self.mapper(latent_states[:, None])
latent_states = self.final_layer_norm(latent_states)
latent_states = self.proj_out(latent_states)
if return_uncond_vector:
return latent_states, self.uncond_vector
return latent_states
class PaintByExampleMapper(nn.Module):
def __init__(self, config):
super().__init__()
num_layers = (config.num_hidden_layers + 1) // 5
hid_size = config.hidden_size
num_heads = 1
self.blocks = nn.ModuleList(
[
BasicTransformerBlock(hid_size, num_heads, hid_size, activation_fn="gelu", attention_bias=True)
for _ in range(num_layers)
]
)
def forward(self, hidden_states):
for block in self.blocks:
hidden_states = block(hidden_states)
return hidden_states
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import warnings
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor
from ...image_processor import VaeImageProcessor
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from ...utils import logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline
from ..stable_diffusion import StableDiffusionPipelineOutput
from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from .image_encoder import PaintByExampleImageEncoder
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def prepare_mask_and_masked_image(image, mask):
"""
Prepares a pair (image, mask) to be consumed by the Paint by Example pipeline. This means that those inputs will be
converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
``image`` and ``1`` for the ``mask``.
The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
binarized (``mask > 0.5``) and cast to ``torch.float32`` too.
Args:
image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.
It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``
``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.
mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``
``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.
Raises:
ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask
should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.
TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not
(ot the other way around).
Returns:
tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
dimensions: ``batch x channels x height x width``.
"""
if isinstance(image, torch.Tensor):
if not isinstance(mask, torch.Tensor):
raise TypeError(f"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not")
# Batch single image
if image.ndim == 3:
assert image.shape[0] == 3, "Image outside a batch should be of shape (3, H, W)"
image = image.unsqueeze(0)
# Batch and add channel dim for single mask
if mask.ndim == 2:
mask = mask.unsqueeze(0).unsqueeze(0)
# Batch single mask or add channel dim
if mask.ndim == 3:
# Batched mask
if mask.shape[0] == image.shape[0]:
mask = mask.unsqueeze(1)
else:
mask = mask.unsqueeze(0)
assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions"
assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions"
assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size"
assert mask.shape[1] == 1, "Mask image must have a single channel"
# Check image is in [-1, 1]
if image.min() < -1 or image.max() > 1:
raise ValueError("Image should be in [-1, 1] range")
# Check mask is in [0, 1]
if mask.min() < 0 or mask.max() > 1:
raise ValueError("Mask should be in [0, 1] range")
# paint-by-example inverses the mask
mask = 1 - mask
# Binarize mask
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
# Image as float32
image = image.to(dtype=torch.float32)
elif isinstance(mask, torch.Tensor):
raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not")
else:
if isinstance(image, PIL.Image.Image):
image = [image]
image = np.concatenate([np.array(i.convert("RGB"))[None, :] for i in image], axis=0)
image = image.transpose(0, 3, 1, 2)
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
# preprocess mask
if isinstance(mask, PIL.Image.Image):
mask = [mask]
mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0)
mask = mask.astype(np.float32) / 255.0
# paint-by-example inverses the mask
mask = 1 - mask
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
mask = torch.from_numpy(mask)
masked_image = image * mask
return mask, masked_image
class PaintByExamplePipeline(DiffusionPipeline):
r"""
<Tip warning={true}>
🧪 This is an experimental feature!
</Tip>
Pipeline for image-guided image inpainting using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
image_encoder ([`PaintByExampleImageEncoder`]):
Encodes the example input image. The `unet` is conditioned on the example image instead of a text prompt.
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
# TODO: feature_extractor is required to encode initial images (if they are in PIL format),
# we should give a descriptive message if the pipeline doesn't have one.
_optional_components = ["safety_checker"]
def __init__(
self,
vae: AutoencoderKL,
image_encoder: PaintByExampleImageEncoder,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = False,
):
super().__init__()
self.register_modules(
vae=vae,
image_encoder=image_encoder,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_image_variation.StableDiffusionImageVariationPipeline.check_inputs
def check_inputs(self, image, height, width, callback_steps):
if (
not isinstance(image, torch.Tensor)
and not isinstance(image, PIL.Image.Image)
and not isinstance(image, list)
):
raise ValueError(
"`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
f" {type(image)}"
)
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline.prepare_mask_latents
def prepare_mask_latents(
self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
):
# resize the mask to latents shape as we concatenate the mask to the latents
# we do that before converting to dtype to avoid breaking in case we're using cpu_offload
# and half precision
mask = torch.nn.functional.interpolate(
mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
)
mask = mask.to(device=device, dtype=dtype)
masked_image = masked_image.to(device=device, dtype=dtype)
masked_image_latents = self._encode_vae_image(masked_image, generator=generator)
# duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
if mask.shape[0] < batch_size:
if not batch_size % mask.shape[0] == 0:
raise ValueError(
"The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
" of masks that you pass is divisible by the total requested batch size."
)
mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
if masked_image_latents.shape[0] < batch_size:
if not batch_size % masked_image_latents.shape[0] == 0:
raise ValueError(
"The passed images and the required batch size don't match. Images are supposed to be duplicated"
f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
" Make sure the number of images that you pass is divisible by the total requested batch size."
)
masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)
mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
masked_image_latents = (
torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
)
# aligning device to prevent device errors when concating it with the latent model input
masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
return mask, masked_image_latents
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline._encode_vae_image
def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
if isinstance(generator, list):
image_latents = [
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])
for i in range(image.shape[0])
]
image_latents = torch.cat(image_latents, dim=0)
else:
image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)
image_latents = self.vae.config.scaling_factor * image_latents
return image_latents
def _encode_image(self, image, device, num_images_per_prompt, do_classifier_free_guidance):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(images=image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeddings, negative_prompt_embeds = self.image_encoder(image, return_uncond_vector=True)
# duplicate image embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = image_embeddings.shape
image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)
image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
if do_classifier_free_guidance:
negative_prompt_embeds = negative_prompt_embeds.repeat(1, image_embeddings.shape[0], 1)
negative_prompt_embeds = negative_prompt_embeds.view(bs_embed * num_images_per_prompt, 1, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings])
return image_embeddings
@torch.no_grad()
def __call__(
self,
example_image: Union[torch.FloatTensor, PIL.Image.Image],
image: Union[torch.FloatTensor, PIL.Image.Image],
mask_image: Union[torch.FloatTensor, PIL.Image.Image],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 5.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
):
r"""
The call function to the pipeline for generation.
Args:
example_image (`torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]`):
An example image to guide image generation.
image (`torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]`):
`Image` or tensor representing an image batch to be inpainted (parts of the image are masked out with
`mask_image` and repainted according to `prompt`).
mask_image (`torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]`):
`Image` or tensor representing an image batch to mask `image`. White pixels in the mask are repainted,
while black pixels are preserved. If `mask_image` is a PIL image, it is converted to a single channel
(luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3, so the
expected shape would be `(B, H, W, 1)`.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
Example:
```py
>>> import PIL
>>> import requests
>>> import torch
>>> from io import BytesIO
>>> from diffusers import PaintByExamplePipeline
>>> def download_image(url):
... response = requests.get(url)
... return PIL.Image.open(BytesIO(response.content)).convert("RGB")
>>> img_url = (
... "https://raw.githubusercontent.com/Fantasy-Studio/Paint-by-Example/main/examples/image/example_1.png"
... )
>>> mask_url = (
... "https://raw.githubusercontent.com/Fantasy-Studio/Paint-by-Example/main/examples/mask/example_1.png"
... )
>>> example_url = "https://raw.githubusercontent.com/Fantasy-Studio/Paint-by-Example/main/examples/reference/example_1.jpg"
>>> init_image = download_image(img_url).resize((512, 512))
>>> mask_image = download_image(mask_url).resize((512, 512))
>>> example_image = download_image(example_url).resize((512, 512))
>>> pipe = PaintByExamplePipeline.from_pretrained(
... "Fantasy-Studio/Paint-by-Example",
... torch_dtype=torch.float16,
... )
>>> pipe = pipe.to("cuda")
>>> image = pipe(image=init_image, mask_image=mask_image, example_image=example_image).images[0]
>>> image
```
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
"""
# 1. Define call parameters
if isinstance(image, PIL.Image.Image):
batch_size = 1
elif isinstance(image, list):
batch_size = len(image)
else:
batch_size = image.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 2. Preprocess mask and image
mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
height, width = masked_image.shape[-2:]
# 3. Check inputs
self.check_inputs(example_image, height, width, callback_steps)
# 4. Encode input image
image_embeddings = self._encode_image(
example_image, device, num_images_per_prompt, do_classifier_free_guidance
)
# 5. set timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 6. Prepare latent variables
num_channels_latents = self.vae.config.latent_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
image_embeddings.dtype,
device,
generator,
latents,
)
# 7. Prepare mask latent variables
mask, masked_image_latents = self.prepare_mask_latents(
mask,
masked_image,
batch_size * num_images_per_prompt,
height,
width,
image_embeddings.dtype,
device,
generator,
do_classifier_free_guidance,
)
# 8. Check that sizes of mask, masked image and latents match
num_channels_mask = mask.shape[1]
num_channels_masked_image = masked_image_latents.shape[1]
if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
raise ValueError(
f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
" `pipeline.unet` or your `mask_image` or `image` input."
)
# 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 10. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
# concat latents, mask, masked_image_latents in the channel dimension
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
latent_model_input = torch.cat([latent_model_input, masked_image_latents, mask], dim=1)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=image_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, image_embeddings.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/alt_diffusion/__init__.py | from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class AltDiffusionPipelineOutput(BaseOutput):
"""
Output class for Alt Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`List[bool]`)
List indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content or
`None` if safety checking could not be performed.
"""
images: Union[List[PIL.Image.Image], np.ndarray]
nsfw_content_detected: Optional[List[bool]]
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
from .modeling_roberta_series import RobertaSeriesModelWithTransformation
from .pipeline_alt_diffusion import AltDiffusionPipeline
from .pipeline_alt_diffusion_img2img import AltDiffusionImg2ImgPipeline
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import warnings
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from packaging import version
from transformers import CLIPImageProcessor, XLMRobertaTokenizer
from diffusers.utils import is_accelerate_available, is_accelerate_version
from ...configuration_utils import FrozenDict
from ...image_processor import VaeImageProcessor
from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import deprecate, logging, randn_tensor, replace_example_docstring
from ..pipeline_utils import DiffusionPipeline
from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from . import AltDiffusionPipelineOutput, RobertaSeriesModelWithTransformation
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import torch
>>> from diffusers import AltDiffusionPipeline
>>> pipe = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion-m9", torch_dtype=torch.float16)
>>> pipe = pipe.to("cuda")
>>> # "dark elf princess, highly detailed, d & d, fantasy, highly detailed, digital painting, trending on artstation, concept art, sharp focus, illustration, art by artgerm and greg rutkowski and fuji choko and viktoria gavrilenko and hoang lap"
>>> prompt = "黑暗精灵公主，非常详细，幻想，非常详细，数字绘画，概念艺术，敏锐的焦点，插图"
>>> image = pipe(prompt).images[0]
```
"""
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
"""
Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
"""
std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
# rescale the results from guidance (fixes overexposure)
noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
# mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
return noise_cfg
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline with Stable->Alt, CLIPTextModel->RobertaSeriesModelWithTransformation, CLIPTokenizer->XLMRobertaTokenizer, AltDiffusionSafetyChecker->StableDiffusionSafetyChecker
class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
r"""
Pipeline for text-to-image generation using Alt Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.RobertaSeriesModelWithTransformation`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.XLMRobertaTokenizer`]):
A `XLMRobertaTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: RobertaSeriesModelWithTransformation,
tokenizer: XLMRobertaTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Alt Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.vae.enable_slicing()
def disable_vae_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_slicing()
def enable_vae_tiling(self):
r"""
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
processing larger images.
"""
self.vae.enable_tiling()
def disable_vae_tiling(self):
r"""
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_tiling()
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
if self.safety_checker is not None:
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
def decode_latents(self, latents):
warnings.warn(
(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead"
),
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
guidance_rescale: float = 0.0,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
guidance_rescale (`float`, *optional*, defaults to 0.7):
Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are
Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when
using zero terminal SNR.
Examples:
Returns:
[`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
if do_classifier_free_guidance and guidance_rescale > 0.0:
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, has_nsfw_concept)
return AltDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import warnings
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL
import torch
from packaging import version
from transformers import CLIPImageProcessor, XLMRobertaTokenizer
from diffusers.utils import is_accelerate_available, is_accelerate_version
from ...configuration_utils import FrozenDict
from ...image_processor import VaeImageProcessor
from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import PIL_INTERPOLATION, deprecate, logging, randn_tensor, replace_example_docstring
from ..pipeline_utils import DiffusionPipeline
from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from . import AltDiffusionPipelineOutput, RobertaSeriesModelWithTransformation
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import requests
>>> import torch
>>> from PIL import Image
>>> from io import BytesIO
>>> from diffusers import AltDiffusionImg2ImgPipeline
>>> device = "cuda"
>>> model_id_or_path = "BAAI/AltDiffusion-m9"
>>> pipe = AltDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
>>> pipe = pipe.to(device)
>>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
>>> response = requests.get(url)
>>> init_image = Image.open(BytesIO(response.content)).convert("RGB")
>>> init_image = init_image.resize((768, 512))
>>> # "A fantasy landscape, trending on artstation"
>>> prompt = "幻想风景, artstation"
>>> images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images
>>> images[0].save("幻想风景.png")
```
"""
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
def preprocess(image):
warnings.warn(
"The preprocess method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor.preprocess instead",
FutureWarning,
)
if isinstance(image, torch.Tensor):
return image
elif isinstance(image, PIL.Image.Image):
image = [image]
if isinstance(image[0], PIL.Image.Image):
w, h = image[0].size
w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
image = np.concatenate(image, axis=0)
image = np.array(image).astype(np.float32) / 255.0
image = image.transpose(0, 3, 1, 2)
image = 2.0 * image - 1.0
image = torch.from_numpy(image)
elif isinstance(image[0], torch.Tensor):
image = torch.cat(image, dim=0)
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline with Stable->Alt, CLIPTextModel->RobertaSeriesModelWithTransformation, CLIPTokenizer->XLMRobertaTokenizer, AltDiffusionSafetyChecker->StableDiffusionSafetyChecker
class AltDiffusionImg2ImgPipeline(
DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
):
r"""
Pipeline for text-guided image-to-image generation using Alt Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.RobertaSeriesModelWithTransformation`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.XLMRobertaTokenizer`]):
A `XLMRobertaTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: RobertaSeriesModelWithTransformation,
tokenizer: XLMRobertaTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Alt Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
if self.safety_checker is not None:
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
def decode_latents(self, latents):
warnings.warn(
(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead"
),
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self, prompt, strength, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None
):
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
return timesteps, num_inference_steps - t_start
def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
)
image = image.to(device=device, dtype=dtype)
batch_size = batch_size * num_images_per_prompt
if image.shape[1] == 4:
init_latents = image
else:
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective"
f" batch size of {batch_size}. Make sure the batch size matches the length of the generators."
)
elif isinstance(generator, list):
init_latents = [
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
]
init_latents = torch.cat(init_latents, dim=0)
else:
init_latents = self.vae.encode(image).latent_dist.sample(generator)
init_latents = self.vae.config.scaling_factor * init_latents
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
# expand init_latents for batch_size
deprecation_message = (
f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
" images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
" your script to pass as many initial images as text prompts to suppress this warning."
)
deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
additional_image_per_prompt = batch_size // init_latents.shape[0]
init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
raise ValueError(
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
)
else:
init_latents = torch.cat([init_latents], dim=0)
shape = init_latents.shape
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
# get latents
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
latents = init_latents
return latents
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: Union[
torch.FloatTensor,
PIL.Image.Image,
np.ndarray,
List[torch.FloatTensor],
List[PIL.Image.Image],
List[np.ndarray],
] = None,
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: Optional[float] = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
`Image` or tensor representing an image batch to be used as the starting point. Can also accept image
latents as `image`, but if passing latents directly it is not encoded again.
strength (`float`, *optional*, defaults to 0.8):
Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
starting point and more noise is added the higher the `strength`. The number of denoising steps depends
on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
essentially ignores `image`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter is modulated by `strength`.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
Examples:
Returns:
[`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
"""
# 1. Check inputs. Raise error if not correct
self.check_inputs(prompt, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
)
# 4. Preprocess image
image = self.image_processor.preprocess(image)
# 5. set timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
# 6. Prepare latent variables
latents = self.prepare_latents(
image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator
)
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, has_nsfw_concept)
return AltDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/alt_diffusion/modeling_roberta_series.py | from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class TransformationModelOutput(ModelOutput):
"""
Base class for text model's outputs that also contains a pooling of the last hidden states.
Args:
text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
The text embeddings obtained by applying the projection layer to the pooler_output.
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
projection_state: Optional[torch.FloatTensor] = None
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
class RobertaSeriesConfig(XLMRobertaConfig):
def __init__(
self,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
project_dim=512,
pooler_fn="cls",
learn_encoder=False,
use_attention_mask=True,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.project_dim = project_dim
self.pooler_fn = pooler_fn
self.learn_encoder = learn_encoder
self.use_attention_mask = use_attention_mask
class RobertaSeriesModelWithTransformation(RobertaPreTrainedModel):
_keys_to_ignore_on_load_unexpected = [r"pooler", r"logit_scale"]
_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
base_model_prefix = "roberta"
config_class = RobertaSeriesConfig
def __init__(self, config):
super().__init__(config)
self.roberta = XLMRobertaModel(config)
self.transformation = nn.Linear(config.hidden_size, config.project_dim)
self.has_pre_transformation = getattr(config, "has_pre_transformation", False)
if self.has_pre_transformation:
self.transformation_pre = nn.Linear(config.hidden_size, config.project_dim)
self.pre_LN = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.post_init()
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
):
r""" """
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.base_model(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=True if self.has_pre_transformation else output_hidden_states,
return_dict=return_dict,
)
if self.has_pre_transformation:
sequence_output2 = outputs["hidden_states"][-2]
sequence_output2 = self.pre_LN(sequence_output2)
projection_state2 = self.transformation_pre(sequence_output2)
return TransformationModelOutput(
projection_state=projection_state2,
last_hidden_state=outputs.last_hidden_state,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
else:
projection_state = self.transformation(outputs.last_hidden_state)
return TransformationModelOutput(
projection_state=projection_state,
last_hidden_state=outputs.last_hidden_state,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/score_sde_ve/__init__.py | from .pipeline_score_sde_ve import ScoreSdeVePipeline
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/score_sde_ve/pipeline_score_sde_ve.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNet2DModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class ScoreSdeVePipeline(DiffusionPipeline):
r"""
Pipeline for unconditional image generation.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Parameters:
unet ([`UNet2DModel`]):
A `UNet2DModel` to denoise the encoded image.
scheduler ([`ScoreSdeVeScheduler`]):
A `ScoreSdeVeScheduler` to be used in combination with `unet` to denoise the encoded image.
"""
unet: UNet2DModel
scheduler: ScoreSdeVeScheduler
def __init__(self, unet: UNet2DModel, scheduler: ScoreSdeVeScheduler):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
num_inference_steps: int = 2000,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
**kwargs,
) -> Union[ImagePipelineOutput, Tuple]:
r"""
The call function to the pipeline for generation.
Args:
batch_size (`int`, *optional*, defaults to 1):
The number of images to generate.
generator (`torch.Generator`, `optional`):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
output_type (`str`, `optional`, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images.
"""
img_size = self.unet.config.sample_size
shape = (batch_size, 3, img_size, img_size)
model = self.unet
sample = randn_tensor(shape, generator=generator) * self.scheduler.init_noise_sigma
sample = sample.to(self.device)
self.scheduler.set_timesteps(num_inference_steps)
self.scheduler.set_sigmas(num_inference_steps)
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
sigma_t = self.scheduler.sigmas[i] * torch.ones(shape[0], device=self.device)
# correction step
for _ in range(self.scheduler.config.correct_steps):
model_output = self.unet(sample, sigma_t).sample
sample = self.scheduler.step_correct(model_output, sample, generator=generator).prev_sample
# prediction step
model_output = model(sample, sigma_t).sample
output = self.scheduler.step_pred(model_output, t, sample, generator=generator)
sample, sample_mean = output.prev_sample, output.prev_sample_mean
sample = sample_mean.clamp(0, 1)
sample = sample.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
sample = self.numpy_to_pil(sample)
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=sample)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/latent_diffusion/__init__.py | from ...utils import OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.utils.checkpoint
from transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutput
from transformers.utils import logging
from ...models import AutoencoderKL, UNet2DConditionModel, UNet2DModel, VQModel
from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class LDMTextToImagePipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using latent diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Parameters:
vqvae ([`VQModel`]):
Vector-quantized (VQ) model to encode and decode images to and from latent representations.
bert ([`LDMBertModel`]):
Text-encoder model based on [`~transformers.BERT`].
tokenizer ([`~transformers.BertTokenizer`]):
A `BertTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
"""
def __init__(
self,
vqvae: Union[VQModel, AutoencoderKL],
bert: PreTrainedModel,
tokenizer: PreTrainedTokenizer,
unet: Union[UNet2DModel, UNet2DConditionModel],
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
):
super().__init__()
self.register_modules(vqvae=vqvae, bert=bert, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
self.vae_scale_factor = 2 ** (len(self.vqvae.config.block_out_channels) - 1)
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 1.0,
eta: Optional[float] = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
**kwargs,
) -> Union[Tuple, ImagePipelineOutput]:
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 1.0):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple.
Example:
```py
>>> from diffusers import DiffusionPipeline
>>> # load model and scheduler
>>> ldm = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")
>>> # run pipeline in inference (sample random noise and denoise)
>>> prompt = "A painting of a squirrel eating a burger"
>>> images = ldm([prompt], num_inference_steps=50, eta=0.3, guidance_scale=6).images
>>> # save images
>>> for idx, image in enumerate(images):
... image.save(f"squirrel-{idx}.png")
```
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
# get unconditional embeddings for classifier free guidance
if guidance_scale != 1.0:
uncond_input = self.tokenizer(
[""] * batch_size, padding="max_length", max_length=77, truncation=True, return_tensors="pt"
)
negative_prompt_embeds = self.bert(uncond_input.input_ids.to(self._execution_device))[0]
# get prompt text embeddings
text_input = self.tokenizer(prompt, padding="max_length", max_length=77, truncation=True, return_tensors="pt")
prompt_embeds = self.bert(text_input.input_ids.to(self._execution_device))[0]
# get the initial random noise unless the user supplied it
latents_shape = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(
latents_shape, generator=generator, device=self._execution_device, dtype=prompt_embeds.dtype
)
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
latents = latents.to(self._execution_device)
self.scheduler.set_timesteps(num_inference_steps)
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_kwargs = {}
if accepts_eta:
extra_kwargs["eta"] = eta
for t in self.progress_bar(self.scheduler.timesteps):
if guidance_scale == 1.0:
# guidance_scale of 1 means no guidance
latents_input = latents
context = prompt_embeds
else:
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
latents_input = torch.cat([latents] * 2)
context = torch.cat([negative_prompt_embeds, prompt_embeds])
# predict the noise residual
noise_pred = self.unet(latents_input, t, encoder_hidden_states=context).sample
# perform guidance
if guidance_scale != 1.0:
noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_kwargs).prev_sample
# scale and decode the image latents with vae
latents = 1 / self.vqvae.config.scaling_factor * latents
image = self.vqvae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
################################################################################
# Code for the text transformer model
################################################################################
""" PyTorch LDMBERT model."""
logger = logging.get_logger(__name__)
LDMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"ldm-bert",
# See all LDMBert models at https://huggingface.co/models?filter=ldmbert
]
LDMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"ldm-bert": "https://huggingface.co/valhalla/ldm-bert/blob/main/config.json",
}
""" LDMBERT model configuration"""
class LDMBertConfig(PretrainedConfig):
model_type = "ldmbert"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__(
self,
vocab_size=30522,
max_position_embeddings=77,
encoder_layers=32,
encoder_ffn_dim=5120,
encoder_attention_heads=8,
head_dim=64,
encoder_layerdrop=0.0,
activation_function="gelu",
d_model=1280,
dropout=0.1,
attention_dropout=0.0,
activation_dropout=0.0,
init_std=0.02,
classifier_dropout=0.0,
scale_embedding=False,
use_cache=True,
pad_token_id=0,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.d_model = d_model
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.head_dim = head_dim
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.activation_function = activation_function
self.init_std = init_std
self.encoder_layerdrop = encoder_layerdrop
self.classifier_dropout = classifier_dropout
self.use_cache = use_cache
self.num_hidden_layers = encoder_layers
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(pad_token_id=pad_token_id, **kwargs)
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->LDMBert
class LDMBertAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
head_dim: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = False,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = head_dim
self.inner_dim = head_dim * num_heads
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.k_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias)
self.out_proj = nn.Linear(self.inner_dim, embed_dim)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned across GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.inner_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped, past_key_value
class LDMBertEncoderLayer(nn.Module):
def __init__(self, config: LDMBertConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = LDMBertAttention(
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_heads,
head_dim=config.head_dim,
dropout=config.attention_dropout,
)
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.FloatTensor,
attention_mask: torch.FloatTensor,
layer_head_mask: torch.FloatTensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
hidden_states, attn_weights, _ = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
if hidden_states.dtype == torch.float16 and (
torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
):
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
# Copied from transformers.models.bart.modeling_bart.BartPretrainedModel with Bart->LDMBert
class LDMBertPreTrainedModel(PreTrainedModel):
config_class = LDMBertConfig
base_model_prefix = "model"
_supports_gradient_checkpointing = True
_keys_to_ignore_on_load_unexpected = [r"encoder\.version", r"decoder\.version"]
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (LDMBertEncoder,)):
module.gradient_checkpointing = value
@property
def dummy_inputs(self):
pad_token = self.config.pad_token_id
input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
dummy_inputs = {
"attention_mask": input_ids.ne(pad_token),
"input_ids": input_ids,
}
return dummy_inputs
class LDMBertEncoder(LDMBertPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`LDMBertEncoderLayer`].
Args:
config: LDMBertConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: LDMBertConfig):
super().__init__(config)
self.dropout = config.dropout
embed_dim = config.d_model
self.padding_idx = config.pad_token_id
self.max_source_positions = config.max_position_embeddings
self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim)
self.embed_positions = nn.Embedding(config.max_position_embeddings, embed_dim)
self.layers = nn.ModuleList([LDMBertEncoderLayer(config) for _ in range(config.encoder_layers)])
self.layer_norm = nn.LayerNorm(embed_dim)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`BartTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.BaseModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
seq_len = input_shape[1]
if position_ids is None:
position_ids = torch.arange(seq_len, dtype=torch.long, device=inputs_embeds.device).expand((1, -1))
embed_pos = self.embed_positions(position_ids)
hidden_states = inputs_embeds + embed_pos
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
if head_mask.size()[0] != (len(self.layers)):
raise ValueError(
f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(encoder_layer),
hidden_states,
attention_mask,
(head_mask[idx] if head_mask is not None else None),
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
hidden_states = self.layer_norm(hidden_states)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
class LDMBertModel(LDMBertPreTrainedModel):
_no_split_modules = []
def __init__(self, config: LDMBertConfig):
super().__init__(config)
self.model = LDMBertEncoder(config)
self.to_logits = nn.Linear(config.hidden_size, config.vocab_size)
def forward(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
outputs = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
return outputs
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py | import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNet2DModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def preprocess(image):
w, h = image.size
w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.0 * image - 1.0
class LDMSuperResolutionPipeline(DiffusionPipeline):
r"""
A pipeline for image super-resolution using latent diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Parameters:
vqvae ([`VQModel`]):
Vector-quantized (VQ) model to encode and decode images to and from latent representations.
unet ([`UNet2DModel`]):
A `UNet2DModel` to denoise the encoded image.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`],
[`EulerAncestralDiscreteScheduler`], [`DPMSolverMultistepScheduler`], or [`PNDMScheduler`].
"""
def __init__(
self,
vqvae: VQModel,
unet: UNet2DModel,
scheduler: Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
],
):
super().__init__()
self.register_modules(vqvae=vqvae, unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
image: Union[torch.Tensor, PIL.Image.Image] = None,
batch_size: Optional[int] = 1,
num_inference_steps: Optional[int] = 100,
eta: Optional[float] = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
) -> Union[Tuple, ImagePipelineOutput]:
r"""
The call function to the pipeline for generation.
Args:
image (`torch.Tensor` or `PIL.Image.Image`):
`Image` or tensor representing an image batch to be used as the starting point for the process.
batch_size (`int`, *optional*, defaults to 1):
Number of images to generate.
num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple.
Example:
```py
>>> import requests
>>> from PIL import Image
>>> from io import BytesIO
>>> from diffusers import LDMSuperResolutionPipeline
>>> import torch
>>> # load model and scheduler
>>> pipeline = LDMSuperResolutionPipeline.from_pretrained("CompVis/ldm-super-resolution-4x-openimages")
>>> pipeline = pipeline.to("cuda")
>>> # let's download an image
>>> url = (
... "https://user-images.githubusercontent.com/38061659/199705896-b48e17b8-b231-47cd-a270-4ffa5a93fa3e.png"
... )
>>> response = requests.get(url)
>>> low_res_img = Image.open(BytesIO(response.content)).convert("RGB")
>>> low_res_img = low_res_img.resize((128, 128))
>>> # run pipeline in inference (sample random noise and denoise)
>>> upscaled_image = pipeline(low_res_img, num_inference_steps=100, eta=1).images[0]
>>> # save image
>>> upscaled_image.save("ldm_generated_image.png")
```
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images
"""
if isinstance(image, PIL.Image.Image):
batch_size = 1
elif isinstance(image, torch.Tensor):
batch_size = image.shape[0]
else:
raise ValueError(f"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(image)}")
if isinstance(image, PIL.Image.Image):
image = preprocess(image)
height, width = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
latents_shape = (batch_size, self.unet.config.in_channels // 2, height, width)
latents_dtype = next(self.unet.parameters()).dtype
latents = randn_tensor(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
image = image.to(device=self.device, dtype=latents_dtype)
# set timesteps and move to the correct device
self.scheduler.set_timesteps(num_inference_steps, device=self.device)
timesteps_tensor = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_kwargs = {}
if accepts_eta:
extra_kwargs["eta"] = eta
for t in self.progress_bar(timesteps_tensor):
# concat latents and low resolution image in the channel dimension.
latents_input = torch.cat([latents, image], dim=1)
latents_input = self.scheduler.scale_model_input(latents_input, t)
# predict the noise residual
noise_pred = self.unet(latents_input, t).sample
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_kwargs).prev_sample
# decode the image latents with the VQVAE
image = self.vqvae.decode(latents).sample
image = torch.clamp(image, -1.0, 1.0)
image = image / 2 + 0.5
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py | from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
from ...models.activations import get_activation
from ...models.attention import Attention
from ...models.attention_processor import (
AttentionProcessor,
AttnAddedKVProcessor,
AttnAddedKVProcessor2_0,
AttnProcessor,
)
from ...models.dual_transformer_2d import DualTransformer2DModel
from ...models.embeddings import (
GaussianFourierProjection,
ImageHintTimeEmbedding,
ImageProjection,
ImageTimeEmbedding,
TextImageProjection,
TextImageTimeEmbedding,
TextTimeEmbedding,
TimestepEmbedding,
Timesteps,
)
from ...models.transformer_2d import Transformer2DModel
from ...models.unet_2d_condition import UNet2DConditionOutput
from ...utils import is_torch_version, logging
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def get_down_block(
down_block_type,
num_layers,
in_channels,
out_channels,
temb_channels,
add_downsample,
resnet_eps,
resnet_act_fn,
num_attention_heads,
resnet_groups=None,
cross_attention_dim=None,
downsample_padding=None,
dual_cross_attention=False,
use_linear_projection=False,
only_cross_attention=False,
upcast_attention=False,
resnet_time_scale_shift="default",
resnet_skip_time_act=False,
resnet_out_scale_factor=1.0,
cross_attention_norm=None,
):
down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type
if down_block_type == "DownBlockFlat":
return DownBlockFlat(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
add_downsample=add_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
downsample_padding=downsample_padding,
resnet_time_scale_shift=resnet_time_scale_shift,
)
elif down_block_type == "CrossAttnDownBlockFlat":
if cross_attention_dim is None:
raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockFlat")
return CrossAttnDownBlockFlat(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
add_downsample=add_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
downsample_padding=downsample_padding,
cross_attention_dim=cross_attention_dim,
num_attention_heads=num_attention_heads,
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
resnet_time_scale_shift=resnet_time_scale_shift,
)
raise ValueError(f"{down_block_type} is not supported.")
def get_up_block(
up_block_type,
num_layers,
in_channels,
out_channels,
prev_output_channel,
temb_channels,
add_upsample,
resnet_eps,
resnet_act_fn,
num_attention_heads,
resnet_groups=None,
cross_attention_dim=None,
dual_cross_attention=False,
use_linear_projection=False,
only_cross_attention=False,
upcast_attention=False,
resnet_time_scale_shift="default",
resnet_skip_time_act=False,
resnet_out_scale_factor=1.0,
cross_attention_norm=None,
):
up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
if up_block_type == "UpBlockFlat":
return UpBlockFlat(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
resnet_time_scale_shift=resnet_time_scale_shift,
)
elif up_block_type == "CrossAttnUpBlockFlat":
if cross_attention_dim is None:
raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockFlat")
return CrossAttnUpBlockFlat(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
cross_attention_dim=cross_attention_dim,
num_attention_heads=num_attention_heads,
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
resnet_time_scale_shift=resnet_time_scale_shift,
)
raise ValueError(f"{up_block_type} is not supported.")
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel with UNet2DConditionModel->UNetFlatConditionModel, nn.Conv2d->LinearMultiDim, Block2D->BlockFlat
class UNetFlatConditionModel(ModelMixin, ConfigMixin):
r"""
A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
shaped output.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Parameters:
sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
Height and width of input/output sample.
in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
Whether to flip the sin to cos in the time embedding.
freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlockFlat", "CrossAttnDownBlockFlat", "CrossAttnDownBlockFlat", "DownBlockFlat")`):
The tuple of downsample blocks to use.
mid_block_type (`str`, *optional*, defaults to `"UNetMidBlockFlatCrossAttn"`):
Block type for middle of UNet, it can be either `UNetMidBlockFlatCrossAttn` or
`UNetMidBlockFlatSimpleCrossAttn`. If `None`, the mid block layer is skipped.
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlockFlat", "CrossAttnUpBlockFlat", "CrossAttnUpBlockFlat", "CrossAttnUpBlockFlat")`):
The tuple of upsample blocks to use.
only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
Whether to include self-attention in the basic transformer blocks, see
[`~models.attention.BasicTransformerBlock`].
block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
The tuple of output channels for each block.
layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
If `None`, normalization and activation layers is skipped in post-processing.
norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
The dimension of the cross attention features.
transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
[`~models.unet_2d_blocks.CrossAttnDownBlockFlat`], [`~models.unet_2d_blocks.CrossAttnUpBlockFlat`],
[`~models.unet_2d_blocks.UNetMidBlockFlatCrossAttn`].
encoder_hid_dim (`int`, *optional*, defaults to None):
If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
dimension to `cross_attention_dim`.
encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
num_attention_heads (`int`, *optional*):
The number of attention heads. If not defined, defaults to `attention_head_dim`
resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
for ResNet blocks (see [`~models.resnet.ResnetBlockFlat`]). Choose from `default` or `scale_shift`.
class_embed_type (`str`, *optional*, defaults to `None`):
The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
`"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
addition_embed_type (`str`, *optional*, defaults to `None`):
Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
"text". "text" will use the `TextTimeEmbedding` layer.
addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
Dimension for the timestep embeddings.
num_class_embeds (`int`, *optional*, defaults to `None`):
Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
class conditioning with `class_embed_type` equal to `None`.
time_embedding_type (`str`, *optional*, defaults to `positional`):
The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
time_embedding_dim (`int`, *optional*, defaults to `None`):
An optional override for the dimension of the projected time embedding.
time_embedding_act_fn (`str`, *optional*, defaults to `None`):
Optional activation function to use only once on the time embeddings before they are passed to the rest of
the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
timestep_post_act (`str`, *optional*, defaults to `None`):
The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
time_cond_proj_dim (`int`, *optional*, defaults to `None`):
The dimension of `cond_proj` layer in the timestep embedding.
conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer.
conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer.
projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when
`class_embed_type="projection"`. Required when `class_embed_type="projection"`.
class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
embeddings with the class embeddings.
mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
Whether to use cross attention with the mid block when using the `UNetMidBlockFlatSimpleCrossAttn`. If
`only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the
`only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`
otherwise.
"""
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
sample_size: Optional[int] = None,
in_channels: int = 4,
out_channels: int = 4,
center_input_sample: bool = False,
flip_sin_to_cos: bool = True,
freq_shift: int = 0,
down_block_types: Tuple[str] = (
"CrossAttnDownBlockFlat",
"CrossAttnDownBlockFlat",
"CrossAttnDownBlockFlat",
"DownBlockFlat",
),
mid_block_type: Optional[str] = "UNetMidBlockFlatCrossAttn",
up_block_types: Tuple[str] = (
"UpBlockFlat",
"CrossAttnUpBlockFlat",
"CrossAttnUpBlockFlat",
"CrossAttnUpBlockFlat",
),
only_cross_attention: Union[bool, Tuple[bool]] = False,
block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
layers_per_block: Union[int, Tuple[int]] = 2,
downsample_padding: int = 1,
mid_block_scale_factor: float = 1,
act_fn: str = "silu",
norm_num_groups: Optional[int] = 32,
norm_eps: float = 1e-5,
cross_attention_dim: Union[int, Tuple[int]] = 1280,
transformer_layers_per_block: Union[int, Tuple[int]] = 1,
encoder_hid_dim: Optional[int] = None,
encoder_hid_dim_type: Optional[str] = None,
attention_head_dim: Union[int, Tuple[int]] = 8,
num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
dual_cross_attention: bool = False,
use_linear_projection: bool = False,
class_embed_type: Optional[str] = None,
addition_embed_type: Optional[str] = None,
addition_time_embed_dim: Optional[int] = None,
num_class_embeds: Optional[int] = None,
upcast_attention: bool = False,
resnet_time_scale_shift: str = "default",
resnet_skip_time_act: bool = False,
resnet_out_scale_factor: int = 1.0,
time_embedding_type: str = "positional",
time_embedding_dim: Optional[int] = None,
time_embedding_act_fn: Optional[str] = None,
timestep_post_act: Optional[str] = None,
time_cond_proj_dim: Optional[int] = None,
conv_in_kernel: int = 3,
conv_out_kernel: int = 3,
projection_class_embeddings_input_dim: Optional[int] = None,
class_embeddings_concat: bool = False,
mid_block_only_cross_attention: Optional[bool] = None,
cross_attention_norm: Optional[str] = None,
addition_embed_type_num_heads=64,
):
super().__init__()
self.sample_size = sample_size
if num_attention_heads is not None:
raise ValueError(
"At the moment it is not possible to define the number of attention heads via `num_attention_heads`"
" because of a naming issue as described in"
" https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing"
" `num_attention_heads` will only be supported in diffusers v0.19."
)
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
num_attention_heads = num_attention_heads or attention_head_dim
# Check inputs
if len(down_block_types) != len(up_block_types):
raise ValueError(
"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`:"
f" {down_block_types}. `up_block_types`: {up_block_types}."
)
if len(block_out_channels) != len(down_block_types):
raise ValueError(
"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`:"
f" {block_out_channels}. `down_block_types`: {down_block_types}."
)
if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
raise ValueError(
"Must provide the same number of `only_cross_attention` as `down_block_types`."
f" `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
)
if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
raise ValueError(
"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`:"
f" {num_attention_heads}. `down_block_types`: {down_block_types}."
)
if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
raise ValueError(
"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`:"
f" {attention_head_dim}. `down_block_types`: {down_block_types}."
)
if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
raise ValueError(
"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`:"
f" {cross_attention_dim}. `down_block_types`: {down_block_types}."
)
if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
raise ValueError(
"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`:"
f" {layers_per_block}. `down_block_types`: {down_block_types}."
)
# input
conv_in_padding = (conv_in_kernel - 1) // 2
self.conv_in = LinearMultiDim(
in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
)
# time
if time_embedding_type == "fourier":
time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
if time_embed_dim % 2 != 0:
raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
self.time_proj = GaussianFourierProjection(
time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
)
timestep_input_dim = time_embed_dim
elif time_embedding_type == "positional":
time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
timestep_input_dim = block_out_channels[0]
else:
raise ValueError(
f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
)
self.time_embedding = TimestepEmbedding(
timestep_input_dim,
time_embed_dim,
act_fn=act_fn,
post_act_fn=timestep_post_act,
cond_proj_dim=time_cond_proj_dim,
)
if encoder_hid_dim_type is None and encoder_hid_dim is not None:
encoder_hid_dim_type = "text_proj"
self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")
if encoder_hid_dim is None and encoder_hid_dim_type is not None:
raise ValueError(
f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
)
if encoder_hid_dim_type == "text_proj":
self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
elif encoder_hid_dim_type == "text_image_proj":
# image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
# case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
self.encoder_hid_proj = TextImageProjection(
text_embed_dim=encoder_hid_dim,
image_embed_dim=cross_attention_dim,
cross_attention_dim=cross_attention_dim,
)
elif encoder_hid_dim_type == "image_proj":
# Kandinsky 2.2
self.encoder_hid_proj = ImageProjection(
image_embed_dim=encoder_hid_dim,
cross_attention_dim=cross_attention_dim,
)
elif encoder_hid_dim_type is not None:
raise ValueError(
f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
)
else:
self.encoder_hid_proj = None
# class embedding
if class_embed_type is None and num_class_embeds is not None:
self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
elif class_embed_type == "timestep":
self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
elif class_embed_type == "identity":
self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
elif class_embed_type == "projection":
if projection_class_embeddings_input_dim is None:
raise ValueError(
"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
)
# The projection `class_embed_type` is the same as the timestep `class_embed_type` except
# 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
# 2. it projects from an arbitrary input dimension.
#
# Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
# When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
# As a result, `TimestepEmbedding` can be passed arbitrary vectors.
self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
elif class_embed_type == "simple_projection":
if projection_class_embeddings_input_dim is None:
raise ValueError(
"`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
)
self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
else:
self.class_embedding = None
if addition_embed_type == "text":
if encoder_hid_dim is not None:
text_time_embedding_from_dim = encoder_hid_dim
else:
text_time_embedding_from_dim = cross_attention_dim
self.add_embedding = TextTimeEmbedding(
text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
)
elif addition_embed_type == "text_image":
# text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
# case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
self.add_embedding = TextImageTimeEmbedding(
text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
)
elif addition_embed_type == "text_time":
self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
elif addition_embed_type == "image":
# Kandinsky 2.2
self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
elif addition_embed_type == "image_hint":
# Kandinsky 2.2 ControlNet
self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
elif addition_embed_type is not None:
raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
if time_embedding_act_fn is None:
self.time_embed_act = None
else:
self.time_embed_act = get_activation(time_embedding_act_fn)
self.down_blocks = nn.ModuleList([])
self.up_blocks = nn.ModuleList([])
if isinstance(only_cross_attention, bool):
if mid_block_only_cross_attention is None:
mid_block_only_cross_attention = only_cross_attention
only_cross_attention = [only_cross_attention] * len(down_block_types)
if mid_block_only_cross_attention is None:
mid_block_only_cross_attention = False
if isinstance(num_attention_heads, int):
num_attention_heads = (num_attention_heads,) * len(down_block_types)
if isinstance(attention_head_dim, int):
attention_head_dim = (attention_head_dim,) * len(down_block_types)
if isinstance(cross_attention_dim, int):
cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
if isinstance(layers_per_block, int):
layers_per_block = [layers_per_block] * len(down_block_types)
if isinstance(transformer_layers_per_block, int):
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
if class_embeddings_concat:
# The time embeddings are concatenated with the class embeddings. The dimension of the
# time embeddings passed to the down, middle, and up blocks is twice the dimension of the
# regular time embeddings
blocks_time_embed_dim = time_embed_dim * 2
else:
blocks_time_embed_dim = time_embed_dim
# down
output_channel = block_out_channels[0]
for i, down_block_type in enumerate(down_block_types):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
down_block = get_down_block(
down_block_type,
num_layers=layers_per_block[i],
transformer_layers_per_block=transformer_layers_per_block[i],
in_channels=input_channel,
out_channels=output_channel,
temb_channels=blocks_time_embed_dim,
add_downsample=not is_final_block,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
cross_attention_dim=cross_attention_dim[i],
num_attention_heads=num_attention_heads[i],
downsample_padding=downsample_padding,
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention[i],
upcast_attention=upcast_attention,
resnet_time_scale_shift=resnet_time_scale_shift,
resnet_skip_time_act=resnet_skip_time_act,
resnet_out_scale_factor=resnet_out_scale_factor,
cross_attention_norm=cross_attention_norm,
attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
)
self.down_blocks.append(down_block)
# mid
if mid_block_type == "UNetMidBlockFlatCrossAttn":
self.mid_block = UNetMidBlockFlatCrossAttn(
transformer_layers_per_block=transformer_layers_per_block[-1],
in_channels=block_out_channels[-1],
temb_channels=blocks_time_embed_dim,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
output_scale_factor=mid_block_scale_factor,
resnet_time_scale_shift=resnet_time_scale_shift,
cross_attention_dim=cross_attention_dim[-1],
num_attention_heads=num_attention_heads[-1],
resnet_groups=norm_num_groups,
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
upcast_attention=upcast_attention,
)
elif mid_block_type == "UNetMidBlockFlatSimpleCrossAttn":
self.mid_block = UNetMidBlockFlatSimpleCrossAttn(
in_channels=block_out_channels[-1],
temb_channels=blocks_time_embed_dim,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
output_scale_factor=mid_block_scale_factor,
cross_attention_dim=cross_attention_dim[-1],
attention_head_dim=attention_head_dim[-1],
resnet_groups=norm_num_groups,
resnet_time_scale_shift=resnet_time_scale_shift,
skip_time_act=resnet_skip_time_act,
only_cross_attention=mid_block_only_cross_attention,
cross_attention_norm=cross_attention_norm,
)
elif mid_block_type is None:
self.mid_block = None
else:
raise ValueError(f"unknown mid_block_type : {mid_block_type}")
# count how many layers upsample the images
self.num_upsamplers = 0
# up
reversed_block_out_channels = list(reversed(block_out_channels))
reversed_num_attention_heads = list(reversed(num_attention_heads))
reversed_layers_per_block = list(reversed(layers_per_block))
reversed_cross_attention_dim = list(reversed(cross_attention_dim))
reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
only_cross_attention = list(reversed(only_cross_attention))
output_channel = reversed_block_out_channels[0]
for i, up_block_type in enumerate(up_block_types):
is_final_block = i == len(block_out_channels) - 1
prev_output_channel = output_channel
output_channel = reversed_block_out_channels[i]
input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
# add upsample block for all BUT final layer
if not is_final_block:
add_upsample = True
self.num_upsamplers += 1
else:
add_upsample = False
up_block = get_up_block(
up_block_type,
num_layers=reversed_layers_per_block[i] + 1,
transformer_layers_per_block=reversed_transformer_layers_per_block[i],
in_channels=input_channel,
out_channels=output_channel,
prev_output_channel=prev_output_channel,
temb_channels=blocks_time_embed_dim,
add_upsample=add_upsample,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
cross_attention_dim=reversed_cross_attention_dim[i],
num_attention_heads=reversed_num_attention_heads[i],
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention[i],
upcast_attention=upcast_attention,
resnet_time_scale_shift=resnet_time_scale_shift,
resnet_skip_time_act=resnet_skip_time_act,
resnet_out_scale_factor=resnet_out_scale_factor,
cross_attention_norm=cross_attention_norm,
attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
)
self.up_blocks.append(up_block)
prev_output_channel = output_channel
# out
if norm_num_groups is not None:
self.conv_norm_out = nn.GroupNorm(
num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
)
self.conv_act = get_activation(act_fn)
else:
self.conv_norm_out = None
self.conv_act = None
conv_out_padding = (conv_out_kernel - 1) // 2
self.conv_out = LinearMultiDim(
block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
)
@property
def attn_processors(self) -> Dict[str, AttentionProcessor]:
r"""
Returns:
`dict` of attention processors: A dictionary containing all attention processors used in the model with
indexed by its weight name.
"""
# set recursively
processors = {}
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
if hasattr(module, "set_processor"):
processors[f"{name}.processor"] = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
return processors
for name, module in self.named_children():
fn_recursive_add_processors(name, module, processors)
return processors
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
r"""
Sets the attention processor to use to compute attention.
Parameters:
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
The instantiated processor class or a dictionary of processor classes that will be set as the processor
for **all** `Attention` layers.
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
processor. This is strongly recommended when setting trainable attention processors.
"""
count = len(self.attn_processors.keys())
if isinstance(processor, dict) and len(processor) != count:
raise ValueError(
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
)
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
if hasattr(module, "set_processor"):
if not isinstance(processor, dict):
module.set_processor(processor)
else:
module.set_processor(processor.pop(f"{name}.processor"))
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
for name, module in self.named_children():
fn_recursive_attn_processor(name, module, processor)
def set_default_attn_processor(self):
"""
Disables custom attention processors and sets the default attention implementation.
"""
self.set_attn_processor(AttnProcessor())
def set_attention_slice(self, slice_size):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module splits the input tensor in slices to compute attention in
several steps. This is useful for saving some memory in exchange for a small decrease in speed.
Args:
slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
`"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
must be a multiple of `slice_size`.
"""
sliceable_head_dims = []
def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
if hasattr(module, "set_attention_slice"):
sliceable_head_dims.append(module.sliceable_head_dim)
for child in module.children():
fn_recursive_retrieve_sliceable_dims(child)
# retrieve number of attention layers
for module in self.children():
fn_recursive_retrieve_sliceable_dims(module)
num_sliceable_layers = len(sliceable_head_dims)
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
slice_size = [dim // 2 for dim in sliceable_head_dims]
elif slice_size == "max":
# make smallest slice possible
slice_size = num_sliceable_layers * [1]
slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
if len(slice_size) != len(sliceable_head_dims):
raise ValueError(
f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
)
for i in range(len(slice_size)):
size = slice_size[i]
dim = sliceable_head_dims[i]
if size is not None and size > dim:
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
# Recursively walk through all the children.
# Any children which exposes the set_attention_slice method
# gets the message
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
if hasattr(module, "set_attention_slice"):
module.set_attention_slice(slice_size.pop())
for child in module.children():
fn_recursive_set_attention_slice(child, slice_size)
reversed_slice_size = list(reversed(slice_size))
for module in self.children():
fn_recursive_set_attention_slice(module, reversed_slice_size)
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (CrossAttnDownBlockFlat, DownBlockFlat, CrossAttnUpBlockFlat, UpBlockFlat)):
module.gradient_checkpointing = value
def forward(
self,
sample: torch.FloatTensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
class_labels: Optional[torch.Tensor] = None,
timestep_cond: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
mid_block_additional_residual: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
return_dict: bool = True,
) -> Union[UNet2DConditionOutput, Tuple]:
r"""
The [`UNetFlatConditionModel`] forward method.
Args:
sample (`torch.FloatTensor`):
The noisy input tensor with the following shape `(batch, channel, height, width)`.
timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
encoder_hidden_states (`torch.FloatTensor`):
The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
encoder_attention_mask (`torch.Tensor`):
A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
`True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
which adds large negative values to the attention scores corresponding to "discard" tokens.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
tuple.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
added_cond_kwargs: (`dict`, *optional*):
A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that
are passed along to the UNet blocks.
Returns:
[`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise
a `tuple` is returned where the first element is the sample tensor.
"""
# By default samples have to be AT least a multiple of the overall upsampling factor.
# The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
# However, the upsampling interpolation output size can be forced to fit any upsampling size
# on the fly if necessary.
default_overall_up_factor = 2**self.num_upsamplers
# upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
forward_upsample_size = False
upsample_size = None
if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
logger.info("Forward upsample size to force interpolation output size.")
forward_upsample_size = True
# ensure attention_mask is a bias, and give it a singleton query_tokens dimension
# expects mask of shape:
# [batch, key_tokens]
# adds singleton query_tokens dimension:
# [batch, 1, key_tokens]
# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
# [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)
# [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
if attention_mask is not None:
# assume that mask is expressed as:
# (1 = keep, 0 = discard)
# convert mask into a bias that can be added to attention scores:
# (keep = +0, discard = -10000.0)
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
attention_mask = attention_mask.unsqueeze(1)
# convert encoder_attention_mask to a bias the same way we do for attention_mask
if encoder_attention_mask is not None:
encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
# 0. center input if necessary
if self.config.center_input_sample:
sample = 2 * sample - 1.0
# 1. time
timesteps = timestep
if not torch.is_tensor(timesteps):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timesteps = timesteps.expand(sample.shape[0])
t_emb = self.time_proj(timesteps)
# `Timesteps` does not contain any weights and will always return f32 tensors
# but time_embedding might actually be running in fp16. so we need to cast here.
# there might be better ways to encapsulate this.
t_emb = t_emb.to(dtype=sample.dtype)
emb = self.time_embedding(t_emb, timestep_cond)
aug_emb = None
if self.class_embedding is not None:
if class_labels is None:
raise ValueError("class_labels should be provided when num_class_embeds > 0")
if self.config.class_embed_type == "timestep":
class_labels = self.time_proj(class_labels)
# `Timesteps` does not contain any weights and will always return f32 tensors
# there might be better ways to encapsulate this.
class_labels = class_labels.to(dtype=sample.dtype)
class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
if self.config.class_embeddings_concat:
emb = torch.cat([emb, class_emb], dim=-1)
else:
emb = emb + class_emb
if self.config.addition_embed_type == "text":
aug_emb = self.add_embedding(encoder_hidden_states)
elif self.config.addition_embed_type == "text_image":
# Kandinsky 2.1 - style
if "image_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires"
" the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
)
image_embs = added_cond_kwargs.get("image_embeds")
text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)
aug_emb = self.add_embedding(text_embs, image_embs)
elif self.config.addition_embed_type == "text_time":
# SDXL - style
if "text_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires"
" the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
)
text_embeds = added_cond_kwargs.get("text_embeds")
if "time_ids" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires"
" the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
)
time_ids = added_cond_kwargs.get("time_ids")
time_embeds = self.add_time_proj(time_ids.flatten())
time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
add_embeds = add_embeds.to(emb.dtype)
aug_emb = self.add_embedding(add_embeds)
elif self.config.addition_embed_type == "image":
# Kandinsky 2.2 - style
if "image_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the"
" keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
)
image_embs = added_cond_kwargs.get("image_embeds")
aug_emb = self.add_embedding(image_embs)
elif self.config.addition_embed_type == "image_hint":
# Kandinsky 2.2 - style
if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires"
" the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`"
)
image_embs = added_cond_kwargs.get("image_embeds")
hint = added_cond_kwargs.get("hint")
aug_emb, hint = self.add_embedding(image_embs, hint)
sample = torch.cat([sample, hint], dim=1)
emb = emb + aug_emb if aug_emb is not None else emb
if self.time_embed_act is not None:
emb = self.time_embed_act(emb)
if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":
# Kadinsky 2.1 - style
if "image_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which"
" requires the keyword argument `image_embeds` to be passed in `added_conditions`"
)
image_embeds = added_cond_kwargs.get("image_embeds")
encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)
elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj":
# Kandinsky 2.2 - style
if "image_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires"
" the keyword argument `image_embeds` to be passed in `added_conditions`"
)
image_embeds = added_cond_kwargs.get("image_embeds")
encoder_hidden_states = self.encoder_hid_proj(image_embeds)
# 2. pre-process
sample = self.conv_in(sample)
# 3. down
is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
is_adapter = mid_block_additional_residual is None and down_block_additional_residuals is not None
down_block_res_samples = (sample,)
for downsample_block in self.down_blocks:
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
# For t2i-adapter CrossAttnDownBlockFlat
additional_residuals = {}
if is_adapter and len(down_block_additional_residuals) > 0:
additional_residuals["additional_residuals"] = down_block_additional_residuals.pop(0)
sample, res_samples = downsample_block(
hidden_states=sample,
temb=emb,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
cross_attention_kwargs=cross_attention_kwargs,
encoder_attention_mask=encoder_attention_mask,
**additional_residuals,
)
else:
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
if is_adapter and len(down_block_additional_residuals) > 0:
sample += down_block_additional_residuals.pop(0)
down_block_res_samples += res_samples
if is_controlnet:
new_down_block_res_samples = ()
for down_block_res_sample, down_block_additional_residual in zip(
down_block_res_samples, down_block_additional_residuals
):
down_block_res_sample = down_block_res_sample + down_block_additional_residual
new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)
down_block_res_samples = new_down_block_res_samples
# 4. mid
if self.mid_block is not None:
sample = self.mid_block(
sample,
emb,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
cross_attention_kwargs=cross_attention_kwargs,
encoder_attention_mask=encoder_attention_mask,
)
if is_controlnet:
sample = sample + mid_block_additional_residual
# 5. up
for i, upsample_block in enumerate(self.up_blocks):
is_final_block = i == len(self.up_blocks) - 1
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
# if we have not reached the final block and need to forward the
# upsample size, we do it here
if not is_final_block and forward_upsample_size:
upsample_size = down_block_res_samples[-1].shape[2:]
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
sample = upsample_block(
hidden_states=sample,
temb=emb,
res_hidden_states_tuple=res_samples,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
upsample_size=upsample_size,
attention_mask=attention_mask,
encoder_attention_mask=encoder_attention_mask,
)
else:
sample = upsample_block(
hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
)
# 6. post-process
if self.conv_norm_out:
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
sample = self.conv_out(sample)
if not return_dict:
return (sample,)
return UNet2DConditionOutput(sample=sample)
class LinearMultiDim(nn.Linear):
def __init__(self, in_features, out_features=None, second_dim=4, *args, **kwargs):
in_features = [in_features, second_dim, 1] if isinstance(in_features, int) else list(in_features)
if out_features is None:
out_features = in_features
out_features = [out_features, second_dim, 1] if isinstance(out_features, int) else list(out_features)
self.in_features_multidim = in_features
self.out_features_multidim = out_features
super().__init__(np.array(in_features).prod(), np.array(out_features).prod())
def forward(self, input_tensor, *args, **kwargs):
shape = input_tensor.shape
n_dim = len(self.in_features_multidim)
input_tensor = input_tensor.reshape(*shape[0:-n_dim], self.in_features)
output_tensor = super().forward(input_tensor)
output_tensor = output_tensor.view(*shape[0:-n_dim], *self.out_features_multidim)
return output_tensor
class ResnetBlockFlat(nn.Module):
def __init__(
self,
*,
in_channels,
out_channels=None,
dropout=0.0,
temb_channels=512,
groups=32,
groups_out=None,
pre_norm=True,
eps=1e-6,
time_embedding_norm="default",
use_in_shortcut=None,
second_dim=4,
**kwargs,
):
super().__init__()
self.pre_norm = pre_norm
self.pre_norm = True
in_channels = [in_channels, second_dim, 1] if isinstance(in_channels, int) else list(in_channels)
self.in_channels_prod = np.array(in_channels).prod()
self.channels_multidim = in_channels
if out_channels is not None:
out_channels = [out_channels, second_dim, 1] if isinstance(out_channels, int) else list(out_channels)
out_channels_prod = np.array(out_channels).prod()
self.out_channels_multidim = out_channels
else:
out_channels_prod = self.in_channels_prod
self.out_channels_multidim = self.channels_multidim
self.time_embedding_norm = time_embedding_norm
if groups_out is None:
groups_out = groups
self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=self.in_channels_prod, eps=eps, affine=True)
self.conv1 = torch.nn.Conv2d(self.in_channels_prod, out_channels_prod, kernel_size=1, padding=0)
if temb_channels is not None:
self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels_prod)
else:
self.time_emb_proj = None
self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels_prod, eps=eps, affine=True)
self.dropout = torch.nn.Dropout(dropout)
self.conv2 = torch.nn.Conv2d(out_channels_prod, out_channels_prod, kernel_size=1, padding=0)
self.nonlinearity = nn.SiLU()
self.use_in_shortcut = (
self.in_channels_prod != out_channels_prod if use_in_shortcut is None else use_in_shortcut
)
self.conv_shortcut = None
if self.use_in_shortcut:
self.conv_shortcut = torch.nn.Conv2d(
self.in_channels_prod, out_channels_prod, kernel_size=1, stride=1, padding=0
)
def forward(self, input_tensor, temb):
shape = input_tensor.shape
n_dim = len(self.channels_multidim)
input_tensor = input_tensor.reshape(*shape[0:-n_dim], self.in_channels_prod, 1, 1)
input_tensor = input_tensor.view(-1, self.in_channels_prod, 1, 1)
hidden_states = input_tensor
hidden_states = self.norm1(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.conv1(hidden_states)
if temb is not None:
temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None]
hidden_states = hidden_states + temb
hidden_states = self.norm2(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.conv2(hidden_states)
if self.conv_shortcut is not None:
input_tensor = self.conv_shortcut(input_tensor)
output_tensor = input_tensor + hidden_states
output_tensor = output_tensor.view(*shape[0:-n_dim], -1)
output_tensor = output_tensor.view(*shape[0:-n_dim], *self.out_channels_multidim)
return output_tensor
# Copied from diffusers.models.unet_2d_blocks.DownBlock2D with DownBlock2D->DownBlockFlat, ResnetBlock2D->ResnetBlockFlat, Downsample2D->LinearMultiDim
class DownBlockFlat(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
output_scale_factor=1.0,
add_downsample=True,
downsample_padding=1,
):
super().__init__()
resnets = []
for i in range(num_layers):
in_channels = in_channels if i == 0 else out_channels
resnets.append(
ResnetBlockFlat(
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
self.resnets = nn.ModuleList(resnets)
if add_downsample:
self.downsamplers = nn.ModuleList(
[
LinearMultiDim(
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
)
]
)
else:
self.downsamplers = None
self.gradient_checkpointing = False
def forward(self, hidden_states, temb=None):
output_states = ()
for resnet in self.resnets:
if self.training and self.gradient_checkpointing:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs)
return custom_forward
if is_torch_version(">=", "1.11.0"):
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet), hidden_states, temb, use_reentrant=False
)
else:
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet), hidden_states, temb
)
else:
hidden_states = resnet(hidden_states, temb)
output_states = output_states + (hidden_states,)
if self.downsamplers is not None:
for downsampler in self.downsamplers:
hidden_states = downsampler(hidden_states)
output_states = output_states + (hidden_states,)
return hidden_states, output_states
# Copied from diffusers.models.unet_2d_blocks.CrossAttnDownBlock2D with CrossAttnDownBlock2D->CrossAttnDownBlockFlat, ResnetBlock2D->ResnetBlockFlat, Downsample2D->LinearMultiDim
class CrossAttnDownBlockFlat(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
transformer_layers_per_block: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
num_attention_heads=1,
cross_attention_dim=1280,
output_scale_factor=1.0,
downsample_padding=1,
add_downsample=True,
dual_cross_attention=False,
use_linear_projection=False,
only_cross_attention=False,
upcast_attention=False,
):
super().__init__()
resnets = []
attentions = []
self.has_cross_attention = True
self.num_attention_heads = num_attention_heads
for i in range(num_layers):
in_channels = in_channels if i == 0 else out_channels
resnets.append(
ResnetBlockFlat(
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
if not dual_cross_attention:
attentions.append(
Transformer2DModel(
num_attention_heads,
out_channels // num_attention_heads,
in_channels=out_channels,
num_layers=transformer_layers_per_block,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
upcast_attention=upcast_attention,
)
)
else:
attentions.append(
DualTransformer2DModel(
num_attention_heads,
out_channels // num_attention_heads,
in_channels=out_channels,
num_layers=1,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
)
)
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
if add_downsample:
self.downsamplers = nn.ModuleList(
[
LinearMultiDim(
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
)
]
)
else:
self.downsamplers = None
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.FloatTensor,
temb: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
additional_residuals=None,
):
output_states = ()
blocks = list(zip(self.resnets, self.attentions))
for i, (resnet, attn) in enumerate(blocks):
if self.training and self.gradient_checkpointing:
def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
if return_dict is not None:
return module(*inputs, return_dict=return_dict)
else:
return module(*inputs)
return custom_forward
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet),
hidden_states,
temb,
**ckpt_kwargs,
)
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(attn, return_dict=False),
hidden_states,
encoder_hidden_states,
None, # timestep
None, # class_labels
cross_attention_kwargs,
attention_mask,
encoder_attention_mask,
**ckpt_kwargs,
)[0]
else:
hidden_states = resnet(hidden_states, temb)
hidden_states = attn(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
attention_mask=attention_mask,
encoder_attention_mask=encoder_attention_mask,
return_dict=False,
)[0]
# apply additional residuals to the output of the last pair of resnet and attention blocks
if i == len(blocks) - 1 and additional_residuals is not None:
hidden_states = hidden_states + additional_residuals
output_states = output_states + (hidden_states,)
if self.downsamplers is not None:
for downsampler in self.downsamplers:
hidden_states = downsampler(hidden_states)
output_states = output_states + (hidden_states,)
return hidden_states, output_states
# Copied from diffusers.models.unet_2d_blocks.UpBlock2D with UpBlock2D->UpBlockFlat, ResnetBlock2D->ResnetBlockFlat, Upsample2D->LinearMultiDim
class UpBlockFlat(nn.Module):
def __init__(
self,
in_channels: int,
prev_output_channel: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
output_scale_factor=1.0,
add_upsample=True,
):
super().__init__()
resnets = []
for i in range(num_layers):
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
resnet_in_channels = prev_output_channel if i == 0 else out_channels
resnets.append(
ResnetBlockFlat(
in_channels=resnet_in_channels + res_skip_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
self.resnets = nn.ModuleList(resnets)
if add_upsample:
self.upsamplers = nn.ModuleList([LinearMultiDim(out_channels, use_conv=True, out_channels=out_channels)])
else:
self.upsamplers = None
self.gradient_checkpointing = False
def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
for resnet in self.resnets:
# pop res hidden states
res_hidden_states = res_hidden_states_tuple[-1]
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
if self.training and self.gradient_checkpointing:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs)
return custom_forward
if is_torch_version(">=", "1.11.0"):
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet), hidden_states, temb, use_reentrant=False
)
else:
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet), hidden_states, temb
)
else:
hidden_states = resnet(hidden_states, temb)
if self.upsamplers is not None:
for upsampler in self.upsamplers:
hidden_states = upsampler(hidden_states, upsample_size)
return hidden_states
# Copied from diffusers.models.unet_2d_blocks.CrossAttnUpBlock2D with CrossAttnUpBlock2D->CrossAttnUpBlockFlat, ResnetBlock2D->ResnetBlockFlat, Upsample2D->LinearMultiDim
class CrossAttnUpBlockFlat(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
prev_output_channel: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
transformer_layers_per_block: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
num_attention_heads=1,
cross_attention_dim=1280,
output_scale_factor=1.0,
add_upsample=True,
dual_cross_attention=False,
use_linear_projection=False,
only_cross_attention=False,
upcast_attention=False,
):
super().__init__()
resnets = []
attentions = []
self.has_cross_attention = True
self.num_attention_heads = num_attention_heads
for i in range(num_layers):
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
resnet_in_channels = prev_output_channel if i == 0 else out_channels
resnets.append(
ResnetBlockFlat(
in_channels=resnet_in_channels + res_skip_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
if not dual_cross_attention:
attentions.append(
Transformer2DModel(
num_attention_heads,
out_channels // num_attention_heads,
in_channels=out_channels,
num_layers=transformer_layers_per_block,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
upcast_attention=upcast_attention,
)
)
else:
attentions.append(
DualTransformer2DModel(
num_attention_heads,
out_channels // num_attention_heads,
in_channels=out_channels,
num_layers=1,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
)
)
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
if add_upsample:
self.upsamplers = nn.ModuleList([LinearMultiDim(out_channels, use_conv=True, out_channels=out_channels)])
else:
self.upsamplers = None
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.FloatTensor,
res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
temb: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
upsample_size: Optional[int] = None,
attention_mask: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
):
for resnet, attn in zip(self.resnets, self.attentions):
# pop res hidden states
res_hidden_states = res_hidden_states_tuple[-1]
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
if self.training and self.gradient_checkpointing:
def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
if return_dict is not None:
return module(*inputs, return_dict=return_dict)
else:
return module(*inputs)
return custom_forward
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet),
hidden_states,
temb,
**ckpt_kwargs,
)
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(attn, return_dict=False),
hidden_states,
encoder_hidden_states,
None, # timestep
None, # class_labels
cross_attention_kwargs,
attention_mask,
encoder_attention_mask,
**ckpt_kwargs,
)[0]
else:
hidden_states = resnet(hidden_states, temb)
hidden_states = attn(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
attention_mask=attention_mask,
encoder_attention_mask=encoder_attention_mask,
return_dict=False,
)[0]
if self.upsamplers is not None:
for upsampler in self.upsamplers:
hidden_states = upsampler(hidden_states, upsample_size)
return hidden_states
# Copied from diffusers.models.unet_2d_blocks.UNetMidBlock2DCrossAttn with UNetMidBlock2DCrossAttn->UNetMidBlockFlatCrossAttn, ResnetBlock2D->ResnetBlockFlat
class UNetMidBlockFlatCrossAttn(nn.Module):
def __init__(
self,
in_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
transformer_layers_per_block: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
num_attention_heads=1,
output_scale_factor=1.0,
cross_attention_dim=1280,
dual_cross_attention=False,
use_linear_projection=False,
upcast_attention=False,
):
super().__init__()
self.has_cross_attention = True
self.num_attention_heads = num_attention_heads
resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
# there is always at least one resnet
resnets = [
ResnetBlockFlat(
in_channels=in_channels,
out_channels=in_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
]
attentions = []
for _ in range(num_layers):
if not dual_cross_attention:
attentions.append(
Transformer2DModel(
num_attention_heads,
in_channels // num_attention_heads,
in_channels=in_channels,
num_layers=transformer_layers_per_block,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
use_linear_projection=use_linear_projection,
upcast_attention=upcast_attention,
)
)
else:
attentions.append(
DualTransformer2DModel(
num_attention_heads,
in_channels // num_attention_heads,
in_channels=in_channels,
num_layers=1,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
)
)
resnets.append(
ResnetBlockFlat(
in_channels=in_channels,
out_channels=in_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
def forward(
self,
hidden_states: torch.FloatTensor,
temb: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
) -> torch.FloatTensor:
hidden_states = self.resnets[0](hidden_states, temb)
for attn, resnet in zip(self.attentions, self.resnets[1:]):
hidden_states = attn(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
attention_mask=attention_mask,
encoder_attention_mask=encoder_attention_mask,
return_dict=False,
)[0]
hidden_states = resnet(hidden_states, temb)
return hidden_states
# Copied from diffusers.models.unet_2d_blocks.UNetMidBlock2DSimpleCrossAttn with UNetMidBlock2DSimpleCrossAttn->UNetMidBlockFlatSimpleCrossAttn, ResnetBlock2D->ResnetBlockFlat
class UNetMidBlockFlatSimpleCrossAttn(nn.Module):
def __init__(
self,
in_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
attention_head_dim=1,
output_scale_factor=1.0,
cross_attention_dim=1280,
skip_time_act=False,
only_cross_attention=False,
cross_attention_norm=None,
):
super().__init__()
self.has_cross_attention = True
self.attention_head_dim = attention_head_dim
resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
self.num_heads = in_channels // self.attention_head_dim
# there is always at least one resnet
resnets = [
ResnetBlockFlat(
in_channels=in_channels,
out_channels=in_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
skip_time_act=skip_time_act,
)
]
attentions = []
for _ in range(num_layers):
processor = (
AttnAddedKVProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnAddedKVProcessor()
)
attentions.append(
Attention(
query_dim=in_channels,
cross_attention_dim=in_channels,
heads=self.num_heads,
dim_head=self.attention_head_dim,
added_kv_proj_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
bias=True,
upcast_softmax=True,
only_cross_attention=only_cross_attention,
cross_attention_norm=cross_attention_norm,
processor=processor,
)
)
resnets.append(
ResnetBlockFlat(
in_channels=in_channels,
out_channels=in_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
skip_time_act=skip_time_act,
)
)
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
def forward(
self,
hidden_states: torch.FloatTensor,
temb: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
):
cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
if attention_mask is None:
# if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask.
mask = None if encoder_hidden_states is None else encoder_attention_mask
else:
# when attention_mask is defined: we don't even check for encoder_attention_mask.
# this is to maintain compatibility with UnCLIP, which uses 'attention_mask' param for cross-attn masks.
# TODO: UnCLIP should express cross-attn mask via encoder_attention_mask param instead of via attention_mask.
# then we can simplify this whole if/else block to:
# mask = attention_mask if encoder_hidden_states is None else encoder_attention_mask
mask = attention_mask
hidden_states = self.resnets[0](hidden_states, temb)
for attn, resnet in zip(self.attentions, self.resnets[1:]):
# attn
hidden_states = attn(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=mask,
**cross_attention_kwargs,
)
# resnet
hidden_states = resnet(hidden_states, temb)
return hidden_states
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/versatile_diffusion/__init__.py | from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
)
else:
from .modeling_text_unet import UNetFlatConditionModel
from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import warnings
from typing import Callable, List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from transformers import (
CLIPImageProcessor,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionModelWithProjection,
)
from ...image_processor import VaeImageProcessor
from ...models import AutoencoderKL, DualTransformer2DModel, Transformer2DModel, UNet2DConditionModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from .modeling_text_unet import UNetFlatConditionModel
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class VersatileDiffusionDualGuidedPipeline(DiffusionPipeline):
r"""
Pipeline for image-text dual-guided generation using Versatile Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Parameters:
vqvae ([`VQModel`]):
Vector-quantized (VQ) model to encode and decode images to and from latent representations.
bert ([`LDMBertModel`]):
Text-encoder model based on [`~transformers.BERT`].
tokenizer ([`~transformers.BertTokenizer`]):
A `BertTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
"""
tokenizer: CLIPTokenizer
image_feature_extractor: CLIPImageProcessor
text_encoder: CLIPTextModelWithProjection
image_encoder: CLIPVisionModelWithProjection
image_unet: UNet2DConditionModel
text_unet: UNetFlatConditionModel
vae: AutoencoderKL
scheduler: KarrasDiffusionSchedulers
_optional_components = ["text_unet"]
def __init__(
self,
tokenizer: CLIPTokenizer,
image_feature_extractor: CLIPImageProcessor,
text_encoder: CLIPTextModelWithProjection,
image_encoder: CLIPVisionModelWithProjection,
image_unet: UNet2DConditionModel,
text_unet: UNetFlatConditionModel,
vae: AutoencoderKL,
scheduler: KarrasDiffusionSchedulers,
):
super().__init__()
self.register_modules(
tokenizer=tokenizer,
image_feature_extractor=image_feature_extractor,
text_encoder=text_encoder,
image_encoder=image_encoder,
image_unet=image_unet,
text_unet=text_unet,
vae=vae,
scheduler=scheduler,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
if self.text_unet is not None and (
"dual_cross_attention" not in self.image_unet.config or not self.image_unet.config.dual_cross_attention
):
# if loading from a universal checkpoint rather than a saved dual-guided pipeline
self._convert_to_dual_attention()
def remove_unused_weights(self):
self.register_modules(text_unet=None)
def _convert_to_dual_attention(self):
"""
Replace image_unet's `Transformer2DModel` blocks with `DualTransformer2DModel` that contains transformer blocks
from both `image_unet` and `text_unet`
"""
for name, module in self.image_unet.named_modules():
if isinstance(module, Transformer2DModel):
parent_name, index = name.rsplit(".", 1)
index = int(index)
image_transformer = self.image_unet.get_submodule(parent_name)[index]
text_transformer = self.text_unet.get_submodule(parent_name)[index]
config = image_transformer.config
dual_transformer = DualTransformer2DModel(
num_attention_heads=config.num_attention_heads,
attention_head_dim=config.attention_head_dim,
in_channels=config.in_channels,
num_layers=config.num_layers,
dropout=config.dropout,
norm_num_groups=config.norm_num_groups,
cross_attention_dim=config.cross_attention_dim,
attention_bias=config.attention_bias,
sample_size=config.sample_size,
num_vector_embeds=config.num_vector_embeds,
activation_fn=config.activation_fn,
num_embeds_ada_norm=config.num_embeds_ada_norm,
)
dual_transformer.transformers[0] = image_transformer
dual_transformer.transformers[1] = text_transformer
self.image_unet.get_submodule(parent_name)[index] = dual_transformer
self.image_unet.register_to_config(dual_cross_attention=True)
def _revert_dual_attention(self):
"""
Revert the image_unet `DualTransformer2DModel` blocks back to `Transformer2DModel` with image_unet weights Call
this function if you reuse `image_unet` in another pipeline, e.g. `VersatileDiffusionPipeline`
"""
for name, module in self.image_unet.named_modules():
if isinstance(module, DualTransformer2DModel):
parent_name, index = name.rsplit(".", 1)
index = int(index)
self.image_unet.get_submodule(parent_name)[index] = module.transformers[0]
self.image_unet.register_to_config(dual_cross_attention=False)
def _encode_text_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
"""
def normalize_embeddings(encoder_output):
embeds = self.text_encoder.text_projection(encoder_output.last_hidden_state)
embeds_pooled = encoder_output.text_embeds
embeds = embeds / torch.norm(embeds_pooled.unsqueeze(1), dim=-1, keepdim=True)
return embeds
batch_size = len(prompt)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids
if not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = normalize_embeddings(prompt_embeds)
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens = [""] * batch_size
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = normalize_embeddings(negative_prompt_embeds)
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
def _encode_image_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
"""
def normalize_embeddings(encoder_output):
embeds = self.image_encoder.vision_model.post_layernorm(encoder_output.last_hidden_state)
embeds = self.image_encoder.visual_projection(embeds)
embeds_pooled = embeds[:, 0:1]
embeds = embeds / torch.norm(embeds_pooled, dim=-1, keepdim=True)
return embeds
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
image_input = self.image_feature_extractor(images=prompt, return_tensors="pt")
pixel_values = image_input.pixel_values.to(device).to(self.image_encoder.dtype)
image_embeddings = self.image_encoder(pixel_values)
image_embeddings = normalize_embeddings(image_embeddings)
# duplicate image embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = image_embeddings.shape
image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)
image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_images = [np.zeros((512, 512, 3)) + 0.5] * batch_size
uncond_images = self.image_feature_extractor(images=uncond_images, return_tensors="pt")
pixel_values = uncond_images.pixel_values.to(device).to(self.image_encoder.dtype)
negative_prompt_embeds = self.image_encoder(pixel_values)
negative_prompt_embeds = normalize_embeddings(negative_prompt_embeds)
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and conditional embeddings into a single batch
# to avoid doing two forward passes
image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings])
return image_embeddings
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(self, prompt, image, height, width, callback_steps):
if not isinstance(prompt, str) and not isinstance(prompt, PIL.Image.Image) and not isinstance(prompt, list):
raise ValueError(f"`prompt` has to be of type `str` `PIL.Image` or `list` but is {type(prompt)}")
if not isinstance(image, str) and not isinstance(image, PIL.Image.Image) and not isinstance(image, list):
raise ValueError(f"`image` has to be of type `str` `PIL.Image` or `list` but is {type(image)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def set_transformer_params(self, mix_ratio: float = 0.5, condition_types: Tuple = ("text", "image")):
for name, module in self.image_unet.named_modules():
if isinstance(module, DualTransformer2DModel):
module.mix_ratio = mix_ratio
for i, type in enumerate(condition_types):
if type == "text":
module.condition_lengths[i] = self.text_encoder.config.max_position_embeddings
module.transformer_index_for_condition[i] = 1 # use the second (text) transformer
else:
module.condition_lengths[i] = 257
module.transformer_index_for_condition[i] = 0 # use the first (image) transformer
@torch.no_grad()
def __call__(
self,
prompt: Union[PIL.Image.Image, List[PIL.Image.Image]],
image: Union[str, List[str]],
text_to_image_strength: float = 0.5,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
**kwargs,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide image generation.
height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
Examples:
```py
>>> from diffusers import VersatileDiffusionDualGuidedPipeline
>>> import torch
>>> import requests
>>> from io import BytesIO
>>> from PIL import Image
>>> # let's download an initial image
>>> url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"
>>> response = requests.get(url)
>>> image = Image.open(BytesIO(response.content)).convert("RGB")
>>> text = "a red car in the sun"
>>> pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained(
... "shi-labs/versatile-diffusion", torch_dtype=torch.float16
... )
>>> pipe.remove_unused_weights()
>>> pipe = pipe.to("cuda")
>>> generator = torch.Generator(device="cuda").manual_seed(0)
>>> text_to_image_strength = 0.75
>>> image = pipe(
... prompt=text, image=image, text_to_image_strength=text_to_image_strength, generator=generator
... ).images[0]
>>> image.save("./car_variation.png")
```
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images.
"""
# 0. Default height and width to unet
height = height or self.image_unet.config.sample_size * self.vae_scale_factor
width = width or self.image_unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(prompt, image, height, width, callback_steps)
# 2. Define call parameters
prompt = [prompt] if not isinstance(prompt, list) else prompt
image = [image] if not isinstance(image, list) else image
batch_size = len(prompt)
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompts
prompt_embeds = self._encode_text_prompt(prompt, device, num_images_per_prompt, do_classifier_free_guidance)
image_embeddings = self._encode_image_prompt(image, device, num_images_per_prompt, do_classifier_free_guidance)
dual_prompt_embeddings = torch.cat([prompt_embeds, image_embeddings], dim=1)
prompt_types = ("text", "image")
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.image_unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
dual_prompt_embeddings.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs.
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Combine the attention blocks of the image and text UNets
self.set_transformer_params(text_to_image_strength, prompt_types)
# 8. Denoising loop
for i, t in enumerate(self.progress_bar(timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.image_unet(latent_model_input, t, encoder_hidden_states=dual_prompt_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
else:
image = latents
image = self.image_processor.postprocess(image, output_type=output_type)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import warnings
from typing import Callable, List, Optional, Union
import torch
import torch.utils.checkpoint
from transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer
from ...image_processor import VaeImageProcessor
from ...models import AutoencoderKL, Transformer2DModel, UNet2DConditionModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from .modeling_text_unet import UNetFlatConditionModel
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class VersatileDiffusionTextToImagePipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using Versatile Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Parameters:
vqvae ([`VQModel`]):
Vector-quantized (VQ) model to encode and decode images to and from latent representations.
bert ([`LDMBertModel`]):
Text-encoder model based on [`~transformers.BERT`].
tokenizer ([`~transformers.BertTokenizer`]):
A `BertTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
"""
tokenizer: CLIPTokenizer
image_feature_extractor: CLIPImageProcessor
text_encoder: CLIPTextModelWithProjection
image_unet: UNet2DConditionModel
text_unet: UNetFlatConditionModel
vae: AutoencoderKL
scheduler: KarrasDiffusionSchedulers
_optional_components = ["text_unet"]
def __init__(
self,
tokenizer: CLIPTokenizer,
text_encoder: CLIPTextModelWithProjection,
image_unet: UNet2DConditionModel,
text_unet: UNetFlatConditionModel,
vae: AutoencoderKL,
scheduler: KarrasDiffusionSchedulers,
):
super().__init__()
self.register_modules(
tokenizer=tokenizer,
text_encoder=text_encoder,
image_unet=image_unet,
text_unet=text_unet,
vae=vae,
scheduler=scheduler,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
if self.text_unet is not None:
self._swap_unet_attention_blocks()
def _swap_unet_attention_blocks(self):
"""
Swap the `Transformer2DModel` blocks between the image and text UNets
"""
for name, module in self.image_unet.named_modules():
if isinstance(module, Transformer2DModel):
parent_name, index = name.rsplit(".", 1)
index = int(index)
self.image_unet.get_submodule(parent_name)[index], self.text_unet.get_submodule(parent_name)[index] = (
self.text_unet.get_submodule(parent_name)[index],
self.image_unet.get_submodule(parent_name)[index],
)
def remove_unused_weights(self):
self.register_modules(text_unet=None)
def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
"""
def normalize_embeddings(encoder_output):
embeds = self.text_encoder.text_projection(encoder_output.last_hidden_state)
embeds_pooled = encoder_output.text_embeds
embeds = embeds / torch.norm(embeds_pooled.unsqueeze(1), dim=-1, keepdim=True)
return embeds
batch_size = len(prompt) if isinstance(prompt, list) else 1
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids
if not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = normalize_embeddings(prompt_embeds)
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = normalize_embeddings(negative_prompt_embeds)
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
**kwargs,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide image generation.
height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
Examples:
```py
>>> from diffusers import VersatileDiffusionTextToImagePipeline
>>> import torch
>>> pipe = VersatileDiffusionTextToImagePipeline.from_pretrained(
... "shi-labs/versatile-diffusion", torch_dtype=torch.float16
... )
>>> pipe.remove_unused_weights()
>>> pipe = pipe.to("cuda")
>>> generator = torch.Generator(device="cuda").manual_seed(0)
>>> image = pipe("an astronaut riding on a horse on mars", generator=generator).images[0]
>>> image.save("./astronaut.png")
```
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images.
"""
# 0. Default height and width to unet
height = height or self.image_unet.config.sample_size * self.vae_scale_factor
width = width or self.image_unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(prompt, height, width, callback_steps)
# 2. Define call parameters
batch_size = 1 if isinstance(prompt, str) else len(prompt)
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
prompt_embeds = self._encode_prompt(
prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.image_unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs.
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Denoising loop
for i, t in enumerate(self.progress_bar(timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.image_unet(latent_model_input, t, encoder_hidden_states=prompt_embeds).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
else:
image = latents
image = self.image_processor.postprocess(image, output_type=output_type)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import warnings
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from ...image_processor import VaeImageProcessor
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class VersatileDiffusionImageVariationPipeline(DiffusionPipeline):
r"""
Pipeline for image variation using Versatile Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Parameters:
vqvae ([`VQModel`]):
Vector-quantized (VQ) model to encode and decode images to and from latent representations.
bert ([`LDMBertModel`]):
Text-encoder model based on [`~transformers.BERT`].
tokenizer ([`~transformers.BertTokenizer`]):
A `BertTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
"""
image_feature_extractor: CLIPImageProcessor
image_encoder: CLIPVisionModelWithProjection
image_unet: UNet2DConditionModel
vae: AutoencoderKL
scheduler: KarrasDiffusionSchedulers
def __init__(
self,
image_feature_extractor: CLIPImageProcessor,
image_encoder: CLIPVisionModelWithProjection,
image_unet: UNet2DConditionModel,
vae: AutoencoderKL,
scheduler: KarrasDiffusionSchedulers,
):
super().__init__()
self.register_modules(
image_feature_extractor=image_feature_extractor,
image_encoder=image_encoder,
image_unet=image_unet,
vae=vae,
scheduler=scheduler,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
"""
def normalize_embeddings(encoder_output):
embeds = self.image_encoder.vision_model.post_layernorm(encoder_output.last_hidden_state)
embeds = self.image_encoder.visual_projection(embeds)
embeds_pooled = embeds[:, 0:1]
embeds = embeds / torch.norm(embeds_pooled, dim=-1, keepdim=True)
return embeds
if isinstance(prompt, torch.Tensor) and len(prompt.shape) == 4:
prompt = list(prompt)
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
image_input = self.image_feature_extractor(images=prompt, return_tensors="pt")
pixel_values = image_input.pixel_values.to(device).to(self.image_encoder.dtype)
image_embeddings = self.image_encoder(pixel_values)
image_embeddings = normalize_embeddings(image_embeddings)
# duplicate image embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = image_embeddings.shape
image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)
image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_images: List[str]
if negative_prompt is None:
uncond_images = [np.zeros((512, 512, 3)) + 0.5] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, PIL.Image.Image):
uncond_images = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_images = negative_prompt
uncond_images = self.image_feature_extractor(images=uncond_images, return_tensors="pt")
pixel_values = uncond_images.pixel_values.to(device).to(self.image_encoder.dtype)
negative_prompt_embeds = self.image_encoder(pixel_values)
negative_prompt_embeds = normalize_embeddings(negative_prompt_embeds)
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and conditional embeddings into a single batch
# to avoid doing two forward passes
image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings])
return image_embeddings
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_image_variation.StableDiffusionImageVariationPipeline.check_inputs
def check_inputs(self, image, height, width, callback_steps):
if (
not isinstance(image, torch.Tensor)
and not isinstance(image, PIL.Image.Image)
and not isinstance(image, list)
):
raise ValueError(
"`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
f" {type(image)}"
)
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@torch.no_grad()
def __call__(
self,
image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.Tensor],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
**kwargs,
):
r"""
The call function to the pipeline for generation.
Args:
image (`PIL.Image.Image`, `List[PIL.Image.Image]` or `torch.Tensor`):
The image prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
Examples:
```py
>>> from diffusers import VersatileDiffusionImageVariationPipeline
>>> import torch
>>> import requests
>>> from io import BytesIO
>>> from PIL import Image
>>> # let's download an initial image
>>> url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"
>>> response = requests.get(url)
>>> image = Image.open(BytesIO(response.content)).convert("RGB")
>>> pipe = VersatileDiffusionImageVariationPipeline.from_pretrained(
... "shi-labs/versatile-diffusion", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> generator = torch.Generator(device="cuda").manual_seed(0)
>>> image = pipe(image, generator=generator).images[0]
>>> image.save("./car_variation.png")
```
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images.
"""
# 0. Default height and width to unet
height = height or self.image_unet.config.sample_size * self.vae_scale_factor
width = width or self.image_unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(image, height, width, callback_steps)
# 2. Define call parameters
batch_size = 1 if isinstance(image, PIL.Image.Image) else len(image)
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
image_embeddings = self._encode_prompt(
image, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.image_unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
image_embeddings.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs.
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Denoising loop
for i, t in enumerate(self.progress_bar(timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.image_unet(latent_model_input, t, encoder_hidden_states=image_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
else:
image = latents
image = self.image_processor.postprocess(image, output_type=output_type)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion.py | import inspect
from typing import Callable, List, Optional, Union
import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModel
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline
from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class VersatileDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
tokenizer: CLIPTokenizer
image_feature_extractor: CLIPImageProcessor
text_encoder: CLIPTextModel
image_encoder: CLIPVisionModel
image_unet: UNet2DConditionModel
text_unet: UNet2DConditionModel
vae: AutoencoderKL
scheduler: KarrasDiffusionSchedulers
def __init__(
self,
tokenizer: CLIPTokenizer,
image_feature_extractor: CLIPImageProcessor,
text_encoder: CLIPTextModel,
image_encoder: CLIPVisionModel,
image_unet: UNet2DConditionModel,
text_unet: UNet2DConditionModel,
vae: AutoencoderKL,
scheduler: KarrasDiffusionSchedulers,
):
super().__init__()
self.register_modules(
tokenizer=tokenizer,
image_feature_extractor=image_feature_extractor,
text_encoder=text_encoder,
image_encoder=image_encoder,
image_unet=image_unet,
text_unet=text_unet,
vae=vae,
scheduler=scheduler,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
@torch.no_grad()
def image_variation(
self,
image: Union[torch.FloatTensor, PIL.Image.Image],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
):
r"""
The call function to the pipeline for generation.
Args:
image (`PIL.Image.Image`, `List[PIL.Image.Image]` or `torch.Tensor`):
The image prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
Examples:
```py
>>> from diffusers import VersatileDiffusionPipeline
>>> import torch
>>> import requests
>>> from io import BytesIO
>>> from PIL import Image
>>> # let's download an initial image
>>> url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"
>>> response = requests.get(url)
>>> image = Image.open(BytesIO(response.content)).convert("RGB")
>>> pipe = VersatileDiffusionPipeline.from_pretrained(
... "shi-labs/versatile-diffusion", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> generator = torch.Generator(device="cuda").manual_seed(0)
>>> image = pipe.image_variation(image, generator=generator).images[0]
>>> image.save("./car_variation.png")
```
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
"""
expected_components = inspect.signature(VersatileDiffusionImageVariationPipeline.__init__).parameters.keys()
components = {name: component for name, component in self.components.items() if name in expected_components}
return VersatileDiffusionImageVariationPipeline(**components)(
image=image,
height=height,
width=width,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
eta=eta,
generator=generator,
latents=latents,
output_type=output_type,
return_dict=return_dict,
callback=callback,
callback_steps=callback_steps,
)
@torch.no_grad()
def text_to_image(
self,
prompt: Union[str, List[str]],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide image generation.
height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
Examples:
```py
>>> from diffusers import VersatileDiffusionPipeline
>>> import torch
>>> pipe = VersatileDiffusionPipeline.from_pretrained(
... "shi-labs/versatile-diffusion", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> generator = torch.Generator(device="cuda").manual_seed(0)
>>> image = pipe.text_to_image("an astronaut riding on a horse on mars", generator=generator).images[0]
>>> image.save("./astronaut.png")
```
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
"""
expected_components = inspect.signature(VersatileDiffusionTextToImagePipeline.__init__).parameters.keys()
components = {name: component for name, component in self.components.items() if name in expected_components}
temp_pipeline = VersatileDiffusionTextToImagePipeline(**components)
output = temp_pipeline(
prompt=prompt,
height=height,
width=width,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
eta=eta,
generator=generator,
latents=latents,
output_type=output_type,
return_dict=return_dict,
callback=callback,
callback_steps=callback_steps,
)
# swap the attention blocks back to the original state
temp_pipeline._swap_unet_attention_blocks()
return output
@torch.no_grad()
def dual_guided(
self,
prompt: Union[PIL.Image.Image, List[PIL.Image.Image]],
image: Union[str, List[str]],
text_to_image_strength: float = 0.5,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide image generation.
height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
Examples:
```py
>>> from diffusers import VersatileDiffusionPipeline
>>> import torch
>>> import requests
>>> from io import BytesIO
>>> from PIL import Image
>>> # let's download an initial image
>>> url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"
>>> response = requests.get(url)
>>> image = Image.open(BytesIO(response.content)).convert("RGB")
>>> text = "a red car in the sun"
>>> pipe = VersatileDiffusionPipeline.from_pretrained(
... "shi-labs/versatile-diffusion", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> generator = torch.Generator(device="cuda").manual_seed(0)
>>> text_to_image_strength = 0.75
>>> image = pipe.dual_guided(
... prompt=text, image=image, text_to_image_strength=text_to_image_strength, generator=generator
... ).images[0]
>>> image.save("./car_variation.png")
```
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images.
"""
expected_components = inspect.signature(VersatileDiffusionDualGuidedPipeline.__init__).parameters.keys()
components = {name: component for name, component in self.components.items() if name in expected_components}
temp_pipeline = VersatileDiffusionDualGuidedPipeline(**components)
output = temp_pipeline(
prompt=prompt,
image=image,
text_to_image_strength=text_to_image_strength,
height=height,
width=width,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
num_images_per_prompt=num_images_per_prompt,
eta=eta,
generator=generator,
latents=latents,
output_type=output_type,
return_dict=return_dict,
callback=callback,
callback_steps=callback_steps,
)
temp_pipeline._revert_dual_attention()
return output
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/repaint/pipeline_repaint.py | # Copyright 2023 ETH Zurich Computer Vision Lab and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNet2DModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
def _preprocess_image(image: Union[List, PIL.Image.Image, torch.Tensor]):
warnings.warn(
"The preprocess method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor.preprocess instead",
FutureWarning,
)
if isinstance(image, torch.Tensor):
return image
elif isinstance(image, PIL.Image.Image):
image = [image]
if isinstance(image[0], PIL.Image.Image):
w, h = image[0].size
w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
image = np.concatenate(image, axis=0)
image = np.array(image).astype(np.float32) / 255.0
image = image.transpose(0, 3, 1, 2)
image = 2.0 * image - 1.0
image = torch.from_numpy(image)
elif isinstance(image[0], torch.Tensor):
image = torch.cat(image, dim=0)
return image
def _preprocess_mask(mask: Union[List, PIL.Image.Image, torch.Tensor]):
if isinstance(mask, torch.Tensor):
return mask
elif isinstance(mask, PIL.Image.Image):
mask = [mask]
if isinstance(mask[0], PIL.Image.Image):
w, h = mask[0].size
w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
mask = [np.array(m.convert("L").resize((w, h), resample=PIL_INTERPOLATION["nearest"]))[None, :] for m in mask]
mask = np.concatenate(mask, axis=0)
mask = mask.astype(np.float32) / 255.0
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
mask = torch.from_numpy(mask)
elif isinstance(mask[0], torch.Tensor):
mask = torch.cat(mask, dim=0)
return mask
class RePaintPipeline(DiffusionPipeline):
r"""
Pipeline for image inpainting using RePaint.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Parameters:
unet ([`UNet2DModel`]):
A `UNet2DModel` to denoise the encoded image latents.
scheduler ([`RePaintScheduler`]):
A `RePaintScheduler` to be used in combination with `unet` to denoise the encoded image.
"""
unet: UNet2DModel
scheduler: RePaintScheduler
def __init__(self, unet, scheduler):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
image: Union[torch.Tensor, PIL.Image.Image],
mask_image: Union[torch.Tensor, PIL.Image.Image],
num_inference_steps: int = 250,
eta: float = 0.0,
jump_length: int = 10,
jump_n_sample: int = 10,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
) -> Union[ImagePipelineOutput, Tuple]:
r"""
The call function to the pipeline for generation.
Args:
image (`torch.FloatTensor` or `PIL.Image.Image`):
The original image to inpaint on.
mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
The mask_image where 0.0 define which part of the original image to inpaint.
num_inference_steps (`int`, *optional*, defaults to 1000):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
eta (`float`):
The weight of the added noise in a diffusion step. Its value is between 0.0 and 1.0; 0.0 corresponds to
DDIM and 1.0 is the DDPM scheduler.
jump_length (`int`, *optional*, defaults to 10):
The number of steps taken forward in time before going backward in time for a single jump ("j" in
RePaint paper). Take a look at Figure 9 and 10 in the [paper](https://arxiv.org/pdf/2201.09865.pdf).
jump_n_sample (`int`, *optional*, defaults to 10):
The number of times to make a forward time jump for a given chosen time sample. Take a look at Figure 9
and 10 in the [paper](https://arxiv.org/pdf/2201.09865.pdf).
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
output_type (`str`, `optional`, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple.
Example:
```py
>>> from io import BytesIO
>>> import torch
>>> import PIL
>>> import requests
>>> from diffusers import RePaintPipeline, RePaintScheduler
>>> def download_image(url):
... response = requests.get(url)
... return PIL.Image.open(BytesIO(response.content)).convert("RGB")
>>> img_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/celeba_hq_256.png"
>>> mask_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"
>>> # Load the original image and the mask as PIL images
>>> original_image = download_image(img_url).resize((256, 256))
>>> mask_image = download_image(mask_url).resize((256, 256))
>>> # Load the RePaint scheduler and pipeline based on a pretrained DDPM model
>>> scheduler = RePaintScheduler.from_pretrained("google/ddpm-ema-celebahq-256")
>>> pipe = RePaintPipeline.from_pretrained("google/ddpm-ema-celebahq-256", scheduler=scheduler)
>>> pipe = pipe.to("cuda")
>>> generator = torch.Generator(device="cuda").manual_seed(0)
>>> output = pipe(
... image=original_image,
... mask_image=mask_image,
... num_inference_steps=250,
... eta=0.0,
... jump_length=10,
... jump_n_sample=10,
... generator=generator,
... )
>>> inpainted_image = output.images[0]
```
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images.
"""
original_image = image
original_image = _preprocess_image(original_image)
original_image = original_image.to(device=self._execution_device, dtype=self.unet.dtype)
mask_image = _preprocess_mask(mask_image)
mask_image = mask_image.to(device=self._execution_device, dtype=self.unet.dtype)
batch_size = original_image.shape[0]
# sample gaussian noise to begin the loop
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
image_shape = original_image.shape
image = randn_tensor(image_shape, generator=generator, device=self._execution_device, dtype=self.unet.dtype)
# set step values
self.scheduler.set_timesteps(num_inference_steps, jump_length, jump_n_sample, self._execution_device)
self.scheduler.eta = eta
t_last = self.scheduler.timesteps[0] + 1
generator = generator[0] if isinstance(generator, list) else generator
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
if t < t_last:
# predict the noise residual
model_output = self.unet(image, t).sample
# compute previous image: x_t -> x_t-1
image = self.scheduler.step(model_output, t, image, original_image, mask_image, generator).prev_sample
else:
# compute the reverse: x_t-1 -> x_t
image = self.scheduler.undo_step(image, t_last, generator)
t_last = t
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/repaint/__init__.py | from .pipeline_repaint import RePaintPipeline
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/vq_diffusion/__init__.py | from ...utils import OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import *
else:
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/vq_diffusion/pipeline_vq_diffusion.py | # Copyright 2023 Microsoft and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, Transformer2DModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class LearnedClassifierFreeSamplingEmbeddings(ModelMixin, ConfigMixin):
"""
Utility class for storing learned text embeddings for classifier free sampling
"""
@register_to_config
def __init__(self, learnable: bool, hidden_size: Optional[int] = None, length: Optional[int] = None):
super().__init__()
self.learnable = learnable
if self.learnable:
assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
assert length is not None, "learnable=True requires `length` to be set"
embeddings = torch.zeros(length, hidden_size)
else:
embeddings = None
self.embeddings = torch.nn.Parameter(embeddings)
class VQDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using VQ Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
vqvae ([`VQModel`]):
Vector Quantized Variational Auto-Encoder (VAE) model to encode and decode images to and from latent
representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
transformer ([`Transformer2DModel`]):
A conditional `Transformer2DModel` to denoise the encoded image latents.
scheduler ([`VQDiffusionScheduler`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
"""
vqvae: VQModel
text_encoder: CLIPTextModel
tokenizer: CLIPTokenizer
transformer: Transformer2DModel
learned_classifier_free_sampling_embeddings: LearnedClassifierFreeSamplingEmbeddings
scheduler: VQDiffusionScheduler
def __init__(
self,
vqvae: VQModel,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
transformer: Transformer2DModel,
scheduler: VQDiffusionScheduler,
learned_classifier_free_sampling_embeddings: LearnedClassifierFreeSamplingEmbeddings,
):
super().__init__()
self.register_modules(
vqvae=vqvae,
transformer=transformer,
text_encoder=text_encoder,
tokenizer=tokenizer,
scheduler=scheduler,
learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings,
)
def _encode_prompt(self, prompt, num_images_per_prompt, do_classifier_free_guidance):
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
prompt_embeds = self.text_encoder(text_input_ids.to(self.device))[0]
# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
# While CLIP does normalize the pooled output of the text transformer when combining
# the image and text embeddings, CLIP does not directly normalize the last hidden state.
#
# CLIP normalizing the pooled output.
# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
prompt_embeds = prompt_embeds / prompt_embeds.norm(dim=-1, keepdim=True)
# duplicate text embeddings for each generation per prompt
prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
if self.learned_classifier_free_sampling_embeddings.learnable:
negative_prompt_embeds = self.learned_classifier_free_sampling_embeddings.embeddings
negative_prompt_embeds = negative_prompt_embeds.unsqueeze(0).repeat(batch_size, 1, 1)
else:
uncond_tokens = [""] * batch_size
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
negative_prompt_embeds = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# See comment for normalizing text embeddings
negative_prompt_embeds = negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1, keepdim=True)
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
num_inference_steps: int = 100,
guidance_scale: float = 5.0,
truncation_rate: float = 1.0,
num_images_per_prompt: int = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
) -> Union[ImagePipelineOutput, Tuple]:
"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide image generation.
num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
truncation_rate (`float`, *optional*, defaults to 1.0 (equivalent to no truncation)):
Used to "truncate" the predicted classes for x_0 such that the cumulative probability for a pixel is at
most `truncation_rate`. The lowest probabilities that would increase the cumulative probability above
`truncation_rate` are set to zero.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor` of shape (batch), *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Must be valid embedding indices.If not provided, a latents tensor will be generated of
completely masked latent pixels.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images.
"""
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
batch_size = batch_size * num_images_per_prompt
do_classifier_free_guidance = guidance_scale > 1.0
prompt_embeds = self._encode_prompt(prompt, num_images_per_prompt, do_classifier_free_guidance)
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
# get the initial completely masked latents unless the user supplied it
latents_shape = (batch_size, self.transformer.num_latent_pixels)
if latents is None:
mask_class = self.transformer.num_vector_embeds - 1
latents = torch.full(latents_shape, mask_class).to(self.device)
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
raise ValueError(
"Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"
f" {self.transformer.num_vector_embeds - 1} (inclusive)."
)
latents = latents.to(self.device)
# set timesteps
self.scheduler.set_timesteps(num_inference_steps, device=self.device)
timesteps_tensor = self.scheduler.timesteps.to(self.device)
sample = latents
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
# expand the sample if we are doing classifier free guidance
latent_model_input = torch.cat([sample] * 2) if do_classifier_free_guidance else sample
# predict the un-noised image
# model_output == `log_p_x_0`
model_output = self.transformer(latent_model_input, encoder_hidden_states=prompt_embeds, timestep=t).sample
if do_classifier_free_guidance:
model_output_uncond, model_output_text = model_output.chunk(2)
model_output = model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
model_output -= torch.logsumexp(model_output, dim=1, keepdim=True)
model_output = self.truncate(model_output, truncation_rate)
# remove `log(0)`'s (`-inf`s)
model_output = model_output.clamp(-70)
# compute the previous noisy sample x_t -> x_t-1
sample = self.scheduler.step(model_output, timestep=t, sample=sample, generator=generator).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, t, sample)
embedding_channels = self.vqvae.config.vq_embed_dim
embeddings_shape = (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
embeddings = self.vqvae.quantize.get_codebook_entry(sample, shape=embeddings_shape)
image = self.vqvae.decode(embeddings, force_not_quantize=True).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
def truncate(self, log_p_x_0: torch.FloatTensor, truncation_rate: float) -> torch.FloatTensor:
"""
Truncates `log_p_x_0` such that for each column vector, the total cumulative probability is `truncation_rate`
The lowest probabilities that would increase the cumulative probability above `truncation_rate` are set to
zero.
"""
sorted_log_p_x_0, indices = torch.sort(log_p_x_0, 1, descending=True)
sorted_p_x_0 = torch.exp(sorted_log_p_x_0)
keep_mask = sorted_p_x_0.cumsum(dim=1) < truncation_rate
# Ensure that at least the largest probability is not zeroed out
all_true = torch.full_like(keep_mask[:, 0:1, :], True)
keep_mask = torch.cat((all_true, keep_mask), dim=1)
keep_mask = keep_mask[:, :-1, :]
keep_mask = keep_mask.gather(1, indices.argsort(1))
rv = log_p_x_0.clone()
rv[~keep_mask] = -torch.inf # -inf = log(0)
return rv
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/dance_diffusion/__init__.py | from .pipeline_dance_diffusion import DanceDiffusionPipeline
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/dance_diffusion/pipeline_dance_diffusion.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class DanceDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline for audio generation.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Parameters:
unet ([`UNet1DModel`]):
A `UNet1DModel` to denoise the encoded audio.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of
[`IPNDMScheduler`].
"""
def __init__(self, unet, scheduler):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
num_inference_steps: int = 100,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
audio_length_in_s: Optional[float] = None,
return_dict: bool = True,
) -> Union[AudioPipelineOutput, Tuple]:
r"""
The call function to the pipeline for generation.
Args:
batch_size (`int`, *optional*, defaults to 1):
The number of audio samples to generate.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher-quality audio sample at
the expense of slower inference.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
audio_length_in_s (`float`, *optional*, defaults to `self.unet.config.sample_size/self.unet.config.sample_rate`):
The length of the generated audio sample in seconds.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.AudioPipelineOutput`] instead of a plain tuple.
Example:
```py
from diffusers import DiffusionPipeline
from scipy.io.wavfile import write
model_id = "harmonai/maestro-150k"
pipe = DiffusionPipeline.from_pretrained(model_id)
pipe = pipe.to("cuda")
audios = pipe(audio_length_in_s=4.0).audios
# To save locally
for i, audio in enumerate(audios):
write(f"maestro_test_{i}.wav", pipe.unet.sample_rate, audio.transpose())
# To dislay in google colab
import IPython.display as ipd
for audio in audios:
display(ipd.Audio(audio, rate=pipe.unet.sample_rate))
```
Returns:
[`~pipelines.AudioPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.AudioPipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated audio.
"""
if audio_length_in_s is None:
audio_length_in_s = self.unet.config.sample_size / self.unet.config.sample_rate
sample_size = audio_length_in_s * self.unet.config.sample_rate
down_scale_factor = 2 ** len(self.unet.up_blocks)
if sample_size < 3 * down_scale_factor:
raise ValueError(
f"{audio_length_in_s} is too small. Make sure it's bigger or equal to"
f" {3 * down_scale_factor / self.unet.config.sample_rate}."
)
original_sample_size = int(sample_size)
if sample_size % down_scale_factor != 0:
sample_size = (
(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
) * down_scale_factor
logger.info(
f"{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"
f" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"
" process."
)
sample_size = int(sample_size)
dtype = next(self.unet.parameters()).dtype
shape = (batch_size, self.unet.config.in_channels, sample_size)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
audio = randn_tensor(shape, generator=generator, device=self._execution_device, dtype=dtype)
# set step values
self.scheduler.set_timesteps(num_inference_steps, device=audio.device)
self.scheduler.timesteps = self.scheduler.timesteps.to(dtype)
for t in self.progress_bar(self.scheduler.timesteps):
# 1. predict noise model_output
model_output = self.unet(audio, t).sample
# 2. compute previous audio sample: x_t -> t_t-1
audio = self.scheduler.step(model_output, t, audio).prev_sample
audio = audio.clamp(-1, 1).float().cpu().numpy()
audio = audio[:, :, :original_sample_size]
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=audio)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/README.md | # Stable Diffusion
## Overview
Stable Diffusion was proposed in [Stable Diffusion Announcement](https://stability.ai/blog/stable-diffusion-announcement) by Patrick Esser and Robin Rombach and the Stability AI team.
The summary of the model is the following:
*Stable Diffusion is a text-to-image model that will empower billions of people to create stunning art within seconds. It is a breakthrough in speed and quality meaning that it can run on consumer GPUs. You can see some of the amazing output that has been created by this model without pre or post-processing on this page. The model itself builds upon the work of the team at CompVis and Runway in their widely used latent diffusion model combined with insights from the conditional diffusion models by our lead generative AI developer Katherine Crowson, Dall-E 2 by Open AI, Imagen by Google Brain and many others. We are delighted that AI media generation is a cooperative field and hope it can continue this way to bring the gift of creativity to all.*
## Tips:
- Stable Diffusion has the same architecture as [Latent Diffusion](https://arxiv.org/abs/2112.10752) but uses a frozen CLIP Text Encoder instead of training the text encoder jointly with the diffusion model.
- An in-detail explanation of the Stable Diffusion model can be found under [Stable Diffusion with 🧨 Diffusers](https://huggingface.co/blog/stable_diffusion).
- If you don't want to rely on the Hugging Face Hub and having to pass a authentication token, you can
download the weights with `git lfs install; git clone https://huggingface.co/runwayml/stable-diffusion-v1-5` and instead pass the local path to the cloned folder to `from_pretrained` as shown below.
- Stable Diffusion can work with a variety of different samplers as is shown below.
## Available Pipelines:
| Pipeline | Tasks | Colab
|---|---|:---:|
| [pipeline_stable_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py) | *Text-to-Image Generation* | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
| [pipeline_stable_diffusion_img2img](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py) | *Image-to-Image Text-Guided Generation* | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb)
| [pipeline_stable_diffusion_inpaint](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py) | *Text-Guided Image Inpainting* | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb)
## Examples:
### Using Stable Diffusion without being logged into the Hub.
If you want to download the model weights using a single Python line, you need to be logged in via `huggingface-cli login`.
```python
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
```
This however can make it difficult to build applications on top of `diffusers` as you will always have to pass the token around. A potential way to solve this issue is by downloading the weights to a local path `"./stable-diffusion-v1-5"`:
```
git lfs install
git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
```
and simply passing the local path to `from_pretrained`:
```python
from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained("./stable-diffusion-v1-5")
```
### Text-to-Image with default PLMS scheduler
```python
# make sure you're logged in with `huggingface-cli login`
from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
pipe = pipe.to("cuda")
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
image.save("astronaut_rides_horse.png")
```
### Text-to-Image with DDIM scheduler
```python
# make sure you're logged in with `huggingface-cli login`
from diffusers import StableDiffusionPipeline, DDIMScheduler
scheduler = DDIMScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
scheduler=scheduler,
).to("cuda")
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
image.save("astronaut_rides_horse.png")
```
### Text-to-Image with K-LMS scheduler
```python
# make sure you're logged in with `huggingface-cli login`
from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
lms = LMSDiscreteScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
scheduler=lms,
).to("cuda")
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
image.save("astronaut_rides_horse.png")
```
### CycleDiffusion using Stable Diffusion and DDIM scheduler
```python
import requests
import torch
from PIL import Image
from io import BytesIO
from diffusers import CycleDiffusionPipeline, DDIMScheduler
# load the scheduler. CycleDiffusion only supports stochastic schedulers.
# load the pipeline
# make sure you're logged in with `huggingface-cli login`
model_id_or_path = "CompVis/stable-diffusion-v1-4"
scheduler = DDIMScheduler.from_pretrained(model_id_or_path, subfolder="scheduler")
pipe = CycleDiffusionPipeline.from_pretrained(model_id_or_path, scheduler=scheduler).to("cuda")
# let's download an initial image
url = "https://raw.githubusercontent.com/ChenWu98/cycle-diffusion/main/data/dalle2/An%20astronaut%20riding%20a%20horse.png"
response = requests.get(url)
init_image = Image.open(BytesIO(response.content)).convert("RGB")
init_image = init_image.resize((512, 512))
init_image.save("horse.png")
# let's specify a prompt
source_prompt = "An astronaut riding a horse"
prompt = "An astronaut riding an elephant"
# call the pipeline
image = pipe(
prompt=prompt,
source_prompt=source_prompt,
image=init_image,
num_inference_steps=100,
eta=0.1,
strength=0.8,
guidance_scale=2,
source_guidance_scale=1,
).images[0]
image.save("horse_to_elephant.png")
# let's try another example
# See more samples at the original repo: https://github.com/ChenWu98/cycle-diffusion
url = "https://raw.githubusercontent.com/ChenWu98/cycle-diffusion/main/data/dalle2/A%20black%20colored%20car.png"
response = requests.get(url)
init_image = Image.open(BytesIO(response.content)).convert("RGB")
init_image = init_image.resize((512, 512))
init_image.save("black.png")
source_prompt = "A black colored car"
prompt = "A blue colored car"
# call the pipeline
torch.manual_seed(0)
image = pipe(
prompt=prompt,
source_prompt=source_prompt,
image=init_image,
num_inference_steps=100,
eta=0.1,
strength=0.85,
guidance_scale=3,
source_guidance_scale=1,
).images[0]
image.save("black_to_blue.png")
```
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/__init__.py | from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_available,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
@dataclass
class StableDiffusionPipelineOutput(BaseOutput):
"""
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`List[bool]`)
List indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content or
`None` if safety checking could not be performed.
"""
images: Union[List[PIL.Image.Image], np.ndarray]
nsfw_content_detected: Optional[List[bool]]
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_cycle_diffusion import CycleDiffusionPipeline
from .pipeline_stable_diffusion import StableDiffusionPipeline
from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
from .pipeline_stable_diffusion_img2img import StableDiffusionImg2ImgPipeline
from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
from .pipeline_stable_diffusion_instruct_pix2pix import StableDiffusionInstructPix2PixPipeline
from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
from .pipeline_stable_diffusion_ldm3d import StableDiffusionLDM3DPipeline
from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from .pipeline_stable_unclip import StableUnCLIPPipeline
from .pipeline_stable_unclip_img2img import StableUnCLIPImg2ImgPipeline
from .safety_checker import StableDiffusionSafetyChecker
from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.26.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
StableDiffusionDepth2ImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionPix2PixZeroPipeline,
)
else:
from .pipeline_stable_diffusion_depth2img import StableDiffusionDepth2ImgPipeline
from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
from .pipeline_stable_diffusion_pix2pix_zero import StableDiffusionPix2PixZeroPipeline
try:
if not (
is_torch_available()
and is_transformers_available()
and is_k_diffusion_available()
and is_k_diffusion_version(">=", "0.0.12")
):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline
try:
if not (is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_onnx_objects import * # noqa F403
else:
from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
from .pipeline_onnx_stable_diffusion_img2img import OnnxStableDiffusionImg2ImgPipeline
from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline
if is_transformers_available() and is_flax_available():
import flax
@flax.struct.dataclass
class FlaxStableDiffusionPipelineOutput(BaseOutput):
"""
Output class for Flax-based Stable Diffusion pipelines.
Args:
images (`np.ndarray`):
Denoised images of array shape of `(batch_size, height, width, num_channels)`.
nsfw_content_detected (`List[bool]`):
List indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content
or `None` if safety checking could not be performed.
"""
images: np.ndarray
nsfw_content_detected: List[bool]
from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
from .pipeline_flax_stable_diffusion_img2img import FlaxStableDiffusionImg2ImgPipeline
from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_k_diffusion.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
import inspect
import warnings
from typing import Callable, List, Optional, Union
import torch
from k_diffusion.external import CompVisDenoiser, CompVisVDenoiser
from k_diffusion.sampling import BrownianTreeNoiseSampler, get_sigmas_karras
from ...image_processor import VaeImageProcessor
from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from ...schedulers import LMSDiscreteScheduler
from ...utils import is_accelerate_available, is_accelerate_version, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class ModelWrapper:
def __init__(self, model, alphas_cumprod):
self.model = model
self.alphas_cumprod = alphas_cumprod
def apply_model(self, *args, **kwargs):
if len(args) == 3:
encoder_hidden_states = args[-1]
args = args[:2]
if kwargs.get("cond", None) is not None:
encoder_hidden_states = kwargs.pop("cond")
return self.model(*args, encoder_hidden_states=encoder_hidden_states, **kwargs).sample
class StableDiffusionKDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
r"""
Pipeline for text-to-image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
<Tip warning={true}>
This is an experimental pipeline and is likely to change in the future.
</Tip>
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae,
text_encoder,
tokenizer,
unet,
scheduler,
safety_checker,
feature_extractor,
requires_safety_checker: bool = True,
):
super().__init__()
logger.info(
f"{self.__class__} is an experimntal pipeline and is likely to change in the future. We recommend to use"
" this pipeline for fast experimentation / iteration if needed, but advice to rely on existing pipelines"
" as defined in https://huggingface.co/docs/diffusers/api/schedulers#implemented-schedulers for"
" production settings."
)
# get correct sigmas from LMS
scheduler = LMSDiscreteScheduler.from_config(scheduler.config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.register_to_config(requires_safety_checker=requires_safety_checker)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
model = ModelWrapper(unet, scheduler.alphas_cumprod)
if scheduler.config.prediction_type == "v_prediction":
self.k_diffusion_model = CompVisVDenoiser(model)
else:
self.k_diffusion_model = CompVisDenoiser(model)
def set_scheduler(self, scheduler_type: str):
library = importlib.import_module("k_diffusion")
sampling = getattr(library, "sampling")
self.sampler = getattr(sampling, scheduler_type)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
if self.safety_checker is not None:
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
return latents
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
use_karras_sigmas: Optional[bool] = False,
noise_sampler_seed: Optional[int] = None,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale`
is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
use_karras_sigmas (`bool`, *optional*, defaults to `False`):
Use karras sigmas. For example, specifying `sample_dpmpp_2m` to `set_scheduler` will be equivalent to
`DPM++2M` in stable-diffusion-webui. On top of that, setting this option to True will make it `DPM++2M
Karras`.
noise_sampler_seed (`int`, *optional*, defaults to `None`):
The random seed to use for the noise sampler. If `None`, a random seed will be generated.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = True
if guidance_scale <= 1.0:
raise ValueError("has to use guidance_scale")
# 3. Encode input prompt
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=prompt_embeds.device)
# 5. Prepare sigmas
if use_karras_sigmas:
sigma_min: float = self.k_diffusion_model.sigmas[0].item()
sigma_max: float = self.k_diffusion_model.sigmas[-1].item()
sigmas = get_sigmas_karras(n=num_inference_steps, sigma_min=sigma_min, sigma_max=sigma_max)
sigmas = sigmas.to(device)
else:
sigmas = self.scheduler.sigmas
sigmas = sigmas.to(prompt_embeds.dtype)
# 6. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
latents = latents * sigmas[0]
self.k_diffusion_model.sigmas = self.k_diffusion_model.sigmas.to(latents.device)
self.k_diffusion_model.log_sigmas = self.k_diffusion_model.log_sigmas.to(latents.device)
# 7. Define model function
def model_fn(x, t):
latent_model_input = torch.cat([x] * 2)
t = torch.cat([t] * 2)
noise_pred = self.k_diffusion_model(latent_model_input, t, cond=prompt_embeds)
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
return noise_pred
# 8. Run k-diffusion solver
sampler_kwargs = {}
if "noise_sampler" in inspect.signature(self.sampler).parameters:
min_sigma, max_sigma = sigmas[sigmas > 0].min(), sigmas.max()
noise_sampler = BrownianTreeNoiseSampler(latents, min_sigma, max_sigma, noise_sampler_seed)
sampler_kwargs["noise_sampler"] = noise_sampler
latents = self.sampler(model_fn, latents, sigmas, **sampler_kwargs)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import warnings
from typing import Any, Callable, Dict, List, Optional, Union
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
from diffusers.utils.import_utils import is_accelerate_available
from ...image_processor import VaeImageProcessor
from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel
from ...models.embeddings import get_timestep_embedding
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import is_accelerate_version, logging, randn_tensor, replace_example_docstring
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import requests
>>> import torch
>>> from PIL import Image
>>> from io import BytesIO
>>> from diffusers import StableUnCLIPImg2ImgPipeline
>>> pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(
... "fusing/stable-unclip-2-1-l-img2img", torch_dtype=torch.float16
... ) # TODO update model path
>>> pipe = pipe.to("cuda")
>>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
>>> response = requests.get(url)
>>> init_image = Image.open(BytesIO(response.content)).convert("RGB")
>>> init_image = init_image.resize((768, 512))
>>> prompt = "A fantasy landscape, trending on artstation"
>>> images = pipe(prompt, init_image).images
>>> images[0].save("fantasy_landscape.png")
```
"""
class StableUnCLIPImg2ImgPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
"""
Pipeline for text-guided image-to-image generation using stable unCLIP.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
feature_extractor ([`CLIPImageProcessor`]):
Feature extractor for image pre-processing before being encoded.
image_encoder ([`CLIPVisionModelWithProjection`]):
CLIP vision model for encoding images.
image_normalizer ([`StableUnCLIPImageNormalizer`]):
Used to normalize the predicted image embeddings before the noise is applied and un-normalize the image
embeddings after the noise has been applied.
image_noising_scheduler ([`KarrasDiffusionSchedulers`]):
Noise schedule for adding noise to the predicted image embeddings. The amount of noise to add is determined
by the `noise_level`.
tokenizer (`~transformers.CLIPTokenizer`):
A [`~transformers.CLIPTokenizer`)].
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen [`~transformers.CLIPTextModel`] text-encoder.
unet ([`UNet2DConditionModel`]):
A [`UNet2DConditionModel`] to denoise the encoded image latents.
scheduler ([`KarrasDiffusionSchedulers`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents.
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
"""
_exclude_from_cpu_offload = ["image_normalizer"]
# image encoding components
feature_extractor: CLIPImageProcessor
image_encoder: CLIPVisionModelWithProjection
# image noising components
image_normalizer: StableUnCLIPImageNormalizer
image_noising_scheduler: KarrasDiffusionSchedulers
# regular denoising components
tokenizer: CLIPTokenizer
text_encoder: CLIPTextModel
unet: UNet2DConditionModel
scheduler: KarrasDiffusionSchedulers
vae: AutoencoderKL
def __init__(
self,
# image encoding components
feature_extractor: CLIPImageProcessor,
image_encoder: CLIPVisionModelWithProjection,
# image noising components
image_normalizer: StableUnCLIPImageNormalizer,
image_noising_scheduler: KarrasDiffusionSchedulers,
# regular denoising components
tokenizer: CLIPTokenizer,
text_encoder: CLIPTextModel,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
# vae
vae: AutoencoderKL,
):
super().__init__()
self.register_modules(
feature_extractor=feature_extractor,
image_encoder=image_encoder,
image_normalizer=image_normalizer,
image_noising_scheduler=image_noising_scheduler,
tokenizer=tokenizer,
text_encoder=text_encoder,
unet=unet,
scheduler=scheduler,
vae=vae,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.vae.enable_slicing()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
def disable_vae_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_slicing()
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
for cpu_offloaded_model in [self.text_encoder, self.image_encoder, self.unet, self.vae]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
def _encode_image(
self,
image,
device,
batch_size,
num_images_per_prompt,
do_classifier_free_guidance,
noise_level,
generator,
image_embeds,
):
dtype = next(self.image_encoder.parameters()).dtype
if isinstance(image, PIL.Image.Image):
# the image embedding should repeated so it matches the total batch size of the prompt
repeat_by = batch_size
else:
# assume the image input is already properly batched and just needs to be repeated so
# it matches the num_images_per_prompt.
#
# NOTE(will) this is probably missing a few number of side cases. I.e. batched/non-batched
# `image_embeds`. If those happen to be common use cases, let's think harder about
# what the expected dimensions of inputs should be and how we handle the encoding.
repeat_by = num_images_per_prompt
if image_embeds is None:
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(images=image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeds = self.image_encoder(image).image_embeds
image_embeds = self.noise_image_embeddings(
image_embeds=image_embeds,
noise_level=noise_level,
generator=generator,
)
# duplicate image embeddings for each generation per prompt, using mps friendly method
image_embeds = image_embeds.unsqueeze(1)
bs_embed, seq_len, _ = image_embeds.shape
image_embeds = image_embeds.repeat(1, repeat_by, 1)
image_embeds = image_embeds.view(bs_embed * repeat_by, seq_len, -1)
image_embeds = image_embeds.squeeze(1)
if do_classifier_free_guidance:
negative_prompt_embeds = torch.zeros_like(image_embeds)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
image_embeds = torch.cat([negative_prompt_embeds, image_embeds])
return image_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self,
prompt,
image,
height,
width,
callback_steps,
noise_level,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
image_embeds=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Please make sure to define only one of the two."
)
if prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
if prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
"Provide either `negative_prompt` or `negative_prompt_embeds`. Cannot leave both `negative_prompt` and `negative_prompt_embeds` undefined."
)
if prompt is not None and negative_prompt is not None:
if type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
if noise_level < 0 or noise_level >= self.image_noising_scheduler.config.num_train_timesteps:
raise ValueError(
f"`noise_level` must be between 0 and {self.image_noising_scheduler.config.num_train_timesteps - 1}, inclusive."
)
if image is not None and image_embeds is not None:
raise ValueError(
"Provide either `image` or `image_embeds`. Please make sure to define only one of the two."
)
if image is None and image_embeds is None:
raise ValueError(
"Provide either `image` or `image_embeds`. Cannot leave both `image` and `image_embeds` undefined."
)
if image is not None:
if (
not isinstance(image, torch.Tensor)
and not isinstance(image, PIL.Image.Image)
and not isinstance(image, list)
):
raise ValueError(
"`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
f" {type(image)}"
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_unclip.StableUnCLIPPipeline.noise_image_embeddings
def noise_image_embeddings(
self,
image_embeds: torch.Tensor,
noise_level: int,
noise: Optional[torch.FloatTensor] = None,
generator: Optional[torch.Generator] = None,
):
"""
Add noise to the image embeddings. The amount of noise is controlled by a `noise_level` input. A higher
`noise_level` increases the variance in the final un-noised images.
The noise is applied in two ways:
1. A noise schedule is applied directly to the embeddings.
2. A vector of sinusoidal time embeddings are appended to the output.
In both cases, the amount of noise is controlled by the same `noise_level`.
The embeddings are normalized before the noise is applied and un-normalized after the noise is applied.
"""
if noise is None:
noise = randn_tensor(
image_embeds.shape, generator=generator, device=image_embeds.device, dtype=image_embeds.dtype
)
noise_level = torch.tensor([noise_level] * image_embeds.shape[0], device=image_embeds.device)
self.image_normalizer.to(image_embeds.device)
image_embeds = self.image_normalizer.scale(image_embeds)
image_embeds = self.image_noising_scheduler.add_noise(image_embeds, timesteps=noise_level, noise=noise)
image_embeds = self.image_normalizer.unscale(image_embeds)
noise_level = get_timestep_embedding(
timesteps=noise_level, embedding_dim=image_embeds.shape[-1], flip_sin_to_cos=True, downscale_freq_shift=0
)
# `get_timestep_embeddings` does not contain any weights and will always return f32 tensors,
# but we might actually be running in fp16. so we need to cast here.
# there might be better ways to encapsulate this.
noise_level = noise_level.to(image_embeds.dtype)
image_embeds = torch.cat((image_embeds, noise_level), 1)
return image_embeds
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
image: Union[torch.FloatTensor, PIL.Image.Image] = None,
prompt: Union[str, List[str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 20,
guidance_scale: float = 10,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
noise_level: int = 0,
image_embeds: Optional[torch.FloatTensor] = None,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, either `prompt_embeds` will be
used or prompt is initialized to `""`.
image (`torch.FloatTensor` or `PIL.Image.Image`):
`Image` or tensor representing an image batch. The image is encoded to its CLIP embedding which the
`unet` is conditioned on. The image is _not_ encoded by the `vae` and then used as the latents in the
denoising process like it is in the standard Stable Diffusion text-guided image variation process.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 20):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 10.0):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
noise_level (`int`, *optional*, defaults to `0`):
The amount of noise to add to the image embeddings. A higher `noise_level` increases the variance in
the final un-noised images. See [`StableUnCLIPPipeline.noise_image_embeddings`] for more details.
image_embeds (`torch.FloatTensor`, *optional*):
Pre-generated CLIP embeddings to condition the `unet` on. These latents are not used in the denoising
process. If you want to provide pre-generated latents, pass them to `__call__` as `latents`.
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
[`~ pipeline_utils.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning
a tuple, the first element is a list with the generated images.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
if prompt is None and prompt_embeds is None:
prompt = len(image) * [""] if isinstance(image, list) else ""
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt=prompt,
image=image,
height=height,
width=width,
callback_steps=callback_steps,
noise_level=noise_level,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
image_embeds=image_embeds,
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
batch_size = batch_size * num_images_per_prompt
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds = self._encode_prompt(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=do_classifier_free_guidance,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
)
# 4. Encoder input image
noise_level = torch.tensor([noise_level], device=device)
image_embeds = self._encode_image(
image=image,
device=device,
batch_size=batch_size,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=do_classifier_free_guidance,
noise_level=noise_level,
generator=generator,
image_embeds=image_embeds,
)
# 5. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 6. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size=batch_size,
num_channels_latents=num_channels_latents,
height=height,
width=width,
dtype=prompt_embeds.dtype,
device=device,
generator=generator,
latents=latents,
)
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 8. Denoising loop
for i, t in enumerate(self.progress_bar(timesteps)):
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
class_labels=image_embeds,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
# 9. Post-processing
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
else:
image = latents
image = self.image_processor.postprocess(image, output_type=output_type)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import warnings
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL
import torch
from packaging import version
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from ...configuration_utils import FrozenDict
from ...image_processor import VaeImageProcessor
from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import (
PIL_INTERPOLATION,
deprecate,
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import requests
>>> import torch
>>> from PIL import Image
>>> from io import BytesIO
>>> from diffusers import StableDiffusionImg2ImgPipeline
>>> device = "cuda"
>>> model_id_or_path = "runwayml/stable-diffusion-v1-5"
>>> pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
>>> pipe = pipe.to(device)
>>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
>>> response = requests.get(url)
>>> init_image = Image.open(BytesIO(response.content)).convert("RGB")
>>> init_image = init_image.resize((768, 512))
>>> prompt = "A fantasy landscape, trending on artstation"
>>> images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images
>>> images[0].save("fantasy_landscape.png")
```
"""
def preprocess(image):
warnings.warn(
"The preprocess method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor.preprocess instead",
FutureWarning,
)
if isinstance(image, torch.Tensor):
return image
elif isinstance(image, PIL.Image.Image):
image = [image]
if isinstance(image[0], PIL.Image.Image):
w, h = image[0].size
w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
image = np.concatenate(image, axis=0)
image = np.array(image).astype(np.float32) / 255.0
image = image.transpose(0, 3, 1, 2)
image = 2.0 * image - 1.0
image = torch.from_numpy(image)
elif isinstance(image[0], torch.Tensor):
image = torch.cat(image, dim=0)
return image
class StableDiffusionImg2ImgPipeline(
DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
):
r"""
Pipeline for text-guided image-to-image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
if self.safety_checker is not None:
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self, prompt, strength, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None
):
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
return timesteps, num_inference_steps - t_start
def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
)
image = image.to(device=device, dtype=dtype)
batch_size = batch_size * num_images_per_prompt
if image.shape[1] == 4:
init_latents = image
else:
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
elif isinstance(generator, list):
init_latents = [
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
]
init_latents = torch.cat(init_latents, dim=0)
else:
init_latents = self.vae.encode(image).latent_dist.sample(generator)
init_latents = self.vae.config.scaling_factor * init_latents
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
# expand init_latents for batch_size
deprecation_message = (
f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
" images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
" your script to pass as many initial images as text prompts to suppress this warning."
)
deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
additional_image_per_prompt = batch_size // init_latents.shape[0]
init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
raise ValueError(
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
)
else:
init_latents = torch.cat([init_latents], dim=0)
shape = init_latents.shape
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
# get latents
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
latents = init_latents
return latents
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: Union[
torch.FloatTensor,
PIL.Image.Image,
np.ndarray,
List[torch.FloatTensor],
List[PIL.Image.Image],
List[np.ndarray],
] = None,
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: Optional[float] = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
`Image` or tensor representing an image batch to be used as the starting point. Can also accept image
latents as `image`, but if passing latents directly it is not encoded again.
strength (`float`, *optional*, defaults to 0.8):
Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
starting point and more noise is added the higher the `strength`. The number of denoising steps depends
on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
essentially ignores `image`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter is modulated by `strength`.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
"""
# 1. Check inputs. Raise error if not correct
self.check_inputs(prompt, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
)
# 4. Preprocess image
image = self.image_processor.preprocess(image)
# 5. set timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
# 6. Prepare latent variables
latents = self.prepare_latents(
image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator
)
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import warnings
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput
from ...image_processor import VaeImageProcessor
from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, PriorTransformer, UNet2DConditionModel
from ...models.embeddings import get_timestep_embedding
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import torch
>>> from diffusers import StableUnCLIPPipeline
>>> pipe = StableUnCLIPPipeline.from_pretrained(
... "fusing/stable-unclip-2-1-l", torch_dtype=torch.float16
... ) # TODO update model path
>>> pipe = pipe.to("cuda")
>>> prompt = "a photo of an astronaut riding a horse on mars"
>>> images = pipe(prompt).images
>>> images[0].save("astronaut_horse.png")
```
"""
class StableUnCLIPPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
"""
Pipeline for text-to-image generation using stable unCLIP.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
prior_tokenizer ([`CLIPTokenizer`]):
A [`CLIPTokenizer`].
prior_text_encoder ([`CLIPTextModelWithProjection`]):
Frozen [`CLIPTextModelWithProjection`] text-encoder.
prior ([`PriorTransformer`]):
The canonincal unCLIP prior to approximate the image embedding from the text embedding.
prior_scheduler ([`KarrasDiffusionSchedulers`]):
Scheduler used in the prior denoising process.
image_normalizer ([`StableUnCLIPImageNormalizer`]):
Used to normalize the predicted image embeddings before the noise is applied and un-normalize the image
embeddings after the noise has been applied.
image_noising_scheduler ([`KarrasDiffusionSchedulers`]):
Noise schedule for adding noise to the predicted image embeddings. The amount of noise to add is determined
by the `noise_level`.
tokenizer ([`CLIPTokenizer`]):
A [`CLIPTokenizer`].
text_encoder ([`CLIPTextModel`]):
Frozen [`CLIPTextModel`] text-encoder.
unet ([`UNet2DConditionModel`]):
A [`UNet2DConditionModel`] to denoise the encoded image latents.
scheduler ([`KarrasDiffusionSchedulers`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents.
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
"""
_exclude_from_cpu_offload = ["prior", "image_normalizer"]
# prior components
prior_tokenizer: CLIPTokenizer
prior_text_encoder: CLIPTextModelWithProjection
prior: PriorTransformer
prior_scheduler: KarrasDiffusionSchedulers
# image noising components
image_normalizer: StableUnCLIPImageNormalizer
image_noising_scheduler: KarrasDiffusionSchedulers
# regular denoising components
tokenizer: CLIPTokenizer
text_encoder: CLIPTextModel
unet: UNet2DConditionModel
scheduler: KarrasDiffusionSchedulers
vae: AutoencoderKL
def __init__(
self,
# prior components
prior_tokenizer: CLIPTokenizer,
prior_text_encoder: CLIPTextModelWithProjection,
prior: PriorTransformer,
prior_scheduler: KarrasDiffusionSchedulers,
# image noising components
image_normalizer: StableUnCLIPImageNormalizer,
image_noising_scheduler: KarrasDiffusionSchedulers,
# regular denoising components
tokenizer: CLIPTokenizer,
text_encoder: CLIPTextModelWithProjection,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
# vae
vae: AutoencoderKL,
):
super().__init__()
self.register_modules(
prior_tokenizer=prior_tokenizer,
prior_text_encoder=prior_text_encoder,
prior=prior,
prior_scheduler=prior_scheduler,
image_normalizer=image_normalizer,
image_noising_scheduler=image_noising_scheduler,
tokenizer=tokenizer,
text_encoder=text_encoder,
unet=unet,
scheduler=scheduler,
vae=vae,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.vae.enable_slicing()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
def disable_vae_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_slicing()
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
for cpu_offloaded_model in [self.text_encoder, self.prior_text_encoder, self.unet, self.vae]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
# Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline._encode_prompt with _encode_prompt->_encode_prior_prompt, tokenizer->prior_tokenizer, text_encoder->prior_text_encoder
def _encode_prior_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,
text_attention_mask: Optional[torch.Tensor] = None,
):
if text_model_output is None:
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
text_inputs = self.prior_tokenizer(
prompt,
padding="max_length",
max_length=self.prior_tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
text_mask = text_inputs.attention_mask.bool().to(device)
untruncated_ids = self.prior_tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.prior_tokenizer.batch_decode(
untruncated_ids[:, self.prior_tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.prior_tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids[:, : self.prior_tokenizer.model_max_length]
prior_text_encoder_output = self.prior_text_encoder(text_input_ids.to(device))
prompt_embeds = prior_text_encoder_output.text_embeds
prior_text_encoder_hidden_states = prior_text_encoder_output.last_hidden_state
else:
batch_size = text_model_output[0].shape[0]
prompt_embeds, prior_text_encoder_hidden_states = text_model_output[0], text_model_output[1]
text_mask = text_attention_mask
prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
prior_text_encoder_hidden_states = prior_text_encoder_hidden_states.repeat_interleave(
num_images_per_prompt, dim=0
)
text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
uncond_tokens = [""] * batch_size
uncond_input = self.prior_tokenizer(
uncond_tokens,
padding="max_length",
max_length=self.prior_tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
uncond_text_mask = uncond_input.attention_mask.bool().to(device)
negative_prompt_embeds_prior_text_encoder_output = self.prior_text_encoder(
uncond_input.input_ids.to(device)
)
negative_prompt_embeds = negative_prompt_embeds_prior_text_encoder_output.text_embeds
uncond_prior_text_encoder_hidden_states = (
negative_prompt_embeds_prior_text_encoder_output.last_hidden_state
)
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
seq_len = uncond_prior_text_encoder_hidden_states.shape[1]
uncond_prior_text_encoder_hidden_states = uncond_prior_text_encoder_hidden_states.repeat(
1, num_images_per_prompt, 1
)
uncond_prior_text_encoder_hidden_states = uncond_prior_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt, seq_len, -1
)
uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
prior_text_encoder_hidden_states = torch.cat(
[uncond_prior_text_encoder_hidden_states, prior_text_encoder_hidden_states]
)
text_mask = torch.cat([uncond_text_mask, text_mask])
return prompt_embeds, prior_text_encoder_hidden_states, text_mask
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs with prepare_extra_step_kwargs->prepare_prior_extra_step_kwargs, scheduler->prior_scheduler
def prepare_prior_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the prior_scheduler step, since not all prior_schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other prior_schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.prior_scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the prior_scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.prior_scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
noise_level,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Please make sure to define only one of the two."
)
if prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
if prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
"Provide either `negative_prompt` or `negative_prompt_embeds`. Cannot leave both `negative_prompt` and `negative_prompt_embeds` undefined."
)
if prompt is not None and negative_prompt is not None:
if type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
if noise_level < 0 or noise_level >= self.image_noising_scheduler.config.num_train_timesteps:
raise ValueError(
f"`noise_level` must be between 0 and {self.image_noising_scheduler.config.num_train_timesteps - 1}, inclusive."
)
# Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
latents = latents * scheduler.init_noise_sigma
return latents
def noise_image_embeddings(
self,
image_embeds: torch.Tensor,
noise_level: int,
noise: Optional[torch.FloatTensor] = None,
generator: Optional[torch.Generator] = None,
):
"""
Add noise to the image embeddings. The amount of noise is controlled by a `noise_level` input. A higher
`noise_level` increases the variance in the final un-noised images.
The noise is applied in two ways:
1. A noise schedule is applied directly to the embeddings.
2. A vector of sinusoidal time embeddings are appended to the output.
In both cases, the amount of noise is controlled by the same `noise_level`.
The embeddings are normalized before the noise is applied and un-normalized after the noise is applied.
"""
if noise is None:
noise = randn_tensor(
image_embeds.shape, generator=generator, device=image_embeds.device, dtype=image_embeds.dtype
)
noise_level = torch.tensor([noise_level] * image_embeds.shape[0], device=image_embeds.device)
self.image_normalizer.to(image_embeds.device)
image_embeds = self.image_normalizer.scale(image_embeds)
image_embeds = self.image_noising_scheduler.add_noise(image_embeds, timesteps=noise_level, noise=noise)
image_embeds = self.image_normalizer.unscale(image_embeds)
noise_level = get_timestep_embedding(
timesteps=noise_level, embedding_dim=image_embeds.shape[-1], flip_sin_to_cos=True, downscale_freq_shift=0
)
# `get_timestep_embeddings` does not contain any weights and will always return f32 tensors,
# but we might actually be running in fp16. so we need to cast here.
# there might be better ways to encapsulate this.
noise_level = noise_level.to(image_embeds.dtype)
image_embeds = torch.cat((image_embeds, noise_level), 1)
return image_embeds
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
# regular denoising process args
prompt: Optional[Union[str, List[str]]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 20,
guidance_scale: float = 10.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
noise_level: int = 0,
# prior args
prior_num_inference_steps: int = 25,
prior_guidance_scale: float = 4.0,
prior_latents: Optional[torch.FloatTensor] = None,
):
"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 20):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 10.0):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
noise_level (`int`, *optional*, defaults to `0`):
The amount of noise to add to the image embeddings. A higher `noise_level` increases the variance in
the final un-noised images. See [`StableUnCLIPPipeline.noise_image_embeddings`] for more details.
prior_num_inference_steps (`int`, *optional*, defaults to 25):
The number of denoising steps in the prior denoising process. More denoising steps usually lead to a
higher quality image at the expense of slower inference.
prior_guidance_scale (`float`, *optional*, defaults to 4.0):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
prior_latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
embedding generation in the prior denoising process. Can be used to tweak the same generation with
different prompts. If not provided, a latents tensor is generated by sampling using the supplied random
`generator`.
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
[`~ pipeline_utils.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning
a tuple, the first element is a list with the generated images.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt=prompt,
height=height,
width=width,
callback_steps=callback_steps,
noise_level=noise_level,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
batch_size = batch_size * num_images_per_prompt
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
prior_do_classifier_free_guidance = prior_guidance_scale > 1.0
# 3. Encode input prompt
prior_prompt_embeds, prior_text_encoder_hidden_states, prior_text_mask = self._encode_prior_prompt(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=prior_do_classifier_free_guidance,
)
# 4. Prepare prior timesteps
self.prior_scheduler.set_timesteps(prior_num_inference_steps, device=device)
prior_timesteps_tensor = self.prior_scheduler.timesteps
# 5. Prepare prior latent variables
embedding_dim = self.prior.config.embedding_dim
prior_latents = self.prepare_latents(
(batch_size, embedding_dim),
prior_prompt_embeds.dtype,
device,
generator,
prior_latents,
self.prior_scheduler,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
prior_extra_step_kwargs = self.prepare_prior_extra_step_kwargs(generator, eta)
# 7. Prior denoising loop
for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([prior_latents] * 2) if prior_do_classifier_free_guidance else prior_latents
latent_model_input = self.prior_scheduler.scale_model_input(latent_model_input, t)
predicted_image_embedding = self.prior(
latent_model_input,
timestep=t,
proj_embedding=prior_prompt_embeds,
encoder_hidden_states=prior_text_encoder_hidden_states,
attention_mask=prior_text_mask,
).predicted_image_embedding
if prior_do_classifier_free_guidance:
predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)
predicted_image_embedding = predicted_image_embedding_uncond + prior_guidance_scale * (
predicted_image_embedding_text - predicted_image_embedding_uncond
)
prior_latents = self.prior_scheduler.step(
predicted_image_embedding,
timestep=t,
sample=prior_latents,
**prior_extra_step_kwargs,
return_dict=False,
)[0]
if callback is not None and i % callback_steps == 0:
callback(i, t, prior_latents)
prior_latents = self.prior.post_process_latents(prior_latents)
image_embeds = prior_latents
# done prior
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 8. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds = self._encode_prompt(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=do_classifier_free_guidance,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
)
# 9. Prepare image embeddings
image_embeds = self.noise_image_embeddings(
image_embeds=image_embeds,
noise_level=noise_level,
generator=generator,
)
if do_classifier_free_guidance:
negative_prompt_embeds = torch.zeros_like(image_embeds)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
image_embeds = torch.cat([negative_prompt_embeds, image_embeds])
# 10. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 11. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
latents = self.prepare_latents(
shape=shape,
dtype=prompt_embeds.dtype,
device=device,
generator=generator,
latents=latents,
scheduler=self.scheduler,
)
# 12. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 13. Denoising loop
for i, t in enumerate(self.progress_bar(timesteps)):
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
class_labels=image_embeds,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
else:
image = latents
image = self.image_processor.postprocess(image, output_type=output_type)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_controlnet.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
"stable diffusion controlnet",
"0.22.0",
"Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.",
standard_warn=False,
stacklevel=3,
)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/stable_unclip_image_normalizer.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class StableUnCLIPImageNormalizer(ModelMixin, ConfigMixin):
"""
This class is used to hold the mean and standard deviation of the CLIP embedder used in stable unCLIP.
It is used to normalize the image embeddings before the noise is applied and un-normalize the noised image
embeddings.
"""
@register_to_config
def __init__(
self,
embedding_dim: int = 768,
):
super().__init__()
self.mean = nn.Parameter(torch.zeros(1, embedding_dim))
self.std = nn.Parameter(torch.ones(1, embedding_dim))
def to(
self,
torch_device: Optional[Union[str, torch.device]] = None,
torch_dtype: Optional[torch.dtype] = None,
):
self.mean = nn.Parameter(self.mean.to(torch_device).to(torch_dtype))
self.std = nn.Parameter(self.std.to(torch_device).to(torch_dtype))
return self
def scale(self, embeds):
embeds = (embeds - self.mean) * 1.0 / self.std
return embeds
def unscale(self, embeds):
embeds = (embeds * self.std) + self.mean
return embeds
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py | # Copyright 2023 The Intel Labs Team Authors and the HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from ...image_processor import VaeImageProcessorLDM3D
from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import (
BaseOutput,
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline
from .safety_checker import StableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import torch
>>> from diffusers import StableDiffusionPipeline
>>> pipe = StableDiffusionLDM3DPipeline.from_pretrained("Intel/ldm3d")
>>> pipe = pipe.to("cuda")
>>> prompt = "a photo of an astronaut riding a horse on mars"
>>> output = pipe(prompt)
>>> rgb_image, depth_image = output.rgb, output.depth
>>> rgb_image[0].save("astronaut_ldm3d_rgb.jpg")
>>> depth_image[0].save("astronaut_ldm3d_depth.png")
```
"""
@dataclass
class LDM3DPipelineOutput(BaseOutput):
"""
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`List[bool]`)
List indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content or
`None` if safety checking could not be performed.
"""
rgb: Union[List[PIL.Image.Image], np.ndarray]
depth: Union[List[PIL.Image.Image], np.ndarray]
nsfw_content_detected: Optional[List[bool]]
class StableDiffusionLDM3DPipeline(
DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
):
r"""
Pipeline for text-to-image and 3D generation using LDM3D.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessorLDM3D(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.vae.enable_slicing()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
def disable_vae_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_slicing()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
def enable_vae_tiling(self):
r"""
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
processing larger images.
"""
self.vae.enable_tiling()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
def disable_vae_tiling(self):
r"""
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_tiling()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
if self.safety_checker is not None:
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
rgb_feature_extractor_input = feature_extractor_input[0]
safety_checker_input = self.feature_extractor(rgb_feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 49,
guidance_scale: float = 5.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 5.0):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
rgb, depth = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return ((rgb, depth), has_nsfw_concept)
return LDM3DPipelineOutput(rgb=rgb, depth=depth, nsfw_content_detected=has_nsfw_concept)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_panorama.py | # Copyright 2023 MultiDiffusion Authors and The HuggingFace Team. All rights reserved."
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import inspect
import warnings
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from ...image_processor import VaeImageProcessor
from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import DDIMScheduler
from ...utils import logging, randn_tensor, replace_example_docstring
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import torch
>>> from diffusers import StableDiffusionPanoramaPipeline, DDIMScheduler
>>> model_ckpt = "stabilityai/stable-diffusion-2-base"
>>> scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder="scheduler")
>>> pipe = StableDiffusionPanoramaPipeline.from_pretrained(
... model_ckpt, scheduler=scheduler, torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> prompt = "a photo of the dolomites"
>>> image = pipe(prompt).images[0]
```
"""
class StableDiffusionPanoramaPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
r"""
Pipeline for text-to-image generation using MultiDiffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: DDIMScheduler,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.vae.enable_slicing()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
def disable_vae_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_slicing()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
def decode_latents_with_padding(self, latents, padding=8):
# Add padding to latents for circular inference
# padding is the number of latents to add on each side
# it would slightly increase the memory usage, but remove the boundary artifacts
latents = 1 / self.vae.config.scaling_factor * latents
latents_left = latents[..., :padding]
latents_right = latents[..., -padding:]
latents = torch.cat((latents_right, latents, latents_left), axis=-1)
image = self.vae.decode(latents, return_dict=False)[0]
padding_pix = self.vae_scale_factor * padding
image = image[..., padding_pix:-padding_pix]
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def get_views(self, panorama_height, panorama_width, window_size=64, stride=8, circular_padding=False):
# Here, we define the mappings F_i (see Eq. 7 in the MultiDiffusion paper https://arxiv.org/abs/2302.08113)
# if panorama's height/width < window_size, num_blocks of height/width should return 1
panorama_height /= 8
panorama_width /= 8
num_blocks_height = (panorama_height - window_size) // stride + 1 if panorama_height > window_size else 1
if circular_padding:
num_blocks_width = panorama_width // stride if panorama_width > window_size else 1
else:
num_blocks_width = (panorama_width - window_size) // stride + 1 if panorama_width > window_size else 1
total_num_blocks = int(num_blocks_height * num_blocks_width)
views = []
for i in range(total_num_blocks):
h_start = int((i // num_blocks_width) * stride)
h_end = h_start + window_size
w_start = int((i % num_blocks_width) * stride)
w_end = w_start + window_size
views.append((h_start, h_end, w_start, w_end))
return views
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = 512,
width: Optional[int] = 2048,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
view_batch_size: int = 1,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
circular_padding: bool = False,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 2048):
The width in pixels of the generated image. The width is kept high because the pipeline is supposed
generate panorama-like images.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
view_batch_size (`int`, *optional*, defaults to 1):
The batch size to denoise split views. For some GPUs with high performance, higher view batch size can
speedup the generation and increase the VRAM usage.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
circular_padding (`bool`, *optional*, defaults to `False`):
If set to `True`, circular padding is applied to ensure there are no stitching artifacts. Circular
padding allows the model to seamlessly generate a transition from the rightmost part of the image to
the leftmost part, maintaining consistency in a 360-degree sense.
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Define panorama grid and initialize views for synthesis.
# prepare batch grid
views = self.get_views(height, width, circular_padding=circular_padding)
views_batch = [views[i : i + view_batch_size] for i in range(0, len(views), view_batch_size)]
views_scheduler_status = [copy.deepcopy(self.scheduler.__dict__)] * len(views_batch)
count = torch.zeros_like(latents)
value = torch.zeros_like(latents)
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 8. Denoising loop
# Each denoising step also includes refinement of the latents with respect to the
# views.
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
count.zero_()
value.zero_()
# generate views
# Here, we iterate through different spatial crops of the latents and denoise them. These
# denoised (latent) crops are then averaged to produce the final latent
# for the current timestep via MultiDiffusion. Please see Sec. 4.1 in the
# MultiDiffusion paper for more details: https://arxiv.org/abs/2302.08113
# Batch views denoise
for j, batch_view in enumerate(views_batch):
vb_size = len(batch_view)
# get the latents corresponding to the current view coordinates
if circular_padding:
latents_for_view = []
for h_start, h_end, w_start, w_end in batch_view:
if w_end > latents.shape[3]:
# Add circular horizontal padding
latent_view = torch.cat(
(
latents[:, :, h_start:h_end, w_start:],
latents[:, :, h_start:h_end, : w_end - latents.shape[3]],
),
axis=-1,
)
else:
latent_view = latents[:, :, h_start:h_end, w_start:w_end]
latents_for_view.append(latent_view)
latents_for_view = torch.cat(latents_for_view)
else:
latents_for_view = torch.cat(
[
latents[:, :, h_start:h_end, w_start:w_end]
for h_start, h_end, w_start, w_end in batch_view
]
)
# rematch block's scheduler status
self.scheduler.__dict__.update(views_scheduler_status[j])
# expand the latents if we are doing classifier free guidance
latent_model_input = (
latents_for_view.repeat_interleave(2, dim=0)
if do_classifier_free_guidance
else latents_for_view
)
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# repeat prompt_embeds for batch
prompt_embeds_input = torch.cat([prompt_embeds] * vb_size)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds_input,
cross_attention_kwargs=cross_attention_kwargs,
).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred[::2], noise_pred[1::2]
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents_denoised_batch = self.scheduler.step(
noise_pred, t, latents_for_view, **extra_step_kwargs
).prev_sample
# save views scheduler status after sample
views_scheduler_status[j] = copy.deepcopy(self.scheduler.__dict__)
# extract value from batch
for latents_view_denoised, (h_start, h_end, w_start, w_end) in zip(
latents_denoised_batch.chunk(vb_size), batch_view
):
if circular_padding and w_end > latents.shape[3]:
# Case for circular padding
value[:, :, h_start:h_end, w_start:] += latents_view_denoised[
:, :, h_start:h_end, : latents.shape[3] - w_start
]
value[:, :, h_start:h_end, : w_end - latents.shape[3]] += latents_view_denoised[
:, :, h_start:h_end, latents.shape[3] - w_start :
]
count[:, :, h_start:h_end, w_start:] += 1
count[:, :, h_start:h_end, : w_end - latents.shape[3]] += 1
else:
value[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised
count[:, :, h_start:h_end, w_start:w_end] += 1
# take the MultiDiffusion step. Eq. 5 in MultiDiffusion paper: https://arxiv.org/abs/2302.08113
latents = torch.where(count > 0, value / count, value)
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if not output_type == "latent":
if circular_padding:
image = self.decode_latents_with_padding(latents)
else:
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import warnings
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from ...image_processor import VaeImageProcessor
from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel
from ...models.attention_processor import (
AttnProcessor2_0,
LoRAAttnProcessor2_0,
LoRAXFormersAttnProcessor,
XFormersAttnProcessor,
)
from ...schedulers import DDPMScheduler, KarrasDiffusionSchedulers
from ...utils import deprecate, is_accelerate_available, is_accelerate_version, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def preprocess(image):
warnings.warn(
"The preprocess method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor.preprocess instead",
FutureWarning,
)
if isinstance(image, torch.Tensor):
return image
elif isinstance(image, PIL.Image.Image):
image = [image]
if isinstance(image[0], PIL.Image.Image):
w, h = image[0].size
w, h = (x - x % 64 for x in (w, h)) # resize to integer multiple of 64
image = [np.array(i.resize((w, h)))[None, :] for i in image]
image = np.concatenate(image, axis=0)
image = np.array(image).astype(np.float32) / 255.0
image = image.transpose(0, 3, 1, 2)
image = 2.0 * image - 1.0
image = torch.from_numpy(image)
elif isinstance(image[0], torch.Tensor):
image = torch.cat(image, dim=0)
return image
class StableDiffusionUpscalePipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
r"""
Pipeline for text-guided image super-resolution using Stable Diffusion 2.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
low_res_scheduler ([`SchedulerMixin`]):
A scheduler used to add initial noise to the low resolution conditioning image. It must be an instance of
[`DDPMScheduler`].
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
"""
_optional_components = ["watermarker", "safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
low_res_scheduler: DDPMScheduler,
scheduler: KarrasDiffusionSchedulers,
safety_checker: Optional[Any] = None,
feature_extractor: Optional[CLIPImageProcessor] = None,
watermarker: Optional[Any] = None,
max_noise_level: int = 350,
):
super().__init__()
if hasattr(
vae, "config"
): # check if vae has a config attribute `scaling_factor` and if it is set to 0.08333, else set it to 0.08333 and deprecate
is_vae_scaling_factor_set_to_0_08333 = (
hasattr(vae.config, "scaling_factor") and vae.config.scaling_factor == 0.08333
)
if not is_vae_scaling_factor_set_to_0_08333:
deprecation_message = (
"The configuration file of the vae does not contain `scaling_factor` or it is set to"
f" {vae.config.scaling_factor}, which seems highly unlikely. If your checkpoint is a fine-tuned"
" version of `stabilityai/stable-diffusion-x4-upscaler` you should change 'scaling_factor' to"
" 0.08333 Please make sure to update the config accordingly, as not doing so might lead to"
" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging"
" Face Hub, it would be very nice if you could open a Pull Request for the `vae/config.json` file"
)
deprecate("wrong scaling_factor", "1.0.0", deprecation_message, standard_warn=False)
vae.register_to_config(scaling_factor=0.08333)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
low_res_scheduler=low_res_scheduler,
scheduler=scheduler,
safety_checker=safety_checker,
watermarker=watermarker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, resample="bicubic")
self.register_to_config(max_noise_level=max_noise_level)
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
if cpu_offloaded_model is not None:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
image, nsfw_detected, watermark_detected = self.safety_checker(
images=image,
clip_input=safety_checker_input.pixel_values.to(dtype=dtype),
)
else:
nsfw_detected = None
watermark_detected = None
if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
self.unet_offload_hook.offload()
return image, nsfw_detected, watermark_detected
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
def check_inputs(
self,
prompt,
image,
noise_level,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
if (
not isinstance(image, torch.Tensor)
and not isinstance(image, PIL.Image.Image)
and not isinstance(image, np.ndarray)
and not isinstance(image, list)
):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `np.ndarray`, `PIL.Image.Image` or `list` but is {type(image)}"
)
# verify batch size of prompt and image are same if image is a list or tensor or numpy array
if isinstance(image, list) or isinstance(image, torch.Tensor) or isinstance(image, np.ndarray):
if isinstance(prompt, str):
batch_size = 1
else:
batch_size = len(prompt)
if isinstance(image, list):
image_batch_size = len(image)
else:
image_batch_size = image.shape[0]
if batch_size != image_batch_size:
raise ValueError(
f"`prompt` has batch size {batch_size} and `image` has batch size {image_batch_size}."
" Please make sure that passed `prompt` matches the batch size of `image`."
)
# check noise level
if noise_level > self.config.max_noise_level:
raise ValueError(f"`noise_level` has to be <= {self.config.max_noise_level} but is {noise_level}")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height, width)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def upcast_vae(self):
dtype = self.vae.dtype
self.vae.to(dtype=torch.float32)
use_torch_2_0_or_xformers = isinstance(
self.vae.decoder.mid_block.attentions[0].processor,
(
AttnProcessor2_0,
XFormersAttnProcessor,
LoRAXFormersAttnProcessor,
LoRAAttnProcessor2_0,
),
)
# if xformers or torch_2_0 is used attention block does not need
# to be in float32 which can save lots of memory
if use_torch_2_0_or_xformers:
self.vae.post_quant_conv.to(dtype)
self.vae.decoder.conv_in.to(dtype)
self.vae.decoder.mid_block.to(dtype)
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: Union[
torch.FloatTensor,
PIL.Image.Image,
np.ndarray,
List[torch.FloatTensor],
List[PIL.Image.Image],
List[np.ndarray],
] = None,
num_inference_steps: int = 75,
guidance_scale: float = 9.0,
noise_level: int = 20,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
`Image` or tensor representing an image batch to be upscaled.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
Examples:
```py
>>> import requests
>>> from PIL import Image
>>> from io import BytesIO
>>> from diffusers import StableDiffusionUpscalePipeline
>>> import torch
>>> # load model and scheduler
>>> model_id = "stabilityai/stable-diffusion-x4-upscaler"
>>> pipeline = StableDiffusionUpscalePipeline.from_pretrained(
... model_id, revision="fp16", torch_dtype=torch.float16
... )
>>> pipeline = pipeline.to("cuda")
>>> # let's download an image
>>> url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png"
>>> response = requests.get(url)
>>> low_res_img = Image.open(BytesIO(response.content)).convert("RGB")
>>> low_res_img = low_res_img.resize((128, 128))
>>> prompt = "a white cat"
>>> upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]
>>> upscaled_image.save("upsampled_cat.png")
```
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
"""
# 1. Check inputs
self.check_inputs(
prompt,
image,
noise_level,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
)
if image is None:
raise ValueError("`image` input cannot be undefined.")
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
)
# 4. Preprocess image
image = self.image_processor.preprocess(image)
image = image.to(dtype=prompt_embeds.dtype, device=device)
# 5. set timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Add noise to image
noise_level = torch.tensor([noise_level], dtype=torch.long, device=device)
noise = randn_tensor(image.shape, generator=generator, device=device, dtype=prompt_embeds.dtype)
image = self.low_res_scheduler.add_noise(image, noise, noise_level)
batch_multiplier = 2 if do_classifier_free_guidance else 1
image = torch.cat([image] * batch_multiplier * num_images_per_prompt)
noise_level = torch.cat([noise_level] * image.shape[0])
# 6. Prepare latent variables
height, width = image.shape[2:]
num_channels_latents = self.vae.config.latent_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 7. Check that sizes of image and latents match
num_channels_image = image.shape[1]
if num_channels_latents + num_channels_image != self.unet.config.in_channels:
raise ValueError(
f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
f" `num_channels_image`: {num_channels_image} "
f" = {num_channels_latents+num_channels_image}. Please verify the config of"
" `pipeline.unet` or your `image` input."
)
# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 9. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
# concat latents, mask, masked_image_latents in the channel dimension
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
latent_model_input = torch.cat([latent_model_input, image], dim=1)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
class_labels=noise_level,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
# 10. Post-processing
# make sure the VAE is in float32 mode, as it overflows in float16
if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
self.upcast_vae()
latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
# post-processing
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept, _ = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# 11. Apply watermark
if output_type == "pil" and self.watermarker is not None:
image = self.watermarker.apply_watermark(image)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline # noqa: F401
deprecate(
"stable diffusion controlnet",
"0.22.0",
"Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.",
standard_warn=False,
stacklevel=3,
)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/safety_checker_flax.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def jax_cosine_distance(emb_1, emb_2, eps=1e-12):
norm_emb_1 = jnp.divide(emb_1.T, jnp.clip(jnp.linalg.norm(emb_1, axis=1), a_min=eps)).T
norm_emb_2 = jnp.divide(emb_2.T, jnp.clip(jnp.linalg.norm(emb_2, axis=1), a_min=eps)).T
return jnp.matmul(norm_emb_1, norm_emb_2.T)
class FlaxStableDiffusionSafetyCheckerModule(nn.Module):
config: CLIPConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.vision_model = FlaxCLIPVisionModule(self.config.vision_config)
self.visual_projection = nn.Dense(self.config.projection_dim, use_bias=False, dtype=self.dtype)
self.concept_embeds = self.param("concept_embeds", jax.nn.initializers.ones, (17, self.config.projection_dim))
self.special_care_embeds = self.param(
"special_care_embeds", jax.nn.initializers.ones, (3, self.config.projection_dim)
)
self.concept_embeds_weights = self.param("concept_embeds_weights", jax.nn.initializers.ones, (17,))
self.special_care_embeds_weights = self.param("special_care_embeds_weights", jax.nn.initializers.ones, (3,))
def __call__(self, clip_input):
pooled_output = self.vision_model(clip_input)[1]
image_embeds = self.visual_projection(pooled_output)
special_cos_dist = jax_cosine_distance(image_embeds, self.special_care_embeds)
cos_dist = jax_cosine_distance(image_embeds, self.concept_embeds)
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign image inputs
adjustment = 0.0
special_scores = special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
special_scores = jnp.round(special_scores, 3)
is_special_care = jnp.any(special_scores > 0, axis=1, keepdims=True)
# Use a lower threshold if an image has any special care concept
special_adjustment = is_special_care * 0.01
concept_scores = cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
concept_scores = jnp.round(concept_scores, 3)
has_nsfw_concepts = jnp.any(concept_scores > 0, axis=1)
return has_nsfw_concepts
class FlaxStableDiffusionSafetyChecker(FlaxPreTrainedModel):
config_class = CLIPConfig
main_input_name = "clip_input"
module_class = FlaxStableDiffusionSafetyCheckerModule
def __init__(
self,
config: CLIPConfig,
input_shape: Optional[Tuple] = None,
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
**kwargs,
):
if input_shape is None:
input_shape = (1, 224, 224, 3)
module = self.module_class(config=config, dtype=dtype, **kwargs)
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
def init_weights(self, rng: jax.random.KeyArray, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensor
clip_input = jax.random.normal(rng, input_shape)
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
random_params = self.module.init(rngs, clip_input)["params"]
return random_params
def __call__(
self,
clip_input,
params: dict = None,
):
clip_input = jnp.transpose(clip_input, (0, 2, 3, 1))
return self.module.apply(
{"params": params or self.params},
jnp.array(clip_input, dtype=jnp.float32),
rngs={},
)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import warnings
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL
import torch
from packaging import version
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers.utils import is_accelerate_available, is_accelerate_version
from ...configuration_utils import FrozenDict
from ...image_processor import VaeImageProcessor
from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import DDIMScheduler
from ...utils import PIL_INTERPOLATION, deprecate, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
def preprocess(image):
warnings.warn(
"The preprocess method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor.preprocess instead",
FutureWarning,
)
if isinstance(image, torch.Tensor):
return image
elif isinstance(image, PIL.Image.Image):
image = [image]
if isinstance(image[0], PIL.Image.Image):
w, h = image[0].size
w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
image = np.concatenate(image, axis=0)
image = np.array(image).astype(np.float32) / 255.0
image = image.transpose(0, 3, 1, 2)
image = 2.0 * image - 1.0
image = torch.from_numpy(image)
elif isinstance(image[0], torch.Tensor):
image = torch.cat(image, dim=0)
return image
def posterior_sample(scheduler, latents, timestep, clean_latents, generator, eta):
# 1. get previous step value (=t-1)
prev_timestep = timestep - scheduler.config.num_train_timesteps // scheduler.num_inference_steps
if prev_timestep <= 0:
return clean_latents
# 2. compute alphas, betas
alpha_prod_t = scheduler.alphas_cumprod[timestep]
alpha_prod_t_prev = (
scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else scheduler.final_alpha_cumprod
)
variance = scheduler._get_variance(timestep, prev_timestep)
std_dev_t = eta * variance ** (0.5)
# direction pointing to x_t
e_t = (latents - alpha_prod_t ** (0.5) * clean_latents) / (1 - alpha_prod_t) ** (0.5)
dir_xt = (1.0 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * e_t
noise = std_dev_t * randn_tensor(
clean_latents.shape, dtype=clean_latents.dtype, device=clean_latents.device, generator=generator
)
prev_latents = alpha_prod_t_prev ** (0.5) * clean_latents + dir_xt + noise
return prev_latents
def compute_noise(scheduler, prev_latents, latents, timestep, noise_pred, eta):
# 1. get previous step value (=t-1)
prev_timestep = timestep - scheduler.config.num_train_timesteps // scheduler.num_inference_steps
# 2. compute alphas, betas
alpha_prod_t = scheduler.alphas_cumprod[timestep]
alpha_prod_t_prev = (
scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else scheduler.final_alpha_cumprod
)
beta_prod_t = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)
# 4. Clip "predicted x_0"
if scheduler.config.clip_sample:
pred_original_sample = torch.clamp(pred_original_sample, -1, 1)
# 5. compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
variance = scheduler._get_variance(timestep, prev_timestep)
std_dev_t = eta * variance ** (0.5)
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * noise_pred
noise = (prev_latents - (alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction)) / (
variance ** (0.5) * eta
)
return noise
class CycleDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
r"""
Pipeline for text-guided image to image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can only be an
instance of [`DDIMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: DDIMScheduler,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
if self.safety_checker is not None:
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.check_inputs
def check_inputs(
self, prompt, strength, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None
):
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
return timesteps, num_inference_steps - t_start
def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
image = image.to(device=device, dtype=dtype)
batch_size = image.shape[0]
if image.shape[1] == 4:
init_latents = image
else:
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if isinstance(generator, list):
init_latents = [
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
]
init_latents = torch.cat(init_latents, dim=0)
else:
init_latents = self.vae.encode(image).latent_dist.sample(generator)
init_latents = self.vae.config.scaling_factor * init_latents
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
# expand init_latents for batch_size
deprecation_message = (
f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
" images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
" your script to pass as many initial images as text prompts to suppress this warning."
)
deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
additional_image_per_prompt = batch_size // init_latents.shape[0]
init_latents = torch.cat([init_latents] * additional_image_per_prompt * num_images_per_prompt, dim=0)
elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
raise ValueError(
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
)
else:
init_latents = torch.cat([init_latents] * num_images_per_prompt, dim=0)
# add noise to latents using the timestep
shape = init_latents.shape
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
# get latents
clean_latents = init_latents
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
latents = init_latents
return latents, clean_latents
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
source_prompt: Union[str, List[str]],
image: Union[
torch.FloatTensor,
PIL.Image.Image,
np.ndarray,
List[torch.FloatTensor],
List[PIL.Image.Image],
List[np.ndarray],
] = None,
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
source_guidance_scale: Optional[float] = 1,
num_images_per_prompt: Optional[int] = 1,
eta: Optional[float] = 0.1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
image (`torch.FloatTensor` `np.ndarray`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
`Image` or tensor representing an image batch to be used as the starting point. Can also accept image
latents as `image`, but if passing latents directly it is not encoded again.
strength (`float`, *optional*, defaults to 0.8):
Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
starting point and more noise is added the higher the `strength`. The number of denoising steps depends
on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
essentially ignores `image`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter is modulated by `strength`.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
source_guidance_scale (`float`, *optional*, defaults to 1):
Guidance scale for the source prompt. This is useful to control the amount of influence the source
prompt has for encoding.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
Example:
```py
import requests
import torch
from PIL import Image
from io import BytesIO
from diffusers import CycleDiffusionPipeline, DDIMScheduler
# load the pipeline
# make sure you're logged in with `huggingface-cli login`
model_id_or_path = "CompVis/stable-diffusion-v1-4"
scheduler = DDIMScheduler.from_pretrained(model_id_or_path, subfolder="scheduler")
pipe = CycleDiffusionPipeline.from_pretrained(model_id_or_path, scheduler=scheduler).to("cuda")
# let's download an initial image
url = "https://raw.githubusercontent.com/ChenWu98/cycle-diffusion/main/data/dalle2/An%20astronaut%20riding%20a%20horse.png"
response = requests.get(url)
init_image = Image.open(BytesIO(response.content)).convert("RGB")
init_image = init_image.resize((512, 512))
init_image.save("horse.png")
# let's specify a prompt
source_prompt = "An astronaut riding a horse"
prompt = "An astronaut riding an elephant"
# call the pipeline
image = pipe(
prompt=prompt,
source_prompt=source_prompt,
image=init_image,
num_inference_steps=100,
eta=0.1,
strength=0.8,
guidance_scale=2,
source_guidance_scale=1,
).images[0]
image.save("horse_to_elephant.png")
# let's try another example
# See more samples at the original repo: https://github.com/ChenWu98/cycle-diffusion
url = (
"https://raw.githubusercontent.com/ChenWu98/cycle-diffusion/main/data/dalle2/A%20black%20colored%20car.png"
)
response = requests.get(url)
init_image = Image.open(BytesIO(response.content)).convert("RGB")
init_image = init_image.resize((512, 512))
init_image.save("black.png")
source_prompt = "A black colored car"
prompt = "A blue colored car"
# call the pipeline
torch.manual_seed(0)
image = pipe(
prompt=prompt,
source_prompt=source_prompt,
image=init_image,
num_inference_steps=100,
eta=0.1,
strength=0.85,
guidance_scale=3,
source_guidance_scale=1,
).images[0]
image.save("black_to_blue.png")
```
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
"""
# 1. Check inputs
self.check_inputs(prompt, strength, callback_steps)
# 2. Define call parameters
batch_size = 1 if isinstance(prompt, str) else len(prompt)
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
prompt_embeds=prompt_embeds,
lora_scale=text_encoder_lora_scale,
)
source_prompt_embeds = self._encode_prompt(
source_prompt, device, num_images_per_prompt, do_classifier_free_guidance, None
)
# 4. Preprocess image
image = self.image_processor.preprocess(image)
# 5. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
# 6. Prepare latent variables
latents, clean_latents = self.prepare_latents(
image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator
)
source_latents = latents
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
generator = extra_step_kwargs.pop("generator", None)
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
source_latent_model_input = (
torch.cat([source_latents] * 2) if do_classifier_free_guidance else source_latents
)
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
source_latent_model_input = self.scheduler.scale_model_input(source_latent_model_input, t)
# predict the noise residual
if do_classifier_free_guidance:
concat_latent_model_input = torch.stack(
[
source_latent_model_input[0],
latent_model_input[0],
source_latent_model_input[1],
latent_model_input[1],
],
dim=0,
)
concat_prompt_embeds = torch.stack(
[
source_prompt_embeds[0],
prompt_embeds[0],
source_prompt_embeds[1],
prompt_embeds[1],
],
dim=0,
)
else:
concat_latent_model_input = torch.cat(
[
source_latent_model_input,
latent_model_input,
],
dim=0,
)
concat_prompt_embeds = torch.cat(
[
source_prompt_embeds,
prompt_embeds,
],
dim=0,
)
concat_noise_pred = self.unet(
concat_latent_model_input,
t,
cross_attention_kwargs=cross_attention_kwargs,
encoder_hidden_states=concat_prompt_embeds,
).sample
# perform guidance
if do_classifier_free_guidance:
(
source_noise_pred_uncond,
noise_pred_uncond,
source_noise_pred_text,
noise_pred_text,
) = concat_noise_pred.chunk(4, dim=0)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
source_noise_pred = source_noise_pred_uncond + source_guidance_scale * (
source_noise_pred_text - source_noise_pred_uncond
)
else:
(source_noise_pred, noise_pred) = concat_noise_pred.chunk(2, dim=0)
# Sample source_latents from the posterior distribution.
prev_source_latents = posterior_sample(
self.scheduler, source_latents, t, clean_latents, generator=generator, **extra_step_kwargs
)
# Compute noise.
noise = compute_noise(
self.scheduler, prev_source_latents, source_latents, t, source_noise_pred, **extra_step_kwargs
)
source_latents = prev_source_latents
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(
noise_pred, t, latents, variance_noise=noise, **extra_step_kwargs
).prev_sample
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
# 9. Post-processing
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint_legacy.py | import inspect
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPTokenizer
from ...configuration_utils import FrozenDict
from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from ...utils import deprecate, logging
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def preprocess(image):
w, h = image.size
w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL.Image.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
return 2.0 * image - 1.0
def preprocess_mask(mask, scale_factor=8):
mask = mask.convert("L")
w, h = mask.size
w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL.Image.NEAREST)
mask = np.array(mask).astype(np.float32) / 255.0
mask = np.tile(mask, (4, 1, 1))
mask = mask[None].transpose(0, 1, 2, 3) # what does this step do?
mask = 1 - mask # repaint white, keep black
return mask
class OnnxStableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
r"""
Pipeline for text-guided image inpainting using Stable Diffusion. This is a *legacy feature* for Onnx pipelines to
provide compatibility with StableDiffusionInpaintPipelineLegacy and may be removed in the future.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
vae_encoder: OnnxRuntimeModel
vae_decoder: OnnxRuntimeModel
text_encoder: OnnxRuntimeModel
tokenizer: CLIPTokenizer
unet: OnnxRuntimeModel
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
safety_checker: OnnxRuntimeModel
feature_extractor: CLIPImageProcessor
def __init__(
self,
vae_encoder: OnnxRuntimeModel,
vae_decoder: OnnxRuntimeModel,
text_encoder: OnnxRuntimeModel,
tokenizer: CLIPTokenizer,
unet: OnnxRuntimeModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: OnnxRuntimeModel,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
self.register_modules(
vae_encoder=vae_encoder,
vae_decoder=vae_decoder,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion.OnnxStableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt: Union[str, List[str]],
num_images_per_prompt: Optional[int],
do_classifier_free_guidance: bool,
negative_prompt: Optional[str],
prompt_embeds: Optional[np.ndarray] = None,
negative_prompt_embeds: Optional[np.ndarray] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`):
prompt to be encoded
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
prompt_embeds (`np.ndarray`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`np.ndarray`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
"""
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="np").input_ids
if not np.array_equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]
prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt] * batch_size
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="np",
)
negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0]
if do_classifier_free_guidance:
negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
def check_inputs(
self,
prompt,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
def __call__(
self,
prompt: Union[str, List[str]],
image: Union[np.ndarray, PIL.Image.Image] = None,
mask_image: Union[np.ndarray, PIL.Image.Image] = None,
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: Optional[float] = 0.0,
generator: Optional[np.random.RandomState] = None,
prompt_embeds: Optional[np.ndarray] = None,
negative_prompt_embeds: Optional[np.ndarray] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
callback_steps: int = 1,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
image (`nd.ndarray` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process. This is the image whose masked region will be inpainted.
mask_image (`nd.ndarray` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.uu
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
will be used as a starting point, adding more noise to it the larger the `strength`. The number of
denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter will be modulated by `strength`.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (?) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`np.random.RandomState`, *optional*):
A np.random.RandomState to make generation deterministic.
prompt_embeds (`np.ndarray`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`np.ndarray`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
# check inputs. Raise error if not correct
self.check_inputs(prompt, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)
# define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
if generator is None:
generator = np.random
# set timesteps
self.scheduler.set_timesteps(num_inference_steps)
if isinstance(image, PIL.Image.Image):
image = preprocess(image)
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
prompt_embeds = self._encode_prompt(
prompt,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
)
latents_dtype = prompt_embeds.dtype
image = image.astype(latents_dtype)
# encode the init image into latents and scale the latents
init_latents = self.vae_encoder(sample=image)[0]
init_latents = 0.18215 * init_latents
# Expand init_latents for batch_size and num_images_per_prompt
init_latents = np.concatenate([init_latents] * num_images_per_prompt, axis=0)
init_latents_orig = init_latents
# preprocess mask
if not isinstance(mask_image, np.ndarray):
mask_image = preprocess_mask(mask_image, 8)
mask_image = mask_image.astype(latents_dtype)
mask = np.concatenate([mask_image] * num_images_per_prompt, axis=0)
# check sizes
if not mask.shape == init_latents.shape:
raise ValueError("The mask and image should be the same size!")
# get the original timestep using init_timestep
offset = self.scheduler.config.get("steps_offset", 0)
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
timesteps = self.scheduler.timesteps.numpy()[-init_timestep]
timesteps = np.array([timesteps] * batch_size * num_images_per_prompt)
# add noise to latents using the timesteps
noise = generator.randn(*init_latents.shape).astype(latents_dtype)
init_latents = self.scheduler.add_noise(
torch.from_numpy(init_latents), torch.from_numpy(noise), torch.from_numpy(timesteps)
)
init_latents = init_latents.numpy()
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (?) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to ? in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
latents = init_latents
t_start = max(num_inference_steps - init_timestep + offset, 0)
timesteps = self.scheduler.timesteps[t_start:].numpy()
timestep_dtype = next(
(input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
)
timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
for i, t in enumerate(self.progress_bar(timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
timestep = np.array([t], dtype=timestep_dtype)
noise_pred = self.unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)[
0
]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(
torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
).prev_sample
latents = latents.numpy()
init_latents_proper = self.scheduler.add_noise(
torch.from_numpy(init_latents_orig), torch.from_numpy(noise), torch.from_numpy(np.array([t]))
)
init_latents_proper = init_latents_proper.numpy()
latents = (init_latents_proper * mask) + (latents * (1 - mask))
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
latents = 1 / 0.18215 * latents
# image = self.vae_decoder(latent_sample=latents)[0]
# it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
image = np.concatenate(
[self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
)
image = np.clip(image / 2 + 0.5, 0, 1)
image = image.transpose((0, 2, 3, 1))
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(
self.numpy_to_pil(image), return_tensors="np"
).pixel_values.astype(image.dtype)
# There will throw an error if use safety_checker batchsize>1
images, has_nsfw_concept = [], []
for i in range(image.shape[0]):
image_i, has_nsfw_concept_i = self.safety_checker(
clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
)
images.append(image_i)
has_nsfw_concept.append(has_nsfw_concept_i[0])
image = np.concatenate(images)
else:
has_nsfw_concept = None
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPTokenizer
from ...configuration_utils import FrozenDict
from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from ...utils import PIL_INTERPOLATION, deprecate, logging
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
NUM_UNET_INPUT_CHANNELS = 9
NUM_LATENT_CHANNELS = 4
def prepare_mask_and_masked_image(image, mask, latents_shape):
image = np.array(image.convert("RGB").resize((latents_shape[1] * 8, latents_shape[0] * 8)))
image = image[None].transpose(0, 3, 1, 2)
image = image.astype(np.float32) / 127.5 - 1.0
image_mask = np.array(mask.convert("L").resize((latents_shape[1] * 8, latents_shape[0] * 8)))
masked_image = image * (image_mask < 127.5)
mask = mask.resize((latents_shape[1], latents_shape[0]), PIL_INTERPOLATION["nearest"])
mask = np.array(mask.convert("L"))
mask = mask.astype(np.float32) / 255.0
mask = mask[None, None]
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
return mask, masked_image
class OnnxStableDiffusionInpaintPipeline(DiffusionPipeline):
r"""
Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
vae_encoder: OnnxRuntimeModel
vae_decoder: OnnxRuntimeModel
text_encoder: OnnxRuntimeModel
tokenizer: CLIPTokenizer
unet: OnnxRuntimeModel
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
safety_checker: OnnxRuntimeModel
feature_extractor: CLIPImageProcessor
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae_encoder: OnnxRuntimeModel,
vae_decoder: OnnxRuntimeModel,
text_encoder: OnnxRuntimeModel,
tokenizer: CLIPTokenizer,
unet: OnnxRuntimeModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: OnnxRuntimeModel,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
logger.info("`OnnxStableDiffusionInpaintPipeline` is experimental and will very likely change in the future.")
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
self.register_modules(
vae_encoder=vae_encoder,
vae_decoder=vae_decoder,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion.OnnxStableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt: Union[str, List[str]],
num_images_per_prompt: Optional[int],
do_classifier_free_guidance: bool,
negative_prompt: Optional[str],
prompt_embeds: Optional[np.ndarray] = None,
negative_prompt_embeds: Optional[np.ndarray] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`):
prompt to be encoded
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
prompt_embeds (`np.ndarray`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`np.ndarray`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
"""
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="np").input_ids
if not np.array_equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]
prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt] * batch_size
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="np",
)
negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0]
if do_classifier_free_guidance:
negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion.OnnxStableDiffusionPipeline.check_inputs
def check_inputs(
self,
prompt: Union[str, List[str]],
height: Optional[int],
width: Optional[int],
callback_steps: int,
negative_prompt: Optional[str] = None,
prompt_embeds: Optional[np.ndarray] = None,
negative_prompt_embeds: Optional[np.ndarray] = None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
image: PIL.Image.Image,
mask_image: PIL.Image.Image,
height: Optional[int] = 512,
width: Optional[int] = 512,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[np.random.RandomState] = None,
latents: Optional[np.ndarray] = None,
prompt_embeds: Optional[np.ndarray] = None,
negative_prompt_embeds: Optional[np.ndarray] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
callback_steps: int = 1,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
image (`PIL.Image.Image`):
`Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
be masked out with `mask_image` and repainted according to `prompt`.
mask_image (`PIL.Image.Image`):
`Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
instead of 3, so the expected shape would be `(B, H, W, 1)`.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`np.random.RandomState`, *optional*):
A np.random.RandomState to make generation deterministic.
latents (`np.ndarray`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
prompt_embeds (`np.ndarray`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`np.ndarray`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
# check inputs. Raise error if not correct
self.check_inputs(
prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
)
# define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if generator is None:
generator = np.random
# set timesteps
self.scheduler.set_timesteps(num_inference_steps)
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
prompt_embeds = self._encode_prompt(
prompt,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
)
num_channels_latents = NUM_LATENT_CHANNELS
latents_shape = (batch_size * num_images_per_prompt, num_channels_latents, height // 8, width // 8)
latents_dtype = prompt_embeds.dtype
if latents is None:
latents = generator.randn(*latents_shape).astype(latents_dtype)
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
# prepare mask and masked_image
mask, masked_image = prepare_mask_and_masked_image(image, mask_image, latents_shape[-2:])
mask = mask.astype(latents.dtype)
masked_image = masked_image.astype(latents.dtype)
masked_image_latents = self.vae_encoder(sample=masked_image)[0]
masked_image_latents = 0.18215 * masked_image_latents
# duplicate mask and masked_image_latents for each generation per prompt
mask = mask.repeat(batch_size * num_images_per_prompt, 0)
masked_image_latents = masked_image_latents.repeat(batch_size * num_images_per_prompt, 0)
mask = np.concatenate([mask] * 2) if do_classifier_free_guidance else mask
masked_image_latents = (
np.concatenate([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
)
num_channels_mask = mask.shape[1]
num_channels_masked_image = masked_image_latents.shape[1]
unet_input_channels = NUM_UNET_INPUT_CHANNELS
if num_channels_latents + num_channels_mask + num_channels_masked_image != unet_input_channels:
raise ValueError(
"Incorrect configuration settings! The config of `pipeline.unet` expects"
f" {unet_input_channels} but received `num_channels_latents`: {num_channels_latents} +"
f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
" `pipeline.unet` or your `mask_image` or `image` input."
)
# set timesteps
self.scheduler.set_timesteps(num_inference_steps)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * np.float64(self.scheduler.init_noise_sigma)
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
timestep_dtype = next(
(input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
)
timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
# concat latents, mask, masked_image_latnets in the channel dimension
latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)
latent_model_input = latent_model_input.cpu().numpy()
latent_model_input = np.concatenate([latent_model_input, mask, masked_image_latents], axis=1)
# predict the noise residual
timestep = np.array([t], dtype=timestep_dtype)
noise_pred = self.unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)[
0
]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
scheduler_output = self.scheduler.step(
torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
)
latents = scheduler_output.prev_sample.numpy()
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
latents = 1 / 0.18215 * latents
# image = self.vae_decoder(latent_sample=latents)[0]
# it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
image = np.concatenate(
[self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
)
image = np.clip(image / 2 + 0.5, 0, 1)
image = image.transpose((0, 2, 3, 1))
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(
self.numpy_to_pil(image), return_tensors="np"
).pixel_values.astype(image.dtype)
# safety_checker does not support batched inputs yet
images, has_nsfw_concept = [], []
for i in range(image.shape[0]):
image_i, has_nsfw_concept_i = self.safety_checker(
clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
)
images.append(image_i)
has_nsfw_concept.append(has_nsfw_concept_i[0])
image = np.concatenate(images)
else:
has_nsfw_concept = None
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_model_editing.py | # Copyright 2023 TIME Authors and The HuggingFace Team. All rights reserved."
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import inspect
import warnings
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from ...image_processor import VaeImageProcessor
from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import PNDMScheduler
from ...schedulers.scheduling_utils import SchedulerMixin
from ...utils import logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
AUGS_CONST = ["A photo of ", "An image of ", "A picture of "]
class StableDiffusionModelEditingPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
r"""
Pipeline for text-to-image model editing.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPFeatureExtractor`]):
A `CLIPFeatureExtractor` to extract features from generated images; used as inputs to the `safety_checker`.
with_to_k ([`bool`]):
Whether to edit the key projection matrices along with the value projection matrices.
with_augs ([`list`]):
Textual augmentations to apply while editing the text-to-image model. Set to `[]` for no augmentations.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: SchedulerMixin,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
requires_safety_checker: bool = True,
with_to_k: bool = True,
with_augs: list = AUGS_CONST,
):
super().__init__()
if isinstance(scheduler, PNDMScheduler):
logger.error("PNDMScheduler for this pipeline is currently not supported.")
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
self.with_to_k = with_to_k
self.with_augs = with_augs
# get cross-attention layers
ca_layers = []
def append_ca(net_):
if net_.__class__.__name__ == "CrossAttention":
ca_layers.append(net_)
elif hasattr(net_, "children"):
for net__ in net_.children():
append_ca(net__)
# recursively find all cross-attention layers in unet
for net in self.unet.named_children():
if "down" in net[0]:
append_ca(net[1])
elif "up" in net[0]:
append_ca(net[1])
elif "mid" in net[0]:
append_ca(net[1])
# get projection matrices
self.ca_clip_layers = [l for l in ca_layers if l.to_v.in_features == 768]
self.projection_matrices = [l.to_v for l in self.ca_clip_layers]
self.og_matrices = [copy.deepcopy(l.to_v) for l in self.ca_clip_layers]
if self.with_to_k:
self.projection_matrices = self.projection_matrices + [l.to_k for l in self.ca_clip_layers]
self.og_matrices = self.og_matrices + [copy.deepcopy(l.to_k) for l in self.ca_clip_layers]
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.vae.enable_slicing()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
def disable_vae_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_slicing()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@torch.no_grad()
def edit_model(
self,
source_prompt: str,
destination_prompt: str,
lamb: float = 0.1,
restart_params: bool = True,
):
r"""
Apply model editing via closed-form solution (see Eq. 5 in the TIME [paper](https://arxiv.org/abs/2303.08084)).
Args:
source_prompt (`str`):
The source prompt containing the concept to be edited.
destination_prompt (`str`):
The destination prompt. Must contain all words from `source_prompt` with additional ones to specify the
target edit.
lamb (`float`, *optional*, defaults to 0.1):
The lambda parameter specifying the regularization intesity. Smaller values increase the editing power.
restart_params (`bool`, *optional*, defaults to True):
Restart the model parameters to their pre-trained version before editing. This is done to avoid edit
compounding. When it is `False`, edits accumulate.
"""
# restart LDM parameters
if restart_params:
num_ca_clip_layers = len(self.ca_clip_layers)
for idx_, l in enumerate(self.ca_clip_layers):
l.to_v = copy.deepcopy(self.og_matrices[idx_])
self.projection_matrices[idx_] = l.to_v
if self.with_to_k:
l.to_k = copy.deepcopy(self.og_matrices[num_ca_clip_layers + idx_])
self.projection_matrices[num_ca_clip_layers + idx_] = l.to_k
# set up sentences
old_texts = [source_prompt]
new_texts = [destination_prompt]
# add augmentations
base = old_texts[0] if old_texts[0][0:1] != "A" else "a" + old_texts[0][1:]
for aug in self.with_augs:
old_texts.append(aug + base)
base = new_texts[0] if new_texts[0][0:1] != "A" else "a" + new_texts[0][1:]
for aug in self.with_augs:
new_texts.append(aug + base)
# prepare input k* and v*
old_embs, new_embs = [], []
for old_text, new_text in zip(old_texts, new_texts):
text_input = self.tokenizer(
[old_text, new_text],
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
old_emb, new_emb = text_embeddings
old_embs.append(old_emb)
new_embs.append(new_emb)
# identify corresponding destinations for each token in old_emb
idxs_replaces = []
for old_text, new_text in zip(old_texts, new_texts):
tokens_a = self.tokenizer(old_text).input_ids
tokens_b = self.tokenizer(new_text).input_ids
tokens_a = [self.tokenizer.encode("a ")[1] if self.tokenizer.decode(t) == "an" else t for t in tokens_a]
tokens_b = [self.tokenizer.encode("a ")[1] if self.tokenizer.decode(t) == "an" else t for t in tokens_b]
num_orig_tokens = len(tokens_a)
idxs_replace = []
j = 0
for i in range(num_orig_tokens):
curr_token = tokens_a[i]
while tokens_b[j] != curr_token:
j += 1
idxs_replace.append(j)
j += 1
while j < 77:
idxs_replace.append(j)
j += 1
while len(idxs_replace) < 77:
idxs_replace.append(76)
idxs_replaces.append(idxs_replace)
# prepare batch: for each pair of setences, old context and new values
contexts, valuess = [], []
for old_emb, new_emb, idxs_replace in zip(old_embs, new_embs, idxs_replaces):
context = old_emb.detach()
values = []
with torch.no_grad():
for layer in self.projection_matrices:
values.append(layer(new_emb[idxs_replace]).detach())
contexts.append(context)
valuess.append(values)
# edit the model
for layer_num in range(len(self.projection_matrices)):
# mat1 = \lambda W + \sum{v k^T}
mat1 = lamb * self.projection_matrices[layer_num].weight
# mat2 = \lambda I + \sum{k k^T}
mat2 = lamb * torch.eye(
self.projection_matrices[layer_num].weight.shape[1],
device=self.projection_matrices[layer_num].weight.device,
)
# aggregate sums for mat1, mat2
for context, values in zip(contexts, valuess):
context_vector = context.reshape(context.shape[0], context.shape[1], 1)
context_vector_T = context.reshape(context.shape[0], 1, context.shape[1])
value_vector = values[layer_num].reshape(values[layer_num].shape[0], values[layer_num].shape[1], 1)
for_mat1 = (value_vector @ context_vector_T).sum(dim=0)
for_mat2 = (context_vector @ context_vector_T).sum(dim=0)
mat1 += for_mat1
mat2 += for_mat2
# update projection matrix
self.projection_matrices[layer_num].weight = torch.nn.Parameter(mat1 @ torch.inverse(mat2))
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
Examples:
```py
>>> import torch
>>> from diffusers import StableDiffusionModelEditingPipeline
>>> model_ckpt = "CompVis/stable-diffusion-v1-4"
>>> pipe = StableDiffusionModelEditingPipeline.from_pretrained(model_ckpt)
>>> pipe = pipe.to("cuda")
>>> source_prompt = "A pack of roses"
>>> destination_prompt = "A pack of blue roses"
>>> pipe.edit_model(source_prompt, destination_prompt)
>>> prompt = "A field of roses"
>>> image = pipe(prompt).images[0]
```
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from functools import partial
from typing import Dict, List, Optional, Union
import jax
import jax.numpy as jnp
import numpy as np
from flax.core.frozen_dict import FrozenDict
from flax.jax_utils import unreplicate
from flax.training.common_utils import shard
from PIL import Image
from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel
from ...models import FlaxAutoencoderKL, FlaxUNet2DConditionModel
from ...schedulers import (
FlaxDDIMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, logging, replace_example_docstring
from ..pipeline_flax_utils import FlaxDiffusionPipeline
from . import FlaxStableDiffusionPipelineOutput
from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# Set to True to use python for loop instead of jax.fori_loop for easier debugging
DEBUG = False
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import jax
>>> import numpy as np
>>> import jax.numpy as jnp
>>> from flax.jax_utils import replicate
>>> from flax.training.common_utils import shard
>>> import requests
>>> from io import BytesIO
>>> from PIL import Image
>>> from diffusers import FlaxStableDiffusionImg2ImgPipeline
>>> def create_key(seed=0):
... return jax.random.PRNGKey(seed)
>>> rng = create_key(0)
>>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
>>> response = requests.get(url)
>>> init_img = Image.open(BytesIO(response.content)).convert("RGB")
>>> init_img = init_img.resize((768, 512))
>>> prompts = "A fantasy landscape, trending on artstation"
>>> pipeline, params = FlaxStableDiffusionImg2ImgPipeline.from_pretrained(
... "CompVis/stable-diffusion-v1-4",
... revision="flax",
... dtype=jnp.bfloat16,
... )
>>> num_samples = jax.device_count()
>>> rng = jax.random.split(rng, jax.device_count())
>>> prompt_ids, processed_image = pipeline.prepare_inputs(
... prompt=[prompts] * num_samples, image=[init_img] * num_samples
... )
>>> p_params = replicate(params)
>>> prompt_ids = shard(prompt_ids)
>>> processed_image = shard(processed_image)
>>> output = pipeline(
... prompt_ids=prompt_ids,
... image=processed_image,
... params=p_params,
... prng_seed=rng,
... strength=0.75,
... num_inference_steps=50,
... jit=True,
... height=512,
... width=768,
... ).images
>>> output_images = pipeline.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:])))
```
"""
class FlaxStableDiffusionImg2ImgPipeline(FlaxDiffusionPipeline):
r"""
Flax-based pipeline for text-guided image-to-image generation using Stable Diffusion.
This model inherits from [`FlaxDiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
vae ([`FlaxAutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.FlaxCLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`FlaxUNet2DConditionModel`]):
A `FlaxUNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`FlaxDDIMScheduler`], [`FlaxLMSDiscreteScheduler`], [`FlaxPNDMScheduler`], or
[`FlaxDPMSolverMultistepScheduler`].
safety_checker ([`FlaxStableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
def __init__(
self,
vae: FlaxAutoencoderKL,
text_encoder: FlaxCLIPTextModel,
tokenizer: CLIPTokenizer,
unet: FlaxUNet2DConditionModel,
scheduler: Union[
FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler
],
safety_checker: FlaxStableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
dtype: jnp.dtype = jnp.float32,
):
super().__init__()
self.dtype = dtype
if safety_checker is None:
logger.warn(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
def prepare_inputs(self, prompt: Union[str, List[str]], image: Union[Image.Image, List[Image.Image]]):
if not isinstance(prompt, (str, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if not isinstance(image, (Image.Image, list)):
raise ValueError(f"image has to be of type `PIL.Image.Image` or list but is {type(image)}")
if isinstance(image, Image.Image):
image = [image]
processed_images = jnp.concatenate([preprocess(img, jnp.float32) for img in image])
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
return text_input.input_ids, processed_images
def _get_has_nsfw_concepts(self, features, params):
has_nsfw_concepts = self.safety_checker(features, params)
return has_nsfw_concepts
def _run_safety_checker(self, images, safety_model_params, jit=False):
# safety_model_params should already be replicated when jit is True
pil_images = [Image.fromarray(image) for image in images]
features = self.feature_extractor(pil_images, return_tensors="np").pixel_values
if jit:
features = shard(features)
has_nsfw_concepts = _p_get_has_nsfw_concepts(self, features, safety_model_params)
has_nsfw_concepts = unshard(has_nsfw_concepts)
safety_model_params = unreplicate(safety_model_params)
else:
has_nsfw_concepts = self._get_has_nsfw_concepts(features, safety_model_params)
images_was_copied = False
for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):
if has_nsfw_concept:
if not images_was_copied:
images_was_copied = True
images = images.copy()
images[idx] = np.zeros(images[idx].shape, dtype=np.uint8) # black image
if any(has_nsfw_concepts):
warnings.warn(
"Potential NSFW content was detected in one or more images. A black image will be returned"
" instead. Try again with a different prompt and/or seed."
)
return images, has_nsfw_concepts
def get_timestep_start(self, num_inference_steps, strength):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
return t_start
def _generate(
self,
prompt_ids: jnp.array,
image: jnp.array,
params: Union[Dict, FrozenDict],
prng_seed: jax.random.KeyArray,
start_timestep: int,
num_inference_steps: int,
height: int,
width: int,
guidance_scale: float,
noise: Optional[jnp.array] = None,
neg_prompt_ids: Optional[jnp.array] = None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
# get prompt text embeddings
prompt_embeds = self.text_encoder(prompt_ids, params=params["text_encoder"])[0]
# TODO: currently it is assumed `do_classifier_free_guidance = guidance_scale > 1.0`
# implement this conditional `do_classifier_free_guidance = guidance_scale > 1.0`
batch_size = prompt_ids.shape[0]
max_length = prompt_ids.shape[-1]
if neg_prompt_ids is None:
uncond_input = self.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="np"
).input_ids
else:
uncond_input = neg_prompt_ids
negative_prompt_embeds = self.text_encoder(uncond_input, params=params["text_encoder"])[0]
context = jnp.concatenate([negative_prompt_embeds, prompt_embeds])
latents_shape = (
batch_size,
self.unet.config.in_channels,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
if noise is None:
noise = jax.random.normal(prng_seed, shape=latents_shape, dtype=jnp.float32)
else:
if noise.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {noise.shape}, expected {latents_shape}")
# Create init_latents
init_latent_dist = self.vae.apply({"params": params["vae"]}, image, method=self.vae.encode).latent_dist
init_latents = init_latent_dist.sample(key=prng_seed).transpose((0, 3, 1, 2))
init_latents = self.vae.config.scaling_factor * init_latents
def loop_body(step, args):
latents, scheduler_state = args
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
latents_input = jnp.concatenate([latents] * 2)
t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step]
timestep = jnp.broadcast_to(t, latents_input.shape[0])
latents_input = self.scheduler.scale_model_input(scheduler_state, latents_input, t)
# predict the noise residual
noise_pred = self.unet.apply(
{"params": params["unet"]},
jnp.array(latents_input),
jnp.array(timestep, dtype=jnp.int32),
encoder_hidden_states=context,
).sample
# perform guidance
noise_pred_uncond, noise_prediction_text = jnp.split(noise_pred, 2, axis=0)
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents, scheduler_state = self.scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple()
return latents, scheduler_state
scheduler_state = self.scheduler.set_timesteps(
params["scheduler"], num_inference_steps=num_inference_steps, shape=latents_shape
)
latent_timestep = scheduler_state.timesteps[start_timestep : start_timestep + 1].repeat(batch_size)
latents = self.scheduler.add_noise(params["scheduler"], init_latents, noise, latent_timestep)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * params["scheduler"].init_noise_sigma
if DEBUG:
# run with python for loop
for i in range(start_timestep, num_inference_steps):
latents, scheduler_state = loop_body(i, (latents, scheduler_state))
else:
latents, _ = jax.lax.fori_loop(start_timestep, num_inference_steps, loop_body, (latents, scheduler_state))
# scale and decode the image latents with vae
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.apply({"params": params["vae"]}, latents, method=self.vae.decode).sample
image = (image / 2 + 0.5).clip(0, 1).transpose(0, 2, 3, 1)
return image
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt_ids: jnp.array,
image: jnp.array,
params: Union[Dict, FrozenDict],
prng_seed: jax.random.KeyArray,
strength: float = 0.8,
num_inference_steps: int = 50,
height: Optional[int] = None,
width: Optional[int] = None,
guidance_scale: Union[float, jnp.array] = 7.5,
noise: jnp.array = None,
neg_prompt_ids: jnp.array = None,
return_dict: bool = True,
jit: bool = False,
):
r"""
The call function to the pipeline for generation.
Args:
prompt_ids (`jnp.array`):
The prompt or prompts to guide image generation.
image (`jnp.array`):
Array representing an image batch to be used as the starting point.
params (`Dict` or `FrozenDict`):
Dictionary containing the model parameters/weights.
prng_seed (`jax.random.KeyArray` or `jax.Array`):
Array containing random number generator key.
strength (`float`, *optional*, defaults to 0.8):
Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
starting point and more noise is added the higher the `strength`. The number of denoising steps depends
on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
essentially ignores `image`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter is modulated by `strength`.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
noise (`jnp.array`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. The array is generated by
sampling using the supplied random `generator`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of
a plain tuple.
jit (`bool`, defaults to `False`):
Whether to run `pmap` versions of the generation and safety scoring functions.
<Tip warning={true}>
This argument exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a
future release.
</Tip>
Examples:
Returns:
[`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] is
returned, otherwise a `tuple` is returned where the first element is a list with the generated images
and the second element is a list of `bool`s indicating whether the corresponding generated image
contains "not-safe-for-work" (nsfw) content.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
if isinstance(guidance_scale, float):
# Convert to a tensor so each device gets a copy. Follow the prompt_ids for
# shape information, as they may be sharded (when `jit` is `True`), or not.
guidance_scale = jnp.array([guidance_scale] * prompt_ids.shape[0])
if len(prompt_ids.shape) > 2:
# Assume sharded
guidance_scale = guidance_scale[:, None]
start_timestep = self.get_timestep_start(num_inference_steps, strength)
if jit:
images = _p_generate(
self,
prompt_ids,
image,
params,
prng_seed,
start_timestep,
num_inference_steps,
height,
width,
guidance_scale,
noise,
neg_prompt_ids,
)
else:
images = self._generate(
prompt_ids,
image,
params,
prng_seed,
start_timestep,
num_inference_steps,
height,
width,
guidance_scale,
noise,
neg_prompt_ids,
)
if self.safety_checker is not None:
safety_params = params["safety_checker"]
images_uint8_casted = (images * 255).round().astype("uint8")
num_devices, batch_size = images.shape[:2]
images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3)
images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit)
images = np.asarray(images)
# block images
if any(has_nsfw_concept):
for i, is_nsfw in enumerate(has_nsfw_concept):
if is_nsfw:
images[i] = np.asarray(images_uint8_casted[i])
images = images.reshape(num_devices, batch_size, height, width, 3)
else:
images = np.asarray(images)
has_nsfw_concept = False
if not return_dict:
return (images, has_nsfw_concept)
return FlaxStableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)
# Static argnums are pipe, start_timestep, num_inference_steps, height, width. A change would trigger recompilation.
# Non-static args are (sharded) input tensors mapped over their first dimension (hence, `0`).
@partial(
jax.pmap,
in_axes=(None, 0, 0, 0, 0, None, None, None, None, 0, 0, 0),
static_broadcasted_argnums=(0, 5, 6, 7, 8),
)
def _p_generate(
pipe,
prompt_ids,
image,
params,
prng_seed,
start_timestep,
num_inference_steps,
height,
width,
guidance_scale,
noise,
neg_prompt_ids,
):
return pipe._generate(
prompt_ids,
image,
params,
prng_seed,
start_timestep,
num_inference_steps,
height,
width,
guidance_scale,
noise,
neg_prompt_ids,
)
@partial(jax.pmap, static_broadcasted_argnums=(0,))
def _p_get_has_nsfw_concepts(pipe, features, params):
return pipe._get_has_nsfw_concepts(features, params)
def unshard(x: jnp.ndarray):
# einops.rearrange(x, 'd b ... -> (d b) ...')
num_devices, batch_size = x.shape[:2]
rest = x.shape[2:]
return x.reshape(num_devices * batch_size, *rest)
def preprocess(image, dtype):
w, h = image.size
w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
image = jnp.array(image).astype(dtype) / 255.0
image = image[None].transpose(0, 3, 1, 2)
return 2.0 * image - 1.0
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
import torch.nn.functional as F
from transformers import CLIPTextModel, CLIPTokenizer
from ...image_processor import VaeImageProcessor
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import EulerDiscreteScheduler
from ...utils import logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.preprocess
def preprocess(image):
warnings.warn(
"The preprocess method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor.preprocess instead",
FutureWarning,
)
if isinstance(image, torch.Tensor):
return image
elif isinstance(image, PIL.Image.Image):
image = [image]
if isinstance(image[0], PIL.Image.Image):
w, h = image[0].size
w, h = (x - x % 64 for x in (w, h)) # resize to integer multiple of 64
image = [np.array(i.resize((w, h)))[None, :] for i in image]
image = np.concatenate(image, axis=0)
image = np.array(image).astype(np.float32) / 255.0
image = image.transpose(0, 3, 1, 2)
image = 2.0 * image - 1.0
image = torch.from_numpy(image)
elif isinstance(image[0], torch.Tensor):
image = torch.cat(image, dim=0)
return image
class StableDiffusionLatentUpscalePipeline(DiffusionPipeline):
r"""
Pipeline for upscaling Stable Diffusion output image resolution by a factor of 2.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A [`EulerDiscreteScheduler`] to be used in combination with `unet` to denoise the encoded image latents.
"""
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: EulerDiscreteScheduler,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, resample="bicubic")
def _encode_prompt(self, prompt, device, do_classifier_free_guidance, negative_prompt):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `list(int)`):
prompt to be encoded
device: (`torch.device`):
torch device
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
"""
batch_size = len(prompt) if isinstance(prompt, list) else 1
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_length=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_encoder_out = self.text_encoder(
text_input_ids.to(device),
output_hidden_states=True,
)
text_embeddings = text_encoder_out.hidden_states[-1]
text_pooler_out = text_encoder_out.pooler_output
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_length=True,
return_tensors="pt",
)
uncond_encoder_out = self.text_encoder(
uncond_input.input_ids.to(device),
output_hidden_states=True,
)
uncond_embeddings = uncond_encoder_out.hidden_states[-1]
uncond_pooler_out = uncond_encoder_out.pooler_output
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
text_pooler_out = torch.cat([uncond_pooler_out, text_pooler_out])
return text_embeddings, text_pooler_out
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
def check_inputs(self, prompt, image, callback_steps):
if not isinstance(prompt, str) and not isinstance(prompt, list):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if (
not isinstance(image, torch.Tensor)
and not isinstance(image, PIL.Image.Image)
and not isinstance(image, list)
):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or `list` but is {type(image)}"
)
# verify batch size of prompt and image are same if image is a list or tensor
if isinstance(image, list) or isinstance(image, torch.Tensor):
if isinstance(prompt, str):
batch_size = 1
else:
batch_size = len(prompt)
if isinstance(image, list):
image_batch_size = len(image)
else:
image_batch_size = image.shape[0] if image.ndim == 4 else 1
if batch_size != image_batch_size:
raise ValueError(
f"`prompt` has batch size {batch_size} and `image` has batch size {image_batch_size}."
" Please make sure that passed `prompt` matches the batch size of `image`."
)
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height, width)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
image: Union[
torch.FloatTensor,
PIL.Image.Image,
np.ndarray,
List[torch.FloatTensor],
List[PIL.Image.Image],
List[np.ndarray],
] = None,
num_inference_steps: int = 75,
guidance_scale: float = 9.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide image upscaling.
image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
`Image` or tensor representing an image batch to be upscaled. If it's a tensor, it can be either a
latent output from a Stable Diffusion model or an image tensor in the range `[-1, 1]`. It is considered
a `latent` if `image.shape[1]` is `4`; otherwise, it is considered to be an image representation and
encoded using this pipeline's `vae` encoder.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
Examples:
```py
>>> from diffusers import StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline
>>> import torch
>>> pipeline = StableDiffusionPipeline.from_pretrained(
... "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16
... )
>>> pipeline.to("cuda")
>>> model_id = "stabilityai/sd-x2-latent-upscaler"
>>> upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
>>> upscaler.to("cuda")
>>> prompt = "a photo of an astronaut high resolution, unreal engine, ultra realistic"
>>> generator = torch.manual_seed(33)
>>> low_res_latents = pipeline(prompt, generator=generator, output_type="latent").images
>>> with torch.no_grad():
... image = pipeline.decode_latents(low_res_latents)
>>> image = pipeline.numpy_to_pil(image)[0]
>>> image.save("../images/a1.png")
>>> upscaled_image = upscaler(
... prompt=prompt,
... image=low_res_latents,
... num_inference_steps=20,
... guidance_scale=0,
... generator=generator,
... ).images[0]
>>> upscaled_image.save("../images/a2.png")
```
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images.
"""
# 1. Check inputs
self.check_inputs(prompt, image, callback_steps)
# 2. Define call parameters
batch_size = 1 if isinstance(prompt, str) else len(prompt)
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
if guidance_scale == 0:
prompt = [""] * batch_size
# 3. Encode input prompt
text_embeddings, text_pooler_out = self._encode_prompt(
prompt, device, do_classifier_free_guidance, negative_prompt
)
# 4. Preprocess image
image = self.image_processor.preprocess(image)
image = image.to(dtype=text_embeddings.dtype, device=device)
if image.shape[1] == 3:
# encode image if not in latent-space yet
image = self.vae.encode(image).latent_dist.sample() * self.vae.config.scaling_factor
# 5. set timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
batch_multiplier = 2 if do_classifier_free_guidance else 1
image = image[None, :] if image.ndim == 3 else image
image = torch.cat([image] * batch_multiplier)
# 5. Add noise to image (set to be 0):
# (see below notes from the author):
# "the This step theoretically can make the model work better on out-of-distribution inputs, but mostly just seems to make it match the input less, so it's turned off by default."
noise_level = torch.tensor([0.0], dtype=torch.float32, device=device)
noise_level = torch.cat([noise_level] * image.shape[0])
inv_noise_level = (noise_level**2 + 1) ** (-0.5)
image_cond = F.interpolate(image, scale_factor=2, mode="nearest") * inv_noise_level[:, None, None, None]
image_cond = image_cond.to(text_embeddings.dtype)
noise_level_embed = torch.cat(
[
torch.ones(text_pooler_out.shape[0], 64, dtype=text_pooler_out.dtype, device=device),
torch.zeros(text_pooler_out.shape[0], 64, dtype=text_pooler_out.dtype, device=device),
],
dim=1,
)
timestep_condition = torch.cat([noise_level_embed, text_pooler_out], dim=1)
# 6. Prepare latent variables
height, width = image.shape[2:]
num_channels_latents = self.vae.config.latent_channels
latents = self.prepare_latents(
batch_size,
num_channels_latents,
height * 2, # 2x upscale
width * 2,
text_embeddings.dtype,
device,
generator,
latents,
)
# 7. Check that sizes of image and latents match
num_channels_image = image.shape[1]
if num_channels_latents + num_channels_image != self.unet.config.in_channels:
raise ValueError(
f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
f" `num_channels_image`: {num_channels_image} "
f" = {num_channels_latents+num_channels_image}. Please verify the config of"
" `pipeline.unet` or your `image` input."
)
# 9. Denoising loop
num_warmup_steps = 0
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
sigma = self.scheduler.sigmas[i]
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
scaled_model_input = self.scheduler.scale_model_input(latent_model_input, t)
scaled_model_input = torch.cat([scaled_model_input, image_cond], dim=1)
# preconditioning parameter based on Karras et al. (2022) (table 1)
timestep = torch.log(sigma) * 0.25
noise_pred = self.unet(
scaled_model_input,
timestep,
encoder_hidden_states=text_embeddings,
timestep_cond=timestep_condition,
).sample
# in original repo, the output contains a variance channel that's not used
noise_pred = noise_pred[:, :-1]
# apply preconditioning, based on table 1 in Karras et al. (2022)
inv_sigma = 1 / (sigma**2 + 1)
noise_pred = inv_sigma * latent_model_input + self.scheduler.scale_model_input(sigma, t) * noise_pred
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents).prev_sample
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
else:
image = latents
image = self.image_processor.postprocess(image, output_type=output_type)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_inpaint.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from functools import partial
from typing import Dict, List, Optional, Union
import jax
import jax.numpy as jnp
import numpy as np
from flax.core.frozen_dict import FrozenDict
from flax.jax_utils import unreplicate
from flax.training.common_utils import shard
from packaging import version
from PIL import Image
from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel
from ...models import FlaxAutoencoderKL, FlaxUNet2DConditionModel
from ...schedulers import (
FlaxDDIMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, deprecate, logging, replace_example_docstring
from ..pipeline_flax_utils import FlaxDiffusionPipeline
from . import FlaxStableDiffusionPipelineOutput
from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# Set to True to use python for loop instead of jax.fori_loop for easier debugging
DEBUG = False
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import jax
>>> import numpy as np
>>> from flax.jax_utils import replicate
>>> from flax.training.common_utils import shard
>>> import PIL
>>> import requests
>>> from io import BytesIO
>>> from diffusers import FlaxStableDiffusionInpaintPipeline
>>> def download_image(url):
... response = requests.get(url)
... return PIL.Image.open(BytesIO(response.content)).convert("RGB")
>>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
>>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
>>> init_image = download_image(img_url).resize((512, 512))
>>> mask_image = download_image(mask_url).resize((512, 512))
>>> pipeline, params = FlaxStableDiffusionInpaintPipeline.from_pretrained(
... "xvjiarui/stable-diffusion-2-inpainting"
... )
>>> prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
>>> prng_seed = jax.random.PRNGKey(0)
>>> num_inference_steps = 50
>>> num_samples = jax.device_count()
>>> prompt = num_samples * [prompt]
>>> init_image = num_samples * [init_image]
>>> mask_image = num_samples * [mask_image]
>>> prompt_ids, processed_masked_images, processed_masks = pipeline.prepare_inputs(
... prompt, init_image, mask_image
... )
# shard inputs and rng
>>> params = replicate(params)
>>> prng_seed = jax.random.split(prng_seed, jax.device_count())
>>> prompt_ids = shard(prompt_ids)
>>> processed_masked_images = shard(processed_masked_images)
>>> processed_masks = shard(processed_masks)
>>> images = pipeline(
... prompt_ids, processed_masks, processed_masked_images, params, prng_seed, num_inference_steps, jit=True
... ).images
>>> images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
```
"""
class FlaxStableDiffusionInpaintPipeline(FlaxDiffusionPipeline):
r"""
Flax-based pipeline for text-guided image inpainting using Stable Diffusion.
<Tip warning={true}>
🧪 This is an experimental feature!
</Tip>
This model inherits from [`FlaxDiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
vae ([`FlaxAutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.FlaxCLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`FlaxUNet2DConditionModel`]):
A `FlaxUNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`FlaxDDIMScheduler`], [`FlaxLMSDiscreteScheduler`], [`FlaxPNDMScheduler`], or
[`FlaxDPMSolverMultistepScheduler`].
safety_checker ([`FlaxStableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
def __init__(
self,
vae: FlaxAutoencoderKL,
text_encoder: FlaxCLIPTextModel,
tokenizer: CLIPTokenizer,
unet: FlaxUNet2DConditionModel,
scheduler: Union[
FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler
],
safety_checker: FlaxStableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
dtype: jnp.dtype = jnp.float32,
):
super().__init__()
self.dtype = dtype
if safety_checker is None:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
def prepare_inputs(
self,
prompt: Union[str, List[str]],
image: Union[Image.Image, List[Image.Image]],
mask: Union[Image.Image, List[Image.Image]],
):
if not isinstance(prompt, (str, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if not isinstance(image, (Image.Image, list)):
raise ValueError(f"image has to be of type `PIL.Image.Image` or list but is {type(image)}")
if isinstance(image, Image.Image):
image = [image]
if not isinstance(mask, (Image.Image, list)):
raise ValueError(f"image has to be of type `PIL.Image.Image` or list but is {type(image)}")
if isinstance(mask, Image.Image):
mask = [mask]
processed_images = jnp.concatenate([preprocess_image(img, jnp.float32) for img in image])
processed_masks = jnp.concatenate([preprocess_mask(m, jnp.float32) for m in mask])
# processed_masks[processed_masks < 0.5] = 0
processed_masks = processed_masks.at[processed_masks < 0.5].set(0)
# processed_masks[processed_masks >= 0.5] = 1
processed_masks = processed_masks.at[processed_masks >= 0.5].set(1)
processed_masked_images = processed_images * (processed_masks < 0.5)
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
return text_input.input_ids, processed_masked_images, processed_masks
def _get_has_nsfw_concepts(self, features, params):
has_nsfw_concepts = self.safety_checker(features, params)
return has_nsfw_concepts
def _run_safety_checker(self, images, safety_model_params, jit=False):
# safety_model_params should already be replicated when jit is True
pil_images = [Image.fromarray(image) for image in images]
features = self.feature_extractor(pil_images, return_tensors="np").pixel_values
if jit:
features = shard(features)
has_nsfw_concepts = _p_get_has_nsfw_concepts(self, features, safety_model_params)
has_nsfw_concepts = unshard(has_nsfw_concepts)
safety_model_params = unreplicate(safety_model_params)
else:
has_nsfw_concepts = self._get_has_nsfw_concepts(features, safety_model_params)
images_was_copied = False
for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):
if has_nsfw_concept:
if not images_was_copied:
images_was_copied = True
images = images.copy()
images[idx] = np.zeros(images[idx].shape, dtype=np.uint8) # black image
if any(has_nsfw_concepts):
warnings.warn(
"Potential NSFW content was detected in one or more images. A black image will be returned"
" instead. Try again with a different prompt and/or seed."
)
return images, has_nsfw_concepts
def _generate(
self,
prompt_ids: jnp.array,
mask: jnp.array,
masked_image: jnp.array,
params: Union[Dict, FrozenDict],
prng_seed: jax.random.KeyArray,
num_inference_steps: int,
height: int,
width: int,
guidance_scale: float,
latents: Optional[jnp.array] = None,
neg_prompt_ids: Optional[jnp.array] = None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
# get prompt text embeddings
prompt_embeds = self.text_encoder(prompt_ids, params=params["text_encoder"])[0]
# TODO: currently it is assumed `do_classifier_free_guidance = guidance_scale > 1.0`
# implement this conditional `do_classifier_free_guidance = guidance_scale > 1.0`
batch_size = prompt_ids.shape[0]
max_length = prompt_ids.shape[-1]
if neg_prompt_ids is None:
uncond_input = self.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="np"
).input_ids
else:
uncond_input = neg_prompt_ids
negative_prompt_embeds = self.text_encoder(uncond_input, params=params["text_encoder"])[0]
context = jnp.concatenate([negative_prompt_embeds, prompt_embeds])
latents_shape = (
batch_size,
self.vae.config.latent_channels,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
if latents is None:
latents = jax.random.normal(prng_seed, shape=latents_shape, dtype=self.dtype)
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
prng_seed, mask_prng_seed = jax.random.split(prng_seed)
masked_image_latent_dist = self.vae.apply(
{"params": params["vae"]}, masked_image, method=self.vae.encode
).latent_dist
masked_image_latents = masked_image_latent_dist.sample(key=mask_prng_seed).transpose((0, 3, 1, 2))
masked_image_latents = self.vae.config.scaling_factor * masked_image_latents
del mask_prng_seed
mask = jax.image.resize(mask, (*mask.shape[:-2], *masked_image_latents.shape[-2:]), method="nearest")
# 8. Check that sizes of mask, masked image and latents match
num_channels_latents = self.vae.config.latent_channels
num_channels_mask = mask.shape[1]
num_channels_masked_image = masked_image_latents.shape[1]
if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
raise ValueError(
f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
" `pipeline.unet` or your `mask_image` or `image` input."
)
def loop_body(step, args):
latents, mask, masked_image_latents, scheduler_state = args
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
latents_input = jnp.concatenate([latents] * 2)
mask_input = jnp.concatenate([mask] * 2)
masked_image_latents_input = jnp.concatenate([masked_image_latents] * 2)
t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step]
timestep = jnp.broadcast_to(t, latents_input.shape[0])
latents_input = self.scheduler.scale_model_input(scheduler_state, latents_input, t)
# concat latents, mask, masked_image_latents in the channel dimension
latents_input = jnp.concatenate([latents_input, mask_input, masked_image_latents_input], axis=1)
# predict the noise residual
noise_pred = self.unet.apply(
{"params": params["unet"]},
jnp.array(latents_input),
jnp.array(timestep, dtype=jnp.int32),
encoder_hidden_states=context,
).sample
# perform guidance
noise_pred_uncond, noise_prediction_text = jnp.split(noise_pred, 2, axis=0)
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents, scheduler_state = self.scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple()
return latents, mask, masked_image_latents, scheduler_state
scheduler_state = self.scheduler.set_timesteps(
params["scheduler"], num_inference_steps=num_inference_steps, shape=latents.shape
)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * params["scheduler"].init_noise_sigma
if DEBUG:
# run with python for loop
for i in range(num_inference_steps):
latents, mask, masked_image_latents, scheduler_state = loop_body(
i, (latents, mask, masked_image_latents, scheduler_state)
)
else:
latents, _, _, _ = jax.lax.fori_loop(
0, num_inference_steps, loop_body, (latents, mask, masked_image_latents, scheduler_state)
)
# scale and decode the image latents with vae
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.apply({"params": params["vae"]}, latents, method=self.vae.decode).sample
image = (image / 2 + 0.5).clip(0, 1).transpose(0, 2, 3, 1)
return image
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt_ids: jnp.array,
mask: jnp.array,
masked_image: jnp.array,
params: Union[Dict, FrozenDict],
prng_seed: jax.random.KeyArray,
num_inference_steps: int = 50,
height: Optional[int] = None,
width: Optional[int] = None,
guidance_scale: Union[float, jnp.array] = 7.5,
latents: jnp.array = None,
neg_prompt_ids: jnp.array = None,
return_dict: bool = True,
jit: bool = False,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide image generation.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter is modulated by `strength`.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
latents (`jnp.array`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
array is generated by sampling using the supplied random `generator`.
jit (`bool`, defaults to `False`):
Whether to run `pmap` versions of the generation and safety scoring functions.
<Tip warning={true}>
This argument exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a
future release.
</Tip>
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of
a plain tuple.
Examples:
Returns:
[`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] is
returned, otherwise a `tuple` is returned where the first element is a list with the generated images
and the second element is a list of `bool`s indicating whether the corresponding generated image
contains "not-safe-for-work" (nsfw) content.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
masked_image = jax.image.resize(masked_image, (*masked_image.shape[:-2], height, width), method="bicubic")
mask = jax.image.resize(mask, (*mask.shape[:-2], height, width), method="nearest")
if isinstance(guidance_scale, float):
# Convert to a tensor so each device gets a copy. Follow the prompt_ids for
# shape information, as they may be sharded (when `jit` is `True`), or not.
guidance_scale = jnp.array([guidance_scale] * prompt_ids.shape[0])
if len(prompt_ids.shape) > 2:
# Assume sharded
guidance_scale = guidance_scale[:, None]
if jit:
images = _p_generate(
self,
prompt_ids,
mask,
masked_image,
params,
prng_seed,
num_inference_steps,
height,
width,
guidance_scale,
latents,
neg_prompt_ids,
)
else:
images = self._generate(
prompt_ids,
mask,
masked_image,
params,
prng_seed,
num_inference_steps,
height,
width,
guidance_scale,
latents,
neg_prompt_ids,
)
if self.safety_checker is not None:
safety_params = params["safety_checker"]
images_uint8_casted = (images * 255).round().astype("uint8")
num_devices, batch_size = images.shape[:2]
images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3)
images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit)
images = np.asarray(images)
# block images
if any(has_nsfw_concept):
for i, is_nsfw in enumerate(has_nsfw_concept):
if is_nsfw:
images[i] = np.asarray(images_uint8_casted[i])
images = images.reshape(num_devices, batch_size, height, width, 3)
else:
images = np.asarray(images)
has_nsfw_concept = False
if not return_dict:
return (images, has_nsfw_concept)
return FlaxStableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)
# Static argnums are pipe, num_inference_steps, height, width. A change would trigger recompilation.
# Non-static args are (sharded) input tensors mapped over their first dimension (hence, `0`).
@partial(
jax.pmap,
in_axes=(None, 0, 0, 0, 0, 0, None, None, None, 0, 0, 0),
static_broadcasted_argnums=(0, 6, 7, 8),
)
def _p_generate(
pipe,
prompt_ids,
mask,
masked_image,
params,
prng_seed,
num_inference_steps,
height,
width,
guidance_scale,
latents,
neg_prompt_ids,
):
return pipe._generate(
prompt_ids,
mask,
masked_image,
params,
prng_seed,
num_inference_steps,
height,
width,
guidance_scale,
latents,
neg_prompt_ids,
)
@partial(jax.pmap, static_broadcasted_argnums=(0,))
def _p_get_has_nsfw_concepts(pipe, features, params):
return pipe._get_has_nsfw_concepts(features, params)
def unshard(x: jnp.ndarray):
# einops.rearrange(x, 'd b ... -> (d b) ...')
num_devices, batch_size = x.shape[:2]
rest = x.shape[2:]
return x.reshape(num_devices * batch_size, *rest)
def preprocess_image(image, dtype):
w, h = image.size
w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
image = jnp.array(image).astype(dtype) / 255.0
image = image[None].transpose(0, 3, 1, 2)
return 2.0 * image - 1.0
def preprocess_mask(mask, dtype):
w, h = mask.size
w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
mask = mask.resize((w, h))
mask = jnp.array(mask.convert("L")).astype(dtype) / 255.0
mask = jnp.expand_dims(mask, axis=(0, 1))
return mask
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py | # Copyright 2023 Pix2Pix Zero Authors and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import warnings
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL
import torch
import torch.nn.functional as F
from transformers import (
BlipForConditionalGeneration,
BlipProcessor,
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from ...image_processor import VaeImageProcessor
from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel
from ...models.attention_processor import Attention
from ...schedulers import DDIMScheduler, DDPMScheduler, EulerAncestralDiscreteScheduler, LMSDiscreteScheduler
from ...schedulers.scheduling_ddim_inverse import DDIMInverseScheduler
from ...utils import (
PIL_INTERPOLATION,
BaseOutput,
deprecate,
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
@dataclass
class Pix2PixInversionPipelineOutput(BaseOutput, TextualInversionLoaderMixin):
"""
Output class for Stable Diffusion pipelines.
Args:
latents (`torch.FloatTensor`)
inverted latents tensor
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
"""
latents: torch.FloatTensor
images: Union[List[PIL.Image.Image], np.ndarray]
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import requests
>>> import torch
>>> from diffusers import DDIMScheduler, StableDiffusionPix2PixZeroPipeline
>>> def download(embedding_url, local_filepath):
... r = requests.get(embedding_url)
... with open(local_filepath, "wb") as f:
... f.write(r.content)
>>> model_ckpt = "CompVis/stable-diffusion-v1-4"
>>> pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained(model_ckpt, torch_dtype=torch.float16)
>>> pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
>>> pipeline.to("cuda")
>>> prompt = "a high resolution painting of a cat in the style of van gough"
>>> source_emb_url = "https://hf.co/datasets/sayakpaul/sample-datasets/resolve/main/cat.pt"
>>> target_emb_url = "https://hf.co/datasets/sayakpaul/sample-datasets/resolve/main/dog.pt"
>>> for url in [source_emb_url, target_emb_url]:
... download(url, url.split("/")[-1])
>>> src_embeds = torch.load(source_emb_url.split("/")[-1])
>>> target_embeds = torch.load(target_emb_url.split("/")[-1])
>>> images = pipeline(
... prompt,
... source_embeds=src_embeds,
... target_embeds=target_embeds,
... num_inference_steps=50,
... cross_attention_guidance_amount=0.15,
... ).images
>>> images[0].save("edited_image_dog.png")
```
"""
EXAMPLE_INVERT_DOC_STRING = """
Examples:
```py
>>> import torch
>>> from transformers import BlipForConditionalGeneration, BlipProcessor
>>> from diffusers import DDIMScheduler, DDIMInverseScheduler, StableDiffusionPix2PixZeroPipeline
>>> import requests
>>> from PIL import Image
>>> captioner_id = "Salesforce/blip-image-captioning-base"
>>> processor = BlipProcessor.from_pretrained(captioner_id)
>>> model = BlipForConditionalGeneration.from_pretrained(
... captioner_id, torch_dtype=torch.float16, low_cpu_mem_usage=True
... )
>>> sd_model_ckpt = "CompVis/stable-diffusion-v1-4"
>>> pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained(
... sd_model_ckpt,
... caption_generator=model,
... caption_processor=processor,
... torch_dtype=torch.float16,
... safety_checker=None,
... )
>>> pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
>>> pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
>>> pipeline.enable_model_cpu_offload()
>>> img_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/test_images/cats/cat_6.png"
>>> raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB").resize((512, 512))
>>> # generate caption
>>> caption = pipeline.generate_caption(raw_image)
>>> # "a photography of a cat with flowers and dai dai daie - daie - daie kasaii"
>>> inv_latents = pipeline.invert(caption, image=raw_image).latents
>>> # we need to generate source and target embeds
>>> source_prompts = ["a cat sitting on the street", "a cat playing in the field", "a face of a cat"]
>>> target_prompts = ["a dog sitting on the street", "a dog playing in the field", "a face of a dog"]
>>> source_embeds = pipeline.get_embeds(source_prompts)
>>> target_embeds = pipeline.get_embeds(target_prompts)
>>> # the latents can then be used to edit a real image
>>> # when using Stable Diffusion 2 or other models that use v-prediction
>>> # set `cross_attention_guidance_amount` to 0.01 or less to avoid input latent gradient explosion
>>> image = pipeline(
... caption,
... source_embeds=source_embeds,
... target_embeds=target_embeds,
... num_inference_steps=50,
... cross_attention_guidance_amount=0.15,
... generator=generator,
... latents=inv_latents,
... negative_prompt=caption,
... ).images[0]
>>> image.save("edited_image.png")
```
"""
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
def preprocess(image):
warnings.warn(
"The preprocess method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor.preprocess instead",
FutureWarning,
)
if isinstance(image, torch.Tensor):
return image
elif isinstance(image, PIL.Image.Image):
image = [image]
if isinstance(image[0], PIL.Image.Image):
w, h = image[0].size
w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
image = np.concatenate(image, axis=0)
image = np.array(image).astype(np.float32) / 255.0
image = image.transpose(0, 3, 1, 2)
image = 2.0 * image - 1.0
image = torch.from_numpy(image)
elif isinstance(image[0], torch.Tensor):
image = torch.cat(image, dim=0)
return image
def prepare_unet(unet: UNet2DConditionModel):
"""Modifies the UNet (`unet`) to perform Pix2Pix Zero optimizations."""
pix2pix_zero_attn_procs = {}
for name in unet.attn_processors.keys():
module_name = name.replace(".processor", "")
module = unet.get_submodule(module_name)
if "attn2" in name:
pix2pix_zero_attn_procs[name] = Pix2PixZeroAttnProcessor(is_pix2pix_zero=True)
module.requires_grad_(True)
else:
pix2pix_zero_attn_procs[name] = Pix2PixZeroAttnProcessor(is_pix2pix_zero=False)
module.requires_grad_(False)
unet.set_attn_processor(pix2pix_zero_attn_procs)
return unet
class Pix2PixZeroL2Loss:
def __init__(self):
self.loss = 0.0
def compute_loss(self, predictions, targets):
self.loss += ((predictions - targets) ** 2).sum((1, 2)).mean(0)
class Pix2PixZeroAttnProcessor:
"""An attention processor class to store the attention weights.
In Pix2Pix Zero, it happens during computations in the cross-attention blocks."""
def __init__(self, is_pix2pix_zero=False):
self.is_pix2pix_zero = is_pix2pix_zero
if self.is_pix2pix_zero:
self.reference_cross_attn_map = {}
def __call__(
self,
attn: Attention,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
timestep=None,
loss=None,
):
batch_size, sequence_length, _ = hidden_states.shape
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
query = attn.head_to_batch_dim(query)
key = attn.head_to_batch_dim(key)
value = attn.head_to_batch_dim(value)
attention_probs = attn.get_attention_scores(query, key, attention_mask)
if self.is_pix2pix_zero and timestep is not None:
# new bookkeeping to save the attention weights.
if loss is None:
self.reference_cross_attn_map[timestep.item()] = attention_probs.detach().cpu()
# compute loss
elif loss is not None:
prev_attn_probs = self.reference_cross_attn_map.pop(timestep.item())
loss.compute_loss(attention_probs, prev_attn_probs.to(attention_probs.device))
hidden_states = torch.bmm(attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
return hidden_states
class StableDiffusionPix2PixZeroPipeline(DiffusionPipeline):
r"""
Pipeline for pixel-levl image editing using Pix2Pix Zero. Based on Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`], or [`DDPMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
requires_safety_checker (bool):
Whether the pipeline requires a safety checker. We recommend setting it to True if you're using the
pipeline publicly.
"""
_optional_components = [
"safety_checker",
"feature_extractor",
"caption_generator",
"caption_processor",
"inverse_scheduler",
]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDPMScheduler, DDIMScheduler, EulerAncestralDiscreteScheduler, LMSDiscreteScheduler],
feature_extractor: CLIPImageProcessor,
safety_checker: StableDiffusionSafetyChecker,
inverse_scheduler: DDIMInverseScheduler,
caption_generator: BlipForConditionalGeneration,
caption_processor: BlipProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
caption_processor=caption_processor,
caption_generator=caption_generator,
inverse_scheduler=inverse_scheduler,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
hook = None
for cpu_offloaded_model in [self.vae, self.text_encoder, self.unet, self.vae]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
if self.safety_checker is not None:
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self,
prompt,
source_embeds,
target_embeds,
callback_steps,
prompt_embeds=None,
):
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if source_embeds is None and target_embeds is None:
raise ValueError("`source_embeds` and `target_embeds` cannot be undefined.")
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@torch.no_grad()
def generate_caption(self, images):
"""Generates caption for a given image."""
text = "a photography of"
prev_device = self.caption_generator.device
device = self._execution_device
inputs = self.caption_processor(images, text, return_tensors="pt").to(
device=device, dtype=self.caption_generator.dtype
)
self.caption_generator.to(device)
outputs = self.caption_generator.generate(**inputs, max_new_tokens=128)
# offload caption generator
self.caption_generator.to(prev_device)
caption = self.caption_processor.batch_decode(outputs, skip_special_tokens=True)[0]
return caption
def construct_direction(self, embs_source: torch.Tensor, embs_target: torch.Tensor):
"""Constructs the edit direction to steer the image generation process semantically."""
return (embs_target.mean(0) - embs_source.mean(0)).unsqueeze(0)
@torch.no_grad()
def get_embeds(self, prompt: List[str], batch_size: int = 16) -> torch.FloatTensor:
num_prompts = len(prompt)
embeds = []
for i in range(0, num_prompts, batch_size):
prompt_slice = prompt[i : i + batch_size]
input_ids = self.tokenizer(
prompt_slice,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
).input_ids
input_ids = input_ids.to(self.text_encoder.device)
embeds.append(self.text_encoder(input_ids)[0])
return torch.cat(embeds, dim=0).mean(0)[None]
def prepare_image_latents(self, image, batch_size, dtype, device, generator=None):
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
)
image = image.to(device=device, dtype=dtype)
if image.shape[1] == 4:
latents = image
else:
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if isinstance(generator, list):
latents = [
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
]
latents = torch.cat(latents, dim=0)
else:
latents = self.vae.encode(image).latent_dist.sample(generator)
latents = self.vae.config.scaling_factor * latents
if batch_size != latents.shape[0]:
if batch_size % latents.shape[0] == 0:
# expand image_latents for batch_size
deprecation_message = (
f"You have passed {batch_size} text prompts (`prompt`), but only {latents.shape[0]} initial"
" images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
" your script to pass as many initial images as text prompts to suppress this warning."
)
deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
additional_latents_per_image = batch_size // latents.shape[0]
latents = torch.cat([latents] * additional_latents_per_image, dim=0)
else:
raise ValueError(
f"Cannot duplicate `image` of batch size {latents.shape[0]} to {batch_size} text prompts."
)
else:
latents = torch.cat([latents], dim=0)
return latents
def get_epsilon(self, model_output: torch.Tensor, sample: torch.Tensor, timestep: int):
pred_type = self.inverse_scheduler.config.prediction_type
alpha_prod_t = self.inverse_scheduler.alphas_cumprod[timestep]
beta_prod_t = 1 - alpha_prod_t
if pred_type == "epsilon":
return model_output
elif pred_type == "sample":
return (sample - alpha_prod_t ** (0.5) * model_output) / beta_prod_t ** (0.5)
elif pred_type == "v_prediction":
return (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
f"prediction_type given as {pred_type} must be one of `epsilon`, `sample`, or `v_prediction`"
)
def auto_corr_loss(self, hidden_states, generator=None):
reg_loss = 0.0
for i in range(hidden_states.shape[0]):
for j in range(hidden_states.shape[1]):
noise = hidden_states[i : i + 1, j : j + 1, :, :]
while True:
roll_amount = torch.randint(noise.shape[2] // 2, (1,), generator=generator).item()
reg_loss += (noise * torch.roll(noise, shifts=roll_amount, dims=2)).mean() ** 2
reg_loss += (noise * torch.roll(noise, shifts=roll_amount, dims=3)).mean() ** 2
if noise.shape[2] <= 8:
break
noise = F.avg_pool2d(noise, kernel_size=2)
return reg_loss
def kl_divergence(self, hidden_states):
mean = hidden_states.mean()
var = hidden_states.var()
return var + mean**2 - 1 - torch.log(var + 1e-7)
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Optional[Union[str, List[str]]] = None,
source_embeds: torch.Tensor = None,
target_embeds: torch.Tensor = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
cross_attention_guidance_amount: float = 0.1,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
source_embeds (`torch.Tensor`):
Source concept embeddings. Generation of the embeddings as per the [original
paper](https://arxiv.org/abs/2302.03027). Used in discovering the edit direction.
target_embeds (`torch.Tensor`):
Target concept embeddings. Generation of the embeddings as per the [original
paper](https://arxiv.org/abs/2302.03027). Used in discovering the edit direction.
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
cross_attention_guidance_amount (`float`, defaults to 0.1):
Amount of guidance needed from the reference cross-attention maps.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
# 0. Define the spatial resolutions.
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
source_embeds,
target_embeds,
callback_steps,
prompt_embeds,
)
# 3. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if cross_attention_kwargs is None:
cross_attention_kwargs = {}
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Generate the inverted noise from the input image or any other image
# generated from the input prompt.
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
latents_init = latents.clone()
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 8. Rejig the UNet so that we can obtain the cross-attenion maps and
# use them for guiding the subsequent image generation.
self.unet = prepare_unet(self.unet)
# 7. Denoising loop where we obtain the cross-attention maps.
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs={"timestep": t},
).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
# 8. Compute the edit directions.
edit_direction = self.construct_direction(source_embeds, target_embeds).to(prompt_embeds.device)
# 9. Edit the prompt embeddings as per the edit directions discovered.
prompt_embeds_edit = prompt_embeds.clone()
prompt_embeds_edit[1:2] += edit_direction
# 10. Second denoising loop to generate the edited image.
latents = latents_init
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# we want to learn the latent such that it steers the generation
# process towards the edited direction, so make the make initial
# noise learnable
x_in = latent_model_input.detach().clone()
x_in.requires_grad = True
# optimizer
opt = torch.optim.SGD([x_in], lr=cross_attention_guidance_amount)
with torch.enable_grad():
# initialize loss
loss = Pix2PixZeroL2Loss()
# predict the noise residual
noise_pred = self.unet(
x_in,
t,
encoder_hidden_states=prompt_embeds_edit.detach(),
cross_attention_kwargs={"timestep": t, "loss": loss},
).sample
loss.loss.backward(retain_graph=False)
opt.step()
# recompute the noise
noise_pred = self.unet(
x_in.detach(),
t,
encoder_hidden_states=prompt_embeds_edit,
cross_attention_kwargs={"timestep": None},
).sample
latents = x_in.detach().chunk(2)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
@torch.no_grad()
@replace_example_docstring(EXAMPLE_INVERT_DOC_STRING)
def invert(
self,
prompt: Optional[str] = None,
image: Union[
torch.FloatTensor,
PIL.Image.Image,
np.ndarray,
List[torch.FloatTensor],
List[PIL.Image.Image],
List[np.ndarray],
] = None,
num_inference_steps: int = 50,
guidance_scale: float = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
cross_attention_guidance_amount: float = 0.1,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
lambda_auto_corr: float = 20.0,
lambda_kl: float = 20.0,
num_reg_steps: int = 5,
num_auto_corr_rolls: int = 5,
):
r"""
Function used to generate inverted latents given a prompt and image.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
image (`torch.FloatTensor` `np.ndarray`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
`Image`, or tensor representing an image batch which will be used for conditioning. Can also accpet
image latents as `image`, if passing latents directly, it will not be encoded again.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 1):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
cross_attention_guidance_amount (`float`, defaults to 0.1):
Amount of guidance needed from the reference cross-attention maps.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
lambda_auto_corr (`float`, *optional*, defaults to 20.0):
Lambda parameter to control auto correction
lambda_kl (`float`, *optional*, defaults to 20.0):
Lambda parameter to control Kullback–Leibler divergence output
num_reg_steps (`int`, *optional*, defaults to 5):
Number of regularization loss steps
num_auto_corr_rolls (`int`, *optional*, defaults to 5):
Number of auto correction roll steps
Examples:
Returns:
[`~pipelines.stable_diffusion.pipeline_stable_diffusion_pix2pix_zero.Pix2PixInversionPipelineOutput`] or
`tuple`:
[`~pipelines.stable_diffusion.pipeline_stable_diffusion_pix2pix_zero.Pix2PixInversionPipelineOutput`] if
`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is the inverted
latents tensor and then second is the corresponding decoded image.
"""
# 1. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if cross_attention_kwargs is None:
cross_attention_kwargs = {}
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Preprocess image
image = self.image_processor.preprocess(image)
# 4. Prepare latent variables
latents = self.prepare_image_latents(image, batch_size, self.vae.dtype, device, generator)
# 5. Encode input prompt
num_images_per_prompt = 1
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
prompt_embeds=prompt_embeds,
)
# 4. Prepare timesteps
self.inverse_scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.inverse_scheduler.timesteps
# 6. Rejig the UNet so that we can obtain the cross-attenion maps and
# use them for guiding the subsequent image generation.
self.unet = prepare_unet(self.unet)
# 7. Denoising loop where we obtain the cross-attention maps.
num_warmup_steps = len(timesteps) - num_inference_steps * self.inverse_scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.inverse_scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs={"timestep": t},
).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# regularization of the noise prediction
with torch.enable_grad():
for _ in range(num_reg_steps):
if lambda_auto_corr > 0:
for _ in range(num_auto_corr_rolls):
var = torch.autograd.Variable(noise_pred.detach().clone(), requires_grad=True)
# Derive epsilon from model output before regularizing to IID standard normal
var_epsilon = self.get_epsilon(var, latent_model_input.detach(), t)
l_ac = self.auto_corr_loss(var_epsilon, generator=generator)
l_ac.backward()
grad = var.grad.detach() / num_auto_corr_rolls
noise_pred = noise_pred - lambda_auto_corr * grad
if lambda_kl > 0:
var = torch.autograd.Variable(noise_pred.detach().clone(), requires_grad=True)
# Derive epsilon from model output before regularizing to IID standard normal
var_epsilon = self.get_epsilon(var, latent_model_input.detach(), t)
l_kld = self.kl_divergence(var_epsilon)
l_kld.backward()
grad = var.grad.detach()
noise_pred = noise_pred - lambda_kl * grad
noise_pred = noise_pred.detach()
# compute the previous noisy sample x_t -> x_t-1
latents = self.inverse_scheduler.step(noise_pred, t, latents).prev_sample
# call the callback, if provided
if i == len(timesteps) - 1 or (
(i + 1) > num_warmup_steps and (i + 1) % self.inverse_scheduler.order == 0
):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
inverted_latents = latents.detach().clone()
# 8. Post-processing
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image = self.image_processor.postprocess(image, output_type=output_type)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (inverted_latents, image)
return Pix2PixInversionPipelineOutput(latents=inverted_latents, images=image)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import Callable, List, Optional, Union
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPTokenizer
from ...configuration_utils import FrozenDict
from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from ...utils import deprecate, logging
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
logger = logging.get_logger(__name__)
class OnnxStableDiffusionPipeline(DiffusionPipeline):
vae_encoder: OnnxRuntimeModel
vae_decoder: OnnxRuntimeModel
text_encoder: OnnxRuntimeModel
tokenizer: CLIPTokenizer
unet: OnnxRuntimeModel
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
safety_checker: OnnxRuntimeModel
feature_extractor: CLIPImageProcessor
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae_encoder: OnnxRuntimeModel,
vae_decoder: OnnxRuntimeModel,
text_encoder: OnnxRuntimeModel,
tokenizer: CLIPTokenizer,
unet: OnnxRuntimeModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: OnnxRuntimeModel,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
self.register_modules(
vae_encoder=vae_encoder,
vae_decoder=vae_decoder,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.register_to_config(requires_safety_checker=requires_safety_checker)
def _encode_prompt(
self,
prompt: Union[str, List[str]],
num_images_per_prompt: Optional[int],
do_classifier_free_guidance: bool,
negative_prompt: Optional[str],
prompt_embeds: Optional[np.ndarray] = None,
negative_prompt_embeds: Optional[np.ndarray] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`):
prompt to be encoded
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
prompt_embeds (`np.ndarray`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`np.ndarray`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
"""
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="np").input_ids
if not np.array_equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]
prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt] * batch_size
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="np",
)
negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0]
if do_classifier_free_guidance:
negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
def check_inputs(
self,
prompt: Union[str, List[str]],
height: Optional[int],
width: Optional[int],
callback_steps: int,
negative_prompt: Optional[str] = None,
prompt_embeds: Optional[np.ndarray] = None,
negative_prompt_embeds: Optional[np.ndarray] = None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = 512,
width: Optional[int] = 512,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: Optional[float] = 0.0,
generator: Optional[np.random.RandomState] = None,
latents: Optional[np.ndarray] = None,
prompt_embeds: Optional[np.ndarray] = None,
negative_prompt_embeds: Optional[np.ndarray] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
callback_steps: int = 1,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
image (`PIL.Image.Image` or List[`PIL.Image.Image`] or `torch.FloatTensor`):
`Image`, or tensor representing an image batch which will be upscaled. *
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale`
is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`np.random.RandomState`, *optional*):
One or a list of [numpy generator(s)](TODO) to make generation deterministic.
latents (`np.ndarray`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
prompt_embeds (`np.ndarray`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`np.ndarray`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
# check inputs. Raise error if not correct
self.check_inputs(
prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
)
# define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if generator is None:
generator = np.random
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
prompt_embeds = self._encode_prompt(
prompt,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
)
# get the initial random noise unless the user supplied it
latents_dtype = prompt_embeds.dtype
latents_shape = (batch_size * num_images_per_prompt, 4, height // 8, width // 8)
if latents is None:
latents = generator.randn(*latents_shape).astype(latents_dtype)
elif latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
# set timesteps
self.scheduler.set_timesteps(num_inference_steps)
latents = latents * np.float64(self.scheduler.init_noise_sigma)
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
timestep_dtype = next(
(input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
)
timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)
latent_model_input = latent_model_input.cpu().numpy()
# predict the noise residual
timestep = np.array([t], dtype=timestep_dtype)
noise_pred = self.unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)
noise_pred = noise_pred[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
scheduler_output = self.scheduler.step(
torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
)
latents = scheduler_output.prev_sample.numpy()
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
latents = 1 / 0.18215 * latents
# image = self.vae_decoder(latent_sample=latents)[0]
# it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
image = np.concatenate(
[self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
)
image = np.clip(image / 2 + 0.5, 0, 1)
image = image.transpose((0, 2, 3, 1))
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(
self.numpy_to_pil(image), return_tensors="np"
).pixel_values.astype(image.dtype)
images, has_nsfw_concept = [], []
for i in range(image.shape[0]):
image_i, has_nsfw_concept_i = self.safety_checker(
clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
)
images.append(image_i)
has_nsfw_concept.append(has_nsfw_concept_i[0])
image = np.concatenate(images)
else:
has_nsfw_concept = None
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
class StableDiffusionOnnxPipeline(OnnxStableDiffusionPipeline):
def __init__(
self,
vae_encoder: OnnxRuntimeModel,
vae_decoder: OnnxRuntimeModel,
text_encoder: OnnxRuntimeModel,
tokenizer: CLIPTokenizer,
unet: OnnxRuntimeModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: OnnxRuntimeModel,
feature_extractor: CLIPImageProcessor,
):
deprecation_message = "Please use `OnnxStableDiffusionPipeline` instead of `StableDiffusionOnnxPipeline`."
deprecate("StableDiffusionOnnxPipeline", "1.0.0", deprecation_message)
super().__init__(
vae_encoder=vae_encoder,
vae_decoder=vae_decoder,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from functools import partial
from typing import Dict, List, Optional, Union
import jax
import jax.numpy as jnp
import numpy as np
from flax.core.frozen_dict import FrozenDict
from flax.jax_utils import unreplicate
from flax.training.common_utils import shard
from packaging import version
from PIL import Image
from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel
from ...models import FlaxAutoencoderKL, FlaxUNet2DConditionModel
from ...schedulers import (
FlaxDDIMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
)
from ...utils import deprecate, logging, replace_example_docstring
from ..pipeline_flax_utils import FlaxDiffusionPipeline
from . import FlaxStableDiffusionPipelineOutput
from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# Set to True to use python for loop instead of jax.fori_loop for easier debugging
DEBUG = False
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import jax
>>> import numpy as np
>>> from flax.jax_utils import replicate
>>> from flax.training.common_utils import shard
>>> from diffusers import FlaxStableDiffusionPipeline
>>> pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
... "runwayml/stable-diffusion-v1-5", revision="bf16", dtype=jax.numpy.bfloat16
... )
>>> prompt = "a photo of an astronaut riding a horse on mars"
>>> prng_seed = jax.random.PRNGKey(0)
>>> num_inference_steps = 50
>>> num_samples = jax.device_count()
>>> prompt = num_samples * [prompt]
>>> prompt_ids = pipeline.prepare_inputs(prompt)
# shard inputs and rng
>>> params = replicate(params)
>>> prng_seed = jax.random.split(prng_seed, jax.device_count())
>>> prompt_ids = shard(prompt_ids)
>>> images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
>>> images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
```
"""
class FlaxStableDiffusionPipeline(FlaxDiffusionPipeline):
r"""
Flax-based pipeline for text-to-image generation using Stable Diffusion.
This model inherits from [`FlaxDiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
vae ([`FlaxAutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.FlaxCLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`FlaxUNet2DConditionModel`]):
A `FlaxUNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`FlaxDDIMScheduler`], [`FlaxLMSDiscreteScheduler`], [`FlaxPNDMScheduler`], or
[`FlaxDPMSolverMultistepScheduler`].
safety_checker ([`FlaxStableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
def __init__(
self,
vae: FlaxAutoencoderKL,
text_encoder: FlaxCLIPTextModel,
tokenizer: CLIPTokenizer,
unet: FlaxUNet2DConditionModel,
scheduler: Union[
FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler
],
safety_checker: FlaxStableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
dtype: jnp.dtype = jnp.float32,
):
super().__init__()
self.dtype = dtype
if safety_checker is None:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
def prepare_inputs(self, prompt: Union[str, List[str]]):
if not isinstance(prompt, (str, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
return text_input.input_ids
def _get_has_nsfw_concepts(self, features, params):
has_nsfw_concepts = self.safety_checker(features, params)
return has_nsfw_concepts
def _run_safety_checker(self, images, safety_model_params, jit=False):
# safety_model_params should already be replicated when jit is True
pil_images = [Image.fromarray(image) for image in images]
features = self.feature_extractor(pil_images, return_tensors="np").pixel_values
if jit:
features = shard(features)
has_nsfw_concepts = _p_get_has_nsfw_concepts(self, features, safety_model_params)
has_nsfw_concepts = unshard(has_nsfw_concepts)
safety_model_params = unreplicate(safety_model_params)
else:
has_nsfw_concepts = self._get_has_nsfw_concepts(features, safety_model_params)
images_was_copied = False
for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):
if has_nsfw_concept:
if not images_was_copied:
images_was_copied = True
images = images.copy()
images[idx] = np.zeros(images[idx].shape, dtype=np.uint8) # black image
if any(has_nsfw_concepts):
warnings.warn(
"Potential NSFW content was detected in one or more images. A black image will be returned"
" instead. Try again with a different prompt and/or seed."
)
return images, has_nsfw_concepts
def _generate(
self,
prompt_ids: jnp.array,
params: Union[Dict, FrozenDict],
prng_seed: jax.random.KeyArray,
num_inference_steps: int,
height: int,
width: int,
guidance_scale: float,
latents: Optional[jnp.array] = None,
neg_prompt_ids: Optional[jnp.array] = None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
# get prompt text embeddings
prompt_embeds = self.text_encoder(prompt_ids, params=params["text_encoder"])[0]
# TODO: currently it is assumed `do_classifier_free_guidance = guidance_scale > 1.0`
# implement this conditional `do_classifier_free_guidance = guidance_scale > 1.0`
batch_size = prompt_ids.shape[0]
max_length = prompt_ids.shape[-1]
if neg_prompt_ids is None:
uncond_input = self.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="np"
).input_ids
else:
uncond_input = neg_prompt_ids
negative_prompt_embeds = self.text_encoder(uncond_input, params=params["text_encoder"])[0]
context = jnp.concatenate([negative_prompt_embeds, prompt_embeds])
# Ensure model output will be `float32` before going into the scheduler
guidance_scale = jnp.array([guidance_scale], dtype=jnp.float32)
latents_shape = (
batch_size,
self.unet.config.in_channels,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
if latents is None:
latents = jax.random.normal(prng_seed, shape=latents_shape, dtype=jnp.float32)
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
def loop_body(step, args):
latents, scheduler_state = args
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
latents_input = jnp.concatenate([latents] * 2)
t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step]
timestep = jnp.broadcast_to(t, latents_input.shape[0])
latents_input = self.scheduler.scale_model_input(scheduler_state, latents_input, t)
# predict the noise residual
noise_pred = self.unet.apply(
{"params": params["unet"]},
jnp.array(latents_input),
jnp.array(timestep, dtype=jnp.int32),
encoder_hidden_states=context,
).sample
# perform guidance
noise_pred_uncond, noise_prediction_text = jnp.split(noise_pred, 2, axis=0)
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents, scheduler_state = self.scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple()
return latents, scheduler_state
scheduler_state = self.scheduler.set_timesteps(
params["scheduler"], num_inference_steps=num_inference_steps, shape=latents.shape
)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * params["scheduler"].init_noise_sigma
if DEBUG:
# run with python for loop
for i in range(num_inference_steps):
latents, scheduler_state = loop_body(i, (latents, scheduler_state))
else:
latents, _ = jax.lax.fori_loop(0, num_inference_steps, loop_body, (latents, scheduler_state))
# scale and decode the image latents with vae
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.apply({"params": params["vae"]}, latents, method=self.vae.decode).sample
image = (image / 2 + 0.5).clip(0, 1).transpose(0, 2, 3, 1)
return image
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt_ids: jnp.array,
params: Union[Dict, FrozenDict],
prng_seed: jax.random.KeyArray,
num_inference_steps: int = 50,
height: Optional[int] = None,
width: Optional[int] = None,
guidance_scale: Union[float, jnp.array] = 7.5,
latents: jnp.array = None,
neg_prompt_ids: jnp.array = None,
return_dict: bool = True,
jit: bool = False,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
latents (`jnp.array`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
array is generated by sampling using the supplied random `generator`.
jit (`bool`, defaults to `False`):
Whether to run `pmap` versions of the generation and safety scoring functions.
<Tip warning={true}>
This argument exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a
future release.
</Tip>
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of
a plain tuple.
Examples:
Returns:
[`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] is
returned, otherwise a `tuple` is returned where the first element is a list with the generated images
and the second element is a list of `bool`s indicating whether the corresponding generated image
contains "not-safe-for-work" (nsfw) content.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
if isinstance(guidance_scale, float):
# Convert to a tensor so each device gets a copy. Follow the prompt_ids for
# shape information, as they may be sharded (when `jit` is `True`), or not.
guidance_scale = jnp.array([guidance_scale] * prompt_ids.shape[0])
if len(prompt_ids.shape) > 2:
# Assume sharded
guidance_scale = guidance_scale[:, None]
if jit:
images = _p_generate(
self,
prompt_ids,
params,
prng_seed,
num_inference_steps,
height,
width,
guidance_scale,
latents,
neg_prompt_ids,
)
else:
images = self._generate(
prompt_ids,
params,
prng_seed,
num_inference_steps,
height,
width,
guidance_scale,
latents,
neg_prompt_ids,
)
if self.safety_checker is not None:
safety_params = params["safety_checker"]
images_uint8_casted = (images * 255).round().astype("uint8")
num_devices, batch_size = images.shape[:2]
images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3)
images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit)
images = np.asarray(images)
# block images
if any(has_nsfw_concept):
for i, is_nsfw in enumerate(has_nsfw_concept):
if is_nsfw:
images[i] = np.asarray(images_uint8_casted[i])
images = images.reshape(num_devices, batch_size, height, width, 3)
else:
images = np.asarray(images)
has_nsfw_concept = False
if not return_dict:
return (images, has_nsfw_concept)
return FlaxStableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)
# Static argnums are pipe, num_inference_steps, height, width. A change would trigger recompilation.
# Non-static args are (sharded) input tensors mapped over their first dimension (hence, `0`).
@partial(
jax.pmap,
in_axes=(None, 0, 0, 0, None, None, None, 0, 0, 0),
static_broadcasted_argnums=(0, 4, 5, 6),
)
def _p_generate(
pipe,
prompt_ids,
params,
prng_seed,
num_inference_steps,
height,
width,
guidance_scale,
latents,
neg_prompt_ids,
):
return pipe._generate(
prompt_ids,
params,
prng_seed,
num_inference_steps,
height,
width,
guidance_scale,
latents,
neg_prompt_ids,
)
@partial(jax.pmap, static_broadcasted_argnums=(0,))
def _p_get_has_nsfw_concepts(pipe, features, params):
return pipe._get_has_nsfw_concepts(features, params)
def unshard(x: jnp.ndarray):
# einops.rearrange(x, 'd b ... -> (d b) ...')
num_devices, batch_size = x.shape[:2]
rest = x.shape[2:]
return x.reshape(num_devices * batch_size, *rest)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_paradigms.py | # Copyright 2023 ParaDiGMS authors and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from ...image_processor import VaeImageProcessor
from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import torch
>>> from diffusers import DDPMParallelScheduler
>>> from diffusers import StableDiffusionParadigmsPipeline
>>> scheduler = DDPMParallelScheduler.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="scheduler")
>>> pipe = StableDiffusionParadigmsPipeline.from_pretrained(
... "runwayml/stable-diffusion-v1-5", scheduler=scheduler, torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> ngpu, batch_per_device = torch.cuda.device_count(), 5
>>> pipe.wrapped_unet = torch.nn.DataParallel(pipe.unet, device_ids=[d for d in range(ngpu)])
>>> prompt = "a photo of an astronaut riding a horse on mars"
>>> image = pipe(prompt, parallel=ngpu * batch_per_device, num_inference_steps=1000).images[0]
```
"""
class StableDiffusionParadigmsPipeline(
DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
):
r"""
Pipeline for text-to-image generation using a parallelized version of Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# attribute to wrap the unet with torch.nn.DataParallel when running multiple denoising steps on multiple GPUs
self.wrapped_unet = self.unet
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.vae.enable_slicing()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
def disable_vae_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_slicing()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
def enable_vae_tiling(self):
r"""
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
processing larger images.
"""
self.vae.enable_tiling()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
def disable_vae_tiling(self):
r"""
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_tiling()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
if self.safety_checker is not None:
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def _cumsum(self, input, dim, debug=False):
if debug:
# cumsum_cuda_kernel does not have a deterministic implementation
# so perform cumsum on cpu for debugging purposes
return torch.cumsum(input.cpu().float(), dim=dim).to(input.device)
else:
return torch.cumsum(input, dim=dim)
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
parallel: int = 10,
tolerance: float = 0.1,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
debug: bool = False,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
parallel (`int`, *optional*, defaults to 10):
The batch size to use when doing parallel sampling. More parallelism may lead to faster inference but
requires higher memory usage and can also require more total FLOPs.
tolerance (`float`, *optional*, defaults to 0.1):
The error tolerance for determining when to slide the batch window forward for parallel sampling. Lower
tolerance usually leads to less or no degradation. Higher tolerance is faster but can risk degradation
of sample quality. The tolerance is specified as a ratio of the scheduler's noise magnitude.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
debug (`bool`, *optional*, defaults to `False`):
Whether or not to run in debug mode. In debug mode, `torch.cumsum` is evaluated using the CPU.
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
extra_step_kwargs.pop("generator", None)
# # 7. Denoising loop
scheduler = self.scheduler
parallel = min(parallel, len(scheduler.timesteps))
begin_idx = 0
end_idx = parallel
latents_time_evolution_buffer = torch.stack([latents] * (len(scheduler.timesteps) + 1))
# We must make sure the noise of stochastic schedulers such as DDPM is sampled only once per timestep.
# Sampling inside the parallel denoising loop will mess this up, so we pre-sample the noise vectors outside the denoising loop.
noise_array = torch.zeros_like(latents_time_evolution_buffer)
for j in range(len(scheduler.timesteps)):
base_noise = randn_tensor(
shape=latents.shape, generator=generator, device=latents.device, dtype=prompt_embeds.dtype
)
noise = (self.scheduler._get_variance(scheduler.timesteps[j]) ** 0.5) * base_noise
noise_array[j] = noise.clone()
# We specify the error tolerance as a ratio of the scheduler's noise magnitude. We similarly compute the error tolerance
# outside of the denoising loop to avoid recomputing it at every step.
# We will be dividing the norm of the noise, so we store its inverse here to avoid a division at every step.
inverse_variance_norm = 1.0 / torch.tensor(
[scheduler._get_variance(scheduler.timesteps[j]) for j in range(len(scheduler.timesteps))] + [0]
).to(noise_array.device)
latent_dim = noise_array[0, 0].numel()
inverse_variance_norm = inverse_variance_norm[:, None] / latent_dim
scaled_tolerance = tolerance**2
with self.progress_bar(total=num_inference_steps) as progress_bar:
steps = 0
while begin_idx < len(scheduler.timesteps):
# these have shape (parallel_dim, 2*batch_size, ...)
# parallel_len is at most parallel, but could be less if we are at the end of the timesteps
# we are processing batch window of timesteps spanning [begin_idx, end_idx)
parallel_len = end_idx - begin_idx
block_prompt_embeds = torch.stack([prompt_embeds] * parallel_len)
block_latents = latents_time_evolution_buffer[begin_idx:end_idx]
block_t = scheduler.timesteps[begin_idx:end_idx, None].repeat(1, batch_size * num_images_per_prompt)
t_vec = block_t
if do_classifier_free_guidance:
t_vec = t_vec.repeat(1, 2)
# expand the latents if we are doing classifier free guidance
latent_model_input = (
torch.cat([block_latents] * 2, dim=1) if do_classifier_free_guidance else block_latents
)
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t_vec)
# if parallel_len is small, no need to use multiple GPUs
net = self.wrapped_unet if parallel_len > 3 else self.unet
# predict the noise residual, shape is now [parallel_len * 2 * batch_size * num_images_per_prompt, ...]
model_output = net(
latent_model_input.flatten(0, 1),
t_vec.flatten(0, 1),
encoder_hidden_states=block_prompt_embeds.flatten(0, 1),
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
per_latent_shape = model_output.shape[1:]
if do_classifier_free_guidance:
model_output = model_output.reshape(
parallel_len, 2, batch_size * num_images_per_prompt, *per_latent_shape
)
noise_pred_uncond, noise_pred_text = model_output[:, 0], model_output[:, 1]
model_output = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
model_output = model_output.reshape(
parallel_len * batch_size * num_images_per_prompt, *per_latent_shape
)
block_latents_denoise = scheduler.batch_step_no_noise(
model_output=model_output,
timesteps=block_t.flatten(0, 1),
sample=block_latents.flatten(0, 1),
**extra_step_kwargs,
).reshape(block_latents.shape)
# back to shape (parallel_dim, batch_size, ...)
# now we want to add the pre-sampled noise
# parallel sampling algorithm requires computing the cumulative drift from the beginning
# of the window, so we need to compute cumulative sum of the deltas and the pre-sampled noises.
delta = block_latents_denoise - block_latents
cumulative_delta = self._cumsum(delta, dim=0, debug=debug)
cumulative_noise = self._cumsum(noise_array[begin_idx:end_idx], dim=0, debug=debug)
# if we are using an ODE-like scheduler (like DDIM), we don't want to add noise
if scheduler._is_ode_scheduler:
cumulative_noise = 0
block_latents_new = (
latents_time_evolution_buffer[begin_idx][None,] + cumulative_delta + cumulative_noise
)
cur_error = torch.linalg.norm(
(block_latents_new - latents_time_evolution_buffer[begin_idx + 1 : end_idx + 1]).reshape(
parallel_len, batch_size * num_images_per_prompt, -1
),
dim=-1,
).pow(2)
error_ratio = cur_error * inverse_variance_norm[begin_idx + 1 : end_idx + 1]
# find the first index of the vector error_ratio that is greater than error tolerance
# we can shift the window for the next iteration up to this index
error_ratio = torch.nn.functional.pad(
error_ratio, (0, 0, 0, 1), value=1e9
) # handle the case when everything is below ratio, by padding the end of parallel_len dimension
any_error_at_time = torch.max(error_ratio > scaled_tolerance, dim=1).values.int()
ind = torch.argmax(any_error_at_time).item()
# compute the new begin and end idxs for the window
new_begin_idx = begin_idx + min(1 + ind, parallel)
new_end_idx = min(new_begin_idx + parallel, len(scheduler.timesteps))
# store the computed latents for the current window in the global buffer
latents_time_evolution_buffer[begin_idx + 1 : end_idx + 1] = block_latents_new
# initialize the new sliding window latents with the end of the current window,
# should be better than random initialization
latents_time_evolution_buffer[end_idx : new_end_idx + 1] = latents_time_evolution_buffer[end_idx][
None,
]
steps += 1
progress_bar.update(new_begin_idx - begin_idx)
if callback is not None and steps % callback_steps == 0:
callback(begin_idx, block_t[begin_idx], latents_time_evolution_buffer[begin_idx])
begin_idx = new_begin_idx
end_idx = new_end_idx
latents = latents_time_evolution_buffer[-1]
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import warnings
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL
import torch
from packaging import version
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from ...configuration_utils import FrozenDict
from ...image_processor import VaeImageProcessor
from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import (
PIL_INTERPOLATION,
deprecate,
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
)
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
logger = logging.get_logger(__name__)
def preprocess_image(image, batch_size):
w, h = image.size
w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
image = np.array(image).astype(np.float32) / 255.0
image = np.vstack([image[None].transpose(0, 3, 1, 2)] * batch_size)
image = torch.from_numpy(image)
return 2.0 * image - 1.0
def preprocess_mask(mask, batch_size, scale_factor=8):
if not isinstance(mask, torch.FloatTensor):
mask = mask.convert("L")
w, h = mask.size
w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"])
mask = np.array(mask).astype(np.float32) / 255.0
mask = np.tile(mask, (4, 1, 1))
mask = np.vstack([mask[None]] * batch_size)
mask = 1 - mask # repaint white, keep black
mask = torch.from_numpy(mask)
return mask
else:
valid_mask_channel_sizes = [1, 3]
# if mask channel is fourth tensor dimension, permute dimensions to pytorch standard (B, C, H, W)
if mask.shape[3] in valid_mask_channel_sizes:
mask = mask.permute(0, 3, 1, 2)
elif mask.shape[1] not in valid_mask_channel_sizes:
raise ValueError(
f"Mask channel dimension of size in {valid_mask_channel_sizes} should be second or fourth dimension,"
f" but received mask of shape {tuple(mask.shape)}"
)
# (potentially) reduce mask channel dimension from 3 to 1 for broadcasting to latent shape
mask = mask.mean(dim=1, keepdim=True)
h, w = mask.shape[-2:]
h, w = (x - x % 8 for x in (h, w)) # resize to integer multiple of 8
mask = torch.nn.functional.interpolate(mask, (h // scale_factor, w // scale_factor))
return mask
class StableDiffusionInpaintPipelineLegacy(
DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
):
r"""
Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
In addition the pipeline inherits the following loading methods:
- *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]
- *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`]
- *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`]
as well as the following saving methods:
- *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`]
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
_optional_components = ["feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
deprecation_message = (
f"The class {self.__class__} is deprecated and will be removed in v1.0.0. You can achieve exactly the same functionality"
"by loading your model into `StableDiffusionInpaintPipeline` instead. See https://github.com/huggingface/diffusers/pull/3533"
"for more information."
)
deprecate("legacy is outdated", "1.0.0", deprecation_message, standard_warn=False)
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
if self.safety_checker is not None:
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.check_inputs
def check_inputs(
self, prompt, strength, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None
):
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
return timesteps, num_inference_steps - t_start
def prepare_latents(self, image, timestep, num_images_per_prompt, dtype, device, generator):
image = image.to(device=device, dtype=dtype)
init_latent_dist = self.vae.encode(image).latent_dist
init_latents = init_latent_dist.sample(generator=generator)
init_latents = self.vae.config.scaling_factor * init_latents
# Expand init_latents for batch_size and num_images_per_prompt
init_latents = torch.cat([init_latents] * num_images_per_prompt, dim=0)
init_latents_orig = init_latents
# add noise to latents using the timesteps
noise = randn_tensor(init_latents.shape, generator=generator, device=device, dtype=dtype)
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
latents = init_latents
return latents, init_latents_orig, noise
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: Union[torch.FloatTensor, PIL.Image.Image] = None,
mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
add_predicted_noise: Optional[bool] = False,
eta: Optional[float] = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
image (`torch.FloatTensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process. This is the image whose masked region will be inpainted.
mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
PIL image, it will be converted to a single channel (luminance) before use. If mask is a tensor, the
expected shape should be either `(B, H, W, C)` or `(B, C, H, W)`, where C is 1 or 3.
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`
is 1, the denoising process will be run on the masked area for the full number of iterations specified
in `num_inference_steps`. `image` will be used as a reference for the masked area, adding more noise to
that region the larger the `strength`. If `strength` is 0, no inpainting will occur.
num_inference_steps (`int`, *optional*, defaults to 50):
The reference number of denoising steps. More denoising steps usually lead to a higher quality image at
the expense of slower inference. This parameter will be modulated by `strength`, as explained above.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale`
is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
add_predicted_noise (`bool`, *optional*, defaults to True):
Use predicted noise instead of random noise when constructing noisy versions of the original image in
the reverse diffusion process
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
# 1. Check inputs
self.check_inputs(prompt, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
)
# 4. Preprocess image and mask
if not isinstance(image, torch.FloatTensor):
image = preprocess_image(image, batch_size)
mask_image = preprocess_mask(mask_image, batch_size, self.vae_scale_factor)
# 5. set timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
# 6. Prepare latent variables
# encode the init image into latents and scale the latents
latents, init_latents_orig, noise = self.prepare_latents(
image, latent_timestep, num_images_per_prompt, prompt_embeds.dtype, device, generator
)
# 7. Prepare mask latent
mask = mask_image.to(device=device, dtype=latents.dtype)
mask = torch.cat([mask] * num_images_per_prompt)
# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 9. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
# masking
if add_predicted_noise:
init_latents_proper = self.scheduler.add_noise(
init_latents_orig, noise_pred_uncond, torch.tensor([t])
)
else:
init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t]))
latents = (init_latents_proper * mask) + (latents * (1 - mask))
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
# use original latents corresponding to unmasked portions of the image
latents = (init_latents_orig * mask) + (latents * (1 - mask))
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/safety_checker.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
logger = logging.get_logger(__name__)
def cosine_distance(image_embeds, text_embeds):
normalized_image_embeds = nn.functional.normalize(image_embeds)
normalized_text_embeds = nn.functional.normalize(text_embeds)
return torch.mm(normalized_image_embeds, normalized_text_embeds.t())
class StableDiffusionSafetyChecker(PreTrainedModel):
config_class = CLIPConfig
_no_split_modules = ["CLIPEncoderLayer"]
def __init__(self, config: CLIPConfig):
super().__init__(config)
self.vision_model = CLIPVisionModel(config.vision_config)
self.visual_projection = nn.Linear(config.vision_config.hidden_size, config.projection_dim, bias=False)
self.concept_embeds = nn.Parameter(torch.ones(17, config.projection_dim), requires_grad=False)
self.special_care_embeds = nn.Parameter(torch.ones(3, config.projection_dim), requires_grad=False)
self.concept_embeds_weights = nn.Parameter(torch.ones(17), requires_grad=False)
self.special_care_embeds_weights = nn.Parameter(torch.ones(3), requires_grad=False)
@torch.no_grad()
def forward(self, clip_input, images):
pooled_output = self.vision_model(clip_input)[1] # pooled_output
image_embeds = self.visual_projection(pooled_output)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds).cpu().float().numpy()
cos_dist = cosine_distance(image_embeds, self.concept_embeds).cpu().float().numpy()
result = []
batch_size = image_embeds.shape[0]
for i in range(batch_size):
result_img = {"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []}
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign images
adjustment = 0.0
for concept_idx in range(len(special_cos_dist[0])):
concept_cos = special_cos_dist[i][concept_idx]
concept_threshold = self.special_care_embeds_weights[concept_idx].item()
result_img["special_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)
if result_img["special_scores"][concept_idx] > 0:
result_img["special_care"].append({concept_idx, result_img["special_scores"][concept_idx]})
adjustment = 0.01
for concept_idx in range(len(cos_dist[0])):
concept_cos = cos_dist[i][concept_idx]
concept_threshold = self.concept_embeds_weights[concept_idx].item()
result_img["concept_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(concept_idx)
result.append(result_img)
has_nsfw_concepts = [len(res["bad_concepts"]) > 0 for res in result]
for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):
if has_nsfw_concept:
if torch.is_tensor(images) or torch.is_tensor(images[0]):
images[idx] = torch.zeros_like(images[idx]) # black image
else:
images[idx] = np.zeros(images[idx].shape) # black image
if any(has_nsfw_concepts):
logger.warning(
"Potential NSFW content was detected in one or more images. A black image will be returned instead."
" Try again with a different prompt and/or seed."
)
return images, has_nsfw_concepts
@torch.no_grad()
def forward_onnx(self, clip_input: torch.FloatTensor, images: torch.FloatTensor):
pooled_output = self.vision_model(clip_input)[1] # pooled_output
image_embeds = self.visual_projection(pooled_output)
special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds)
cos_dist = cosine_distance(image_embeds, self.concept_embeds)
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
adjustment = 0.0
special_scores = special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
special_care = torch.any(special_scores > 0, dim=1)
special_adjustment = special_care * 0.01
special_adjustment = special_adjustment.unsqueeze(1).expand(-1, cos_dist.shape[1])
concept_scores = (cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
has_nsfw_concepts = torch.any(concept_scores > 0, dim=1)
images[has_nsfw_concepts] = 0.0 # black image
return images, has_nsfw_concepts
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import warnings
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from packaging import version
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from ...configuration_utils import FrozenDict
from ...image_processor import VaeImageProcessor
from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import (
deprecate,
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import torch
>>> from diffusers import StableDiffusionPipeline
>>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
>>> pipe = pipe.to("cuda")
>>> prompt = "a photo of an astronaut riding a horse on mars"
>>> image = pipe(prompt).images[0]
```
"""
def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
"""
Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
"""
std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
# rescale the results from guidance (fixes overexposure)
noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
# mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
return noise_cfg
class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin):
r"""
Pipeline for text-to-image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.vae.enable_slicing()
def disable_vae_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_slicing()
def enable_vae_tiling(self):
r"""
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
processing larger images.
"""
self.vae.enable_tiling()
def disable_vae_tiling(self):
r"""
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_tiling()
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
if self.safety_checker is not None:
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
guidance_rescale: float = 0.0,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
guidance_rescale (`float`, *optional*, defaults to 0.7):
Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are
Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when
using zero terminal SNR.
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
if do_classifier_free_guidance and guidance_rescale > 0.0:
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import warnings
from typing import Callable, List, Optional, Union
import PIL
import torch
from packaging import version
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from ...configuration_utils import FrozenDict
from ...image_processor import VaeImageProcessor
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import deprecate, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class StableDiffusionImageVariationPipeline(DiffusionPipeline):
r"""
Pipeline to generate image variations from an input image using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
image_encoder ([`~transformers.CLIPVisionModelWithProjection`]):
Frozen CLIP image-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
# TODO: feature_extractor is required to encode images (if they are in PIL format),
# we should give a descriptive message if the pipeline doesn't have one.
_optional_components = ["safety_checker"]
def __init__(
self,
vae: AutoencoderKL,
image_encoder: CLIPVisionModelWithProjection,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if safety_checker is None and requires_safety_checker:
logger.warn(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
image_encoder=image_encoder,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
def _encode_image(self, image, device, num_images_per_prompt, do_classifier_free_guidance):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(images=image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeddings = self.image_encoder(image).image_embeds
image_embeddings = image_embeddings.unsqueeze(1)
# duplicate image embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = image_embeddings.shape
image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)
image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
if do_classifier_free_guidance:
negative_prompt_embeds = torch.zeros_like(image_embeddings)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings])
return image_embeddings
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(self, image, height, width, callback_steps):
if (
not isinstance(image, torch.Tensor)
and not isinstance(image, PIL.Image.Image)
and not isinstance(image, list)
):
raise ValueError(
"`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
f" {type(image)}"
)
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@torch.no_grad()
def __call__(
self,
image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
):
r"""
The call function to the pipeline for generation.
Args:
image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`):
Image or images to guide image generation. If you provide a tensor, it needs to be compatible with
[`CLIPImageProcessor`](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json).
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter is modulated by `strength`.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
Examples:
```py
from diffusers import StableDiffusionImageVariationPipeline
from PIL import Image
from io import BytesIO
import requests
pipe = StableDiffusionImageVariationPipeline.from_pretrained(
"lambdalabs/sd-image-variations-diffusers", revision="v2.0"
)
pipe = pipe.to("cuda")
url = "https://lh3.googleusercontent.com/y-iFOHfLTwkuQSUegpwDdgKmOjRSTvPxat63dQLB25xkTs4lhIbRUFeNBWZzYf370g=s1200"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")
out = pipe(image, num_images_per_prompt=3, guidance_scale=15)
out["images"][0].save("result.jpg")
```
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(image, height, width, callback_steps)
# 2. Define call parameters
if isinstance(image, PIL.Image.Image):
batch_size = 1
elif isinstance(image, list):
batch_size = len(image)
else:
batch_size = image.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input image
image_embeddings = self._encode_image(image, device, num_images_per_prompt, do_classifier_free_guidance)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
image_embeddings.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=image_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, image_embeddings.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_diffedit.py | # Copyright 2023 DiffEdit Authors and Pix2Pix Zero Authors and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import warnings
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL
import torch
from packaging import version
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from ...configuration_utils import FrozenDict
from ...image_processor import VaeImageProcessor
from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import DDIMInverseScheduler, KarrasDiffusionSchedulers
from ...utils import (
PIL_INTERPOLATION,
BaseOutput,
deprecate,
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
@dataclass
class DiffEditInversionPipelineOutput(BaseOutput):
"""
Output class for Stable Diffusion pipelines.
Args:
latents (`torch.FloatTensor`)
inverted latents tensor
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `num_timesteps * batch_size` or numpy array of shape `(num_timesteps,
batch_size, height, width, num_channels)`. PIL images or numpy array present the denoised images of the
diffusion pipeline.
"""
latents: torch.FloatTensor
images: Union[List[PIL.Image.Image], np.ndarray]
EXAMPLE_DOC_STRING = """
```py
>>> import PIL
>>> import requests
>>> import torch
>>> from io import BytesIO
>>> from diffusers import StableDiffusionDiffEditPipeline
>>> def download_image(url):
... response = requests.get(url)
... return PIL.Image.open(BytesIO(response.content)).convert("RGB")
>>> img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"
>>> init_image = download_image(img_url).resize((768, 768))
>>> pipe = StableDiffusionDiffEditPipeline.from_pretrained(
... "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
>>> pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
>>> pipeline.enable_model_cpu_offload()
>>> mask_prompt = "A bowl of fruits"
>>> prompt = "A bowl of pears"
>>> mask_image = pipe.generate_mask(image=init_image, source_prompt=prompt, target_prompt=mask_prompt)
>>> image_latents = pipe.invert(image=init_image, prompt=mask_prompt).latents
>>> image = pipe(prompt=prompt, mask_image=mask_image, image_latents=image_latents).images[0]
```
"""
EXAMPLE_INVERT_DOC_STRING = """
```py
>>> import PIL
>>> import requests
>>> import torch
>>> from io import BytesIO
>>> from diffusers import StableDiffusionDiffEditPipeline
>>> def download_image(url):
... response = requests.get(url)
... return PIL.Image.open(BytesIO(response.content)).convert("RGB")
>>> img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"
>>> init_image = download_image(img_url).resize((768, 768))
>>> pipe = StableDiffusionDiffEditPipeline.from_pretrained(
... "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
>>> pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
>>> pipeline.enable_model_cpu_offload()
>>> prompt = "A bowl of fruits"
>>> inverted_latents = pipe.invert(image=init_image, prompt=prompt).latents
```
"""
def auto_corr_loss(hidden_states, generator=None):
reg_loss = 0.0
for i in range(hidden_states.shape[0]):
for j in range(hidden_states.shape[1]):
noise = hidden_states[i : i + 1, j : j + 1, :, :]
while True:
roll_amount = torch.randint(noise.shape[2] // 2, (1,), generator=generator).item()
reg_loss += (noise * torch.roll(noise, shifts=roll_amount, dims=2)).mean() ** 2
reg_loss += (noise * torch.roll(noise, shifts=roll_amount, dims=3)).mean() ** 2
if noise.shape[2] <= 8:
break
noise = torch.nn.functional.avg_pool2d(noise, kernel_size=2)
return reg_loss
def kl_divergence(hidden_states):
return hidden_states.var() + hidden_states.mean() ** 2 - 1 - torch.log(hidden_states.var() + 1e-7)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
def preprocess(image):
warnings.warn(
"The preprocess method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor.preprocess instead",
FutureWarning,
)
if isinstance(image, torch.Tensor):
return image
elif isinstance(image, PIL.Image.Image):
image = [image]
if isinstance(image[0], PIL.Image.Image):
w, h = image[0].size
w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
image = np.concatenate(image, axis=0)
image = np.array(image).astype(np.float32) / 255.0
image = image.transpose(0, 3, 1, 2)
image = 2.0 * image - 1.0
image = torch.from_numpy(image)
elif isinstance(image[0], torch.Tensor):
image = torch.cat(image, dim=0)
return image
def preprocess_mask(mask, batch_size: int = 1):
if not isinstance(mask, torch.Tensor):
# preprocess mask
if isinstance(mask, PIL.Image.Image) or isinstance(mask, np.ndarray):
mask = [mask]
if isinstance(mask, list):
if isinstance(mask[0], PIL.Image.Image):
mask = [np.array(m.convert("L")).astype(np.float32) / 255.0 for m in mask]
if isinstance(mask[0], np.ndarray):
mask = np.stack(mask, axis=0) if mask[0].ndim < 3 else np.concatenate(mask, axis=0)
mask = torch.from_numpy(mask)
elif isinstance(mask[0], torch.Tensor):
mask = torch.stack(mask, dim=0) if mask[0].ndim < 3 else torch.cat(mask, dim=0)
# Batch and add channel dim for single mask
if mask.ndim == 2:
mask = mask.unsqueeze(0).unsqueeze(0)
# Batch single mask or add channel dim
if mask.ndim == 3:
# Single batched mask, no channel dim or single mask not batched but channel dim
if mask.shape[0] == 1:
mask = mask.unsqueeze(0)
# Batched masks no channel dim
else:
mask = mask.unsqueeze(1)
# Check mask shape
if batch_size > 1:
if mask.shape[0] == 1:
mask = torch.cat([mask] * batch_size)
elif mask.shape[0] > 1 and mask.shape[0] != batch_size:
raise ValueError(
f"`mask_image` with batch size {mask.shape[0]} cannot be broadcasted to batch size {batch_size} "
f"inferred by prompt inputs"
)
if mask.shape[1] != 1:
raise ValueError(f"`mask_image` must have 1 channel, but has {mask.shape[1]} channels")
# Check mask is in [0, 1]
if mask.min() < 0 or mask.max() > 1:
raise ValueError("`mask_image` should be in [0, 1] range")
# Binarize mask
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
return mask
class StableDiffusionDiffEditPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
r"""
<Tip warning={true}>
This is an experimental feature!
</Tip>
Pipeline for text-guided image inpainting using Stable Diffusion and DiffEdit.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
In addition the pipeline inherits the following loading methods:
- *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]
- *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`]
as well as the following saving methods:
- *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`]
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer (`CLIPTokenizer`):
A [`~transformers.CLIPTokenizer`] to tokenize text.
unet ([`UNet2DConditionModel`]):
A [`UNet2DConditionModel`] to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents.
inverse_scheduler (`[DDIMInverseScheduler]`):
A scheduler to be used in combination with `unet` to fill in the unmasked part of the input latents.
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`CLIPImageProcessor`]):
A [`CLIPImageProcessor`] to extract features from generated images; used as inputs to the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor", "inverse_scheduler"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
inverse_scheduler: DDIMInverseScheduler,
requires_safety_checker: bool = True,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "skip_prk_steps") and scheduler.config.skip_prk_steps is False:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration"
" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
" sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
" Hub, it would be very nice if you could open a Pull request for the"
" `scheduler/scheduler_config.json` file"
)
deprecate("skip_prk_steps not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["skip_prk_steps"] = True
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
inverse_scheduler=inverse_scheduler,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.vae.enable_slicing()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
def disable_vae_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_slicing()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
def enable_vae_tiling(self):
r"""
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
processing larger images.
"""
self.vae.enable_tiling()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
def disable_vae_tiling(self):
r"""
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_tiling()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
if self.safety_checker is not None:
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
def check_inputs(
self,
prompt,
strength,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if (strength is None) or (strength is not None and (strength < 0 or strength > 1)):
raise ValueError(
f"The value of `strength` should in [0.0, 1.0] but is, but is {strength} of type {type(strength)}."
)
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
def check_source_inputs(
self,
source_prompt=None,
source_negative_prompt=None,
source_prompt_embeds=None,
source_negative_prompt_embeds=None,
):
if source_prompt is not None and source_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `source_prompt`: {source_prompt} and `source_prompt_embeds`: {source_prompt_embeds}."
" Please make sure to only forward one of the two."
)
elif source_prompt is None and source_prompt_embeds is None:
raise ValueError(
"Provide either `source_image` or `source_prompt_embeds`. Cannot leave all both of the arguments undefined."
)
elif source_prompt is not None and (
not isinstance(source_prompt, str) and not isinstance(source_prompt, list)
):
raise ValueError(f"`source_prompt` has to be of type `str` or `list` but is {type(source_prompt)}")
if source_negative_prompt is not None and source_negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `source_negative_prompt`: {source_negative_prompt} and `source_negative_prompt_embeds`:"
f" {source_negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if source_prompt_embeds is not None and source_negative_prompt_embeds is not None:
if source_prompt_embeds.shape != source_negative_prompt_embeds.shape:
raise ValueError(
"`source_prompt_embeds` and `source_negative_prompt_embeds` must have the same shape when passed"
f" directly, but got: `source_prompt_embeds` {source_prompt_embeds.shape} !="
f" `source_negative_prompt_embeds` {source_negative_prompt_embeds.shape}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
return timesteps, num_inference_steps - t_start
def get_inverse_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
# safety for t_start overflow to prevent empty timsteps slice
if t_start == 0:
return self.inverse_scheduler.timesteps, num_inference_steps
timesteps = self.inverse_scheduler.timesteps[:-t_start]
return timesteps, num_inference_steps - t_start
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_pix2pix_zero.StableDiffusionPix2PixZeroPipeline.prepare_image_latents
def prepare_image_latents(self, image, batch_size, dtype, device, generator=None):
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
)
image = image.to(device=device, dtype=dtype)
if image.shape[1] == 4:
latents = image
else:
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if isinstance(generator, list):
latents = [
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
]
latents = torch.cat(latents, dim=0)
else:
latents = self.vae.encode(image).latent_dist.sample(generator)
latents = self.vae.config.scaling_factor * latents
if batch_size != latents.shape[0]:
if batch_size % latents.shape[0] == 0:
# expand image_latents for batch_size
deprecation_message = (
f"You have passed {batch_size} text prompts (`prompt`), but only {latents.shape[0]} initial"
" images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
" your script to pass as many initial images as text prompts to suppress this warning."
)
deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
additional_latents_per_image = batch_size // latents.shape[0]
latents = torch.cat([latents] * additional_latents_per_image, dim=0)
else:
raise ValueError(
f"Cannot duplicate `image` of batch size {latents.shape[0]} to {batch_size} text prompts."
)
else:
latents = torch.cat([latents], dim=0)
return latents
def get_epsilon(self, model_output: torch.Tensor, sample: torch.Tensor, timestep: int):
pred_type = self.inverse_scheduler.config.prediction_type
alpha_prod_t = self.inverse_scheduler.alphas_cumprod[timestep]
beta_prod_t = 1 - alpha_prod_t
if pred_type == "epsilon":
return model_output
elif pred_type == "sample":
return (sample - alpha_prod_t ** (0.5) * model_output) / beta_prod_t ** (0.5)
elif pred_type == "v_prediction":
return (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
f"prediction_type given as {pred_type} must be one of `epsilon`, `sample`, or `v_prediction`"
)
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def generate_mask(
self,
image: Union[torch.FloatTensor, PIL.Image.Image] = None,
target_prompt: Optional[Union[str, List[str]]] = None,
target_negative_prompt: Optional[Union[str, List[str]]] = None,
target_prompt_embeds: Optional[torch.FloatTensor] = None,
target_negative_prompt_embeds: Optional[torch.FloatTensor] = None,
source_prompt: Optional[Union[str, List[str]]] = None,
source_negative_prompt: Optional[Union[str, List[str]]] = None,
source_prompt_embeds: Optional[torch.FloatTensor] = None,
source_negative_prompt_embeds: Optional[torch.FloatTensor] = None,
num_maps_per_mask: Optional[int] = 10,
mask_encode_strength: Optional[float] = 0.5,
mask_thresholding_ratio: Optional[float] = 3.0,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
output_type: Optional[str] = "np",
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
r"""
Generate a latent mask given a mask prompt, a target prompt, and an image.
Args:
image (`PIL.Image.Image`):
`Image` or tensor representing an image batch to be used for computing the mask.
target_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide semantic mask generation. If not defined, you need to pass
`prompt_embeds`.
target_negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
target_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
target_negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
source_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide semantic mask generation using DiffEdit. If not defined, you need to
pass `source_prompt_embeds` or `source_image` instead.
source_negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide semantic mask generation away from using DiffEdit. If not defined, you
need to pass `source_negative_prompt_embeds` or `source_image` instead.
source_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings to guide the semantic mask generation. Can be used to easily tweak text
inputs (prompt weighting). If not provided, text embeddings are generated from `source_prompt` input
argument.
source_negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings to negatively guide the semantic mask generation. Can be used to easily
tweak text inputs (prompt weighting). If not provided, text embeddings are generated from
`source_negative_prompt` input argument.
num_maps_per_mask (`int`, *optional*, defaults to 10):
The number of noise maps sampled to generate the semantic mask using DiffEdit.
mask_encode_strength (`float`, *optional*, defaults to 0.5):
The strength of the noise maps sampled to generate the semantic mask using DiffEdit. Must be between 0
and 1.
mask_thresholding_ratio (`float`, *optional*, defaults to 3.0):
The maximum multiple of the mean absolute difference used to clamp the semantic guidance map before
mask binarization.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
Examples:
Returns:
`List[PIL.Image.Image]` or `np.array`:
When returning a `List[PIL.Image.Image]`, the list consists of a batch of single-channel binary images
with dimensions `(height // self.vae_scale_factor, width // self.vae_scale_factor)`. If it's
`np.array`, the shape is `(batch_size, height // self.vae_scale_factor, width //
self.vae_scale_factor)`.
"""
# 1. Check inputs (Provide dummy argument for callback_steps)
self.check_inputs(
target_prompt,
mask_encode_strength,
1,
target_negative_prompt,
target_prompt_embeds,
target_negative_prompt_embeds,
)
self.check_source_inputs(
source_prompt,
source_negative_prompt,
source_prompt_embeds,
source_negative_prompt_embeds,
)
if (num_maps_per_mask is None) or (
num_maps_per_mask is not None and (not isinstance(num_maps_per_mask, int) or num_maps_per_mask <= 0)
):
raise ValueError(
f"`num_maps_per_mask` has to be a positive integer but is {num_maps_per_mask} of type"
f" {type(num_maps_per_mask)}."
)
if mask_thresholding_ratio is None or mask_thresholding_ratio <= 0:
raise ValueError(
f"`mask_thresholding_ratio` has to be positive but is {mask_thresholding_ratio} of type"
f" {type(mask_thresholding_ratio)}."
)
# 2. Define call parameters
if target_prompt is not None and isinstance(target_prompt, str):
batch_size = 1
elif target_prompt is not None and isinstance(target_prompt, list):
batch_size = len(target_prompt)
else:
batch_size = target_prompt_embeds.shape[0]
if cross_attention_kwargs is None:
cross_attention_kwargs = {}
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompts
(cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None)
target_prompt_embeds = self._encode_prompt(
target_prompt,
device,
num_maps_per_mask,
do_classifier_free_guidance,
target_negative_prompt,
prompt_embeds=target_prompt_embeds,
negative_prompt_embeds=target_negative_prompt_embeds,
)
source_prompt_embeds = self._encode_prompt(
source_prompt,
device,
num_maps_per_mask,
do_classifier_free_guidance,
source_negative_prompt,
prompt_embeds=source_prompt_embeds,
negative_prompt_embeds=source_negative_prompt_embeds,
)
# 4. Preprocess image
image = self.image_processor.preprocess(image).repeat_interleave(num_maps_per_mask, dim=0)
# 5. Set timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, _ = self.get_timesteps(num_inference_steps, mask_encode_strength, device)
encode_timestep = timesteps[0]
# 6. Prepare image latents and add noise with specified strength
image_latents = self.prepare_image_latents(
image, batch_size * num_maps_per_mask, self.vae.dtype, device, generator
)
noise = randn_tensor(image_latents.shape, generator=generator, device=device, dtype=self.vae.dtype)
image_latents = self.scheduler.add_noise(image_latents, noise, encode_timestep)
latent_model_input = torch.cat([image_latents] * (4 if do_classifier_free_guidance else 2))
latent_model_input = self.scheduler.scale_model_input(latent_model_input, encode_timestep)
# 7. Predict the noise residual
prompt_embeds = torch.cat([source_prompt_embeds, target_prompt_embeds])
noise_pred = self.unet(
latent_model_input,
encode_timestep,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
).sample
if do_classifier_free_guidance:
noise_pred_neg_src, noise_pred_source, noise_pred_uncond, noise_pred_target = noise_pred.chunk(4)
noise_pred_source = noise_pred_neg_src + guidance_scale * (noise_pred_source - noise_pred_neg_src)
noise_pred_target = noise_pred_uncond + guidance_scale * (noise_pred_target - noise_pred_uncond)
else:
noise_pred_source, noise_pred_target = noise_pred.chunk(2)
# 8. Compute the mask from the absolute difference of predicted noise residuals
# TODO: Consider smoothing mask guidance map
mask_guidance_map = (
torch.abs(noise_pred_target - noise_pred_source)
.reshape(batch_size, num_maps_per_mask, *noise_pred_target.shape[-3:])
.mean([1, 2])
)
clamp_magnitude = mask_guidance_map.mean() * mask_thresholding_ratio
semantic_mask_image = mask_guidance_map.clamp(0, clamp_magnitude) / clamp_magnitude
semantic_mask_image = torch.where(semantic_mask_image <= 0.5, 0, 1)
mask_image = semantic_mask_image.cpu().numpy()
# 9. Convert to Numpy array or PIL.
if output_type == "pil":
mask_image = self.image_processor.numpy_to_pil(mask_image)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
return mask_image
@torch.no_grad()
@replace_example_docstring(EXAMPLE_INVERT_DOC_STRING)
def invert(
self,
prompt: Optional[Union[str, List[str]]] = None,
image: Union[torch.FloatTensor, PIL.Image.Image] = None,
num_inference_steps: int = 50,
inpaint_strength: float = 0.8,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
decode_latents: bool = False,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
lambda_auto_corr: float = 20.0,
lambda_kl: float = 20.0,
num_reg_steps: int = 0,
num_auto_corr_rolls: int = 5,
):
r"""
Generate inverted latents given a prompt and image.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
image (`PIL.Image.Image`):
`Image` or tensor representing an image batch to produce the inverted latents guided by `prompt`.
inpaint_strength (`float`, *optional*, defaults to 0.8):
Indicates extent of the noising process to run latent inversion. Must be between 0 and 1. When
`strength` is 1, the inversion process iss ru for the full number of iterations specified in
`num_inference_steps`. `image` is used as a reference for the inversion process, adding more noise the
larger the `strength`. If `strength` is 0, no inpainting occurs.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
decode_latents (`bool`, *optional*, defaults to `False`):
Whether or not to decode the inverted latents into a generated image. Setting this argument to `True`
decodes all inverted latents for each timestep into a list of generated images.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.DiffEditInversionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
lambda_auto_corr (`float`, *optional*, defaults to 20.0):
Lambda parameter to control auto correction.
lambda_kl (`float`, *optional*, defaults to 20.0):
Lambda parameter to control Kullback–Leibler divergence output.
num_reg_steps (`int`, *optional*, defaults to 0):
Number of regularization loss steps.
num_auto_corr_rolls (`int`, *optional*, defaults to 5):
Number of auto correction roll steps.
Examples:
Returns:
[`~pipelines.stable_diffusion.pipeline_stable_diffusion_diffedit.DiffEditInversionPipelineOutput`] or
`tuple`:
If `return_dict` is `True`,
[`~pipelines.stable_diffusion.pipeline_stable_diffusion_diffedit.DiffEditInversionPipelineOutput`] is
returned, otherwise a `tuple` is returned where the first element is the inverted latents tensors
ordered by increasing noise, and the second is the corresponding decoded images if `decode_latents` is
`True`, otherwise `None`.
"""
# 1. Check inputs
self.check_inputs(
prompt,
inpaint_strength,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
)
if image is None:
raise ValueError("`image` input cannot be undefined.")
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if cross_attention_kwargs is None:
cross_attention_kwargs = {}
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Preprocess image
image = self.image_processor.preprocess(image)
# 4. Prepare latent variables
num_images_per_prompt = 1
latents = self.prepare_image_latents(
image, batch_size * num_images_per_prompt, self.vae.dtype, device, generator
)
# 5. Encode input prompt
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
)
# 6. Prepare timesteps
self.inverse_scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_inverse_timesteps(num_inference_steps, inpaint_strength, device)
# 7. Noising loop where we obtain the intermediate noised latent image for each timestep.
num_warmup_steps = len(timesteps) - num_inference_steps * self.inverse_scheduler.order
inverted_latents = []
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.inverse_scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# regularization of the noise prediction (not in original code or paper but borrowed from Pix2PixZero)
if num_reg_steps > 0:
with torch.enable_grad():
for _ in range(num_reg_steps):
if lambda_auto_corr > 0:
for _ in range(num_auto_corr_rolls):
var = torch.autograd.Variable(noise_pred.detach().clone(), requires_grad=True)
# Derive epsilon from model output before regularizing to IID standard normal
var_epsilon = self.get_epsilon(var, latent_model_input.detach(), t)
l_ac = auto_corr_loss(var_epsilon, generator=generator)
l_ac.backward()
grad = var.grad.detach() / num_auto_corr_rolls
noise_pred = noise_pred - lambda_auto_corr * grad
if lambda_kl > 0:
var = torch.autograd.Variable(noise_pred.detach().clone(), requires_grad=True)
# Derive epsilon from model output before regularizing to IID standard normal
var_epsilon = self.get_epsilon(var, latent_model_input.detach(), t)
l_kld = kl_divergence(var_epsilon)
l_kld.backward()
grad = var.grad.detach()
noise_pred = noise_pred - lambda_kl * grad
noise_pred = noise_pred.detach()
# compute the previous noisy sample x_t -> x_t-1
latents = self.inverse_scheduler.step(noise_pred, t, latents).prev_sample
inverted_latents.append(latents.detach().clone())
# call the callback, if provided
if i == len(timesteps) - 1 or (
(i + 1) > num_warmup_steps and (i + 1) % self.inverse_scheduler.order == 0
):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
assert len(inverted_latents) == len(timesteps)
latents = torch.stack(list(reversed(inverted_latents)), 1)
# 8. Post-processing
image = None
if decode_latents:
image = self.decode_latents(latents.flatten(0, 1))
# 9. Convert to PIL.
if decode_latents and output_type == "pil":
image = self.image_processor.numpy_to_pil(image)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (latents, image)
return DiffEditInversionPipelineOutput(latents=latents, images=image)
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Optional[Union[str, List[str]]] = None,
mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
image_latents: Union[torch.FloatTensor, PIL.Image.Image] = None,
inpaint_strength: Optional[float] = 0.8,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
mask_image (`PIL.Image.Image`):
`Image` or tensor representing an image batch to mask the generated image. White pixels in the mask are
repainted, while black pixels are preserved. If `mask_image` is a PIL image, it is converted to a
single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
instead of 3, so the expected shape would be `(B, 1, H, W)`.
image_latents (`PIL.Image.Image` or `torch.FloatTensor`):
Partially noised image latents from the inversion process to be used as inputs for image generation.
inpaint_strength (`float`, *optional*, defaults to 0.8):
Indicates extent to inpaint the masked area. Must be between 0 and 1. When `strength` is 1, the
denoising process is run on the masked area for the full number of iterations specified in
`num_inference_steps`. `image_latents` is used as a reference for the masked area, adding more noise to
that region the larger the `strength`. If `strength` is 0, no inpainting occurs.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
"""
# 1. Check inputs
self.check_inputs(
prompt,
inpaint_strength,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
)
if mask_image is None:
raise ValueError(
"`mask_image` input cannot be undefined. Use `generate_mask()` to compute `mask_image` from text prompts."
)
if image_latents is None:
raise ValueError(
"`image_latents` input cannot be undefined. Use `invert()` to compute `image_latents` from input images."
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if cross_attention_kwargs is None:
cross_attention_kwargs = {}
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
)
# 4. Preprocess mask
mask_image = preprocess_mask(mask_image, batch_size)
latent_height, latent_width = mask_image.shape[-2:]
mask_image = torch.cat([mask_image] * num_images_per_prompt)
mask_image = mask_image.to(device=device, dtype=prompt_embeds.dtype)
# 5. Set timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, inpaint_strength, device)
# 6. Preprocess image latents
if isinstance(image_latents, list) and any(isinstance(l, torch.Tensor) and l.ndim == 5 for l in image_latents):
image_latents = torch.cat(image_latents).detach()
elif isinstance(image_latents, torch.Tensor) and image_latents.ndim == 5:
image_latents = image_latents.detach()
else:
image_latents = self.image_processor.preprocess(image_latents).detach()
latent_shape = (self.vae.config.latent_channels, latent_height, latent_width)
if image_latents.shape[-3:] != latent_shape:
raise ValueError(
f"Each latent image in `image_latents` must have shape {latent_shape}, "
f"but has shape {image_latents.shape[-3:]}"
)
if image_latents.ndim == 4:
image_latents = image_latents.reshape(batch_size, len(timesteps), *latent_shape)
if image_latents.shape[:2] != (batch_size, len(timesteps)):
raise ValueError(
f"`image_latents` must have batch size {batch_size} with latent images from {len(timesteps)}"
f" timesteps, but has batch size {image_latents.shape[0]} with latent images from"
f" {image_latents.shape[1]} timesteps."
)
image_latents = image_latents.transpose(0, 1).repeat_interleave(num_images_per_prompt, dim=1)
image_latents = image_latents.to(device=device, dtype=prompt_embeds.dtype)
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 8. Denoising loop
latents = image_latents[0].clone()
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# mask with inverted latents from appropriate timestep - use original image latent for last step
latents = latents * mask_image + image_latents[i] * (1 - mask_image)
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import math
import warnings
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import torch
from torch.nn import functional as F
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from ...image_processor import VaeImageProcessor
from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel
from ...models.attention_processor import Attention
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import logging, randn_tensor, replace_example_docstring
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
logger = logging.get_logger(__name__)
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import torch
>>> from diffusers import StableDiffusionAttendAndExcitePipeline
>>> pipe = StableDiffusionAttendAndExcitePipeline.from_pretrained(
... "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16
... ).to("cuda")
>>> prompt = "a cat and a frog"
>>> # use get_indices function to find out indices of the tokens you want to alter
>>> pipe.get_indices(prompt)
{0: '<|startoftext|>', 1: 'a</w>', 2: 'cat</w>', 3: 'and</w>', 4: 'a</w>', 5: 'frog</w>', 6: '<|endoftext|>'}
>>> token_indices = [2, 5]
>>> seed = 6141
>>> generator = torch.Generator("cuda").manual_seed(seed)
>>> images = pipe(
... prompt=prompt,
... token_indices=token_indices,
... guidance_scale=7.5,
... generator=generator,
... num_inference_steps=50,
... max_iter_to_alter=25,
... ).images
>>> image = images[0]
>>> image.save(f"../images/{prompt}_{seed}.png")
```
"""
class AttentionStore:
@staticmethod
def get_empty_store():
return {"down": [], "mid": [], "up": []}
def __call__(self, attn, is_cross: bool, place_in_unet: str):
if self.cur_att_layer >= 0 and is_cross:
if attn.shape[1] == np.prod(self.attn_res):
self.step_store[place_in_unet].append(attn)
self.cur_att_layer += 1
if self.cur_att_layer == self.num_att_layers:
self.cur_att_layer = 0
self.between_steps()
def between_steps(self):
self.attention_store = self.step_store
self.step_store = self.get_empty_store()
def get_average_attention(self):
average_attention = self.attention_store
return average_attention
def aggregate_attention(self, from_where: List[str]) -> torch.Tensor:
"""Aggregates the attention across the different layers and heads at the specified resolution."""
out = []
attention_maps = self.get_average_attention()
for location in from_where:
for item in attention_maps[location]:
cross_maps = item.reshape(-1, self.attn_res[0], self.attn_res[1], item.shape[-1])
out.append(cross_maps)
out = torch.cat(out, dim=0)
out = out.sum(0) / out.shape[0]
return out
def reset(self):
self.cur_att_layer = 0
self.step_store = self.get_empty_store()
self.attention_store = {}
def __init__(self, attn_res):
"""
Initialize an empty AttentionStore :param step_index: used to visualize only a specific step in the diffusion
process
"""
self.num_att_layers = -1
self.cur_att_layer = 0
self.step_store = self.get_empty_store()
self.attention_store = {}
self.curr_step_index = 0
self.attn_res = attn_res
class AttendExciteAttnProcessor:
def __init__(self, attnstore, place_in_unet):
super().__init__()
self.attnstore = attnstore
self.place_in_unet = place_in_unet
def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):
batch_size, sequence_length, _ = hidden_states.shape
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
query = attn.to_q(hidden_states)
is_cross = encoder_hidden_states is not None
encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
query = attn.head_to_batch_dim(query)
key = attn.head_to_batch_dim(key)
value = attn.head_to_batch_dim(value)
attention_probs = attn.get_attention_scores(query, key, attention_mask)
# only need to store attention maps during the Attend and Excite process
if attention_probs.requires_grad:
self.attnstore(attention_probs, is_cross, self.place_in_unet)
hidden_states = torch.bmm(attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
return hidden_states
class StableDiffusionAttendAndExcitePipeline(DiffusionPipeline, TextualInversionLoaderMixin):
r"""
Pipeline for text-to-image generation using Stable Diffusion and Attend-and-Excite.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.vae.enable_slicing()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
def disable_vae_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_slicing()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self,
prompt,
indices,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
indices_is_list_ints = isinstance(indices, list) and isinstance(indices[0], int)
indices_is_list_list_ints = (
isinstance(indices, list) and isinstance(indices[0], list) and isinstance(indices[0][0], int)
)
if not indices_is_list_ints and not indices_is_list_list_ints:
raise TypeError("`indices` must be a list of ints or a list of a list of ints")
if indices_is_list_ints:
indices_batch_size = 1
elif indices_is_list_list_ints:
indices_batch_size = len(indices)
if prompt is not None and isinstance(prompt, str):
prompt_batch_size = 1
elif prompt is not None and isinstance(prompt, list):
prompt_batch_size = len(prompt)
elif prompt_embeds is not None:
prompt_batch_size = prompt_embeds.shape[0]
if indices_batch_size != prompt_batch_size:
raise ValueError(
f"indices batch size must be same as prompt batch size. indices batch size: {indices_batch_size}, prompt batch size: {prompt_batch_size}"
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@staticmethod
def _compute_max_attention_per_index(
attention_maps: torch.Tensor,
indices: List[int],
) -> List[torch.Tensor]:
"""Computes the maximum attention value for each of the tokens we wish to alter."""
attention_for_text = attention_maps[:, :, 1:-1]
attention_for_text *= 100
attention_for_text = torch.nn.functional.softmax(attention_for_text, dim=-1)
# Shift indices since we removed the first token
indices = [index - 1 for index in indices]
# Extract the maximum values
max_indices_list = []
for i in indices:
image = attention_for_text[:, :, i]
smoothing = GaussianSmoothing().to(attention_maps.device)
input = F.pad(image.unsqueeze(0).unsqueeze(0), (1, 1, 1, 1), mode="reflect")
image = smoothing(input).squeeze(0).squeeze(0)
max_indices_list.append(image.max())
return max_indices_list
def _aggregate_and_get_max_attention_per_token(
self,
indices: List[int],
):
"""Aggregates the attention for each token and computes the max activation value for each token to alter."""
attention_maps = self.attention_store.aggregate_attention(
from_where=("up", "down", "mid"),
)
max_attention_per_index = self._compute_max_attention_per_index(
attention_maps=attention_maps,
indices=indices,
)
return max_attention_per_index
@staticmethod
def _compute_loss(max_attention_per_index: List[torch.Tensor]) -> torch.Tensor:
"""Computes the attend-and-excite loss using the maximum attention value for each token."""
losses = [max(0, 1.0 - curr_max) for curr_max in max_attention_per_index]
loss = max(losses)
return loss
@staticmethod
def _update_latent(latents: torch.Tensor, loss: torch.Tensor, step_size: float) -> torch.Tensor:
"""Update the latent according to the computed loss."""
grad_cond = torch.autograd.grad(loss.requires_grad_(True), [latents], retain_graph=True)[0]
latents = latents - step_size * grad_cond
return latents
def _perform_iterative_refinement_step(
self,
latents: torch.Tensor,
indices: List[int],
loss: torch.Tensor,
threshold: float,
text_embeddings: torch.Tensor,
step_size: float,
t: int,
max_refinement_steps: int = 20,
):
"""
Performs the iterative latent refinement introduced in the paper. Here, we continuously update the latent code
according to our loss objective until the given threshold is reached for all tokens.
"""
iteration = 0
target_loss = max(0, 1.0 - threshold)
while loss > target_loss:
iteration += 1
latents = latents.clone().detach().requires_grad_(True)
self.unet(latents, t, encoder_hidden_states=text_embeddings).sample
self.unet.zero_grad()
# Get max activation value for each subject token
max_attention_per_index = self._aggregate_and_get_max_attention_per_token(
indices=indices,
)
loss = self._compute_loss(max_attention_per_index)
if loss != 0:
latents = self._update_latent(latents, loss, step_size)
logger.info(f"\t Try {iteration}. loss: {loss}")
if iteration >= max_refinement_steps:
logger.info(f"\t Exceeded max number of iterations ({max_refinement_steps})! ")
break
# Run one more time but don't compute gradients and update the latents.
# We just need to compute the new loss - the grad update will occur below
latents = latents.clone().detach().requires_grad_(True)
_ = self.unet(latents, t, encoder_hidden_states=text_embeddings).sample
self.unet.zero_grad()
# Get max activation value for each subject token
max_attention_per_index = self._aggregate_and_get_max_attention_per_token(
indices=indices,
)
loss = self._compute_loss(max_attention_per_index)
logger.info(f"\t Finished with loss of: {loss}")
return loss, latents, max_attention_per_index
def register_attention_control(self):
attn_procs = {}
cross_att_count = 0
for name in self.unet.attn_processors.keys():
if name.startswith("mid_block"):
place_in_unet = "mid"
elif name.startswith("up_blocks"):
place_in_unet = "up"
elif name.startswith("down_blocks"):
place_in_unet = "down"
else:
continue
cross_att_count += 1
attn_procs[name] = AttendExciteAttnProcessor(attnstore=self.attention_store, place_in_unet=place_in_unet)
self.unet.set_attn_processor(attn_procs)
self.attention_store.num_att_layers = cross_att_count
def get_indices(self, prompt: str) -> Dict[str, int]:
"""Utility function to list the indices of the tokens you wish to alte"""
ids = self.tokenizer(prompt).input_ids
indices = {i: tok for tok, i in zip(self.tokenizer.convert_ids_to_tokens(ids), range(len(ids)))}
return indices
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]],
token_indices: Union[List[int], List[List[int]]],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: int = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
max_iter_to_alter: int = 25,
thresholds: dict = {0: 0.05, 10: 0.5, 20: 0.8},
scale_factor: int = 20,
attn_res: Optional[Tuple[int]] = (16, 16),
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
token_indices (`List[int]`):
The token indices to alter with attend-and-excite.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
max_iter_to_alter (`int`, *optional*, defaults to `25`):
Number of denoising steps to apply attend-and-excite. The `max_iter_to_alter` denoising steps are when
attend-and-excite is applied. For example, if `max_iter_to_alter` is `25` and there are a total of `30`
denoising steps, the first `25` denoising steps applies attend-and-excite and the last `5` will not.
thresholds (`dict`, *optional*, defaults to `{0: 0.05, 10: 0.5, 20: 0.8}`):
Dictionary defining the iterations and desired thresholds to apply iterative latent refinement in.
scale_factor (`int`, *optional*, default to 20):
Scale factor to control the step size of each attend-and-excite update.
attn_res (`tuple`, *optional*, default computed from width and height):
The 2D resolution of the semantic attention map.
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
token_indices,
height,
width,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
if attn_res is None:
attn_res = int(np.ceil(width / 32)), int(np.ceil(height / 32))
self.attention_store = AttentionStore(attn_res)
self.register_attention_control()
# default config for step size from original repo
scale_range = np.linspace(1.0, 0.5, len(self.scheduler.timesteps))
step_size = scale_factor * np.sqrt(scale_range)
text_embeddings = (
prompt_embeds[batch_size * num_images_per_prompt :] if do_classifier_free_guidance else prompt_embeds
)
if isinstance(token_indices[0], int):
token_indices = [token_indices]
indices = []
for ind in token_indices:
indices = indices + [ind] * num_images_per_prompt
# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# Attend and excite process
with torch.enable_grad():
latents = latents.clone().detach().requires_grad_(True)
updated_latents = []
for latent, index, text_embedding in zip(latents, indices, text_embeddings):
# Forward pass of denoising with text conditioning
latent = latent.unsqueeze(0)
text_embedding = text_embedding.unsqueeze(0)
self.unet(
latent,
t,
encoder_hidden_states=text_embedding,
cross_attention_kwargs=cross_attention_kwargs,
).sample
self.unet.zero_grad()
# Get max activation value for each subject token
max_attention_per_index = self._aggregate_and_get_max_attention_per_token(
indices=index,
)
loss = self._compute_loss(max_attention_per_index=max_attention_per_index)
# If this is an iterative refinement step, verify we have reached the desired threshold for all
if i in thresholds.keys() and loss > 1.0 - thresholds[i]:
loss, latent, max_attention_per_index = self._perform_iterative_refinement_step(
latents=latent,
indices=index,
loss=loss,
threshold=thresholds[i],
text_embeddings=text_embedding,
step_size=step_size[i],
t=t,
)
# Perform gradient update
if i < max_iter_to_alter:
if loss != 0:
latent = self._update_latent(
latents=latent,
loss=loss,
step_size=step_size[i],
)
logger.info(f"Iteration {i} | Loss: {loss:0.4f}")
updated_latents.append(latent)
latents = torch.cat(updated_latents, dim=0)
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
# 8. Post-processing
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
class GaussianSmoothing(torch.nn.Module):
"""
Arguments:
Apply gaussian smoothing on a 1d, 2d or 3d tensor. Filtering is performed seperately for each channel in the input
using a depthwise convolution.
channels (int, sequence): Number of channels of the input tensors. Output will
have this number of channels as well.
kernel_size (int, sequence): Size of the gaussian kernel. sigma (float, sequence): Standard deviation of the
gaussian kernel. dim (int, optional): The number of dimensions of the data.
Default value is 2 (spatial).
"""
# channels=1, kernel_size=kernel_size, sigma=sigma, dim=2
def __init__(
self,
channels: int = 1,
kernel_size: int = 3,
sigma: float = 0.5,
dim: int = 2,
):
super().__init__()
if isinstance(kernel_size, int):
kernel_size = [kernel_size] * dim
if isinstance(sigma, float):
sigma = [sigma] * dim
# The gaussian kernel is the product of the
# gaussian function of each dimension.
kernel = 1
meshgrids = torch.meshgrid([torch.arange(size, dtype=torch.float32) for size in kernel_size])
for size, std, mgrid in zip(kernel_size, sigma, meshgrids):
mean = (size - 1) / 2
kernel *= 1 / (std * math.sqrt(2 * math.pi)) * torch.exp(-(((mgrid - mean) / (2 * std)) ** 2))
# Make sure sum of values in gaussian kernel equals 1.
kernel = kernel / torch.sum(kernel)
# Reshape to depthwise convolutional weight
kernel = kernel.view(1, 1, *kernel.size())
kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1))
self.register_buffer("weight", kernel)
self.groups = channels
if dim == 1:
self.conv = F.conv1d
elif dim == 2:
self.conv = F.conv2d
elif dim == 3:
self.conv = F.conv3d
else:
raise RuntimeError("Only 1, 2 and 3 dimensions are supported. Received {}.".format(dim))
def forward(self, input):
"""
Arguments:
Apply gaussian filter to input.
input (torch.Tensor): Input to apply gaussian filter on.
Returns:
filtered (torch.Tensor): Filtered output.
"""
return self.conv(input, weight=self.weight.to(input.dtype), groups=self.groups)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Conversion script for the Stable Diffusion checkpoints."""
import re
from contextlib import nullcontext
from io import BytesIO
from typing import Optional
import requests
import torch
from transformers import (
AutoFeatureExtractor,
BertTokenizerFast,
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from ...models import (
AutoencoderKL,
ControlNetModel,
PriorTransformer,
UNet2DConditionModel,
)
from ...schedulers import (
DDIMScheduler,
DDPMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UnCLIPScheduler,
)
from ...utils import is_accelerate_available, is_omegaconf_available, is_safetensors_available, logging
from ...utils.import_utils import BACKENDS_MAPPING
from ..latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
from ..paint_by_example import PaintByExampleImageEncoder
from ..pipeline_utils import DiffusionPipeline
from .safety_checker import StableDiffusionSafetyChecker
from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
if is_accelerate_available():
from accelerate import init_empty_weights
from accelerate.utils import set_module_tensor_to_device
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def shave_segments(path, n_shave_prefix_segments=1):
"""
Removes segments. Positive values shave the first segments, negative shave the last segments.
"""
if n_shave_prefix_segments >= 0:
return ".".join(path.split(".")[n_shave_prefix_segments:])
else:
return ".".join(path.split(".")[:n_shave_prefix_segments])
def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside resnets to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item.replace("in_layers.0", "norm1")
new_item = new_item.replace("in_layers.2", "conv1")
new_item = new_item.replace("out_layers.0", "norm2")
new_item = new_item.replace("out_layers.3", "conv2")
new_item = new_item.replace("emb_layers.1", "time_emb_proj")
new_item = new_item.replace("skip_connection", "conv_shortcut")
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside resnets to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item
new_item = new_item.replace("nin_shortcut", "conv_shortcut")
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def renew_attention_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside attentions to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item
# new_item = new_item.replace('norm.weight', 'group_norm.weight')
# new_item = new_item.replace('norm.bias', 'group_norm.bias')
# new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
# new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
# new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside attentions to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item
new_item = new_item.replace("norm.weight", "group_norm.weight")
new_item = new_item.replace("norm.bias", "group_norm.bias")
new_item = new_item.replace("q.weight", "to_q.weight")
new_item = new_item.replace("q.bias", "to_q.bias")
new_item = new_item.replace("k.weight", "to_k.weight")
new_item = new_item.replace("k.bias", "to_k.bias")
new_item = new_item.replace("v.weight", "to_v.weight")
new_item = new_item.replace("v.bias", "to_v.bias")
new_item = new_item.replace("proj_out.weight", "to_out.0.weight")
new_item = new_item.replace("proj_out.bias", "to_out.0.bias")
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def assign_to_checkpoint(
paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None
):
"""
This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits
attention layers, and takes into account additional replacements that may arise.
Assigns the weights to the new checkpoint.
"""
assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
# Splits the attention layers into three variables.
if attention_paths_to_split is not None:
for path, path_map in attention_paths_to_split.items():
old_tensor = old_checkpoint[path]
channels = old_tensor.shape[0] // 3
target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
query, key, value = old_tensor.split(channels // num_heads, dim=1)
checkpoint[path_map["query"]] = query.reshape(target_shape)
checkpoint[path_map["key"]] = key.reshape(target_shape)
checkpoint[path_map["value"]] = value.reshape(target_shape)
for path in paths:
new_path = path["new"]
# These have already been assigned
if attention_paths_to_split is not None and new_path in attention_paths_to_split:
continue
# Global renaming happens here
new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")
new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
new_path = new_path.replace("middle_block.2", "mid_block.resnets.1")
if additional_replacements is not None:
for replacement in additional_replacements:
new_path = new_path.replace(replacement["old"], replacement["new"])
# proj_attn.weight has to be converted from conv 1D to linear
is_attn_weight = "proj_attn.weight" in new_path or ("attentions" in new_path and "to_" in new_path)
shape = old_checkpoint[path["old"]].shape
if is_attn_weight and len(shape) == 3:
checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
elif is_attn_weight and len(shape) == 4:
checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0, 0]
else:
checkpoint[new_path] = old_checkpoint[path["old"]]
def conv_attn_to_linear(checkpoint):
keys = list(checkpoint.keys())
attn_keys = ["query.weight", "key.weight", "value.weight"]
for key in keys:
if ".".join(key.split(".")[-2:]) in attn_keys:
if checkpoint[key].ndim > 2:
checkpoint[key] = checkpoint[key][:, :, 0, 0]
elif "proj_attn.weight" in key:
if checkpoint[key].ndim > 2:
checkpoint[key] = checkpoint[key][:, :, 0]
def create_unet_diffusers_config(original_config, image_size: int, controlnet=False):
"""
Creates a config for the diffusers based on the config of the LDM model.
"""
if controlnet:
unet_params = original_config.model.params.control_stage_config.params
else:
if "unet_config" in original_config.model.params and original_config.model.params.unet_config is not None:
unet_params = original_config.model.params.unet_config.params
else:
unet_params = original_config.model.params.network_config.params
vae_params = original_config.model.params.first_stage_config.params.ddconfig
block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]
down_block_types = []
resolution = 1
for i in range(len(block_out_channels)):
block_type = "CrossAttnDownBlock2D" if resolution in unet_params.attention_resolutions else "DownBlock2D"
down_block_types.append(block_type)
if i != len(block_out_channels) - 1:
resolution *= 2
up_block_types = []
for i in range(len(block_out_channels)):
block_type = "CrossAttnUpBlock2D" if resolution in unet_params.attention_resolutions else "UpBlock2D"
up_block_types.append(block_type)
resolution //= 2
if unet_params.transformer_depth is not None:
transformer_layers_per_block = (
unet_params.transformer_depth
if isinstance(unet_params.transformer_depth, int)
else list(unet_params.transformer_depth)
)
else:
transformer_layers_per_block = 1
vae_scale_factor = 2 ** (len(vae_params.ch_mult) - 1)
head_dim = unet_params.num_heads if "num_heads" in unet_params else None
use_linear_projection = (
unet_params.use_linear_in_transformer if "use_linear_in_transformer" in unet_params else False
)
if use_linear_projection:
# stable diffusion 2-base-512 and 2-768
if head_dim is None:
head_dim_mult = unet_params.model_channels // unet_params.num_head_channels
head_dim = [head_dim_mult * c for c in list(unet_params.channel_mult)]
class_embed_type = None
addition_embed_type = None
addition_time_embed_dim = None
projection_class_embeddings_input_dim = None
context_dim = None
if unet_params.context_dim is not None:
context_dim = (
unet_params.context_dim if isinstance(unet_params.context_dim, int) else unet_params.context_dim[0]
)
if "num_classes" in unet_params:
if unet_params.num_classes == "sequential":
if context_dim in [2048, 1280]:
# SDXL
addition_embed_type = "text_time"
addition_time_embed_dim = 256
else:
class_embed_type = "projection"
assert "adm_in_channels" in unet_params
projection_class_embeddings_input_dim = unet_params.adm_in_channels
else:
raise NotImplementedError(f"Unknown conditional unet num_classes config: {unet_params.num_classes}")
config = {
"sample_size": image_size // vae_scale_factor,
"in_channels": unet_params.in_channels,
"down_block_types": tuple(down_block_types),
"block_out_channels": tuple(block_out_channels),
"layers_per_block": unet_params.num_res_blocks,
"cross_attention_dim": context_dim,
"attention_head_dim": head_dim,
"use_linear_projection": use_linear_projection,
"class_embed_type": class_embed_type,
"addition_embed_type": addition_embed_type,
"addition_time_embed_dim": addition_time_embed_dim,
"projection_class_embeddings_input_dim": projection_class_embeddings_input_dim,
"transformer_layers_per_block": transformer_layers_per_block,
}
if controlnet:
config["conditioning_channels"] = unet_params.hint_channels
else:
config["out_channels"] = unet_params.out_channels
config["up_block_types"] = tuple(up_block_types)
return config
def create_vae_diffusers_config(original_config, image_size: int):
"""
Creates a config for the diffusers based on the config of the LDM model.
"""
vae_params = original_config.model.params.first_stage_config.params.ddconfig
_ = original_config.model.params.first_stage_config.params.embed_dim
block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult]
down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)
up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)
config = {
"sample_size": image_size,
"in_channels": vae_params.in_channels,
"out_channels": vae_params.out_ch,
"down_block_types": tuple(down_block_types),
"up_block_types": tuple(up_block_types),
"block_out_channels": tuple(block_out_channels),
"latent_channels": vae_params.z_channels,
"layers_per_block": vae_params.num_res_blocks,
}
return config
def create_diffusers_schedular(original_config):
schedular = DDIMScheduler(
num_train_timesteps=original_config.model.params.timesteps,
beta_start=original_config.model.params.linear_start,
beta_end=original_config.model.params.linear_end,
beta_schedule="scaled_linear",
)
return schedular
def create_ldm_bert_config(original_config):
bert_params = original_config.model.parms.cond_stage_config.params
config = LDMBertConfig(
d_model=bert_params.n_embed,
encoder_layers=bert_params.n_layer,
encoder_ffn_dim=bert_params.n_embed * 4,
)
return config
def convert_ldm_unet_checkpoint(
checkpoint, config, path=None, extract_ema=False, controlnet=False, skip_extract_state_dict=False
):
"""
Takes a state dict and a config, and returns a converted checkpoint.
"""
if skip_extract_state_dict:
unet_state_dict = checkpoint
else:
# extract state_dict for UNet
unet_state_dict = {}
keys = list(checkpoint.keys())
if controlnet:
unet_key = "control_model."
else:
unet_key = "model.diffusion_model."
# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema:
logger.warning(f"Checkpoint {path} has both EMA and non-EMA weights.")
logger.warning(
"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."
)
for key in keys:
if key.startswith("model.diffusion_model"):
flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key)
else:
if sum(k.startswith("model_ema") for k in keys) > 100:
logger.warning(
"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
" weights (usually better for inference), please make sure to add the `--extract_ema` flag."
)
for key in keys:
if key.startswith(unet_key):
unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
new_checkpoint = {}
new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"]
new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"]
new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"]
if config["class_embed_type"] is None:
# No parameters to port
...
elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"]
new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"]
new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"]
new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"]
else:
raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}")
if config["addition_embed_type"] == "text_time":
new_checkpoint["add_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"]
new_checkpoint["add_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"]
new_checkpoint["add_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"]
new_checkpoint["add_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"]
new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]
if not controlnet:
new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]
# Retrieves the keys for the input blocks only
num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})
input_blocks = {
layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
for layer_id in range(num_input_blocks)
}
# Retrieves the keys for the middle blocks only
num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})
middle_blocks = {
layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
for layer_id in range(num_middle_blocks)
}
# Retrieves the keys for the output blocks only
num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})
output_blocks = {
layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
for layer_id in range(num_output_blocks)
}
for i in range(1, num_input_blocks):
block_id = (i - 1) // (config["layers_per_block"] + 1)
layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)
resnets = [
key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
]
attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(
f"input_blocks.{i}.0.op.weight"
)
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(
f"input_blocks.{i}.0.op.bias"
)
paths = renew_resnet_paths(resnets)
meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
if len(attentions):
paths = renew_attention_paths(attentions)
meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
resnet_0 = middle_blocks[0]
attentions = middle_blocks[1]
resnet_1 = middle_blocks[2]
resnet_0_paths = renew_resnet_paths(resnet_0)
assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)
resnet_1_paths = renew_resnet_paths(resnet_1)
assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)
attentions_paths = renew_attention_paths(attentions)
meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(
attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
for i in range(num_output_blocks):
block_id = i // (config["layers_per_block"] + 1)
layer_in_block_id = i % (config["layers_per_block"] + 1)
output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
output_block_list = {}
for layer in output_block_layers:
layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
if layer_id in output_block_list:
output_block_list[layer_id].append(layer_name)
else:
output_block_list[layer_id] = [layer_name]
if len(output_block_list) > 1:
resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
resnet_0_paths = renew_resnet_paths(resnets)
paths = renew_resnet_paths(resnets)
meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
output_block_list = {k: sorted(v) for k, v in output_block_list.items()}
if ["conv.bias", "conv.weight"] in output_block_list.values():
index = list(output_block_list.values()).index(["conv.bias", "conv.weight"])
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
f"output_blocks.{i}.{index}.conv.weight"
]
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
f"output_blocks.{i}.{index}.conv.bias"
]
# Clear attentions as they have been attributed above.
if len(attentions) == 2:
attentions = []
if len(attentions):
paths = renew_attention_paths(attentions)
meta_path = {
"old": f"output_blocks.{i}.1",
"new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
else:
resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
for path in resnet_0_paths:
old_path = ".".join(["output_blocks", str(i), path["old"]])
new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])
new_checkpoint[new_path] = unet_state_dict[old_path]
if controlnet:
# conditioning embedding
orig_index = 0
new_checkpoint["controlnet_cond_embedding.conv_in.weight"] = unet_state_dict.pop(
f"input_hint_block.{orig_index}.weight"
)
new_checkpoint["controlnet_cond_embedding.conv_in.bias"] = unet_state_dict.pop(
f"input_hint_block.{orig_index}.bias"
)
orig_index += 2
diffusers_index = 0
while diffusers_index < 6:
new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.weight"] = unet_state_dict.pop(
f"input_hint_block.{orig_index}.weight"
)
new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.bias"] = unet_state_dict.pop(
f"input_hint_block.{orig_index}.bias"
)
diffusers_index += 1
orig_index += 2
new_checkpoint["controlnet_cond_embedding.conv_out.weight"] = unet_state_dict.pop(
f"input_hint_block.{orig_index}.weight"
)
new_checkpoint["controlnet_cond_embedding.conv_out.bias"] = unet_state_dict.pop(
f"input_hint_block.{orig_index}.bias"
)
# down blocks
for i in range(num_input_blocks):
new_checkpoint[f"controlnet_down_blocks.{i}.weight"] = unet_state_dict.pop(f"zero_convs.{i}.0.weight")
new_checkpoint[f"controlnet_down_blocks.{i}.bias"] = unet_state_dict.pop(f"zero_convs.{i}.0.bias")
# mid block
new_checkpoint["controlnet_mid_block.weight"] = unet_state_dict.pop("middle_block_out.0.weight")
new_checkpoint["controlnet_mid_block.bias"] = unet_state_dict.pop("middle_block_out.0.bias")
return new_checkpoint
def convert_ldm_vae_checkpoint(checkpoint, config):
# extract state dict for VAE
vae_state_dict = {}
keys = list(checkpoint.keys())
vae_key = "first_stage_model." if any(k.startswith("first_stage_model.") for k in keys) else ""
for key in keys:
if key.startswith(vae_key):
vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
new_checkpoint = {}
new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"]
new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"]
new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]
new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"]
new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"]
new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]
new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
# Retrieves the keys for the encoder down blocks only
num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
down_blocks = {
layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
}
# Retrieves the keys for the decoder up blocks only
num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
up_blocks = {
layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)
}
for i in range(num_down_blocks):
resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
f"encoder.down.{i}.downsample.conv.weight"
)
new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
f"encoder.down.{i}.downsample.conv.bias"
)
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
num_mid_res_blocks = 2
for i in range(1, num_mid_res_blocks + 1):
resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
paths = renew_vae_attention_paths(mid_attentions)
meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
conv_attn_to_linear(new_checkpoint)
for i in range(num_up_blocks):
block_id = num_up_blocks - 1 - i
resnets = [
key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
]
if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
f"decoder.up.{block_id}.upsample.conv.weight"
]
new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
f"decoder.up.{block_id}.upsample.conv.bias"
]
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
num_mid_res_blocks = 2
for i in range(1, num_mid_res_blocks + 1):
resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
paths = renew_vae_attention_paths(mid_attentions)
meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
conv_attn_to_linear(new_checkpoint)
return new_checkpoint
def convert_ldm_bert_checkpoint(checkpoint, config):
def _copy_attn_layer(hf_attn_layer, pt_attn_layer):
hf_attn_layer.q_proj.weight.data = pt_attn_layer.to_q.weight
hf_attn_layer.k_proj.weight.data = pt_attn_layer.to_k.weight
hf_attn_layer.v_proj.weight.data = pt_attn_layer.to_v.weight
hf_attn_layer.out_proj.weight = pt_attn_layer.to_out.weight
hf_attn_layer.out_proj.bias = pt_attn_layer.to_out.bias
def _copy_linear(hf_linear, pt_linear):
hf_linear.weight = pt_linear.weight
hf_linear.bias = pt_linear.bias
def _copy_layer(hf_layer, pt_layer):
# copy layer norms
_copy_linear(hf_layer.self_attn_layer_norm, pt_layer[0][0])
_copy_linear(hf_layer.final_layer_norm, pt_layer[1][0])
# copy attn
_copy_attn_layer(hf_layer.self_attn, pt_layer[0][1])
# copy MLP
pt_mlp = pt_layer[1][1]
_copy_linear(hf_layer.fc1, pt_mlp.net[0][0])
_copy_linear(hf_layer.fc2, pt_mlp.net[2])
def _copy_layers(hf_layers, pt_layers):
for i, hf_layer in enumerate(hf_layers):
if i != 0:
i += i
pt_layer = pt_layers[i : i + 2]
_copy_layer(hf_layer, pt_layer)
hf_model = LDMBertModel(config).eval()
# copy embeds
hf_model.model.embed_tokens.weight = checkpoint.transformer.token_emb.weight
hf_model.model.embed_positions.weight.data = checkpoint.transformer.pos_emb.emb.weight
# copy layer norm
_copy_linear(hf_model.model.layer_norm, checkpoint.transformer.norm)
# copy hidden layers
_copy_layers(hf_model.model.layers, checkpoint.transformer.attn_layers.layers)
_copy_linear(hf_model.to_logits, checkpoint.transformer.to_logits)
return hf_model
def convert_ldm_clip_checkpoint(checkpoint, local_files_only=False, text_encoder=None):
if text_encoder is None:
config_name = "openai/clip-vit-large-patch14"
config = CLIPTextConfig.from_pretrained(config_name)
ctx = init_empty_weights if is_accelerate_available() else nullcontext
with ctx():
text_model = CLIPTextModel(config)
keys = list(checkpoint.keys())
text_model_dict = {}
remove_prefixes = ["cond_stage_model.transformer", "conditioner.embedders.0.transformer"]
for key in keys:
for prefix in remove_prefixes:
if key.startswith(prefix):
text_model_dict[key[len(prefix + ".") :]] = checkpoint[key]
if is_accelerate_available():
for param_name, param in text_model_dict.items():
set_module_tensor_to_device(text_model, param_name, "cpu", value=param)
else:
if not (hasattr(text_model, "embeddings") and hasattr(text_model.embeddings.position_ids)):
text_model_dict.pop("text_model.embeddings.position_ids", None)
text_model.load_state_dict(text_model_dict)
return text_model
textenc_conversion_lst = [
("positional_embedding", "text_model.embeddings.position_embedding.weight"),
("token_embedding.weight", "text_model.embeddings.token_embedding.weight"),
("ln_final.weight", "text_model.final_layer_norm.weight"),
("ln_final.bias", "text_model.final_layer_norm.bias"),
("text_projection", "text_projection.weight"),
]
textenc_conversion_map = {x[0]: x[1] for x in textenc_conversion_lst}
textenc_transformer_conversion_lst = [
# (stable-diffusion, HF Diffusers)
("resblocks.", "text_model.encoder.layers."),
("ln_1", "layer_norm1"),
("ln_2", "layer_norm2"),
(".c_fc.", ".fc1."),
(".c_proj.", ".fc2."),
(".attn", ".self_attn"),
("ln_final.", "transformer.text_model.final_layer_norm."),
("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
]
protected = {re.escape(x[0]): x[1] for x in textenc_transformer_conversion_lst}
textenc_pattern = re.compile("|".join(protected.keys()))
def convert_paint_by_example_checkpoint(checkpoint):
config = CLIPVisionConfig.from_pretrained("openai/clip-vit-large-patch14")
model = PaintByExampleImageEncoder(config)
keys = list(checkpoint.keys())
text_model_dict = {}
for key in keys:
if key.startswith("cond_stage_model.transformer"):
text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key]
# load clip vision
model.model.load_state_dict(text_model_dict)
# load mapper
keys_mapper = {
k[len("cond_stage_model.mapper.res") :]: v
for k, v in checkpoint.items()
if k.startswith("cond_stage_model.mapper")
}
MAPPING = {
"attn.c_qkv": ["attn1.to_q", "attn1.to_k", "attn1.to_v"],
"attn.c_proj": ["attn1.to_out.0"],
"ln_1": ["norm1"],
"ln_2": ["norm3"],
"mlp.c_fc": ["ff.net.0.proj"],
"mlp.c_proj": ["ff.net.2"],
}
mapped_weights = {}
for key, value in keys_mapper.items():
prefix = key[: len("blocks.i")]
suffix = key.split(prefix)[-1].split(".")[-1]
name = key.split(prefix)[-1].split(suffix)[0][1:-1]
mapped_names = MAPPING[name]
num_splits = len(mapped_names)
for i, mapped_name in enumerate(mapped_names):
new_name = ".".join([prefix, mapped_name, suffix])
shape = value.shape[0] // num_splits
mapped_weights[new_name] = value[i * shape : (i + 1) * shape]
model.mapper.load_state_dict(mapped_weights)
# load final layer norm
model.final_layer_norm.load_state_dict(
{
"bias": checkpoint["cond_stage_model.final_ln.bias"],
"weight": checkpoint["cond_stage_model.final_ln.weight"],
}
)
# load final proj
model.proj_out.load_state_dict(
{
"bias": checkpoint["proj_out.bias"],
"weight": checkpoint["proj_out.weight"],
}
)
# load uncond vector
model.uncond_vector.data = torch.nn.Parameter(checkpoint["learnable_vector"])
return model
def convert_open_clip_checkpoint(
checkpoint, config_name, prefix="cond_stage_model.model.", has_projection=False, **config_kwargs
):
# text_model = CLIPTextModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="text_encoder")
# text_model = CLIPTextModelWithProjection.from_pretrained(
# "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", projection_dim=1280
# )
config = CLIPTextConfig.from_pretrained(config_name, **config_kwargs)
ctx = init_empty_weights if is_accelerate_available() else nullcontext
with ctx():
text_model = CLIPTextModelWithProjection(config) if has_projection else CLIPTextModel(config)
keys = list(checkpoint.keys())
keys_to_ignore = []
if config_name == "stabilityai/stable-diffusion-2" and config.num_hidden_layers == 23:
# make sure to remove all keys > 22
keys_to_ignore += [k for k in keys if k.startswith("cond_stage_model.model.transformer.resblocks.23")]
keys_to_ignore += ["cond_stage_model.model.text_projection"]
text_model_dict = {}
if prefix + "text_projection" in checkpoint:
d_model = int(checkpoint[prefix + "text_projection"].shape[0])
else:
d_model = 1024
text_model_dict["text_model.embeddings.position_ids"] = text_model.text_model.embeddings.get_buffer("position_ids")
for key in keys:
if key in keys_to_ignore:
continue
if key[len(prefix) :] in textenc_conversion_map:
if key.endswith("text_projection"):
value = checkpoint[key].T.contiguous()
else:
value = checkpoint[key]
text_model_dict[textenc_conversion_map[key[len(prefix) :]]] = value
if key.startswith(prefix + "transformer."):
new_key = key[len(prefix + "transformer.") :]
if new_key.endswith(".in_proj_weight"):
new_key = new_key[: -len(".in_proj_weight")]
new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)
text_model_dict[new_key + ".q_proj.weight"] = checkpoint[key][:d_model, :]
text_model_dict[new_key + ".k_proj.weight"] = checkpoint[key][d_model : d_model * 2, :]
text_model_dict[new_key + ".v_proj.weight"] = checkpoint[key][d_model * 2 :, :]
elif new_key.endswith(".in_proj_bias"):
new_key = new_key[: -len(".in_proj_bias")]
new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)
text_model_dict[new_key + ".q_proj.bias"] = checkpoint[key][:d_model]
text_model_dict[new_key + ".k_proj.bias"] = checkpoint[key][d_model : d_model * 2]
text_model_dict[new_key + ".v_proj.bias"] = checkpoint[key][d_model * 2 :]
else:
new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)
text_model_dict[new_key] = checkpoint[key]
if is_accelerate_available():
for param_name, param in text_model_dict.items():
set_module_tensor_to_device(text_model, param_name, "cpu", value=param)
else:
if not (hasattr(text_model, "embeddings") and hasattr(text_model.embeddings.position_ids)):
text_model_dict.pop("text_model.embeddings.position_ids", None)
text_model.load_state_dict(text_model_dict)
return text_model
def stable_unclip_image_encoder(original_config):
"""
Returns the image processor and clip image encoder for the img2img unclip pipeline.
We currently know of two types of stable unclip models which separately use the clip and the openclip image
encoders.
"""
image_embedder_config = original_config.model.params.embedder_config
sd_clip_image_embedder_class = image_embedder_config.target
sd_clip_image_embedder_class = sd_clip_image_embedder_class.split(".")[-1]
if sd_clip_image_embedder_class == "ClipImageEmbedder":
clip_model_name = image_embedder_config.params.model
if clip_model_name == "ViT-L/14":
feature_extractor = CLIPImageProcessor()
image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
else:
raise NotImplementedError(f"Unknown CLIP checkpoint name in stable diffusion checkpoint {clip_model_name}")
elif sd_clip_image_embedder_class == "FrozenOpenCLIPImageEmbedder":
feature_extractor = CLIPImageProcessor()
image_encoder = CLIPVisionModelWithProjection.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K")
else:
raise NotImplementedError(
f"Unknown CLIP image embedder class in stable diffusion checkpoint {sd_clip_image_embedder_class}"
)
return feature_extractor, image_encoder
def stable_unclip_image_noising_components(
original_config, clip_stats_path: Optional[str] = None, device: Optional[str] = None
):
"""
Returns the noising components for the img2img and txt2img unclip pipelines.
Converts the stability noise augmentor into
1. a `StableUnCLIPImageNormalizer` for holding the CLIP stats
2. a `DDPMScheduler` for holding the noise schedule
If the noise augmentor config specifies a clip stats path, the `clip_stats_path` must be provided.
"""
noise_aug_config = original_config.model.params.noise_aug_config
noise_aug_class = noise_aug_config.target
noise_aug_class = noise_aug_class.split(".")[-1]
if noise_aug_class == "CLIPEmbeddingNoiseAugmentation":
noise_aug_config = noise_aug_config.params
embedding_dim = noise_aug_config.timestep_dim
max_noise_level = noise_aug_config.noise_schedule_config.timesteps
beta_schedule = noise_aug_config.noise_schedule_config.beta_schedule
image_normalizer = StableUnCLIPImageNormalizer(embedding_dim=embedding_dim)
image_noising_scheduler = DDPMScheduler(num_train_timesteps=max_noise_level, beta_schedule=beta_schedule)
if "clip_stats_path" in noise_aug_config:
if clip_stats_path is None:
raise ValueError("This stable unclip config requires a `clip_stats_path`")
clip_mean, clip_std = torch.load(clip_stats_path, map_location=device)
clip_mean = clip_mean[None, :]
clip_std = clip_std[None, :]
clip_stats_state_dict = {
"mean": clip_mean,
"std": clip_std,
}
image_normalizer.load_state_dict(clip_stats_state_dict)
else:
raise NotImplementedError(f"Unknown noise augmentor class: {noise_aug_class}")
return image_normalizer, image_noising_scheduler
def convert_controlnet_checkpoint(
checkpoint,
original_config,
checkpoint_path,
image_size,
upcast_attention,
extract_ema,
use_linear_projection=None,
cross_attention_dim=None,
):
ctrlnet_config = create_unet_diffusers_config(original_config, image_size=image_size, controlnet=True)
ctrlnet_config["upcast_attention"] = upcast_attention
ctrlnet_config.pop("sample_size")
if use_linear_projection is not None:
ctrlnet_config["use_linear_projection"] = use_linear_projection
if cross_attention_dim is not None:
ctrlnet_config["cross_attention_dim"] = cross_attention_dim
controlnet = ControlNetModel(**ctrlnet_config)
# Some controlnet ckpt files are distributed independently from the rest of the
# model components i.e. https://huggingface.co/thibaud/controlnet-sd21/
if "time_embed.0.weight" in checkpoint:
skip_extract_state_dict = True
else:
skip_extract_state_dict = False
converted_ctrl_checkpoint = convert_ldm_unet_checkpoint(
checkpoint,
ctrlnet_config,
path=checkpoint_path,
extract_ema=extract_ema,
controlnet=True,
skip_extract_state_dict=skip_extract_state_dict,
)
controlnet.load_state_dict(converted_ctrl_checkpoint)
return controlnet
def download_from_original_stable_diffusion_ckpt(
checkpoint_path: str,
original_config_file: str = None,
image_size: Optional[int] = None,
prediction_type: str = None,
model_type: str = None,
extract_ema: bool = False,
scheduler_type: str = "pndm",
num_in_channels: Optional[int] = None,
upcast_attention: Optional[bool] = None,
device: str = None,
from_safetensors: bool = False,
stable_unclip: Optional[str] = None,
stable_unclip_prior: Optional[str] = None,
clip_stats_path: Optional[str] = None,
controlnet: Optional[bool] = None,
load_safety_checker: bool = True,
pipeline_class: DiffusionPipeline = None,
local_files_only=False,
vae_path=None,
vae=None,
text_encoder=None,
tokenizer=None,
) -> DiffusionPipeline:
"""
Load a Stable Diffusion pipeline object from a CompVis-style `.ckpt`/`.safetensors` file and (ideally) a `.yaml`
config file.
Although many of the arguments can be automatically inferred, some of these rely on brittle checks against the
global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is
recommended that you override the default values and/or supply an `original_config_file` wherever possible.
Args:
checkpoint_path (`str`): Path to `.ckpt` file.
original_config_file (`str`):
Path to `.yaml` config file corresponding to the original architecture. If `None`, will be automatically
inferred by looking for a key that only exists in SD2.0 models.
image_size (`int`, *optional*, defaults to 512):
The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2
Base. Use 768 for Stable Diffusion v2.
prediction_type (`str`, *optional*):
The prediction type that the model was trained on. Use `'epsilon'` for Stable Diffusion v1.X and Stable
Diffusion v2 Base. Use `'v_prediction'` for Stable Diffusion v2.
num_in_channels (`int`, *optional*, defaults to None):
The number of input channels. If `None`, it will be automatically inferred.
scheduler_type (`str`, *optional*, defaults to 'pndm'):
Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler", "euler-ancestral", "dpm",
"ddim"]`.
model_type (`str`, *optional*, defaults to `None`):
The pipeline type. `None` to automatically infer, or one of `["FrozenOpenCLIPEmbedder",
"FrozenCLIPEmbedder", "PaintByExample"]`.
is_img2img (`bool`, *optional*, defaults to `False`):
Whether the model should be loaded as an img2img pipeline.
extract_ema (`bool`, *optional*, defaults to `False`): Only relevant for
checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to
`False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for
inference. Non-EMA weights are usually better to continue fine-tuning.
upcast_attention (`bool`, *optional*, defaults to `None`):
Whether the attention computation should always be upcasted. This is necessary when running stable
diffusion 2.1.
device (`str`, *optional*, defaults to `None`):
The device to use. Pass `None` to determine automatically.
from_safetensors (`str`, *optional*, defaults to `False`):
If `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.
load_safety_checker (`bool`, *optional*, defaults to `True`):
Whether to load the safety checker or not. Defaults to `True`.
pipeline_class (`str`, *optional*, defaults to `None`):
The pipeline class to use. Pass `None` to determine automatically.
local_files_only (`bool`, *optional*, defaults to `False`):
Whether or not to only look at local files (i.e., do not try to download the model).
vae (`AutoencoderKL`, *optional*, defaults to `None`):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. If
this parameter is `None`, the function will load a new instance of [CLIP] by itself, if needed.
text_encoder (`CLIPTextModel`, *optional*, defaults to `None`):
An instance of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel)
to use, specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)
variant. If this parameter is `None`, the function will load a new instance of [CLIP] by itself, if needed.
tokenizer (`CLIPTokenizer`, *optional*, defaults to `None`):
An instance of
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer)
to use. If this parameter is `None`, the function will load a new instance of [CLIPTokenizer] by itself, if
needed.
return: A StableDiffusionPipeline object representing the passed-in `.ckpt`/`.safetensors` file.
"""
# import pipelines here to avoid circular import error when using from_single_file method
from diffusers import (
LDMTextToImagePipeline,
PaintByExamplePipeline,
StableDiffusionControlNetPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionPipeline,
StableDiffusionXLImg2ImgPipeline,
StableUnCLIPImg2ImgPipeline,
StableUnCLIPPipeline,
)
if pipeline_class is None:
pipeline_class = StableDiffusionPipeline if not controlnet else StableDiffusionControlNetPipeline
if prediction_type == "v-prediction":
prediction_type = "v_prediction"
if not is_omegaconf_available():
raise ValueError(BACKENDS_MAPPING["omegaconf"][1])
from omegaconf import OmegaConf
if from_safetensors:
if not is_safetensors_available():
raise ValueError(BACKENDS_MAPPING["safetensors"][1])
from safetensors.torch import load_file as safe_load
checkpoint = safe_load(checkpoint_path, device="cpu")
else:
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
checkpoint = torch.load(checkpoint_path, map_location=device)
else:
checkpoint = torch.load(checkpoint_path, map_location=device)
# Sometimes models don't have the global_step item
if "global_step" in checkpoint:
global_step = checkpoint["global_step"]
else:
logger.debug("global_step key not found in model")
global_step = None
# NOTE: this while loop isn't great but this controlnet checkpoint has one additional
# "state_dict" key https://huggingface.co/thibaud/controlnet-canny-sd21
while "state_dict" in checkpoint:
checkpoint = checkpoint["state_dict"]
if original_config_file is None:
key_name_v2_1 = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
key_name_sd_xl_base = "conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.bias"
key_name_sd_xl_refiner = "conditioner.embedders.0.model.transformer.resblocks.9.mlp.c_proj.bias"
# model_type = "v1"
config_url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
if key_name_v2_1 in checkpoint and checkpoint[key_name_v2_1].shape[-1] == 1024:
# model_type = "v2"
config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml"
if global_step == 110000:
# v2.1 needs to upcast attention
upcast_attention = True
elif key_name_sd_xl_base in checkpoint:
# only base xl has two text embedders
config_url = "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml"
elif key_name_sd_xl_refiner in checkpoint:
# only refiner xl has embedder and one text embedders
config_url = "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_refiner.yaml"
original_config_file = BytesIO(requests.get(config_url).content)
original_config = OmegaConf.load(original_config_file)
# Convert the text model.
if (
model_type is None
and "cond_stage_config" in original_config.model.params
and original_config.model.params.cond_stage_config is not None
):
model_type = original_config.model.params.cond_stage_config.target.split(".")[-1]
logger.debug(f"no `model_type` given, `model_type` inferred as: {model_type}")
elif model_type is None and original_config.model.params.network_config is not None:
if original_config.model.params.network_config.params.context_dim == 2048:
model_type = "SDXL"
else:
model_type = "SDXL-Refiner"
if image_size is None:
image_size = 1024
if num_in_channels is None and pipeline_class == StableDiffusionInpaintPipeline:
num_in_channels = 9
elif num_in_channels is None:
num_in_channels = 4
if "unet_config" in original_config.model.params:
original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels
if (
"parameterization" in original_config["model"]["params"]
and original_config["model"]["params"]["parameterization"] == "v"
):
if prediction_type is None:
# NOTE: For stable diffusion 2 base it is recommended to pass `prediction_type=="epsilon"`
# as it relies on a brittle global step parameter here
prediction_type = "epsilon" if global_step == 875000 else "v_prediction"
if image_size is None:
# NOTE: For stable diffusion 2 base one has to pass `image_size==512`
# as it relies on a brittle global step parameter here
image_size = 512 if global_step == 875000 else 768
else:
if prediction_type is None:
prediction_type = "epsilon"
if image_size is None:
image_size = 512
if controlnet is None and "control_stage_config" in original_config.model.params:
controlnet = convert_controlnet_checkpoint(
checkpoint, original_config, checkpoint_path, image_size, upcast_attention, extract_ema
)
num_train_timesteps = getattr(original_config.model.params, "timesteps", None) or 1000
if model_type in ["SDXL", "SDXL-Refiner"]:
scheduler_dict = {
"beta_schedule": "scaled_linear",
"beta_start": 0.00085,
"beta_end": 0.012,
"interpolation_type": "linear",
"num_train_timesteps": num_train_timesteps,
"prediction_type": "epsilon",
"sample_max_value": 1.0,
"set_alpha_to_one": False,
"skip_prk_steps": True,
"steps_offset": 1,
"timestep_spacing": "leading",
}
scheduler = EulerDiscreteScheduler.from_config(scheduler_dict)
scheduler_type = "euler"
else:
beta_start = getattr(original_config.model.params, "linear_start", None) or 0.02
beta_end = getattr(original_config.model.params, "linear_end", None) or 0.085
scheduler = DDIMScheduler(
beta_end=beta_end,
beta_schedule="scaled_linear",
beta_start=beta_start,
num_train_timesteps=num_train_timesteps,
steps_offset=1,
clip_sample=False,
set_alpha_to_one=False,
prediction_type=prediction_type,
)
# make sure scheduler works correctly with DDIM
scheduler.register_to_config(clip_sample=False)
if scheduler_type == "pndm":
config = dict(scheduler.config)
config["skip_prk_steps"] = True
scheduler = PNDMScheduler.from_config(config)
elif scheduler_type == "lms":
scheduler = LMSDiscreteScheduler.from_config(scheduler.config)
elif scheduler_type == "heun":
scheduler = HeunDiscreteScheduler.from_config(scheduler.config)
elif scheduler_type == "euler":
scheduler = EulerDiscreteScheduler.from_config(scheduler.config)
elif scheduler_type == "euler-ancestral":
scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config)
elif scheduler_type == "dpm":
scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)
elif scheduler_type == "ddim":
scheduler = scheduler
else:
raise ValueError(f"Scheduler of type {scheduler_type} doesn't exist!")
# Convert the UNet2DConditionModel model.
unet_config = create_unet_diffusers_config(original_config, image_size=image_size)
unet_config["upcast_attention"] = upcast_attention
converted_unet_checkpoint = convert_ldm_unet_checkpoint(
checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema
)
ctx = init_empty_weights if is_accelerate_available() else nullcontext
with ctx():
unet = UNet2DConditionModel(**unet_config)
if is_accelerate_available():
for param_name, param in converted_unet_checkpoint.items():
set_module_tensor_to_device(unet, param_name, "cpu", value=param)
else:
unet.load_state_dict(converted_unet_checkpoint)
# Convert the VAE model.
if vae_path is None and vae is None:
vae_config = create_vae_diffusers_config(original_config, image_size=image_size)
converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
if (
"model" in original_config
and "params" in original_config.model
and "scale_factor" in original_config.model.params
):
vae_scaling_factor = original_config.model.params.scale_factor
else:
vae_scaling_factor = 0.18215 # default SD scaling factor
vae_config["scaling_factor"] = vae_scaling_factor
ctx = init_empty_weights if is_accelerate_available() else nullcontext
with ctx():
vae = AutoencoderKL(**vae_config)
if is_accelerate_available():
for param_name, param in converted_vae_checkpoint.items():
set_module_tensor_to_device(vae, param_name, "cpu", value=param)
else:
vae.load_state_dict(converted_vae_checkpoint)
elif vae is None:
vae = AutoencoderKL.from_pretrained(vae_path)
if model_type == "FrozenOpenCLIPEmbedder":
config_name = "stabilityai/stable-diffusion-2"
config_kwargs = {"subfolder": "text_encoder"}
text_model = convert_open_clip_checkpoint(checkpoint, config_name, **config_kwargs)
tokenizer = CLIPTokenizer.from_pretrained("stabilityai/stable-diffusion-2", subfolder="tokenizer")
if stable_unclip is None:
if controlnet:
pipe = pipeline_class(
vae=vae,
text_encoder=text_model,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
controlnet=controlnet,
safety_checker=None,
feature_extractor=None,
requires_safety_checker=False,
)
else:
pipe = pipeline_class(
vae=vae,
text_encoder=text_model,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=None,
feature_extractor=None,
requires_safety_checker=False,
)
else:
image_normalizer, image_noising_scheduler = stable_unclip_image_noising_components(
original_config, clip_stats_path=clip_stats_path, device=device
)
if stable_unclip == "img2img":
feature_extractor, image_encoder = stable_unclip_image_encoder(original_config)
pipe = StableUnCLIPImg2ImgPipeline(
# image encoding components
feature_extractor=feature_extractor,
image_encoder=image_encoder,
# image noising components
image_normalizer=image_normalizer,
image_noising_scheduler=image_noising_scheduler,
# regular denoising components
tokenizer=tokenizer,
text_encoder=text_model,
unet=unet,
scheduler=scheduler,
# vae
vae=vae,
)
elif stable_unclip == "txt2img":
if stable_unclip_prior is None or stable_unclip_prior == "karlo":
karlo_model = "kakaobrain/karlo-v1-alpha"
prior = PriorTransformer.from_pretrained(karlo_model, subfolder="prior")
prior_tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
prior_text_model = CLIPTextModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
prior_scheduler = UnCLIPScheduler.from_pretrained(karlo_model, subfolder="prior_scheduler")
prior_scheduler = DDPMScheduler.from_config(prior_scheduler.config)
else:
raise NotImplementedError(f"unknown prior for stable unclip model: {stable_unclip_prior}")
pipe = StableUnCLIPPipeline(
# prior components
prior_tokenizer=prior_tokenizer,
prior_text_encoder=prior_text_model,
prior=prior,
prior_scheduler=prior_scheduler,
# image noising components
image_normalizer=image_normalizer,
image_noising_scheduler=image_noising_scheduler,
# regular denoising components
tokenizer=tokenizer,
text_encoder=text_model,
unet=unet,
scheduler=scheduler,
# vae
vae=vae,
)
else:
raise NotImplementedError(f"unknown `stable_unclip` type: {stable_unclip}")
elif model_type == "PaintByExample":
vision_model = convert_paint_by_example_checkpoint(checkpoint)
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker")
pipe = PaintByExamplePipeline(
vae=vae,
image_encoder=vision_model,
unet=unet,
scheduler=scheduler,
safety_checker=None,
feature_extractor=feature_extractor,
)
elif model_type == "FrozenCLIPEmbedder":
text_model = convert_ldm_clip_checkpoint(
checkpoint, local_files_only=local_files_only, text_encoder=text_encoder
)
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") if tokenizer is None else tokenizer
if load_safety_checker:
safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker")
else:
safety_checker = None
feature_extractor = None
if controlnet:
pipe = pipeline_class(
vae=vae,
text_encoder=text_model,
tokenizer=tokenizer,
unet=unet,
controlnet=controlnet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
else:
pipe = pipeline_class(
vae=vae,
text_encoder=text_model,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
elif model_type in ["SDXL", "SDXL-Refiner"]:
if model_type == "SDXL":
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
text_encoder = convert_ldm_clip_checkpoint(checkpoint, local_files_only=local_files_only)
tokenizer_2 = CLIPTokenizer.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", pad_token="!")
config_name = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
config_kwargs = {"projection_dim": 1280}
text_encoder_2 = convert_open_clip_checkpoint(
checkpoint, config_name, prefix="conditioner.embedders.1.model.", has_projection=True, **config_kwargs
)
pipe = pipeline_class(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
text_encoder_2=text_encoder_2,
tokenizer_2=tokenizer_2,
unet=unet,
scheduler=scheduler,
force_zeros_for_empty_prompt=True,
)
else:
tokenizer = None
text_encoder = None
tokenizer_2 = CLIPTokenizer.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", pad_token="!")
config_name = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
config_kwargs = {"projection_dim": 1280}
text_encoder_2 = convert_open_clip_checkpoint(
checkpoint, config_name, prefix="conditioner.embedders.0.model.", has_projection=True, **config_kwargs
)
pipe = StableDiffusionXLImg2ImgPipeline(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
text_encoder_2=text_encoder_2,
tokenizer_2=tokenizer_2,
unet=unet,
scheduler=scheduler,
requires_aesthetics_score=True,
force_zeros_for_empty_prompt=False,
)
else:
text_config = create_ldm_bert_config(original_config)
text_model = convert_ldm_bert_checkpoint(checkpoint, text_config)
tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
return pipe
def download_controlnet_from_original_ckpt(
checkpoint_path: str,
original_config_file: str,
image_size: int = 512,
extract_ema: bool = False,
num_in_channels: Optional[int] = None,
upcast_attention: Optional[bool] = None,
device: str = None,
from_safetensors: bool = False,
use_linear_projection: Optional[bool] = None,
cross_attention_dim: Optional[bool] = None,
) -> DiffusionPipeline:
if not is_omegaconf_available():
raise ValueError(BACKENDS_MAPPING["omegaconf"][1])
from omegaconf import OmegaConf
if from_safetensors:
if not is_safetensors_available():
raise ValueError(BACKENDS_MAPPING["safetensors"][1])
from safetensors import safe_open
checkpoint = {}
with safe_open(checkpoint_path, framework="pt", device="cpu") as f:
for key in f.keys():
checkpoint[key] = f.get_tensor(key)
else:
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
checkpoint = torch.load(checkpoint_path, map_location=device)
else:
checkpoint = torch.load(checkpoint_path, map_location=device)
# NOTE: this while loop isn't great but this controlnet checkpoint has one additional
# "state_dict" key https://huggingface.co/thibaud/controlnet-canny-sd21
while "state_dict" in checkpoint:
checkpoint = checkpoint["state_dict"]
original_config = OmegaConf.load(original_config_file)
if num_in_channels is not None:
original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels
if "control_stage_config" not in original_config.model.params:
raise ValueError("`control_stage_config` not present in original config")
controlnet = convert_controlnet_checkpoint(
checkpoint,
original_config,
checkpoint_path,
image_size,
upcast_attention,
extract_ema,
use_linear_projection=use_linear_projection,
cross_attention_dim=cross_attention_dim,
)
return controlnet
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import contextlib
import inspect
import warnings
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL
import torch
from packaging import version
from transformers import CLIPTextModel, CLIPTokenizer, DPTFeatureExtractor, DPTForDepthEstimation
from ...configuration_utils import FrozenDict
from ...image_processor import VaeImageProcessor
from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import PIL_INTERPOLATION, deprecate, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
def preprocess(image):
warnings.warn(
"The preprocess method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor.preprocess instead",
FutureWarning,
)
if isinstance(image, torch.Tensor):
return image
elif isinstance(image, PIL.Image.Image):
image = [image]
if isinstance(image[0], PIL.Image.Image):
w, h = image[0].size
w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
image = np.concatenate(image, axis=0)
image = np.array(image).astype(np.float32) / 255.0
image = image.transpose(0, 3, 1, 2)
image = 2.0 * image - 1.0
image = torch.from_numpy(image)
elif isinstance(image[0], torch.Tensor):
image = torch.cat(image, dim=0)
return image
class StableDiffusionDepth2ImgPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
r"""
Pipeline for text-guided depth-based image-to-image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
"""
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
depth_estimator: DPTForDepthEstimation,
feature_extractor: DPTFeatureExtractor,
):
super().__init__()
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
depth_estimator=depth_estimator,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.check_inputs
def check_inputs(
self, prompt, strength, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None
):
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
return timesteps, num_inference_steps - t_start
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latents
def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
)
image = image.to(device=device, dtype=dtype)
batch_size = batch_size * num_images_per_prompt
if image.shape[1] == 4:
init_latents = image
else:
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
elif isinstance(generator, list):
init_latents = [
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
]
init_latents = torch.cat(init_latents, dim=0)
else:
init_latents = self.vae.encode(image).latent_dist.sample(generator)
init_latents = self.vae.config.scaling_factor * init_latents
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
# expand init_latents for batch_size
deprecation_message = (
f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
" images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
" your script to pass as many initial images as text prompts to suppress this warning."
)
deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
additional_image_per_prompt = batch_size // init_latents.shape[0]
init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
raise ValueError(
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
)
else:
init_latents = torch.cat([init_latents], dim=0)
shape = init_latents.shape
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
# get latents
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
latents = init_latents
return latents
def prepare_depth_map(self, image, depth_map, batch_size, do_classifier_free_guidance, dtype, device):
if isinstance(image, PIL.Image.Image):
image = [image]
else:
image = list(image)
if isinstance(image[0], PIL.Image.Image):
width, height = image[0].size
elif isinstance(image[0], np.ndarray):
width, height = image[0].shape[:-1]
else:
height, width = image[0].shape[-2:]
if depth_map is None:
pixel_values = self.feature_extractor(images=image, return_tensors="pt").pixel_values
pixel_values = pixel_values.to(device=device)
# The DPT-Hybrid model uses batch-norm layers which are not compatible with fp16.
# So we use `torch.autocast` here for half precision inference.
context_manger = torch.autocast("cuda", dtype=dtype) if device.type == "cuda" else contextlib.nullcontext()
with context_manger:
depth_map = self.depth_estimator(pixel_values).predicted_depth
else:
depth_map = depth_map.to(device=device, dtype=dtype)
depth_map = torch.nn.functional.interpolate(
depth_map.unsqueeze(1),
size=(height // self.vae_scale_factor, width // self.vae_scale_factor),
mode="bicubic",
align_corners=False,
)
depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
depth_map = 2.0 * (depth_map - depth_min) / (depth_max - depth_min) - 1.0
depth_map = depth_map.to(dtype)
# duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
if depth_map.shape[0] < batch_size:
repeat_by = batch_size // depth_map.shape[0]
depth_map = depth_map.repeat(repeat_by, 1, 1, 1)
depth_map = torch.cat([depth_map] * 2) if do_classifier_free_guidance else depth_map
return depth_map
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: Union[
torch.FloatTensor,
PIL.Image.Image,
np.ndarray,
List[torch.FloatTensor],
List[PIL.Image.Image],
List[np.ndarray],
] = None,
depth_map: Optional[torch.FloatTensor] = None,
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: Optional[float] = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
`Image` or tensor representing an image batch to be used as the starting point. Can accept image
latents as `image` only if `depth_map` is not `None`.
depth_map (`torch.FloatTensor`, *optional*):
Depth prediction to be used as additional conditioning for the image generation process. If not
defined, it automatically predicts the depth with `self.depth_estimator`.
strength (`float`, *optional*, defaults to 0.8):
Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
starting point and more noise is added the higher the `strength`. The number of denoising steps depends
on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
essentially ignores `image`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter is modulated by `strength`.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
Examples:
```py
>>> import torch
>>> import requests
>>> from PIL import Image
>>> from diffusers import StableDiffusionDepth2ImgPipeline
>>> pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
... "stabilityai/stable-diffusion-2-depth",
... torch_dtype=torch.float16,
... )
>>> pipe.to("cuda")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> init_image = Image.open(requests.get(url, stream=True).raw)
>>> prompt = "two tigers"
>>> n_propmt = "bad, deformed, ugly, bad anotomy"
>>> image = pipe(prompt=prompt, image=init_image, negative_prompt=n_propmt, strength=0.7).images[0]
```
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images.
"""
# 1. Check inputs
self.check_inputs(
prompt,
strength,
callback_steps,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
)
if image is None:
raise ValueError("`image` input cannot be undefined.")
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
)
# 4. Prepare depth mask
depth_mask = self.prepare_depth_map(
image,
depth_map,
batch_size * num_images_per_prompt,
do_classifier_free_guidance,
prompt_embeds.dtype,
device,
)
# 5. Preprocess image
image = self.image_processor.preprocess(image)
# 6. Set timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
# 7. Prepare latent variables
latents = self.prepare_latents(
image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator
)
# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 9. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
latent_model_input = torch.cat([latent_model_input, depth_mask], dim=1)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
else:
image = latents
image = self.image_processor.postprocess(image, output_type=output_type)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL
import torch
from packaging import version
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from ...configuration_utils import FrozenDict
from ...image_processor import VaeImageProcessor
from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AsymmetricAutoencoderKL, AutoencoderKL, UNet2DConditionModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import deprecate, is_accelerate_available, is_accelerate_version, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def prepare_mask_and_masked_image(image, mask, height, width, return_image: bool = False):
"""
Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be
converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
``image`` and ``1`` for the ``mask``.
The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
binarized (``mask > 0.5``) and cast to ``torch.float32`` too.
Args:
image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.
It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``
``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.
mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``
``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.
Raises:
ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask
should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.
TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not
(ot the other way around).
Returns:
tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
dimensions: ``batch x channels x height x width``.
"""
if image is None:
raise ValueError("`image` input cannot be undefined.")
if mask is None:
raise ValueError("`mask_image` input cannot be undefined.")
if isinstance(image, torch.Tensor):
if not isinstance(mask, torch.Tensor):
raise TypeError(f"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not")
# Batch single image
if image.ndim == 3:
assert image.shape[0] == 3, "Image outside a batch should be of shape (3, H, W)"
image = image.unsqueeze(0)
# Batch and add channel dim for single mask
if mask.ndim == 2:
mask = mask.unsqueeze(0).unsqueeze(0)
# Batch single mask or add channel dim
if mask.ndim == 3:
# Single batched mask, no channel dim or single mask not batched but channel dim
if mask.shape[0] == 1:
mask = mask.unsqueeze(0)
# Batched masks no channel dim
else:
mask = mask.unsqueeze(1)
assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions"
assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions"
assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size"
# Check image is in [-1, 1]
if image.min() < -1 or image.max() > 1:
raise ValueError("Image should be in [-1, 1] range")
# Check mask is in [0, 1]
if mask.min() < 0 or mask.max() > 1:
raise ValueError("Mask should be in [0, 1] range")
# Binarize mask
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
# Image as float32
image = image.to(dtype=torch.float32)
elif isinstance(mask, torch.Tensor):
raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not")
else:
# preprocess image
if isinstance(image, (PIL.Image.Image, np.ndarray)):
image = [image]
if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
# resize all images w.r.t passed height an width
image = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in image]
image = [np.array(i.convert("RGB"))[None, :] for i in image]
image = np.concatenate(image, axis=0)
elif isinstance(image, list) and isinstance(image[0], np.ndarray):
image = np.concatenate([i[None, :] for i in image], axis=0)
image = image.transpose(0, 3, 1, 2)
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
# preprocess mask
if isinstance(mask, (PIL.Image.Image, np.ndarray)):
mask = [mask]
if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):
mask = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in mask]
mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0)
mask = mask.astype(np.float32) / 255.0
elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
mask = torch.from_numpy(mask)
masked_image = image * (mask < 0.5)
# n.b. ensure backwards compatibility as old function does not return image
if return_image:
return mask, masked_image, image
return mask, masked_image
class StableDiffusionInpaintPipeline(
DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
):
r"""
Pipeline for text-guided image inpainting using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
Args:
vae ([`AutoencoderKL`, `AsymmetricAutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: Union[AutoencoderKL, AsymmetricAutoencoderKL],
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "skip_prk_steps") and scheduler.config.skip_prk_steps is False:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration"
" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
" sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
" Hub, it would be very nice if you could open a Pull request for the"
" `scheduler/scheduler_config.json` file"
)
deprecate("skip_prk_steps not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["skip_prk_steps"] = True
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
# Check shapes, assume num_channels_latents == 4, num_channels_mask == 1, num_channels_masked == 4
if unet.config.in_channels != 9:
logger.info(f"You have loaded a UNet with {unet.config.in_channels} input channels which.")
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
if self.safety_checker is not None:
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self,
prompt,
height,
width,
strength,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
def prepare_latents(
self,
batch_size,
num_channels_latents,
height,
width,
dtype,
device,
generator,
latents=None,
image=None,
timestep=None,
is_strength_max=True,
return_noise=False,
return_image_latents=False,
):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if (image is None or timestep is None) and not is_strength_max:
raise ValueError(
"Since strength < 1. initial latents are to be initialised as a combination of Image + Noise."
"However, either the image or the noise timestep has not been provided."
)
if return_image_latents or (latents is None and not is_strength_max):
image = image.to(device=device, dtype=dtype)
image_latents = self._encode_vae_image(image=image, generator=generator)
if latents is None:
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
# if strength is 1. then initialise the latents to noise, else initial to image + noise
latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)
# if pure noise then scale the initial latents by the Scheduler's init sigma
latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents
else:
noise = latents.to(device)
latents = noise * self.scheduler.init_noise_sigma
outputs = (latents,)
if return_noise:
outputs += (noise,)
if return_image_latents:
outputs += (image_latents,)
return outputs
def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
if isinstance(generator, list):
image_latents = [
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])
for i in range(image.shape[0])
]
image_latents = torch.cat(image_latents, dim=0)
else:
image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)
image_latents = self.vae.config.scaling_factor * image_latents
return image_latents
def prepare_mask_latents(
self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
):
# resize the mask to latents shape as we concatenate the mask to the latents
# we do that before converting to dtype to avoid breaking in case we're using cpu_offload
# and half precision
mask = torch.nn.functional.interpolate(
mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
)
mask = mask.to(device=device, dtype=dtype)
masked_image = masked_image.to(device=device, dtype=dtype)
masked_image_latents = self._encode_vae_image(masked_image, generator=generator)
# duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
if mask.shape[0] < batch_size:
if not batch_size % mask.shape[0] == 0:
raise ValueError(
"The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
" of masks that you pass is divisible by the total requested batch size."
)
mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
if masked_image_latents.shape[0] < batch_size:
if not batch_size % masked_image_latents.shape[0] == 0:
raise ValueError(
"The passed images and the required batch size don't match. Images are supposed to be duplicated"
f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
" Make sure the number of images that you pass is divisible by the total requested batch size."
)
masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)
mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
masked_image_latents = (
torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
)
# aligning device to prevent device errors when concating it with the latent model input
masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
return mask, masked_image_latents
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
return timesteps, num_inference_steps - t_start
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: Union[torch.FloatTensor, PIL.Image.Image] = None,
mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
height: Optional[int] = None,
width: Optional[int] = None,
strength: float = 1.0,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
image (`PIL.Image.Image`):
`Image` or tensor representing an image batch to be inpainted (which parts of the image to be masked
out with `mask_image` and repainted according to `prompt`).
mask_image (`PIL.Image.Image`):
`Image` or tensor representing an image batch to mask `image`. White pixels in the mask are repainted
while black pixels are preserved. If `mask_image` is a PIL image, it is converted to a single channel
(luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3, so the
expected shape would be `(B, H, W, 1)`.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
strength (`float`, *optional*, defaults to 1.0):
Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
starting point and more noise is added the higher the `strength`. The number of denoising steps depends
on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
essentially ignores `image`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter is modulated by `strength`.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
Examples:
```py
>>> import PIL
>>> import requests
>>> import torch
>>> from io import BytesIO
>>> from diffusers import StableDiffusionInpaintPipeline
>>> def download_image(url):
... response = requests.get(url)
... return PIL.Image.open(BytesIO(response.content)).convert("RGB")
>>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
>>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
>>> init_image = download_image(img_url).resize((512, 512))
>>> mask_image = download_image(mask_url).resize((512, 512))
>>> pipe = StableDiffusionInpaintPipeline.from_pretrained(
... "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
>>> image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
```
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs
self.check_inputs(
prompt,
height,
width,
strength,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
)
# 4. set timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(
num_inference_steps=num_inference_steps, strength=strength, device=device
)
# check that number of inference steps is not < 1 - as this doesn't make sense
if num_inference_steps < 1:
raise ValueError(
f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline"
f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline."
)
# at which timestep to set the initial noise (n.b. 50% if strength is 0.5)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
# create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise
is_strength_max = strength == 1.0
# 5. Preprocess mask and image
mask, masked_image, init_image = prepare_mask_and_masked_image(
image, mask_image, height, width, return_image=True
)
mask_condition = mask.clone()
# 6. Prepare latent variables
num_channels_latents = self.vae.config.latent_channels
num_channels_unet = self.unet.config.in_channels
return_image_latents = num_channels_unet == 4
latents_outputs = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
image=init_image,
timestep=latent_timestep,
is_strength_max=is_strength_max,
return_noise=True,
return_image_latents=return_image_latents,
)
if return_image_latents:
latents, noise, image_latents = latents_outputs
else:
latents, noise = latents_outputs
# 7. Prepare mask latent variables
mask, masked_image_latents = self.prepare_mask_latents(
mask,
masked_image,
batch_size * num_images_per_prompt,
height,
width,
prompt_embeds.dtype,
device,
generator,
do_classifier_free_guidance,
)
# 8. Check that sizes of mask, masked image and latents match
if num_channels_unet == 9:
# default case for runwayml/stable-diffusion-inpainting
num_channels_mask = mask.shape[1]
num_channels_masked_image = masked_image_latents.shape[1]
if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
raise ValueError(
f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
" `pipeline.unet` or your `mask_image` or `image` input."
)
elif num_channels_unet != 4:
raise ValueError(
f"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}."
)
# 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 10. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
# concat latents, mask, masked_image_latents in the channel dimension
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
if num_channels_unet == 9:
latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
if num_channels_unet == 4:
init_latents_proper = image_latents[:1]
init_mask = mask[:1]
if i < len(timesteps) - 1:
noise_timestep = timesteps[i + 1]
init_latents_proper = self.scheduler.add_noise(
init_latents_proper, noise, torch.tensor([noise_timestep])
)
latents = (1 - init_mask) * init_latents_proper + init_mask * latents
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if not output_type == "latent":
condition_kwargs = {}
if isinstance(self.vae, AsymmetricAutoencoderKL):
init_image = init_image.to(device=device, dtype=masked_image_latents.dtype)
init_image_condition = init_image.clone()
init_image = self._encode_vae_image(init_image, generator=generator)
mask_condition = mask_condition.to(device=device, dtype=masked_image_latents.dtype)
condition_kwargs = {"image": init_image_condition, "mask": mask_condition}
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, **condition_kwargs)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_sag.py | # Copyright 2023 Susung Hong and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import warnings
from typing import Any, Callable, Dict, List, Optional, Union
import torch
import torch.nn.functional as F
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from ...image_processor import VaeImageProcessor
from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import logging, randn_tensor, replace_example_docstring
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import torch
>>> from diffusers import StableDiffusionSAGPipeline
>>> pipe = StableDiffusionSAGPipeline.from_pretrained(
... "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> prompt = "a photo of an astronaut riding a horse on mars"
>>> image = pipe(prompt, sag_scale=0.75).images[0]
```
"""
# processes and stores attention probabilities
class CrossAttnStoreProcessor:
def __init__(self):
self.attention_probs = None
def __call__(
self,
attn,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
):
batch_size, sequence_length, _ = hidden_states.shape
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
query = attn.head_to_batch_dim(query)
key = attn.head_to_batch_dim(key)
value = attn.head_to_batch_dim(value)
self.attention_probs = attn.get_attention_scores(query, key, attention_mask)
hidden_states = torch.bmm(self.attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
return hidden_states
# Modified to get self-attention guidance scale in this paper (https://arxiv.org/pdf/2210.00939.pdf) as an input
class StableDiffusionSAGPipeline(DiffusionPipeline, TextualInversionLoaderMixin):
r"""
Pipeline for text-to-image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.vae.enable_slicing()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
def disable_vae_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_slicing()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
lora_scale: Optional[float] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
sag_scale: float = 0.75,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
sag_scale (`float`, *optional*, defaults to 0.75):
Chosen between [0, 1.0] for better quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# and `sag_scale` is` `s` of equation (16)
# of the self-attentnion guidance paper: https://arxiv.org/pdf/2210.00939.pdf
# `sag_scale = 0` means no self-attention guidance
do_self_attention_guidance = sag_scale > 0.0
# 3. Encode input prompt
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Denoising loop
store_processor = CrossAttnStoreProcessor()
self.unet.mid_block.attentions[0].transformer_blocks[0].attn1.processor = store_processor
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
map_size = None
def get_map_size(module, input, output):
nonlocal map_size
map_size = output[0].shape[-2:]
with self.unet.mid_block.attentions[0].register_forward_hook(get_map_size):
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform self-attention guidance with the stored self-attentnion map
if do_self_attention_guidance:
# classifier-free guidance produces two chunks of attention map
# and we only use unconditional one according to equation (25)
# in https://arxiv.org/pdf/2210.00939.pdf
if do_classifier_free_guidance:
# DDIM-like prediction of x0
pred_x0 = self.pred_x0(latents, noise_pred_uncond, t)
# get the stored attention maps
uncond_attn, cond_attn = store_processor.attention_probs.chunk(2)
# self-attention-based degrading of latents
degraded_latents = self.sag_masking(
pred_x0, uncond_attn, map_size, t, self.pred_epsilon(latents, noise_pred_uncond, t)
)
uncond_emb, _ = prompt_embeds.chunk(2)
# forward and give guidance
degraded_pred = self.unet(degraded_latents, t, encoder_hidden_states=uncond_emb).sample
noise_pred += sag_scale * (noise_pred_uncond - degraded_pred)
else:
# DDIM-like prediction of x0
pred_x0 = self.pred_x0(latents, noise_pred, t)
# get the stored attention maps
cond_attn = store_processor.attention_probs
# self-attention-based degrading of latents
degraded_latents = self.sag_masking(
pred_x0, cond_attn, map_size, t, self.pred_epsilon(latents, noise_pred, t)
)
# forward and give guidance
degraded_pred = self.unet(degraded_latents, t, encoder_hidden_states=prompt_embeds).sample
noise_pred += sag_scale * (noise_pred - degraded_pred)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
def sag_masking(self, original_latents, attn_map, map_size, t, eps):
# Same masking process as in SAG paper: https://arxiv.org/pdf/2210.00939.pdf
bh, hw1, hw2 = attn_map.shape
b, latent_channel, latent_h, latent_w = original_latents.shape
h = self.unet.config.attention_head_dim
if isinstance(h, list):
h = h[-1]
# Produce attention mask
attn_map = attn_map.reshape(b, h, hw1, hw2)
attn_mask = attn_map.mean(1, keepdim=False).sum(1, keepdim=False) > 1.0
attn_mask = (
attn_mask.reshape(b, map_size[0], map_size[1])
.unsqueeze(1)
.repeat(1, latent_channel, 1, 1)
.type(attn_map.dtype)
)
attn_mask = F.interpolate(attn_mask, (latent_h, latent_w))
# Blur according to the self-attention mask
degraded_latents = gaussian_blur_2d(original_latents, kernel_size=9, sigma=1.0)
degraded_latents = degraded_latents * attn_mask + original_latents * (1 - attn_mask)
# Noise it again to match the noise level
degraded_latents = self.scheduler.add_noise(degraded_latents, noise=eps, timesteps=t)
return degraded_latents
# Modified from diffusers.schedulers.scheduling_ddim.DDIMScheduler.step
# Note: there are some schedulers that clip or do not return x_0 (PNDMScheduler, DDIMScheduler, etc.)
def pred_x0(self, sample, model_output, timestep):
alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
beta_prod_t = 1 - alpha_prod_t
if self.scheduler.config.prediction_type == "epsilon":
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
elif self.scheduler.config.prediction_type == "sample":
pred_original_sample = model_output
elif self.scheduler.config.prediction_type == "v_prediction":
pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
# predict V
model_output = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
f"prediction_type given as {self.scheduler.config.prediction_type} must be one of `epsilon`, `sample`,"
" or `v_prediction`"
)
return pred_original_sample
def pred_epsilon(self, sample, model_output, timestep):
alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
beta_prod_t = 1 - alpha_prod_t
if self.scheduler.config.prediction_type == "epsilon":
pred_eps = model_output
elif self.scheduler.config.prediction_type == "sample":
pred_eps = (sample - (alpha_prod_t**0.5) * model_output) / (beta_prod_t**0.5)
elif self.scheduler.config.prediction_type == "v_prediction":
pred_eps = (beta_prod_t**0.5) * sample + (alpha_prod_t**0.5) * model_output
else:
raise ValueError(
f"prediction_type given as {self.scheduler.config.prediction_type} must be one of `epsilon`, `sample`,"
" or `v_prediction`"
)
return pred_eps
# Gaussian blur
def gaussian_blur_2d(img, kernel_size, sigma):
ksize_half = (kernel_size - 1) * 0.5
x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)
pdf = torch.exp(-0.5 * (x / sigma).pow(2))
x_kernel = pdf / pdf.sum()
x_kernel = x_kernel.to(device=img.device, dtype=img.dtype)
kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :])
kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1])
padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2]
img = F.pad(img, padding, mode="reflect")
img = F.conv2d(img, kernel2d, groups=img.shape[-3])
return img
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py | from logging import getLogger
from typing import Any, Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline
logger = getLogger(__name__)
NUM_LATENT_CHANNELS = 4
NUM_UNET_INPUT_CHANNELS = 7
ORT_TO_PT_TYPE = {
"float16": torch.float16,
"float32": torch.float32,
}
def preprocess(image):
if isinstance(image, torch.Tensor):
return image
elif isinstance(image, PIL.Image.Image):
image = [image]
if isinstance(image[0], PIL.Image.Image):
w, h = image[0].size
w, h = (x - x % 64 for x in (w, h)) # resize to integer multiple of 32
image = [np.array(i.resize((w, h)))[None, :] for i in image]
image = np.concatenate(image, axis=0)
image = np.array(image).astype(np.float32) / 255.0
image = image.transpose(0, 3, 1, 2)
image = 2.0 * image - 1.0
image = torch.from_numpy(image)
elif isinstance(image[0], torch.Tensor):
image = torch.cat(image, dim=0)
return image
class OnnxStableDiffusionUpscalePipeline(StableDiffusionUpscalePipeline):
def __init__(
self,
vae: OnnxRuntimeModel,
text_encoder: OnnxRuntimeModel,
tokenizer: Any,
unet: OnnxRuntimeModel,
low_res_scheduler: DDPMScheduler,
scheduler: Any,
max_noise_level: int = 350,
):
super().__init__(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
low_res_scheduler=low_res_scheduler,
scheduler=scheduler,
safety_checker=None,
feature_extractor=None,
watermarker=None,
max_noise_level=max_noise_level,
)
def __call__(
self,
prompt: Union[str, List[str]],
image: Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]],
num_inference_steps: int = 75,
guidance_scale: float = 9.0,
noise_level: int = 20,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[np.ndarray] = None,
negative_prompt_embeds: Optional[np.ndarray] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
image (`np.ndarray` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter will be modulated by `strength`.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
noise_level TODO
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`np.random.RandomState`, *optional*):
A np.random.RandomState to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
prompt_embeds (`np.ndarray`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`np.ndarray`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
# 1. Check inputs
self.check_inputs(prompt, image, noise_level, callback_steps)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_embeddings = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
)
latents_dtype = ORT_TO_PT_TYPE[str(text_embeddings.dtype)]
# 4. Preprocess image
image = preprocess(image)
image = image.cpu()
# 5. set timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Add noise to image
noise_level = torch.tensor([noise_level], dtype=torch.long, device=device)
noise = torch.randn(image.shape, generator=generator, device=device, dtype=latents_dtype)
image = self.low_res_scheduler.add_noise(image, noise, noise_level)
batch_multiplier = 2 if do_classifier_free_guidance else 1
image = np.concatenate([image] * batch_multiplier * num_images_per_prompt)
noise_level = np.concatenate([noise_level] * image.shape[0])
# 6. Prepare latent variables
height, width = image.shape[2:]
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
NUM_LATENT_CHANNELS,
height,
width,
latents_dtype,
device,
generator,
latents,
)
# 7. Check that sizes of image and latents match
num_channels_image = image.shape[1]
if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
raise ValueError(
"Incorrect configuration settings! The config of `pipeline.unet` expects"
f" {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +"
f" `num_channels_image`: {num_channels_image} "
f" = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of"
" `pipeline.unet` or your `image` input."
)
# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
timestep_dtype = next(
(input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
)
timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
# 9. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
# concat latents, mask, masked_image_latents in the channel dimension
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
latent_model_input = np.concatenate([latent_model_input, image], axis=1)
# timestep to tensor
timestep = np.array([t], dtype=timestep_dtype)
# predict the noise residual
noise_pred = self.unet(
sample=latent_model_input,
timestep=timestep,
encoder_hidden_states=text_embeddings,
class_labels=noise_level.astype(np.int64),
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(
torch.from_numpy(noise_pred), t, latents, **extra_step_kwargs
).prev_sample
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
# 10. Post-processing
image = self.decode_latents(latents.float())
# 11. Convert to PIL
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
def decode_latents(self, latents):
latents = 1 / 0.08333 * latents
image = self.vae(latent_sample=latents)[0]
image = np.clip(image / 2 + 0.5, 0, 1)
image = image.transpose((0, 2, 3, 1))
return image
def _encode_prompt(
self,
prompt: Union[str, List[str]],
device,
num_images_per_prompt: Optional[int],
do_classifier_free_guidance: bool,
negative_prompt: Optional[str],
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
):
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
# no positional arguments to text_encoder
prompt_embeds = self.text_encoder(
input_ids=text_input_ids.int().to(device),
# attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt)
prompt_embeds = prompt_embeds.reshape(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
# if hasattr(uncond_input, "attention_mask"):
# attention_mask = uncond_input.attention_mask.to(device)
# else:
# attention_mask = None
uncond_embeddings = self.text_encoder(
input_ids=uncond_input.input_ids.int().to(device),
# attention_mask=attention_mask,
)
uncond_embeddings = uncond_embeddings[0]
if do_classifier_free_guidance:
seq_len = uncond_embeddings.shape[1]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt)
uncond_embeddings = uncond_embeddings.reshape(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = np.concatenate([uncond_embeddings, prompt_embeds])
return prompt_embeds
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py | # Copyright 2023 The InstructPix2Pix Authors and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import warnings
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from ...image_processor import VaeImageProcessor
from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import (
PIL_INTERPOLATION,
deprecate,
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
)
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
def preprocess(image):
warnings.warn(
"The preprocess method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor.preprocess instead",
FutureWarning,
)
if isinstance(image, torch.Tensor):
return image
elif isinstance(image, PIL.Image.Image):
image = [image]
if isinstance(image[0], PIL.Image.Image):
w, h = image[0].size
w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
image = np.concatenate(image, axis=0)
image = np.array(image).astype(np.float32) / 255.0
image = image.transpose(0, 3, 1, 2)
image = 2.0 * image - 1.0
image = torch.from_numpy(image)
elif isinstance(image[0], torch.Tensor):
image = torch.cat(image, dim=0)
return image
class StableDiffusionInstructPix2PixPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
r"""
Pipeline for pixel-level image editing by following text instructions (based on Stable Diffusion).
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: Union[
torch.FloatTensor,
PIL.Image.Image,
np.ndarray,
List[torch.FloatTensor],
List[PIL.Image.Image],
List[np.ndarray],
] = None,
num_inference_steps: int = 100,
guidance_scale: float = 7.5,
image_guidance_scale: float = 1.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
image (`torch.FloatTensor` `np.ndarray`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
`Image` or tensor representing an image batch to be repainted according to `prompt`. Can also accept
image latents as `image`, but if passing latents directly it is not encoded again.
num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
image_guidance_scale (`float`, *optional*, defaults to 1.5):
Push the generated image towards the inital `image`. Image guidance scale is enabled by setting
`image_guidance_scale > 1`. Higher image guidance scale encourages generated images that are closely
linked to the source `image`, usually at the expense of lower image quality. This pipeline requires a
value of at least `1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
Examples:
```py
>>> import PIL
>>> import requests
>>> import torch
>>> from io import BytesIO
>>> from diffusers import StableDiffusionInstructPix2PixPipeline
>>> def download_image(url):
... response = requests.get(url)
... return PIL.Image.open(BytesIO(response.content)).convert("RGB")
>>> img_url = "https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png"
>>> image = download_image(img_url).resize((512, 512))
>>> pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
... "timbrooks/instruct-pix2pix", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> prompt = "make the mountains snowy"
>>> image = pipe(prompt=prompt, image=image).images[0]
```
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
"""
# 0. Check inputs
self.check_inputs(prompt, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)
if image is None:
raise ValueError("`image` input cannot be undefined.")
# 1. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0 and image_guidance_scale >= 1.0
# check if scheduler is in sigmas space
scheduler_is_in_sigma_space = hasattr(self.scheduler, "sigmas")
# 2. Encode input prompt
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
)
# 3. Preprocess image
image = self.image_processor.preprocess(image)
# 4. set timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare Image latents
image_latents = self.prepare_image_latents(
image,
batch_size,
num_images_per_prompt,
prompt_embeds.dtype,
device,
do_classifier_free_guidance,
generator,
)
height, width = image_latents.shape[-2:]
height = height * self.vae_scale_factor
width = width * self.vae_scale_factor
# 6. Prepare latent variables
num_channels_latents = self.vae.config.latent_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 7. Check that shapes of latents and image match the UNet channels
num_channels_image = image_latents.shape[1]
if num_channels_latents + num_channels_image != self.unet.config.in_channels:
raise ValueError(
f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
f" `num_channels_image`: {num_channels_image} "
f" = {num_channels_latents+num_channels_image}. Please verify the config of"
" `pipeline.unet` or your `image` input."
)
# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 9. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# Expand the latents if we are doing classifier free guidance.
# The latents are expanded 3 times because for pix2pix the guidance\
# is applied for both the text and the input image.
latent_model_input = torch.cat([latents] * 3) if do_classifier_free_guidance else latents
# concat latents, image_latents in the channel dimension
scaled_latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
scaled_latent_model_input = torch.cat([scaled_latent_model_input, image_latents], dim=1)
# predict the noise residual
noise_pred = self.unet(
scaled_latent_model_input, t, encoder_hidden_states=prompt_embeds, return_dict=False
)[0]
# Hack:
# For karras style schedulers the model does classifer free guidance using the
# predicted_original_sample instead of the noise_pred. So we need to compute the
# predicted_original_sample here if we are using a karras style scheduler.
if scheduler_is_in_sigma_space:
step_index = (self.scheduler.timesteps == t).nonzero()[0].item()
sigma = self.scheduler.sigmas[step_index]
noise_pred = latent_model_input - sigma * noise_pred
# perform guidance
if do_classifier_free_guidance:
noise_pred_text, noise_pred_image, noise_pred_uncond = noise_pred.chunk(3)
noise_pred = (
noise_pred_uncond
+ guidance_scale * (noise_pred_text - noise_pred_image)
+ image_guidance_scale * (noise_pred_image - noise_pred_uncond)
)
# Hack:
# For karras style schedulers the model does classifer free guidance using the
# predicted_original_sample instead of the noise_pred. But the scheduler.step function
# expects the noise_pred and computes the predicted_original_sample internally. So we
# need to overwrite the noise_pred here such that the value of the computed
# predicted_original_sample is correct.
if scheduler_is_in_sigma_space:
noise_pred = (noise_pred - latents) / (-sigma)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
if self.safety_checker is not None:
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_ prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
"""
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
# textual inversion: procecss multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
# pix2pix has two negative embeddings, and unlike in other pipelines latents are ordered [prompt_embeds, negative_prompt_embeds, negative_prompt_embeds]
prompt_embeds = torch.cat([prompt_embeds, negative_prompt_embeds, negative_prompt_embeds])
return prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
def check_inputs(
self, prompt, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None
):
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def prepare_image_latents(
self, image, batch_size, num_images_per_prompt, dtype, device, do_classifier_free_guidance, generator=None
):
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
)
image = image.to(device=device, dtype=dtype)
batch_size = batch_size * num_images_per_prompt
if image.shape[1] == 4:
image_latents = image
else:
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if isinstance(generator, list):
image_latents = [self.vae.encode(image[i : i + 1]).latent_dist.mode() for i in range(batch_size)]
image_latents = torch.cat(image_latents, dim=0)
else:
image_latents = self.vae.encode(image).latent_dist.mode()
if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
# expand image_latents for batch_size
deprecation_message = (
f"You have passed {batch_size} text prompts (`prompt`), but only {image_latents.shape[0]} initial"
" images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
" your script to pass as many initial images as text prompts to suppress this warning."
)
deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
additional_image_per_prompt = batch_size // image_latents.shape[0]
image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)
elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:
raise ValueError(
f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
)
else:
image_latents = torch.cat([image_latents], dim=0)
if do_classifier_free_guidance:
uncond_image_latents = torch.zeros_like(image_latents)
image_latents = torch.cat([image_latents, image_latents, uncond_image_latents], dim=0)
return image_latents
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import warnings
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPTokenizer
from ...configuration_utils import FrozenDict
from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from ...utils import PIL_INTERPOLATION, deprecate, logging
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess with 8->64
def preprocess(image):
warnings.warn(
(
"The preprocess method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor.preprocess instead"
),
FutureWarning,
)
if isinstance(image, torch.Tensor):
return image
elif isinstance(image, PIL.Image.Image):
image = [image]
if isinstance(image[0], PIL.Image.Image):
w, h = image[0].size
w, h = (x - x % 64 for x in (w, h)) # resize to integer multiple of 64
image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
image = np.concatenate(image, axis=0)
image = np.array(image).astype(np.float32) / 255.0
image = image.transpose(0, 3, 1, 2)
image = 2.0 * image - 1.0
image = torch.from_numpy(image)
elif isinstance(image[0], torch.Tensor):
image = torch.cat(image, dim=0)
return image
class OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline):
r"""
Pipeline for text-guided image to image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
vae_encoder: OnnxRuntimeModel
vae_decoder: OnnxRuntimeModel
text_encoder: OnnxRuntimeModel
tokenizer: CLIPTokenizer
unet: OnnxRuntimeModel
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
safety_checker: OnnxRuntimeModel
feature_extractor: CLIPImageProcessor
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae_encoder: OnnxRuntimeModel,
vae_decoder: OnnxRuntimeModel,
text_encoder: OnnxRuntimeModel,
tokenizer: CLIPTokenizer,
unet: OnnxRuntimeModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: OnnxRuntimeModel,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
self.register_modules(
vae_encoder=vae_encoder,
vae_decoder=vae_decoder,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion.OnnxStableDiffusionPipeline._encode_prompt
def _encode_prompt(
self,
prompt: Union[str, List[str]],
num_images_per_prompt: Optional[int],
do_classifier_free_guidance: bool,
negative_prompt: Optional[str],
prompt_embeds: Optional[np.ndarray] = None,
negative_prompt_embeds: Optional[np.ndarray] = None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`):
prompt to be encoded
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
prompt_embeds (`np.ndarray`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`np.ndarray`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
"""
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="np").input_ids
if not np.array_equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]
prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt] * batch_size
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="np",
)
negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0]
if do_classifier_free_guidance:
negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
def check_inputs(
self,
prompt: Union[str, List[str]],
callback_steps: int,
negative_prompt: Optional[Union[str, List[str]]] = None,
prompt_embeds: Optional[np.ndarray] = None,
negative_prompt_embeds: Optional[np.ndarray] = None,
):
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
def __call__(
self,
prompt: Union[str, List[str]],
image: Union[np.ndarray, PIL.Image.Image] = None,
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: Optional[float] = 0.0,
generator: Optional[np.random.RandomState] = None,
prompt_embeds: Optional[np.ndarray] = None,
negative_prompt_embeds: Optional[np.ndarray] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
callback_steps: int = 1,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
image (`np.ndarray` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
will be used as a starting point, adding more noise to it the larger the `strength`. The number of
denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter will be modulated by `strength`.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`np.random.RandomState`, *optional*):
A np.random.RandomState to make generation deterministic.
prompt_embeds (`np.ndarray`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`np.ndarray`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
When returning a tuple, the first element is a list with the generated images, and the second element is a
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content, according to the `safety_checker`.
"""
# check inputs. Raise error if not correct
self.check_inputs(prompt, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)
# define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
if generator is None:
generator = np.random
# set timesteps
self.scheduler.set_timesteps(num_inference_steps)
image = preprocess(image).cpu().numpy()
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
prompt_embeds = self._encode_prompt(
prompt,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
)
latents_dtype = prompt_embeds.dtype
image = image.astype(latents_dtype)
# encode the init image into latents and scale the latents
init_latents = self.vae_encoder(sample=image)[0]
init_latents = 0.18215 * init_latents
if isinstance(prompt, str):
prompt = [prompt]
if len(prompt) > init_latents.shape[0] and len(prompt) % init_latents.shape[0] == 0:
# expand init_latents for batch_size
deprecation_message = (
f"You have passed {len(prompt)} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
" images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
" your script to pass as many initial images as text prompts to suppress this warning."
)
deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
additional_image_per_prompt = len(prompt) // init_latents.shape[0]
init_latents = np.concatenate([init_latents] * additional_image_per_prompt * num_images_per_prompt, axis=0)
elif len(prompt) > init_latents.shape[0] and len(prompt) % init_latents.shape[0] != 0:
raise ValueError(
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {len(prompt)} text prompts."
)
else:
init_latents = np.concatenate([init_latents] * num_images_per_prompt, axis=0)
# get the original timestep using init_timestep
offset = self.scheduler.config.get("steps_offset", 0)
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
timesteps = self.scheduler.timesteps.numpy()[-init_timestep]
timesteps = np.array([timesteps] * batch_size * num_images_per_prompt)
# add noise to latents using the timesteps
noise = generator.randn(*init_latents.shape).astype(latents_dtype)
init_latents = self.scheduler.add_noise(
torch.from_numpy(init_latents), torch.from_numpy(noise), torch.from_numpy(timesteps)
)
init_latents = init_latents.numpy()
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
latents = init_latents
t_start = max(num_inference_steps - init_timestep + offset, 0)
timesteps = self.scheduler.timesteps[t_start:].numpy()
timestep_dtype = next(
(input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
)
timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
for i, t in enumerate(self.progress_bar(timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)
latent_model_input = latent_model_input.cpu().numpy()
# predict the noise residual
timestep = np.array([t], dtype=timestep_dtype)
noise_pred = self.unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)[
0
]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
scheduler_output = self.scheduler.step(
torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
)
latents = scheduler_output.prev_sample.numpy()
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
latents = 1 / 0.18215 * latents
# image = self.vae_decoder(latent_sample=latents)[0]
# it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
image = np.concatenate(
[self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
)
image = np.clip(image / 2 + 0.5, 0, 1)
image = image.transpose((0, 2, 3, 1))
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(
self.numpy_to_pil(image), return_tensors="np"
).pixel_values.astype(image.dtype)
# safety_checker does not support batched inputs yet
images, has_nsfw_concept = [], []
for i in range(image.shape[0]):
image_i, has_nsfw_concept_i = self.safety_checker(
clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
)
images.append(image_i)
has_nsfw_concept.append(has_nsfw_concept_i[0])
image = np.concatenate(images)
else:
has_nsfw_concept = None
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/__init__.py | from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class SemanticStableDiffusionPipelineOutput(BaseOutput):
"""
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`List[bool]`)
List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content or
`None` if safety checking could not be performed.
"""
images: Union[List[PIL.Image.Image], np.ndarray]
nsfw_content_detected: Optional[List[bool]]
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py | import inspect
import warnings
from itertools import repeat
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from ...image_processor import VaeImageProcessor
from ...models import AutoencoderKL, UNet2DConditionModel
from ...pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline
from . import SemanticStableDiffusionPipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class SemanticStableDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using Stable Diffusion with latent editing.
This model inherits from [`DiffusionPipeline`] and builds on the [`StableDiffusionPipeline`]. Check the superclass
documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular
device, etc.).
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`Q16SafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
warnings.warn(
"The decode_latents method is deprecated and will be removed in a future version. Please"
" use VaeImageProcessor instead",
FutureWarning,
)
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: int = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
editing_prompt: Optional[Union[str, List[str]]] = None,
editing_prompt_embeddings: Optional[torch.Tensor] = None,
reverse_editing_direction: Optional[Union[bool, List[bool]]] = False,
edit_guidance_scale: Optional[Union[float, List[float]]] = 5,
edit_warmup_steps: Optional[Union[int, List[int]]] = 10,
edit_cooldown_steps: Optional[Union[int, List[int]]] = None,
edit_threshold: Optional[Union[float, List[float]]] = 0.9,
edit_momentum_scale: Optional[float] = 0.1,
edit_mom_beta: Optional[float] = 0.4,
edit_weights: Optional[List[float]] = None,
sem_guidance: Optional[List[torch.Tensor]] = None,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide image generation.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
editing_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to use for semantic guidance. Semantic guidance is disabled by setting
`editing_prompt = None`. Guidance direction of prompt should be specified via
`reverse_editing_direction`.
editing_prompt_embeddings (`torch.Tensor`, *optional*):
Pre-computed embeddings to use for semantic guidance. Guidance direction of embedding should be
specified via `reverse_editing_direction`.
reverse_editing_direction (`bool` or `List[bool]`, *optional*, defaults to `False`):
Whether the corresponding prompt in `editing_prompt` should be increased or decreased.
edit_guidance_scale (`float` or `List[float]`, *optional*, defaults to 5):
Guidance scale for semantic guidance. If provided as a list, values should correspond to
`editing_prompt`.
edit_warmup_steps (`float` or `List[float]`, *optional*, defaults to 10):
Number of diffusion steps (for each prompt) for which semantic guidance is not applied. Momentum is
calculated for those steps and applied once all warmup periods are over.
edit_cooldown_steps (`float` or `List[float]`, *optional*, defaults to `None`):
Number of diffusion steps (for each prompt) after which semantic guidance is longer applied.
edit_threshold (`float` or `List[float]`, *optional*, defaults to 0.9):
Threshold of semantic guidance.
edit_momentum_scale (`float`, *optional*, defaults to 0.1):
Scale of the momentum to be added to the semantic guidance at each diffusion step. If set to 0.0,
momentum is disabled. Momentum is already built up during warmup (for diffusion steps smaller than
`sld_warmup_steps`). Momentum is only added to latent guidance once all warmup periods are finished.
edit_mom_beta (`float`, *optional*, defaults to 0.4):
Defines how semantic guidance momentum builds up. `edit_mom_beta` indicates how much of the previous
momentum is kept. Momentum is already built up during warmup (for diffusion steps smaller than
`edit_warmup_steps`).
edit_weights (`List[float]`, *optional*, defaults to `None`):
Indicates how much each individual concept should influence the overall guidance. If no weights are
provided all concepts are applied equally.
sem_guidance (`List[torch.Tensor]`, *optional*):
List of pre-generated guidance vectors to be applied at generation. Length of the list has to
correspond to `num_inference_steps`.
Examples:
```py
>>> import torch
>>> from diffusers import SemanticStableDiffusionPipeline
>>> pipe = SemanticStableDiffusionPipeline.from_pretrained(
... "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> out = pipe(
... prompt="a photo of the face of a woman",
... num_images_per_prompt=1,
... guidance_scale=7,
... editing_prompt=[
... "smiling, smile", # Concepts to apply
... "glasses, wearing glasses",
... "curls, wavy hair, curly hair",
... "beard, full beard, mustache",
... ],
... reverse_editing_direction=[
... False,
... False,
... False,
... False,
... ], # Direction of guidance i.e. increase all concepts
... edit_warmup_steps=[10, 10, 10, 10], # Warmup period for each concept
... edit_guidance_scale=[4, 5, 5, 5.4], # Guidance scale for each concept
... edit_threshold=[
... 0.99,
... 0.975,
... 0.925,
... 0.96,
... ], # Threshold for each concept. Threshold equals the percentile of the latent space that will be discarded. I.e. threshold=0.99 uses 1% of the latent dimensions
... edit_momentum_scale=0.3, # Momentum scale that will be added to the latent guidance
... edit_mom_beta=0.6, # Momentum beta
... edit_weights=[1, 1, 1, 1, 1], # Weights of the individual concepts against each other
... )
>>> image = out.images[0]
```
Returns:
[`~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`,
[`~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput`] is returned, otherwise a
`tuple` is returned where the first element is a list with the generated images and the second element
is a list of `bool`s indicating whether the corresponding generated image contains "not-safe-for-work"
(nsfw) content.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(prompt, height, width, callback_steps)
# 2. Define call parameters
batch_size = 1 if isinstance(prompt, str) else len(prompt)
if editing_prompt:
enable_edit_guidance = True
if isinstance(editing_prompt, str):
editing_prompt = [editing_prompt]
enabled_editing_prompts = len(editing_prompt)
elif editing_prompt_embeddings is not None:
enable_edit_guidance = True
enabled_editing_prompts = editing_prompt_embeddings.shape[0]
else:
enabled_editing_prompts = 0
enable_edit_guidance = False
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
if enable_edit_guidance:
# get safety text embeddings
if editing_prompt_embeddings is None:
edit_concepts_input = self.tokenizer(
[x for item in editing_prompt for x in repeat(item, batch_size)],
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
)
edit_concepts_input_ids = edit_concepts_input.input_ids
if edit_concepts_input_ids.shape[-1] > self.tokenizer.model_max_length:
removed_text = self.tokenizer.batch_decode(
edit_concepts_input_ids[:, self.tokenizer.model_max_length :]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
edit_concepts_input_ids = edit_concepts_input_ids[:, : self.tokenizer.model_max_length]
edit_concepts = self.text_encoder(edit_concepts_input_ids.to(self.device))[0]
else:
edit_concepts = editing_prompt_embeddings.to(self.device).repeat(batch_size, 1, 1)
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed_edit, seq_len_edit, _ = edit_concepts.shape
edit_concepts = edit_concepts.repeat(1, num_images_per_prompt, 1)
edit_concepts = edit_concepts.view(bs_embed_edit * num_images_per_prompt, seq_len_edit, -1)
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""]
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
if enable_edit_guidance:
text_embeddings = torch.cat([uncond_embeddings, text_embeddings, edit_concepts])
else:
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
# get the initial random noise unless the user supplied it
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=self.device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
text_embeddings.dtype,
self.device,
generator,
latents,
)
# 6. Prepare extra step kwargs.
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# Initialize edit_momentum to None
edit_momentum = None
self.uncond_estimates = None
self.text_estimates = None
self.edit_estimates = None
self.sem_guidance = None
for i, t in enumerate(self.progress_bar(timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = (
torch.cat([latents] * (2 + enabled_editing_prompts)) if do_classifier_free_guidance else latents
)
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_out = noise_pred.chunk(2 + enabled_editing_prompts) # [b,4, 64, 64]
noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1]
noise_pred_edit_concepts = noise_pred_out[2:]
# default text guidance
noise_guidance = guidance_scale * (noise_pred_text - noise_pred_uncond)
# noise_guidance = (noise_pred_text - noise_pred_edit_concepts[0])
if self.uncond_estimates is None:
self.uncond_estimates = torch.zeros((num_inference_steps + 1, *noise_pred_uncond.shape))
self.uncond_estimates[i] = noise_pred_uncond.detach().cpu()
if self.text_estimates is None:
self.text_estimates = torch.zeros((num_inference_steps + 1, *noise_pred_text.shape))
self.text_estimates[i] = noise_pred_text.detach().cpu()
if self.edit_estimates is None and enable_edit_guidance:
self.edit_estimates = torch.zeros(
(num_inference_steps + 1, len(noise_pred_edit_concepts), *noise_pred_edit_concepts[0].shape)
)
if self.sem_guidance is None:
self.sem_guidance = torch.zeros((num_inference_steps + 1, *noise_pred_text.shape))
if edit_momentum is None:
edit_momentum = torch.zeros_like(noise_guidance)
if enable_edit_guidance:
concept_weights = torch.zeros(
(len(noise_pred_edit_concepts), noise_guidance.shape[0]),
device=self.device,
dtype=noise_guidance.dtype,
)
noise_guidance_edit = torch.zeros(
(len(noise_pred_edit_concepts), *noise_guidance.shape),
device=self.device,
dtype=noise_guidance.dtype,
)
# noise_guidance_edit = torch.zeros_like(noise_guidance)
warmup_inds = []
for c, noise_pred_edit_concept in enumerate(noise_pred_edit_concepts):
self.edit_estimates[i, c] = noise_pred_edit_concept
if isinstance(edit_guidance_scale, list):
edit_guidance_scale_c = edit_guidance_scale[c]
else:
edit_guidance_scale_c = edit_guidance_scale
if isinstance(edit_threshold, list):
edit_threshold_c = edit_threshold[c]
else:
edit_threshold_c = edit_threshold
if isinstance(reverse_editing_direction, list):
reverse_editing_direction_c = reverse_editing_direction[c]
else:
reverse_editing_direction_c = reverse_editing_direction
if edit_weights:
edit_weight_c = edit_weights[c]
else:
edit_weight_c = 1.0
if isinstance(edit_warmup_steps, list):
edit_warmup_steps_c = edit_warmup_steps[c]
else:
edit_warmup_steps_c = edit_warmup_steps
if isinstance(edit_cooldown_steps, list):
edit_cooldown_steps_c = edit_cooldown_steps[c]
elif edit_cooldown_steps is None:
edit_cooldown_steps_c = i + 1
else:
edit_cooldown_steps_c = edit_cooldown_steps
if i >= edit_warmup_steps_c:
warmup_inds.append(c)
if i >= edit_cooldown_steps_c:
noise_guidance_edit[c, :, :, :, :] = torch.zeros_like(noise_pred_edit_concept)
continue
noise_guidance_edit_tmp = noise_pred_edit_concept - noise_pred_uncond
# tmp_weights = (noise_pred_text - noise_pred_edit_concept).sum(dim=(1, 2, 3))
tmp_weights = (noise_guidance - noise_pred_edit_concept).sum(dim=(1, 2, 3))
tmp_weights = torch.full_like(tmp_weights, edit_weight_c) # * (1 / enabled_editing_prompts)
if reverse_editing_direction_c:
noise_guidance_edit_tmp = noise_guidance_edit_tmp * -1
concept_weights[c, :] = tmp_weights
noise_guidance_edit_tmp = noise_guidance_edit_tmp * edit_guidance_scale_c
# torch.quantile function expects float32
if noise_guidance_edit_tmp.dtype == torch.float32:
tmp = torch.quantile(
torch.abs(noise_guidance_edit_tmp).flatten(start_dim=2),
edit_threshold_c,
dim=2,
keepdim=False,
)
else:
tmp = torch.quantile(
torch.abs(noise_guidance_edit_tmp).flatten(start_dim=2).to(torch.float32),
edit_threshold_c,
dim=2,
keepdim=False,
).to(noise_guidance_edit_tmp.dtype)
noise_guidance_edit_tmp = torch.where(
torch.abs(noise_guidance_edit_tmp) >= tmp[:, :, None, None],
noise_guidance_edit_tmp,
torch.zeros_like(noise_guidance_edit_tmp),
)
noise_guidance_edit[c, :, :, :, :] = noise_guidance_edit_tmp
# noise_guidance_edit = noise_guidance_edit + noise_guidance_edit_tmp
warmup_inds = torch.tensor(warmup_inds).to(self.device)
if len(noise_pred_edit_concepts) > warmup_inds.shape[0] > 0:
concept_weights = concept_weights.to("cpu") # Offload to cpu
noise_guidance_edit = noise_guidance_edit.to("cpu")
concept_weights_tmp = torch.index_select(concept_weights.to(self.device), 0, warmup_inds)
concept_weights_tmp = torch.where(
concept_weights_tmp < 0, torch.zeros_like(concept_weights_tmp), concept_weights_tmp
)
concept_weights_tmp = concept_weights_tmp / concept_weights_tmp.sum(dim=0)
# concept_weights_tmp = torch.nan_to_num(concept_weights_tmp)
noise_guidance_edit_tmp = torch.index_select(
noise_guidance_edit.to(self.device), 0, warmup_inds
)
noise_guidance_edit_tmp = torch.einsum(
"cb,cbijk->bijk", concept_weights_tmp, noise_guidance_edit_tmp
)
noise_guidance_edit_tmp = noise_guidance_edit_tmp
noise_guidance = noise_guidance + noise_guidance_edit_tmp
self.sem_guidance[i] = noise_guidance_edit_tmp.detach().cpu()
del noise_guidance_edit_tmp
del concept_weights_tmp
concept_weights = concept_weights.to(self.device)
noise_guidance_edit = noise_guidance_edit.to(self.device)
concept_weights = torch.where(
concept_weights < 0, torch.zeros_like(concept_weights), concept_weights
)
concept_weights = torch.nan_to_num(concept_weights)
noise_guidance_edit = torch.einsum("cb,cbijk->bijk", concept_weights, noise_guidance_edit)
noise_guidance_edit = noise_guidance_edit + edit_momentum_scale * edit_momentum
edit_momentum = edit_mom_beta * edit_momentum + (1 - edit_mom_beta) * noise_guidance_edit
if warmup_inds.shape[0] == len(noise_pred_edit_concepts):
noise_guidance = noise_guidance + noise_guidance_edit
self.sem_guidance[i] = noise_guidance_edit.detach().cpu()
if sem_guidance is not None:
edit_guidance = sem_guidance[i].to(self.device)
noise_guidance = noise_guidance + edit_guidance
noise_pred = noise_pred_uncond + noise_guidance
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
# 8. Post-processing
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, self.device, text_embeddings.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
if not return_dict:
return (image, has_nsfw_concept)
return SemanticStableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/kandinsky/text_encoder.py | import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class MCLIPConfig(XLMRobertaConfig):
model_type = "M-CLIP"
def __init__(self, transformerDimSize=1024, imageDimSize=768, **kwargs):
self.transformerDimensions = transformerDimSize
self.numDims = imageDimSize
super().__init__(**kwargs)
class MultilingualCLIP(PreTrainedModel):
config_class = MCLIPConfig
def __init__(self, config, *args, **kwargs):
super().__init__(config, *args, **kwargs)
self.transformer = XLMRobertaModel(config)
self.LinearTransformation = torch.nn.Linear(
in_features=config.transformerDimensions, out_features=config.numDims
)
def forward(self, input_ids, attention_mask):
embs = self.transformer(input_ids=input_ids, attention_mask=attention_mask)[0]
embs2 = (embs * attention_mask.unsqueeze(2)).sum(dim=1) / attention_mask.sum(dim=1)[:, None]
return self.LinearTransformation(embs2), embs
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/kandinsky/__init__.py | from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import *
else:
from .pipeline_kandinsky import KandinskyPipeline
from .pipeline_kandinsky_combined import (
KandinskyCombinedPipeline,
KandinskyImg2ImgCombinedPipeline,
KandinskyInpaintCombinedPipeline,
)
from .pipeline_kandinsky_img2img import KandinskyImg2ImgPipeline
from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
from .text_encoder import MultilingualCLIP
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection
from ...models import PriorTransformer
from ...schedulers import UnCLIPScheduler
from ...utils import (
BaseOutput,
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
>>> import torch
>>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-1-prior")
>>> pipe_prior.to("cuda")
>>> prompt = "red cat, 4k photo"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> negative_image_emb = out.negative_image_embeds
>>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
>>> pipe.to("cuda")
>>> image = pipe(
... prompt,
... image_embeds=image_emb,
... negative_image_embeds=negative_image_emb,
... height=768,
... width=768,
... num_inference_steps=100,
... ).images
>>> image[0].save("cat.png")
```
"""
EXAMPLE_INTERPOLATE_DOC_STRING = """
Examples:
```py
>>> from diffusers import KandinskyPriorPipeline, KandinskyPipeline
>>> from diffusers.utils import load_image
>>> import PIL
>>> import torch
>>> from torchvision import transforms
>>> pipe_prior = KandinskyPriorPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16
... )
>>> pipe_prior.to("cuda")
>>> img1 = load_image(
... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
... "/kandinsky/cat.png"
... )
>>> img2 = load_image(
... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
... "/kandinsky/starry_night.jpeg"
... )
>>> images_texts = ["a cat", img1, img2]
>>> weights = [0.3, 0.3, 0.4]
>>> image_emb, zero_image_emb = pipe_prior.interpolate(images_texts, weights)
>>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
>>> pipe.to("cuda")
>>> image = pipe(
... "",
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=150,
... ).images[0]
>>> image.save("starry_cat.png")
```
"""
@dataclass
class KandinskyPriorPipelineOutput(BaseOutput):
"""
Output class for KandinskyPriorPipeline.
Args:
image_embeds (`torch.FloatTensor`)
clip image embeddings for text prompt
negative_image_embeds (`List[PIL.Image.Image]` or `np.ndarray`)
clip image embeddings for unconditional tokens
"""
image_embeds: Union[torch.FloatTensor, np.ndarray]
negative_image_embeds: Union[torch.FloatTensor, np.ndarray]
class KandinskyPriorPipeline(DiffusionPipeline):
"""
Pipeline for generating image prior for Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
prior ([`PriorTransformer`]):
The canonincal unCLIP prior to approximate the image embedding from the text embedding.
image_encoder ([`CLIPVisionModelWithProjection`]):
Frozen image-encoder.
text_encoder ([`CLIPTextModelWithProjection`]):
Frozen text-encoder.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
scheduler ([`UnCLIPScheduler`]):
A scheduler to be used in combination with `prior` to generate image embedding.
"""
_exclude_from_cpu_offload = ["prior"]
def __init__(
self,
prior: PriorTransformer,
image_encoder: CLIPVisionModelWithProjection,
text_encoder: CLIPTextModelWithProjection,
tokenizer: CLIPTokenizer,
scheduler: UnCLIPScheduler,
image_processor: CLIPImageProcessor,
):
super().__init__()
self.register_modules(
prior=prior,
text_encoder=text_encoder,
tokenizer=tokenizer,
scheduler=scheduler,
image_encoder=image_encoder,
image_processor=image_processor,
)
@torch.no_grad()
@replace_example_docstring(EXAMPLE_INTERPOLATE_DOC_STRING)
def interpolate(
self,
images_and_prompts: List[Union[str, PIL.Image.Image, torch.FloatTensor]],
weights: List[float],
num_images_per_prompt: int = 1,
num_inference_steps: int = 25,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
negative_prior_prompt: Optional[str] = None,
negative_prompt: str = "",
guidance_scale: float = 4.0,
device=None,
):
"""
Function invoked when using the prior pipeline for interpolation.
Args:
images_and_prompts (`List[Union[str, PIL.Image.Image, torch.FloatTensor]]`):
list of prompts and images to guide the image generation.
weights: (`List[float]`):
list of weights for each condition in `images_and_prompts`
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
num_inference_steps (`int`, *optional*, defaults to 25):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
negative_prior_prompt (`str`, *optional*):
The prompt not to guide the prior diffusion process. Ignored when not using guidance (i.e., ignored if
`guidance_scale` is less than `1`).
negative_prompt (`str` or `List[str]`, *optional*):
The prompt not to guide the image generation. Ignored when not using guidance (i.e., ignored if
`guidance_scale` is less than `1`).
guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
Examples:
Returns:
[`KandinskyPriorPipelineOutput`] or `tuple`
"""
device = device or self.device
if len(images_and_prompts) != len(weights):
raise ValueError(
f"`images_and_prompts` contains {len(images_and_prompts)} items and `weights` contains {len(weights)} items - they should be lists of same length"
)
image_embeddings = []
for cond, weight in zip(images_and_prompts, weights):
if isinstance(cond, str):
image_emb = self(
cond,
num_inference_steps=num_inference_steps,
num_images_per_prompt=num_images_per_prompt,
generator=generator,
latents=latents,
negative_prompt=negative_prior_prompt,
guidance_scale=guidance_scale,
).image_embeds
elif isinstance(cond, (PIL.Image.Image, torch.Tensor)):
if isinstance(cond, PIL.Image.Image):
cond = (
self.image_processor(cond, return_tensors="pt")
.pixel_values[0]
.unsqueeze(0)
.to(dtype=self.image_encoder.dtype, device=device)
)
image_emb = self.image_encoder(cond)["image_embeds"]
else:
raise ValueError(
f"`images_and_prompts` can only contains elements to be of type `str`, `PIL.Image.Image` or `torch.Tensor` but is {type(cond)}"
)
image_embeddings.append(image_emb * weight)
image_emb = torch.cat(image_embeddings).sum(dim=0, keepdim=True)
out_zero = self(
negative_prompt,
num_inference_steps=num_inference_steps,
num_images_per_prompt=num_images_per_prompt,
generator=generator,
latents=latents,
negative_prompt=negative_prior_prompt,
guidance_scale=guidance_scale,
)
zero_image_emb = out_zero.negative_image_embeds if negative_prompt == "" else out_zero.image_embeds
return KandinskyPriorPipelineOutput(image_embeds=image_emb, negative_image_embeds=zero_image_emb)
# Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
latents = latents * scheduler.init_noise_sigma
return latents
def get_zero_embed(self, batch_size=1, device=None):
device = device or self.device
zero_img = torch.zeros(1, 3, self.image_encoder.config.image_size, self.image_encoder.config.image_size).to(
device=device, dtype=self.image_encoder.dtype
)
zero_image_emb = self.image_encoder(zero_img)["image_embeds"]
zero_image_emb = zero_image_emb.repeat(batch_size, 1)
return zero_image_emb
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
):
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
text_mask = text_inputs.attention_mask.bool().to(device)
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
text_encoder_output = self.text_encoder(text_input_ids.to(device))
prompt_embeds = text_encoder_output.text_embeds
text_encoder_hidden_states = text_encoder_output.last_hidden_state
prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
uncond_text_mask = uncond_input.attention_mask.bool().to(device)
negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))
negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds
uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
seq_len = uncond_text_encoder_hidden_states.shape[1]
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt, seq_len, -1
)
uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
text_mask = torch.cat([uncond_text_mask, text_mask])
return prompt_embeds, text_encoder_hidden_states, text_mask
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
for cpu_offloaded_model in [self.text_encoder, self.prior]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.prior_hook = hook
_, hook = cpu_offload_with_hook(self.image_encoder, device, prev_module_hook=self.prior_hook)
self.final_offload_hook = hook
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: int = 1,
num_inference_steps: int = 25,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
guidance_scale: float = 4.0,
output_type: Optional[str] = "pt",
return_dict: bool = True,
):
"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
num_inference_steps (`int`, *optional*, defaults to 25):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
output_type (`str`, *optional*, defaults to `"pt"`):
The output format of the generate image. Choose between: `"np"` (`np.array`) or `"pt"`
(`torch.Tensor`).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
Examples:
Returns:
[`KandinskyPriorPipelineOutput`] or `tuple`
"""
if isinstance(prompt, str):
prompt = [prompt]
elif not isinstance(prompt, list):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if isinstance(negative_prompt, str):
negative_prompt = [negative_prompt]
elif not isinstance(negative_prompt, list) and negative_prompt is not None:
raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")
# if the negative prompt is defined we double the batch size to
# directly retrieve the negative prompt embedding
if negative_prompt is not None:
prompt = prompt + negative_prompt
negative_prompt = 2 * negative_prompt
device = self._execution_device
batch_size = len(prompt)
batch_size = batch_size * num_images_per_prompt
do_classifier_free_guidance = guidance_scale > 1.0
prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(
prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
# prior
self.scheduler.set_timesteps(num_inference_steps, device=device)
prior_timesteps_tensor = self.scheduler.timesteps
embedding_dim = self.prior.config.embedding_dim
latents = self.prepare_latents(
(batch_size, embedding_dim),
prompt_embeds.dtype,
device,
generator,
latents,
self.scheduler,
)
for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
predicted_image_embedding = self.prior(
latent_model_input,
timestep=t,
proj_embedding=prompt_embeds,
encoder_hidden_states=text_encoder_hidden_states,
attention_mask=text_mask,
).predicted_image_embedding
if do_classifier_free_guidance:
predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)
predicted_image_embedding = predicted_image_embedding_uncond + guidance_scale * (
predicted_image_embedding_text - predicted_image_embedding_uncond
)
if i + 1 == prior_timesteps_tensor.shape[0]:
prev_timestep = None
else:
prev_timestep = prior_timesteps_tensor[i + 1]
latents = self.scheduler.step(
predicted_image_embedding,
timestep=t,
sample=latents,
generator=generator,
prev_timestep=prev_timestep,
).prev_sample
latents = self.prior.post_process_latents(latents)
image_embeddings = latents
# if negative prompt has been defined, we retrieve split the image embedding into two
if negative_prompt is None:
zero_embeds = self.get_zero_embed(latents.shape[0], device=latents.device)
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
else:
image_embeddings, zero_embeds = image_embeddings.chunk(2)
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.prior_hook.offload()
if output_type not in ["pt", "np"]:
raise ValueError(f"Only the output types `pt` and `np` are supported not output_type={output_type}")
if output_type == "np":
image_embeddings = image_embeddings.cpu().numpy()
zero_embeds = zero_embeds.cpu().numpy()
if not return_dict:
return (image_embeddings, zero_embeds)
return KandinskyPriorPipelineOutput(image_embeds=image_embeddings, negative_image_embeds=zero_embeds)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNet2DConditionModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from .text_encoder import MultilingualCLIP
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> from diffusers import KandinskyImg2ImgPipeline, KandinskyPriorPipeline
>>> from diffusers.utils import load_image
>>> import torch
>>> pipe_prior = KandinskyPriorPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16
... )
>>> pipe_prior.to("cuda")
>>> prompt = "A red cartoon frog, 4k"
>>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)
>>> pipe = KandinskyImg2ImgPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16
... )
>>> pipe.to("cuda")
>>> init_image = load_image(
... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
... "/kandinsky/frog.png"
... )
>>> image = pipe(
... prompt,
... image=init_image,
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=100,
... strength=0.2,
... ).images
>>> image[0].save("red_frog.png")
```
"""
def get_new_h_w(h, w, scale_factor=8):
new_h = h // scale_factor**2
if h % scale_factor**2 != 0:
new_h += 1
new_w = w // scale_factor**2
if w % scale_factor**2 != 0:
new_w += 1
return new_h * scale_factor, new_w * scale_factor
def prepare_image(pil_image, w=512, h=512):
pil_image = pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)
arr = np.array(pil_image.convert("RGB"))
arr = arr.astype(np.float32) / 127.5 - 1
arr = np.transpose(arr, [2, 0, 1])
image = torch.from_numpy(arr).unsqueeze(0)
return image
class KandinskyImg2ImgPipeline(DiffusionPipeline):
"""
Pipeline for image-to-image generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
text_encoder ([`MultilingualCLIP`]):
Frozen text-encoder.
tokenizer ([`XLMRobertaTokenizer`]):
Tokenizer of class
scheduler ([`DDIMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ image encoder and decoder
"""
def __init__(
self,
text_encoder: MultilingualCLIP,
movq: VQModel,
tokenizer: XLMRobertaTokenizer,
unet: UNet2DConditionModel,
scheduler: DDIMScheduler,
):
super().__init__()
self.register_modules(
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
movq=movq,
)
self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def prepare_latents(self, latents, latent_timestep, shape, dtype, device, generator, scheduler):
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
latents = latents * scheduler.init_noise_sigma
shape = latents.shape
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
latents = self.add_noise(latents, noise, latent_timestep)
return latents
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
):
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=77,
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids.to(device)
text_mask = text_inputs.attention_mask.to(device)
prompt_embeds, text_encoder_hidden_states = self.text_encoder(
input_ids=text_input_ids, attention_mask=text_mask
)
prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=77,
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
uncond_text_input_ids = uncond_input.input_ids.to(device)
uncond_text_mask = uncond_input.attention_mask.to(device)
negative_prompt_embeds, uncond_text_encoder_hidden_states = self.text_encoder(
input_ids=uncond_text_input_ids, attention_mask=uncond_text_mask
)
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
seq_len = uncond_text_encoder_hidden_states.shape[1]
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt, seq_len, -1
)
uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
text_mask = torch.cat([uncond_text_mask, text_mask])
return prompt_embeds, text_encoder_hidden_states, text_mask
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
# add_noise method to overwrite the one in schedule because it use a different beta schedule for adding noise vs sampling
def add_noise(
self,
original_samples: torch.FloatTensor,
noise: torch.FloatTensor,
timesteps: torch.IntTensor,
) -> torch.FloatTensor:
betas = torch.linspace(0.0001, 0.02, 1000, dtype=torch.float32)
alphas = 1.0 - betas
alphas_cumprod = torch.cumprod(alphas, dim=0)
alphas_cumprod = alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
timesteps = timesteps.to(original_samples.device)
sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
sqrt_alpha_prod = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]],
image: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]],
image_embeds: torch.FloatTensor,
negative_image_embeds: torch.FloatTensor,
negative_prompt: Optional[Union[str, List[str]]] = None,
height: int = 512,
width: int = 512,
num_inference_steps: int = 100,
strength: float = 0.3,
guidance_scale: float = 7.0,
num_images_per_prompt: int = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
output_type: Optional[str] = "pil",
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
return_dict: bool = True,
):
"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
image (`torch.FloatTensor`, `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`):
The clip image embeddings for text prompt, that will be used to condition the image generation.
negative_image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`):
The clip image embeddings for negative text prompt, will be used to condition the image generation.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
strength (`float`, *optional*, defaults to 0.3):
Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
will be used as a starting point, adding more noise to it the larger the `strength`. The number of
denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
(`np.array`) or `"pt"` (`torch.Tensor`).
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
# 1. Define call parameters
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
device = self._execution_device
batch_size = batch_size * num_images_per_prompt
do_classifier_free_guidance = guidance_scale > 1.0
# 2. get text and image embeddings
prompt_embeds, text_encoder_hidden_states, _ = self._encode_prompt(
prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
if isinstance(image_embeds, list):
image_embeds = torch.cat(image_embeds, dim=0)
if isinstance(negative_image_embeds, list):
negative_image_embeds = torch.cat(negative_image_embeds, dim=0)
if do_classifier_free_guidance:
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(
dtype=prompt_embeds.dtype, device=device
)
# 3. pre-processing initial image
if not isinstance(image, list):
image = [image]
if not all(isinstance(i, (PIL.Image.Image, torch.Tensor)) for i in image):
raise ValueError(
f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support PIL image and pytorch tensor"
)
image = torch.cat([prepare_image(i, width, height) for i in image], dim=0)
image = image.to(dtype=prompt_embeds.dtype, device=device)
latents = self.movq.encode(image)["latents"]
latents = latents.repeat_interleave(num_images_per_prompt, dim=0)
# 4. set timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps_tensor, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
# the formular to calculate timestep for add_noise is taken from the original kandinsky repo
latent_timestep = int(self.scheduler.config.num_train_timesteps * strength) - 2
latent_timestep = torch.tensor([latent_timestep] * batch_size, dtype=timesteps_tensor.dtype, device=device)
num_channels_latents = self.unet.config.in_channels
height, width = get_new_h_w(height, width, self.movq_scale_factor)
# 5. Create initial latent
latents = self.prepare_latents(
latents,
latent_timestep,
(batch_size, num_channels_latents, height, width),
text_encoder_hidden_states.dtype,
device,
generator,
self.scheduler,
)
# 6. Denoising loop
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
added_cond_kwargs = {"text_embeds": prompt_embeds, "image_embeds": image_embeds}
noise_pred = self.unet(
sample=latent_model_input,
timestep=t,
encoder_hidden_states=text_encoder_hidden_states,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
if do_classifier_free_guidance:
noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
_, variance_pred_text = variance_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1)
if not (
hasattr(self.scheduler.config, "variance_type")
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
noise_pred, _ = noise_pred.split(latents.shape[1], dim=1)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(
noise_pred,
t,
latents,
generator=generator,
).prev_sample
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
# 7. post-processing
image = self.movq.decode(latents, force_not_quantize=True)["sample"]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}")
if output_type in ["np", "pil"]:
image = image * 0.5 + 0.5
image = image.clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Callable, List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNet2DConditionModel, VQModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from .text_encoder import MultilingualCLIP
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
>>> import torch
>>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")
>>> pipe_prior.to("cuda")
>>> prompt = "red cat, 4k photo"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> negative_image_emb = out.negative_image_embeds
>>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
>>> pipe.to("cuda")
>>> image = pipe(
... prompt,
... image_embeds=image_emb,
... negative_image_embeds=negative_image_emb,
... height=768,
... width=768,
... num_inference_steps=100,
... ).images
>>> image[0].save("cat.png")
```
"""
def get_new_h_w(h, w, scale_factor=8):
new_h = h // scale_factor**2
if h % scale_factor**2 != 0:
new_h += 1
new_w = w // scale_factor**2
if w % scale_factor**2 != 0:
new_w += 1
return new_h * scale_factor, new_w * scale_factor
class KandinskyPipeline(DiffusionPipeline):
"""
Pipeline for text-to-image generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
text_encoder ([`MultilingualCLIP`]):
Frozen text-encoder.
tokenizer ([`XLMRobertaTokenizer`]):
Tokenizer of class
scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ Decoder to generate the image from the latents.
"""
def __init__(
self,
text_encoder: MultilingualCLIP,
tokenizer: XLMRobertaTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, DDPMScheduler],
movq: VQModel,
):
super().__init__()
self.register_modules(
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
movq=movq,
)
self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)
# Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
latents = latents * scheduler.init_noise_sigma
return latents
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
):
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
truncation=True,
max_length=77,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids.to(device)
text_mask = text_inputs.attention_mask.to(device)
prompt_embeds, text_encoder_hidden_states = self.text_encoder(
input_ids=text_input_ids, attention_mask=text_mask
)
prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=77,
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
uncond_text_input_ids = uncond_input.input_ids.to(device)
uncond_text_mask = uncond_input.attention_mask.to(device)
negative_prompt_embeds, uncond_text_encoder_hidden_states = self.text_encoder(
input_ids=uncond_text_input_ids, attention_mask=uncond_text_mask
)
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
seq_len = uncond_text_encoder_hidden_states.shape[1]
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt, seq_len, -1
)
uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
text_mask = torch.cat([uncond_text_mask, text_mask])
return prompt_embeds, text_encoder_hidden_states, text_mask
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]],
image_embeds: Union[torch.FloatTensor, List[torch.FloatTensor]],
negative_image_embeds: Union[torch.FloatTensor, List[torch.FloatTensor]],
negative_prompt: Optional[Union[str, List[str]]] = None,
height: int = 512,
width: int = 512,
num_inference_steps: int = 100,
guidance_scale: float = 4.0,
num_images_per_prompt: int = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
return_dict: bool = True,
):
"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`):
The clip image embeddings for text prompt, that will be used to condition the image generation.
negative_image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`):
The clip image embeddings for negative text prompt, will be used to condition the image generation.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
(`np.array`) or `"pt"` (`torch.Tensor`).
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
device = self._execution_device
batch_size = batch_size * num_images_per_prompt
do_classifier_free_guidance = guidance_scale > 1.0
prompt_embeds, text_encoder_hidden_states, _ = self._encode_prompt(
prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
if isinstance(image_embeds, list):
image_embeds = torch.cat(image_embeds, dim=0)
if isinstance(negative_image_embeds, list):
negative_image_embeds = torch.cat(negative_image_embeds, dim=0)
if do_classifier_free_guidance:
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(
dtype=prompt_embeds.dtype, device=device
)
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps_tensor = self.scheduler.timesteps
num_channels_latents = self.unet.config.in_channels
height, width = get_new_h_w(height, width, self.movq_scale_factor)
# create initial latent
latents = self.prepare_latents(
(batch_size, num_channels_latents, height, width),
text_encoder_hidden_states.dtype,
device,
generator,
latents,
self.scheduler,
)
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
added_cond_kwargs = {"text_embeds": prompt_embeds, "image_embeds": image_embeds}
noise_pred = self.unet(
sample=latent_model_input,
timestep=t,
encoder_hidden_states=text_encoder_hidden_states,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
if do_classifier_free_guidance:
noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
_, variance_pred_text = variance_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1)
if not (
hasattr(self.scheduler.config, "variance_type")
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
noise_pred, _ = noise_pred.split(latents.shape[1], dim=1)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(
noise_pred,
t,
latents,
generator=generator,
).prev_sample
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
# post-processing
image = self.movq.decode(latents, force_not_quantize=True)["sample"]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}")
if output_type in ["np", "pil"]:
image = image * 0.5 + 0.5
image = image.clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from copy import deepcopy
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
import torch.nn.functional as F
from packaging import version
from PIL import Image
from transformers import (
XLMRobertaTokenizer,
)
from ... import __version__
from ...models import UNet2DConditionModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from .text_encoder import MultilingualCLIP
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> from diffusers import KandinskyInpaintPipeline, KandinskyPriorPipeline
>>> from diffusers.utils import load_image
>>> import torch
>>> import numpy as np
>>> pipe_prior = KandinskyPriorPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16
... )
>>> pipe_prior.to("cuda")
>>> prompt = "a hat"
>>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)
>>> pipe = KandinskyInpaintPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-1-inpaint", torch_dtype=torch.float16
... )
>>> pipe.to("cuda")
>>> init_image = load_image(
... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
... "/kandinsky/cat.png"
... )
>>> mask = np.zeros((768, 768), dtype=np.float32)
>>> mask[:250, 250:-250] = 1
>>> out = pipe(
... prompt,
... image=init_image,
... mask_image=mask,
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=50,
... )
>>> image = out.images[0]
>>> image.save("cat_with_hat.png")
```
"""
def get_new_h_w(h, w, scale_factor=8):
new_h = h // scale_factor**2
if h % scale_factor**2 != 0:
new_h += 1
new_w = w // scale_factor**2
if w % scale_factor**2 != 0:
new_w += 1
return new_h * scale_factor, new_w * scale_factor
def prepare_mask(masks):
prepared_masks = []
for mask in masks:
old_mask = deepcopy(mask)
for i in range(mask.shape[1]):
for j in range(mask.shape[2]):
if old_mask[0][i][j] == 1:
continue
if i != 0:
mask[:, i - 1, j] = 0
if j != 0:
mask[:, i, j - 1] = 0
if i != 0 and j != 0:
mask[:, i - 1, j - 1] = 0
if i != mask.shape[1] - 1:
mask[:, i + 1, j] = 0
if j != mask.shape[2] - 1:
mask[:, i, j + 1] = 0
if i != mask.shape[1] - 1 and j != mask.shape[2] - 1:
mask[:, i + 1, j + 1] = 0
prepared_masks.append(mask)
return torch.stack(prepared_masks, dim=0)
def prepare_mask_and_masked_image(image, mask, height, width):
r"""
Prepares a pair (mask, image) to be consumed by the Kandinsky inpaint pipeline. This means that those inputs will
be converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for
the ``image`` and ``1`` for the ``mask``.
The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
binarized (``mask > 0.5``) and cast to ``torch.float32`` too.
Args:
image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.
It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``
``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.
mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``
``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
Raises:
ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask
should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.
TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not
(ot the other way around).
Returns:
tuple[torch.Tensor]: The pair (mask, image) as ``torch.Tensor`` with 4
dimensions: ``batch x channels x height x width``.
"""
if image is None:
raise ValueError("`image` input cannot be undefined.")
if mask is None:
raise ValueError("`mask_image` input cannot be undefined.")
if isinstance(image, torch.Tensor):
if not isinstance(mask, torch.Tensor):
raise TypeError(f"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not")
# Batch single image
if image.ndim == 3:
assert image.shape[0] == 3, "Image outside a batch should be of shape (3, H, W)"
image = image.unsqueeze(0)
# Batch and add channel dim for single mask
if mask.ndim == 2:
mask = mask.unsqueeze(0).unsqueeze(0)
# Batch single mask or add channel dim
if mask.ndim == 3:
# Single batched mask, no channel dim or single mask not batched but channel dim
if mask.shape[0] == 1:
mask = mask.unsqueeze(0)
# Batched masks no channel dim
else:
mask = mask.unsqueeze(1)
assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions"
assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions"
assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size"
# Check image is in [-1, 1]
if image.min() < -1 or image.max() > 1:
raise ValueError("Image should be in [-1, 1] range")
# Check mask is in [0, 1]
if mask.min() < 0 or mask.max() > 1:
raise ValueError("Mask should be in [0, 1] range")
# Binarize mask
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
# Image as float32
image = image.to(dtype=torch.float32)
elif isinstance(mask, torch.Tensor):
raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not")
else:
# preprocess image
if isinstance(image, (PIL.Image.Image, np.ndarray)):
image = [image]
if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
# resize all images w.r.t passed height an width
image = [i.resize((width, height), resample=Image.BICUBIC, reducing_gap=1) for i in image]
image = [np.array(i.convert("RGB"))[None, :] for i in image]
image = np.concatenate(image, axis=0)
elif isinstance(image, list) and isinstance(image[0], np.ndarray):
image = np.concatenate([i[None, :] for i in image], axis=0)
image = image.transpose(0, 3, 1, 2)
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
# preprocess mask
if isinstance(mask, (PIL.Image.Image, np.ndarray)):
mask = [mask]
if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):
mask = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in mask]
mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0)
mask = mask.astype(np.float32) / 255.0
elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
mask = torch.from_numpy(mask)
mask = 1 - mask
return mask, image
class KandinskyInpaintPipeline(DiffusionPipeline):
"""
Pipeline for text-guided image inpainting using Kandinsky2.1
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
text_encoder ([`MultilingualCLIP`]):
Frozen text-encoder.
tokenizer ([`XLMRobertaTokenizer`]):
Tokenizer of class
scheduler ([`DDIMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ image encoder and decoder
"""
def __init__(
self,
text_encoder: MultilingualCLIP,
movq: VQModel,
tokenizer: XLMRobertaTokenizer,
unet: UNet2DConditionModel,
scheduler: DDIMScheduler,
):
super().__init__()
self.register_modules(
text_encoder=text_encoder,
movq=movq,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
)
self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)
self._warn_has_been_called = False
# Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
latents = latents * scheduler.init_noise_sigma
return latents
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
):
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=77,
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids.to(device)
text_mask = text_inputs.attention_mask.to(device)
prompt_embeds, text_encoder_hidden_states = self.text_encoder(
input_ids=text_input_ids, attention_mask=text_mask
)
prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=77,
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
uncond_text_input_ids = uncond_input.input_ids.to(device)
uncond_text_mask = uncond_input.attention_mask.to(device)
negative_prompt_embeds, uncond_text_encoder_hidden_states = self.text_encoder(
input_ids=uncond_text_input_ids, attention_mask=uncond_text_mask
)
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
seq_len = uncond_text_encoder_hidden_states.shape[1]
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt, seq_len, -1
)
uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
text_mask = torch.cat([uncond_text_mask, text_mask])
return prompt_embeds, text_encoder_hidden_states, text_mask
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]],
image: Union[torch.FloatTensor, PIL.Image.Image],
mask_image: Union[torch.FloatTensor, PIL.Image.Image, np.ndarray],
image_embeds: torch.FloatTensor,
negative_image_embeds: torch.FloatTensor,
negative_prompt: Optional[Union[str, List[str]]] = None,
height: int = 512,
width: int = 512,
num_inference_steps: int = 100,
guidance_scale: float = 4.0,
num_images_per_prompt: int = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
return_dict: bool = True,
):
"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
image (`torch.FloatTensor`, `PIL.Image.Image` or `np.ndarray`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
mask_image (`PIL.Image.Image`,`torch.FloatTensor` or `np.ndarray`):
`Image`, or a tensor representing an image batch, to mask `image`. White pixels in the mask will be
repainted, while black pixels will be preserved. You can pass a pytorch tensor as mask only if the
image you passed is a pytorch tensor, and it should contain one color channel (L) instead of 3, so the
expected shape would be either `(B, 1, H, W,)`, `(B, H, W)`, `(1, H, W)` or `(H, W)` If image is an PIL
image or numpy array, mask should also be a either PIL image or numpy array. If it is a PIL image, it
will be converted to a single channel (luminance) before use. If it is a nummpy array, the expected
shape is `(H, W)`.
image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`):
The clip image embeddings for text prompt, that will be used to condition the image generation.
negative_image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`):
The clip image embeddings for negative text prompt, will be used to condition the image generation.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
(`np.array`) or `"pt"` (`torch.Tensor`).
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
if not self._warn_has_been_called and version.parse(version.parse(__version__).base_version) < version.parse(
"0.22.0.dev0"
):
logger.warn(
"Please note that the expected format of `mask_image` has recently been changed. "
"Before diffusers == 0.19.0, Kandinsky Inpainting pipelines repainted black pixels and preserved black pixels. "
"As of diffusers==0.19.0 this behavior has been inverted. Now white pixels are repainted and black pixels are preserved. "
"This way, Kandinsky's masking behavior is aligned with Stable Diffusion. "
"THIS means that you HAVE to invert the input mask to have the same behavior as before as explained in https://github.com/huggingface/diffusers/pull/4207. "
"This warning will be surpressed after the first inference call and will be removed in diffusers>0.22.0"
)
self._warn_has_been_called = True
# Define call parameters
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
device = self._execution_device
batch_size = batch_size * num_images_per_prompt
do_classifier_free_guidance = guidance_scale > 1.0
prompt_embeds, text_encoder_hidden_states, _ = self._encode_prompt(
prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
)
if isinstance(image_embeds, list):
image_embeds = torch.cat(image_embeds, dim=0)
if isinstance(negative_image_embeds, list):
negative_image_embeds = torch.cat(negative_image_embeds, dim=0)
if do_classifier_free_guidance:
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(
dtype=prompt_embeds.dtype, device=device
)
# preprocess image and mask
mask_image, image = prepare_mask_and_masked_image(image, mask_image, height, width)
image = image.to(dtype=prompt_embeds.dtype, device=device)
image = self.movq.encode(image)["latents"]
mask_image = mask_image.to(dtype=prompt_embeds.dtype, device=device)
image_shape = tuple(image.shape[-2:])
mask_image = F.interpolate(
mask_image,
image_shape,
mode="nearest",
)
mask_image = prepare_mask(mask_image)
masked_image = image * mask_image
mask_image = mask_image.repeat_interleave(num_images_per_prompt, dim=0)
masked_image = masked_image.repeat_interleave(num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
mask_image = mask_image.repeat(2, 1, 1, 1)
masked_image = masked_image.repeat(2, 1, 1, 1)
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps_tensor = self.scheduler.timesteps
num_channels_latents = self.movq.config.latent_channels
# get h, w for latents
sample_height, sample_width = get_new_h_w(height, width, self.movq_scale_factor)
# create initial latent
latents = self.prepare_latents(
(batch_size, num_channels_latents, sample_height, sample_width),
text_encoder_hidden_states.dtype,
device,
generator,
latents,
self.scheduler,
)
# Check that sizes of mask, masked image and latents match with expected
num_channels_mask = mask_image.shape[1]
num_channels_masked_image = masked_image.shape[1]
if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
raise ValueError(
f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
" `pipeline.unet` or your `mask_image` or `image` input."
)
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = torch.cat([latent_model_input, masked_image, mask_image], dim=1)
added_cond_kwargs = {"text_embeds": prompt_embeds, "image_embeds": image_embeds}
noise_pred = self.unet(
sample=latent_model_input,
timestep=t,
encoder_hidden_states=text_encoder_hidden_states,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
if do_classifier_free_guidance:
noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
_, variance_pred_text = variance_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1)
if not (
hasattr(self.scheduler.config, "variance_type")
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
noise_pred, _ = noise_pred.split(latents.shape[1], dim=1)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(
noise_pred,
t,
latents,
generator=generator,
).prev_sample
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
# post-processing
image = self.movq.decode(latents, force_not_quantize=True)["sample"]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}")
if output_type in ["np", "pil"]:
image = image * 0.5 + 0.5
image = image.clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionModelWithProjection,
XLMRobertaTokenizer,
)
from ...models import PriorTransformer, UNet2DConditionModel, VQModel
from ...schedulers import DDIMScheduler, DDPMScheduler, UnCLIPScheduler
from ...utils import (
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline
from .pipeline_kandinsky import KandinskyPipeline
from .pipeline_kandinsky_img2img import KandinskyImg2ImgPipeline
from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
from .pipeline_kandinsky_prior import KandinskyPriorPipeline
from .text_encoder import MultilingualCLIP
TEXT2IMAGE_EXAMPLE_DOC_STRING = """
Examples:
```py
from diffusers import AutoPipelineForText2Image
import torch
pipe = AutoPipelineForText2Image.from_pretrained(
"kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()
prompt = "A lion in galaxies, spirals, nebulae, stars, smoke, iridescent, intricate detail, octane render, 8k"
image = pipe(prompt=prompt, num_inference_steps=25).images[0]
```
"""
IMAGE2IMAGE_EXAMPLE_DOC_STRING = """
Examples:
```py
from diffusers import AutoPipelineForImage2Image
import torch
import requests
from io import BytesIO
from PIL import Image
import os
pipe = AutoPipelineForImage2Image.from_pretrained(
"kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()
prompt = "A fantasy landscape, Cinematic lighting"
negative_prompt = "low quality, bad quality"
url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")
image.thumbnail((768, 768))
image = pipe(prompt=prompt, image=original_image, num_inference_steps=25).images[0]
```
"""
INPAINT_EXAMPLE_DOC_STRING = """
Examples:
```py
from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image
import torch
import numpy as np
pipe = AutoPipelineForInpainting.from_pretrained(
"kandinsky-community/kandinsky-2-1-inpaint", torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()
prompt = "A fantasy landscape, Cinematic lighting"
negative_prompt = "low quality, bad quality"
original_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"
)
mask = np.zeros((768, 768), dtype=np.float32)
# Let's mask out an area above the cat's head
mask[:250, 250:-250] = 1
image = pipe(prompt=prompt, image=original_image, mask_image=mask, num_inference_steps=25).images[0]
```
"""
class KandinskyCombinedPipeline(DiffusionPipeline):
"""
Combined Pipeline for text-to-image generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
text_encoder ([`MultilingualCLIP`]):
Frozen text-encoder.
tokenizer ([`XLMRobertaTokenizer`]):
Tokenizer of class
scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ Decoder to generate the image from the latents.
prior_prior ([`PriorTransformer`]):
The canonincal unCLIP prior to approximate the image embedding from the text embedding.
prior_image_encoder ([`CLIPVisionModelWithProjection`]):
Frozen image-encoder.
prior_text_encoder ([`CLIPTextModelWithProjection`]):
Frozen text-encoder.
prior_tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
prior_scheduler ([`UnCLIPScheduler`]):
A scheduler to be used in combination with `prior` to generate image embedding.
"""
_load_connected_pipes = True
def __init__(
self,
text_encoder: MultilingualCLIP,
tokenizer: XLMRobertaTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, DDPMScheduler],
movq: VQModel,
prior_prior: PriorTransformer,
prior_image_encoder: CLIPVisionModelWithProjection,
prior_text_encoder: CLIPTextModelWithProjection,
prior_tokenizer: CLIPTokenizer,
prior_scheduler: UnCLIPScheduler,
prior_image_processor: CLIPImageProcessor,
):
super().__init__()
self.register_modules(
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
movq=movq,
prior_prior=prior_prior,
prior_image_encoder=prior_image_encoder,
prior_text_encoder=prior_text_encoder,
prior_tokenizer=prior_tokenizer,
prior_scheduler=prior_scheduler,
prior_image_processor=prior_image_processor,
)
self.prior_pipe = KandinskyPriorPipeline(
prior=prior_prior,
image_encoder=prior_image_encoder,
text_encoder=prior_text_encoder,
tokenizer=prior_tokenizer,
scheduler=prior_scheduler,
image_processor=prior_image_processor,
)
self.decoder_pipe = KandinskyPipeline(
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
movq=movq,
)
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
"""
self.prior_pipe.enable_model_cpu_offload()
self.decoder_pipe.enable_model_cpu_offload()
def progress_bar(self, iterable=None, total=None):
self.prior_pipe.progress_bar(iterable=iterable, total=total)
self.decoder_pipe.progress_bar(iterable=iterable, total=total)
self.decoder_pipe.enable_model_cpu_offload()
def set_progress_bar_config(self, **kwargs):
self.prior_pipe.set_progress_bar_config(**kwargs)
self.decoder_pipe.set_progress_bar_config(**kwargs)
@torch.no_grad()
@replace_example_docstring(TEXT2IMAGE_EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
num_inference_steps: int = 100,
guidance_scale: float = 4.0,
num_images_per_prompt: int = 1,
height: int = 512,
width: int = 512,
prior_guidance_scale: float = 4.0,
prior_num_inference_steps: int = 25,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
return_dict: bool = True,
):
"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
prior_guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
prior_num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
(`np.array`) or `"pt"` (`torch.Tensor`).
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
prior_outputs = self.prior_pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
num_inference_steps=prior_num_inference_steps,
generator=generator,
latents=latents,
guidance_scale=prior_guidance_scale,
output_type="pt",
return_dict=False,
)
image_embeds = prior_outputs[0]
negative_image_embeds = prior_outputs[1]
prompt = [prompt] if not isinstance(prompt, (list, tuple)) else prompt
if len(prompt) < image_embeds.shape[0] and image_embeds.shape[0] % len(prompt) == 0:
prompt = (image_embeds.shape[0] // len(prompt)) * prompt
outputs = self.decoder_pipe(
prompt=prompt,
image_embeds=image_embeds,
negative_image_embeds=negative_image_embeds,
width=width,
height=height,
num_inference_steps=num_inference_steps,
generator=generator,
guidance_scale=guidance_scale,
output_type=output_type,
callback=callback,
callback_steps=callback_steps,
return_dict=return_dict,
)
return outputs
class KandinskyImg2ImgCombinedPipeline(DiffusionPipeline):
"""
Combined Pipeline for image-to-image generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
text_encoder ([`MultilingualCLIP`]):
Frozen text-encoder.
tokenizer ([`XLMRobertaTokenizer`]):
Tokenizer of class
scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ Decoder to generate the image from the latents.
prior_prior ([`PriorTransformer`]):
The canonincal unCLIP prior to approximate the image embedding from the text embedding.
prior_image_encoder ([`CLIPVisionModelWithProjection`]):
Frozen image-encoder.
prior_text_encoder ([`CLIPTextModelWithProjection`]):
Frozen text-encoder.
prior_tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
prior_scheduler ([`UnCLIPScheduler`]):
A scheduler to be used in combination with `prior` to generate image embedding.
"""
_load_connected_pipes = True
def __init__(
self,
text_encoder: MultilingualCLIP,
tokenizer: XLMRobertaTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, DDPMScheduler],
movq: VQModel,
prior_prior: PriorTransformer,
prior_image_encoder: CLIPVisionModelWithProjection,
prior_text_encoder: CLIPTextModelWithProjection,
prior_tokenizer: CLIPTokenizer,
prior_scheduler: UnCLIPScheduler,
prior_image_processor: CLIPImageProcessor,
):
super().__init__()
self.register_modules(
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
movq=movq,
prior_prior=prior_prior,
prior_image_encoder=prior_image_encoder,
prior_text_encoder=prior_text_encoder,
prior_tokenizer=prior_tokenizer,
prior_scheduler=prior_scheduler,
prior_image_processor=prior_image_processor,
)
self.prior_pipe = KandinskyPriorPipeline(
prior=prior_prior,
image_encoder=prior_image_encoder,
text_encoder=prior_text_encoder,
tokenizer=prior_tokenizer,
scheduler=prior_scheduler,
image_processor=prior_image_processor,
)
self.decoder_pipe = KandinskyImg2ImgPipeline(
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
movq=movq,
)
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
"""
self.prior_pipe.enable_model_cpu_offload()
self.decoder_pipe.enable_model_cpu_offload()
def progress_bar(self, iterable=None, total=None):
self.prior_pipe.progress_bar(iterable=iterable, total=total)
self.decoder_pipe.progress_bar(iterable=iterable, total=total)
self.decoder_pipe.enable_model_cpu_offload()
def set_progress_bar_config(self, **kwargs):
self.prior_pipe.set_progress_bar_config(**kwargs)
self.decoder_pipe.set_progress_bar_config(**kwargs)
@torch.no_grad()
@replace_example_docstring(IMAGE2IMAGE_EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]],
image: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]],
negative_prompt: Optional[Union[str, List[str]]] = None,
num_inference_steps: int = 100,
guidance_scale: float = 4.0,
num_images_per_prompt: int = 1,
strength: float = 0.3,
height: int = 512,
width: int = 512,
prior_guidance_scale: float = 4.0,
prior_num_inference_steps: int = 25,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
return_dict: bool = True,
):
"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process. Can also accpet image latents as `image`, if passing latents directly, it will not be encoded
again.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
strength (`float`, *optional*, defaults to 0.3):
Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
will be used as a starting point, adding more noise to it the larger the `strength`. The number of
denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
prior_guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
prior_num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
(`np.array`) or `"pt"` (`torch.Tensor`).
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
prior_outputs = self.prior_pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
num_inference_steps=prior_num_inference_steps,
generator=generator,
latents=latents,
guidance_scale=prior_guidance_scale,
output_type="pt",
return_dict=False,
)
image_embeds = prior_outputs[0]
negative_image_embeds = prior_outputs[1]
prompt = [prompt] if not isinstance(prompt, (list, tuple)) else prompt
image = [image] if isinstance(prompt, PIL.Image.Image) else image
if len(prompt) < image_embeds.shape[0] and image_embeds.shape[0] % len(prompt) == 0:
prompt = (image_embeds.shape[0] // len(prompt)) * prompt
if (
isinstance(image, (list, tuple))
and len(image) < image_embeds.shape[0]
and image_embeds.shape[0] % len(image) == 0
):
image = (image_embeds.shape[0] // len(image)) * image
outputs = self.decoder_pipe(
prompt=prompt,
image=image,
image_embeds=image_embeds,
negative_image_embeds=negative_image_embeds,
strength=strength,
width=width,
height=height,
num_inference_steps=num_inference_steps,
generator=generator,
guidance_scale=guidance_scale,
output_type=output_type,
callback=callback,
callback_steps=callback_steps,
return_dict=return_dict,
)
return outputs
class KandinskyInpaintCombinedPipeline(DiffusionPipeline):
"""
Combined Pipeline for generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
text_encoder ([`MultilingualCLIP`]):
Frozen text-encoder.
tokenizer ([`XLMRobertaTokenizer`]):
Tokenizer of class
scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):
A scheduler to be used in combination with `unet` to generate image latents.
unet ([`UNet2DConditionModel`]):
Conditional U-Net architecture to denoise the image embedding.
movq ([`VQModel`]):
MoVQ Decoder to generate the image from the latents.
prior_prior ([`PriorTransformer`]):
The canonincal unCLIP prior to approximate the image embedding from the text embedding.
prior_image_encoder ([`CLIPVisionModelWithProjection`]):
Frozen image-encoder.
prior_text_encoder ([`CLIPTextModelWithProjection`]):
Frozen text-encoder.
prior_tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
prior_scheduler ([`UnCLIPScheduler`]):
A scheduler to be used in combination with `prior` to generate image embedding.
"""
_load_connected_pipes = True
def __init__(
self,
text_encoder: MultilingualCLIP,
tokenizer: XLMRobertaTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, DDPMScheduler],
movq: VQModel,
prior_prior: PriorTransformer,
prior_image_encoder: CLIPVisionModelWithProjection,
prior_text_encoder: CLIPTextModelWithProjection,
prior_tokenizer: CLIPTokenizer,
prior_scheduler: UnCLIPScheduler,
prior_image_processor: CLIPImageProcessor,
):
super().__init__()
self.register_modules(
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
movq=movq,
prior_prior=prior_prior,
prior_image_encoder=prior_image_encoder,
prior_text_encoder=prior_text_encoder,
prior_tokenizer=prior_tokenizer,
prior_scheduler=prior_scheduler,
prior_image_processor=prior_image_processor,
)
self.prior_pipe = KandinskyPriorPipeline(
prior=prior_prior,
image_encoder=prior_image_encoder,
text_encoder=prior_text_encoder,
tokenizer=prior_tokenizer,
scheduler=prior_scheduler,
image_processor=prior_image_processor,
)
self.decoder_pipe = KandinskyInpaintPipeline(
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
movq=movq,
)
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
"""
self.prior_pipe.enable_model_cpu_offload()
self.decoder_pipe.enable_model_cpu_offload()
def progress_bar(self, iterable=None, total=None):
self.prior_pipe.progress_bar(iterable=iterable, total=total)
self.decoder_pipe.progress_bar(iterable=iterable, total=total)
self.decoder_pipe.enable_model_cpu_offload()
def set_progress_bar_config(self, **kwargs):
self.prior_pipe.set_progress_bar_config(**kwargs)
self.decoder_pipe.set_progress_bar_config(**kwargs)
@torch.no_grad()
@replace_example_docstring(INPAINT_EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]],
image: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]],
mask_image: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]],
negative_prompt: Optional[Union[str, List[str]]] = None,
num_inference_steps: int = 100,
guidance_scale: float = 4.0,
num_images_per_prompt: int = 1,
height: int = 512,
width: int = 512,
prior_guidance_scale: float = 4.0,
prior_num_inference_steps: int = 25,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
return_dict: bool = True,
):
"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process. Can also accpet image latents as `image`, if passing latents directly, it will not be encoded
again.
mask_image (`np.array`):
Tensor representing an image batch, to mask `image`. White pixels in the mask will be repainted, while
black pixels will be preserved. If `mask_image` is a PIL image, it will be converted to a single
channel (luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3,
so the expected shape would be `(B, H, W, 1)`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
prior_guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
prior_num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
(`np.array`) or `"pt"` (`torch.Tensor`).
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
prior_outputs = self.prior_pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
num_inference_steps=prior_num_inference_steps,
generator=generator,
latents=latents,
guidance_scale=prior_guidance_scale,
output_type="pt",
return_dict=False,
)
image_embeds = prior_outputs[0]
negative_image_embeds = prior_outputs[1]
prompt = [prompt] if not isinstance(prompt, (list, tuple)) else prompt
image = [image] if isinstance(prompt, PIL.Image.Image) else image
mask_image = [mask_image] if isinstance(mask_image, PIL.Image.Image) else mask_image
if len(prompt) < image_embeds.shape[0] and image_embeds.shape[0] % len(prompt) == 0:
prompt = (image_embeds.shape[0] // len(prompt)) * prompt
if (
isinstance(image, (list, tuple))
and len(image) < image_embeds.shape[0]
and image_embeds.shape[0] % len(image) == 0
):
image = (image_embeds.shape[0] // len(image)) * image
if (
isinstance(mask_image, (list, tuple))
and len(mask_image) < image_embeds.shape[0]
and image_embeds.shape[0] % len(mask_image) == 0
):
mask_image = (image_embeds.shape[0] // len(mask_image)) * mask_image
outputs = self.decoder_pipe(
prompt=prompt,
image=image,
mask_image=mask_image,
image_embeds=image_embeds,
negative_image_embeds=negative_image_embeds,
width=width,
height=height,
num_inference_steps=num_inference_steps,
generator=generator,
guidance_scale=guidance_scale,
output_type=output_type,
callback=callback,
callback_steps=callback_steps,
return_dict=return_dict,
)
return outputs
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/deepfloyd_if/__init__.py | from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fast27_timesteps,
smart27_timesteps,
smart50_timesteps,
smart100_timesteps,
smart185_timesteps,
super27_timesteps,
super40_timesteps,
super100_timesteps,
)
@dataclass
class IFPipelineOutput(BaseOutput):
"""
Args:
Output class for Stable Diffusion pipelines.
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
nsfw_detected (`List[bool]`)
List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content or a watermark. `None` if safety checking could not be performed.
watermark_detected (`List[bool]`)
List of flags denoting whether the corresponding generated image likely has a watermark. `None` if safety
checking could not be performed.
"""
images: Union[List[PIL.Image.Image], np.ndarray]
nsfw_detected: Optional[List[bool]]
watermark_detected: Optional[List[bool]]
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_if import IFPipeline
from .pipeline_if_img2img import IFImg2ImgPipeline
from .pipeline_if_img2img_superresolution import IFImg2ImgSuperResolutionPipeline
from .pipeline_if_inpainting import IFInpaintingPipeline
from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
from .pipeline_if_superresolution import IFSuperResolutionPipeline
from .safety_checker import IFSafetyChecker
from .watermark import IFWatermarker
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py | import html
import inspect
import re
import urllib.parse as ul
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer
from ...loaders import LoraLoaderMixin
from ...models import UNet2DConditionModel
from ...schedulers import DDPMScheduler
from ...utils import (
BACKENDS_MAPPING,
PIL_INTERPOLATION,
is_accelerate_available,
is_accelerate_version,
is_bs4_available,
is_ftfy_available,
logging,
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline
from . import IFPipelineOutput
from .safety_checker import IFSafetyChecker
from .watermark import IFWatermarker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
if is_bs4_available():
from bs4 import BeautifulSoup
if is_ftfy_available():
import ftfy
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.resize
def resize(images: PIL.Image.Image, img_size: int) -> PIL.Image.Image:
w, h = images.size
coef = w / h
w, h = img_size, img_size
if coef >= 1:
w = int(round(img_size / 8 * coef) * 8)
else:
h = int(round(img_size / 8 / coef) * 8)
images = images.resize((w, h), resample=PIL_INTERPOLATION["bicubic"], reducing_gap=None)
return images
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> from diffusers import IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline, DiffusionPipeline
>>> from diffusers.utils import pt_to_pil
>>> import torch
>>> from PIL import Image
>>> import requests
>>> from io import BytesIO
>>> url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/person.png"
>>> response = requests.get(url)
>>> original_image = Image.open(BytesIO(response.content)).convert("RGB")
>>> original_image = original_image
>>> url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/glasses_mask.png"
>>> response = requests.get(url)
>>> mask_image = Image.open(BytesIO(response.content))
>>> mask_image = mask_image
>>> pipe = IFInpaintingPipeline.from_pretrained(
... "DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16
... )
>>> pipe.enable_model_cpu_offload()
>>> prompt = "blue sunglasses"
>>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
>>> image = pipe(
... image=original_image,
... mask_image=mask_image,
... prompt_embeds=prompt_embeds,
... negative_prompt_embeds=negative_embeds,
... output_type="pt",
... ).images
>>> # save intermediate image
>>> pil_image = pt_to_pil(image)
>>> pil_image[0].save("./if_stage_I.png")
>>> super_res_1_pipe = IFInpaintingSuperResolutionPipeline.from_pretrained(
... "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16
... )
>>> super_res_1_pipe.enable_model_cpu_offload()
>>> image = super_res_1_pipe(
... image=image,
... mask_image=mask_image,
... original_image=original_image,
... prompt_embeds=prompt_embeds,
... negative_prompt_embeds=negative_embeds,
... ).images
>>> image[0].save("./if_stage_II.png")
```
"""
class IFInpaintingPipeline(DiffusionPipeline, LoraLoaderMixin):
tokenizer: T5Tokenizer
text_encoder: T5EncoderModel
unet: UNet2DConditionModel
scheduler: DDPMScheduler
feature_extractor: Optional[CLIPImageProcessor]
safety_checker: Optional[IFSafetyChecker]
watermarker: Optional[IFWatermarker]
bad_punct_regex = re.compile(
r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
) # noqa
_optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor", "watermarker"]
def __init__(
self,
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
unet: UNet2DConditionModel,
scheduler: DDPMScheduler,
safety_checker: Optional[IFSafetyChecker],
feature_extractor: Optional[CLIPImageProcessor],
watermarker: Optional[IFWatermarker],
requires_safety_checker: bool = True,
):
super().__init__()
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the IF license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
self.register_modules(
tokenizer=tokenizer,
text_encoder=text_encoder,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
watermarker=watermarker,
)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.enable_model_cpu_offload
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
if self.text_encoder is not None:
_, hook = cpu_offload_with_hook(self.text_encoder, device, prev_module_hook=hook)
# Accelerate will move the next model to the device _before_ calling the offload hook of the
# previous model. This will cause both models to be present on the device at the same time.
# IF uses T5 for its text encoder which is really large. We can manually call the offload
# hook for the text encoder to ensure it's moved to the cpu before the unet is moved to
# the GPU.
self.text_encoder_offload_hook = hook
_, hook = cpu_offload_with_hook(self.unet, device, prev_module_hook=hook)
# if the safety checker isn't called, `unet_offload_hook` will have to be called to manually offload the unet
self.unet_offload_hook = hook
if self.safety_checker is not None:
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.remove_all_hooks
def remove_all_hooks(self):
if is_accelerate_available():
from accelerate.hooks import remove_hook_from_module
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
for model in [self.text_encoder, self.unet, self.safety_checker]:
if model is not None:
remove_hook_from_module(model, recurse=True)
self.unet_offload_hook = None
self.text_encoder_offload_hook = None
self.final_offload_hook = None
@torch.no_grad()
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.encode_prompt
def encode_prompt(
self,
prompt,
do_classifier_free_guidance=True,
num_images_per_prompt=1,
device=None,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
clean_caption: bool = False,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`, *optional*):
torch device to place the resulting embeddings on
num_images_per_prompt (`int`, *optional*, defaults to 1):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
"""
if prompt is not None and negative_prompt is not None:
if type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
if device is None:
device = self._execution_device
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
# while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF
max_length = 77
if prompt_embeds is None:
prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=max_length,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {max_length} tokens: {removed_text}"
)
attention_mask = text_inputs.attention_mask.to(device)
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
if self.text_encoder is not None:
dtype = self.text_encoder.dtype
elif self.unet is not None:
dtype = self.unet.dtype
else:
dtype = None
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
attention_mask = uncond_input.attention_mask.to(device)
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
else:
negative_prompt_embeds = None
return prompt_embeds, negative_prompt_embeds
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
image, nsfw_detected, watermark_detected = self.safety_checker(
images=image,
clip_input=safety_checker_input.pixel_values.to(dtype=dtype),
)
else:
nsfw_detected = None
watermark_detected = None
if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
self.unet_offload_hook.offload()
return image, nsfw_detected, watermark_detected
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self,
prompt,
image,
mask_image,
batch_size,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# image
if isinstance(image, list):
check_image_type = image[0]
else:
check_image_type = image
if (
not isinstance(check_image_type, torch.Tensor)
and not isinstance(check_image_type, PIL.Image.Image)
and not isinstance(check_image_type, np.ndarray)
):
raise ValueError(
"`image` has to be of type `torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is"
f" {type(check_image_type)}"
)
if isinstance(image, list):
image_batch_size = len(image)
elif isinstance(image, torch.Tensor):
image_batch_size = image.shape[0]
elif isinstance(image, PIL.Image.Image):
image_batch_size = 1
elif isinstance(image, np.ndarray):
image_batch_size = image.shape[0]
else:
assert False
if batch_size != image_batch_size:
raise ValueError(f"image batch size: {image_batch_size} must be same as prompt batch size {batch_size}")
# mask_image
if isinstance(mask_image, list):
check_image_type = mask_image[0]
else:
check_image_type = mask_image
if (
not isinstance(check_image_type, torch.Tensor)
and not isinstance(check_image_type, PIL.Image.Image)
and not isinstance(check_image_type, np.ndarray)
):
raise ValueError(
"`mask_image` has to be of type `torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is"
f" {type(check_image_type)}"
)
if isinstance(mask_image, list):
image_batch_size = len(mask_image)
elif isinstance(mask_image, torch.Tensor):
image_batch_size = mask_image.shape[0]
elif isinstance(mask_image, PIL.Image.Image):
image_batch_size = 1
elif isinstance(mask_image, np.ndarray):
image_batch_size = mask_image.shape[0]
else:
assert False
if image_batch_size != 1 and batch_size != image_batch_size:
raise ValueError(
f"mask_image batch size: {image_batch_size} must be `1` or the same as prompt batch size {batch_size}"
)
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
def _text_preprocessing(self, text, clean_caption=False):
if clean_caption and not is_bs4_available():
logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
logger.warn("Setting `clean_caption` to False...")
clean_caption = False
if clean_caption and not is_ftfy_available():
logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
logger.warn("Setting `clean_caption` to False...")
clean_caption = False
if not isinstance(text, (tuple, list)):
text = [text]
def process(text: str):
if clean_caption:
text = self._clean_caption(text)
text = self._clean_caption(text)
else:
text = text.lower().strip()
return text
return [process(t) for t in text]
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
def _clean_caption(self, caption):
caption = str(caption)
caption = ul.unquote_plus(caption)
caption = caption.strip().lower()
caption = re.sub("<person>", "person", caption)
# urls:
caption = re.sub(
r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
caption = re.sub(
r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
# html:
caption = BeautifulSoup(caption, features="html.parser").text
# @<nickname>
caption = re.sub(r"@[\w\d]+\b", "", caption)
# 31C0—31EF CJK Strokes
# 31F0—31FF Katakana Phonetic Extensions
# 3200—32FF Enclosed CJK Letters and Months
# 3300—33FF CJK Compatibility
# 3400—4DBF CJK Unified Ideographs Extension A
# 4DC0—4DFF Yijing Hexagram Symbols
# 4E00—9FFF CJK Unified Ideographs
caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
#######################################################
# все виды тире / all types of dash --> "-"
caption = re.sub(
r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa
"-",
caption,
)
# кавычки к одному стандарту
caption = re.sub(r"[`´«»“”¨]", '"', caption)
caption = re.sub(r"[‘’]", "'", caption)
# "
caption = re.sub(r""?", "", caption)
# &
caption = re.sub(r"&", "", caption)
# ip adresses:
caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
# article ids:
caption = re.sub(r"\d:\d\d\s+$", "", caption)
# \n
caption = re.sub(r"\\n", " ", caption)
# "#123"
caption = re.sub(r"#\d{1,3}\b", "", caption)
# "#12345.."
caption = re.sub(r"#\d{5,}\b", "", caption)
# "123456.."
caption = re.sub(r"\b\d{6,}\b", "", caption)
# filenames:
caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)
#
caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT"""
caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT"""
caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT
caption = re.sub(r"\s+\.\s+", r" ", caption) # " . "
# this-is-my-cute-cat / this_is_my_cute_cat
regex2 = re.compile(r"(?:\-|\_)")
if len(re.findall(regex2, caption)) > 3:
caption = re.sub(regex2, " ", caption)
caption = ftfy.fix_text(caption)
caption = html.unescape(html.unescape(caption))
caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640
caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc
caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231
caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
caption = re.sub(r"\bpage\s+\d+\b", "", caption)
caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a...
caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)
caption = re.sub(r"\b\s+\:\s+", r": ", caption)
caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
caption = re.sub(r"\s+", " ", caption)
caption.strip()
caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
caption = re.sub(r"^\.\S+$", "", caption)
return caption.strip()
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.preprocess_image
def preprocess_image(self, image: PIL.Image.Image) -> torch.Tensor:
if not isinstance(image, list):
image = [image]
def numpy_to_pt(images):
if images.ndim == 3:
images = images[..., None]
images = torch.from_numpy(images.transpose(0, 3, 1, 2))
return images
if isinstance(image[0], PIL.Image.Image):
new_image = []
for image_ in image:
image_ = image_.convert("RGB")
image_ = resize(image_, self.unet.sample_size)
image_ = np.array(image_)
image_ = image_.astype(np.float32)
image_ = image_ / 127.5 - 1
new_image.append(image_)
image = new_image
image = np.stack(image, axis=0) # to np
image = numpy_to_pt(image) # to pt
elif isinstance(image[0], np.ndarray):
image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0)
image = numpy_to_pt(image)
elif isinstance(image[0], torch.Tensor):
image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0)
return image
def preprocess_mask_image(self, mask_image) -> torch.Tensor:
if not isinstance(mask_image, list):
mask_image = [mask_image]
if isinstance(mask_image[0], torch.Tensor):
mask_image = torch.cat(mask_image, axis=0) if mask_image[0].ndim == 4 else torch.stack(mask_image, axis=0)
if mask_image.ndim == 2:
# Batch and add channel dim for single mask
mask_image = mask_image.unsqueeze(0).unsqueeze(0)
elif mask_image.ndim == 3 and mask_image.shape[0] == 1:
# Single mask, the 0'th dimension is considered to be
# the existing batch size of 1
mask_image = mask_image.unsqueeze(0)
elif mask_image.ndim == 3 and mask_image.shape[0] != 1:
# Batch of mask, the 0'th dimension is considered to be
# the batching dimension
mask_image = mask_image.unsqueeze(1)
mask_image[mask_image < 0.5] = 0
mask_image[mask_image >= 0.5] = 1
elif isinstance(mask_image[0], PIL.Image.Image):
new_mask_image = []
for mask_image_ in mask_image:
mask_image_ = mask_image_.convert("L")
mask_image_ = resize(mask_image_, self.unet.sample_size)
mask_image_ = np.array(mask_image_)
mask_image_ = mask_image_[None, None, :]
new_mask_image.append(mask_image_)
mask_image = new_mask_image
mask_image = np.concatenate(mask_image, axis=0)
mask_image = mask_image.astype(np.float32) / 255.0
mask_image[mask_image < 0.5] = 0
mask_image[mask_image >= 0.5] = 1
mask_image = torch.from_numpy(mask_image)
elif isinstance(mask_image[0], np.ndarray):
mask_image = np.concatenate([m[None, None, :] for m in mask_image], axis=0)
mask_image[mask_image < 0.5] = 0
mask_image[mask_image >= 0.5] = 1
mask_image = torch.from_numpy(mask_image)
return mask_image
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def prepare_intermediate_images(
self, image, timestep, batch_size, num_images_per_prompt, dtype, device, mask_image, generator=None
):
image_batch_size, channels, height, width = image.shape
batch_size = batch_size * num_images_per_prompt
shape = (batch_size, channels, height, width)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
image = image.repeat_interleave(num_images_per_prompt, dim=0)
noised_image = self.scheduler.add_noise(image, noise, timestep)
image = (1 - mask_image) * image + mask_image * noised_image
return image
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: Union[
PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray]
] = None,
mask_image: Union[
PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray]
] = None,
strength: float = 1.0,
num_inference_steps: int = 50,
timesteps: List[int] = None,
guidance_scale: float = 7.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
clean_caption: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
image (`torch.FloatTensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
mask_image (`PIL.Image.Image`):
`Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
instead of 3, so the expected shape would be `(B, H, W, 1)`.
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
will be used as a starting point, adding more noise to it the larger the `strength`. The number of
denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
timesteps (`List[int]`, *optional*):
Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
timesteps are used. Must be in descending order.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
clean_caption (`bool`, *optional*, defaults to `True`):
Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
be installed. If the dependencies are not installed, the embeddings will be created from the raw
prompt.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
Examples:
Returns:
[`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When
returning a tuple, the first element is a list with the generated images, and the second element is a list
of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw)
or watermarked content, according to the `safety_checker`.
"""
# 1. Check inputs. Raise error if not correct
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
self.check_inputs(
prompt,
image,
mask_image,
batch_size,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
)
# 2. Define call parameters
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
do_classifier_free_guidance,
num_images_per_prompt=num_images_per_prompt,
device=device,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
clean_caption=clean_caption,
)
if do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
dtype = prompt_embeds.dtype
# 4. Prepare timesteps
if timesteps is not None:
self.scheduler.set_timesteps(timesteps=timesteps, device=device)
timesteps = self.scheduler.timesteps
num_inference_steps = len(timesteps)
else:
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
# 5. Prepare intermediate images
image = self.preprocess_image(image)
image = image.to(device=device, dtype=dtype)
mask_image = self.preprocess_mask_image(mask_image)
mask_image = mask_image.to(device=device, dtype=dtype)
if mask_image.shape[0] == 1:
mask_image = mask_image.repeat_interleave(batch_size * num_images_per_prompt, dim=0)
else:
mask_image = mask_image.repeat_interleave(num_images_per_prompt, dim=0)
noise_timestep = timesteps[0:1]
noise_timestep = noise_timestep.repeat(batch_size * num_images_per_prompt)
intermediate_images = self.prepare_intermediate_images(
image, noise_timestep, batch_size, num_images_per_prompt, dtype, device, mask_image, generator
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# HACK: see comment in `enable_model_cpu_offload`
if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None:
self.text_encoder_offload_hook.offload()
# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
model_input = (
torch.cat([intermediate_images] * 2) if do_classifier_free_guidance else intermediate_images
)
model_input = self.scheduler.scale_model_input(model_input, t)
# predict the noise residual
noise_pred = self.unet(
model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1], dim=1)
noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1], dim=1)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
if self.scheduler.config.variance_type not in ["learned", "learned_range"]:
noise_pred, _ = noise_pred.split(model_input.shape[1], dim=1)
# compute the previous noisy sample x_t -> x_t-1
prev_intermediate_images = intermediate_images
intermediate_images = self.scheduler.step(
noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False
)[0]
intermediate_images = (1 - mask_image) * prev_intermediate_images + mask_image * intermediate_images
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, intermediate_images)
image = intermediate_images
if output_type == "pil":
# 8. Post-processing
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
# 9. Run safety checker
image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
# 10. Convert to PIL
image = self.numpy_to_pil(image)
# 11. Apply watermark
if self.watermarker is not None:
self.watermarker.apply_watermark(image, self.unet.config.sample_size)
elif output_type == "pt":
nsfw_detected = None
watermark_detected = None
if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
self.unet_offload_hook.offload()
else:
# 8. Post-processing
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
# 9. Run safety checker
image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, nsfw_detected, watermark_detected)
return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py | import html
import inspect
import re
import urllib.parse as ul
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL
import torch
import torch.nn.functional as F
from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer
from ...loaders import LoraLoaderMixin
from ...models import UNet2DConditionModel
from ...schedulers import DDPMScheduler
from ...utils import (
BACKENDS_MAPPING,
PIL_INTERPOLATION,
is_accelerate_available,
is_accelerate_version,
is_bs4_available,
is_ftfy_available,
logging,
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline
from . import IFPipelineOutput
from .safety_checker import IFSafetyChecker
from .watermark import IFWatermarker
if is_bs4_available():
from bs4 import BeautifulSoup
if is_ftfy_available():
import ftfy
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.resize
def resize(images: PIL.Image.Image, img_size: int) -> PIL.Image.Image:
w, h = images.size
coef = w / h
w, h = img_size, img_size
if coef >= 1:
w = int(round(img_size / 8 * coef) * 8)
else:
h = int(round(img_size / 8 / coef) * 8)
images = images.resize((w, h), resample=PIL_INTERPOLATION["bicubic"], reducing_gap=None)
return images
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> from diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline, DiffusionPipeline
>>> from diffusers.utils import pt_to_pil
>>> import torch
>>> from PIL import Image
>>> import requests
>>> from io import BytesIO
>>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
>>> response = requests.get(url)
>>> original_image = Image.open(BytesIO(response.content)).convert("RGB")
>>> original_image = original_image.resize((768, 512))
>>> pipe = IFImg2ImgPipeline.from_pretrained(
... "DeepFloyd/IF-I-XL-v1.0",
... variant="fp16",
... torch_dtype=torch.float16,
... )
>>> pipe.enable_model_cpu_offload()
>>> prompt = "A fantasy landscape in style minecraft"
>>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
>>> image = pipe(
... image=original_image,
... prompt_embeds=prompt_embeds,
... negative_prompt_embeds=negative_embeds,
... output_type="pt",
... ).images
>>> # save intermediate image
>>> pil_image = pt_to_pil(image)
>>> pil_image[0].save("./if_stage_I.png")
>>> super_res_1_pipe = IFImg2ImgSuperResolutionPipeline.from_pretrained(
... "DeepFloyd/IF-II-L-v1.0",
... text_encoder=None,
... variant="fp16",
... torch_dtype=torch.float16,
... )
>>> super_res_1_pipe.enable_model_cpu_offload()
>>> image = super_res_1_pipe(
... image=image,
... original_image=original_image,
... prompt_embeds=prompt_embeds,
... negative_prompt_embeds=negative_embeds,
... ).images
>>> image[0].save("./if_stage_II.png")
```
"""
class IFImg2ImgSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin):
tokenizer: T5Tokenizer
text_encoder: T5EncoderModel
unet: UNet2DConditionModel
scheduler: DDPMScheduler
image_noising_scheduler: DDPMScheduler
feature_extractor: Optional[CLIPImageProcessor]
safety_checker: Optional[IFSafetyChecker]
watermarker: Optional[IFWatermarker]
bad_punct_regex = re.compile(
r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
) # noqa
_optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor"]
def __init__(
self,
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
unet: UNet2DConditionModel,
scheduler: DDPMScheduler,
image_noising_scheduler: DDPMScheduler,
safety_checker: Optional[IFSafetyChecker],
feature_extractor: Optional[CLIPImageProcessor],
watermarker: Optional[IFWatermarker],
requires_safety_checker: bool = True,
):
super().__init__()
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the IF license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
if unet.config.in_channels != 6:
logger.warn(
"It seems like you have loaded a checkpoint that shall not be used for super resolution from {unet.config._name_or_path} as it accepts {unet.config.in_channels} input channels instead of 6. Please make sure to pass a super resolution checkpoint as the `'unet'`: IFSuperResolutionPipeline.from_pretrained(unet=super_resolution_unet, ...)`."
)
self.register_modules(
tokenizer=tokenizer,
text_encoder=text_encoder,
unet=unet,
scheduler=scheduler,
image_noising_scheduler=image_noising_scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
watermarker=watermarker,
)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.enable_model_cpu_offload
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
if self.text_encoder is not None:
_, hook = cpu_offload_with_hook(self.text_encoder, device, prev_module_hook=hook)
# Accelerate will move the next model to the device _before_ calling the offload hook of the
# previous model. This will cause both models to be present on the device at the same time.
# IF uses T5 for its text encoder which is really large. We can manually call the offload
# hook for the text encoder to ensure it's moved to the cpu before the unet is moved to
# the GPU.
self.text_encoder_offload_hook = hook
_, hook = cpu_offload_with_hook(self.unet, device, prev_module_hook=hook)
# if the safety checker isn't called, `unet_offload_hook` will have to be called to manually offload the unet
self.unet_offload_hook = hook
if self.safety_checker is not None:
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.remove_all_hooks
def remove_all_hooks(self):
if is_accelerate_available():
from accelerate.hooks import remove_hook_from_module
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
for model in [self.text_encoder, self.unet, self.safety_checker]:
if model is not None:
remove_hook_from_module(model, recurse=True)
self.unet_offload_hook = None
self.text_encoder_offload_hook = None
self.final_offload_hook = None
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
def _text_preprocessing(self, text, clean_caption=False):
if clean_caption and not is_bs4_available():
logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
logger.warn("Setting `clean_caption` to False...")
clean_caption = False
if clean_caption and not is_ftfy_available():
logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
logger.warn("Setting `clean_caption` to False...")
clean_caption = False
if not isinstance(text, (tuple, list)):
text = [text]
def process(text: str):
if clean_caption:
text = self._clean_caption(text)
text = self._clean_caption(text)
else:
text = text.lower().strip()
return text
return [process(t) for t in text]
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
def _clean_caption(self, caption):
caption = str(caption)
caption = ul.unquote_plus(caption)
caption = caption.strip().lower()
caption = re.sub("<person>", "person", caption)
# urls:
caption = re.sub(
r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
caption = re.sub(
r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
# html:
caption = BeautifulSoup(caption, features="html.parser").text
# @<nickname>
caption = re.sub(r"@[\w\d]+\b", "", caption)
# 31C0—31EF CJK Strokes
# 31F0—31FF Katakana Phonetic Extensions
# 3200—32FF Enclosed CJK Letters and Months
# 3300—33FF CJK Compatibility
# 3400—4DBF CJK Unified Ideographs Extension A
# 4DC0—4DFF Yijing Hexagram Symbols
# 4E00—9FFF CJK Unified Ideographs
caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
#######################################################
# все виды тире / all types of dash --> "-"
caption = re.sub(
r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa
"-",
caption,
)
# кавычки к одному стандарту
caption = re.sub(r"[`´«»“”¨]", '"', caption)
caption = re.sub(r"[‘’]", "'", caption)
# "
caption = re.sub(r""?", "", caption)
# &
caption = re.sub(r"&", "", caption)
# ip adresses:
caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
# article ids:
caption = re.sub(r"\d:\d\d\s+$", "", caption)
# \n
caption = re.sub(r"\\n", " ", caption)
# "#123"
caption = re.sub(r"#\d{1,3}\b", "", caption)
# "#12345.."
caption = re.sub(r"#\d{5,}\b", "", caption)
# "123456.."
caption = re.sub(r"\b\d{6,}\b", "", caption)
# filenames:
caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)
#
caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT"""
caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT"""
caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT
caption = re.sub(r"\s+\.\s+", r" ", caption) # " . "
# this-is-my-cute-cat / this_is_my_cute_cat
regex2 = re.compile(r"(?:\-|\_)")
if len(re.findall(regex2, caption)) > 3:
caption = re.sub(regex2, " ", caption)
caption = ftfy.fix_text(caption)
caption = html.unescape(html.unescape(caption))
caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640
caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc
caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231
caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
caption = re.sub(r"\bpage\s+\d+\b", "", caption)
caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a...
caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)
caption = re.sub(r"\b\s+\:\s+", r": ", caption)
caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
caption = re.sub(r"\s+", " ", caption)
caption.strip()
caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
caption = re.sub(r"^\.\S+$", "", caption)
return caption.strip()
@torch.no_grad()
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.encode_prompt
def encode_prompt(
self,
prompt,
do_classifier_free_guidance=True,
num_images_per_prompt=1,
device=None,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
clean_caption: bool = False,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`, *optional*):
torch device to place the resulting embeddings on
num_images_per_prompt (`int`, *optional*, defaults to 1):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
"""
if prompt is not None and negative_prompt is not None:
if type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
if device is None:
device = self._execution_device
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
# while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF
max_length = 77
if prompt_embeds is None:
prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=max_length,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {max_length} tokens: {removed_text}"
)
attention_mask = text_inputs.attention_mask.to(device)
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
if self.text_encoder is not None:
dtype = self.text_encoder.dtype
elif self.unet is not None:
dtype = self.unet.dtype
else:
dtype = None
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
attention_mask = uncond_input.attention_mask.to(device)
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
else:
negative_prompt_embeds = None
return prompt_embeds, negative_prompt_embeds
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
image, nsfw_detected, watermark_detected = self.safety_checker(
images=image,
clip_input=safety_checker_input.pixel_values.to(dtype=dtype),
)
else:
nsfw_detected = None
watermark_detected = None
if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
self.unet_offload_hook.offload()
return image, nsfw_detected, watermark_detected
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self,
prompt,
image,
original_image,
batch_size,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# image
if isinstance(image, list):
check_image_type = image[0]
else:
check_image_type = image
if (
not isinstance(check_image_type, torch.Tensor)
and not isinstance(check_image_type, PIL.Image.Image)
and not isinstance(check_image_type, np.ndarray)
):
raise ValueError(
"`image` has to be of type `torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is"
f" {type(check_image_type)}"
)
if isinstance(image, list):
image_batch_size = len(image)
elif isinstance(image, torch.Tensor):
image_batch_size = image.shape[0]
elif isinstance(image, PIL.Image.Image):
image_batch_size = 1
elif isinstance(image, np.ndarray):
image_batch_size = image.shape[0]
else:
assert False
if batch_size != image_batch_size:
raise ValueError(f"image batch size: {image_batch_size} must be same as prompt batch size {batch_size}")
# original_image
if isinstance(original_image, list):
check_image_type = original_image[0]
else:
check_image_type = original_image
if (
not isinstance(check_image_type, torch.Tensor)
and not isinstance(check_image_type, PIL.Image.Image)
and not isinstance(check_image_type, np.ndarray)
):
raise ValueError(
"`original_image` has to be of type `torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is"
f" {type(check_image_type)}"
)
if isinstance(original_image, list):
image_batch_size = len(original_image)
elif isinstance(original_image, torch.Tensor):
image_batch_size = original_image.shape[0]
elif isinstance(original_image, PIL.Image.Image):
image_batch_size = 1
elif isinstance(original_image, np.ndarray):
image_batch_size = original_image.shape[0]
else:
assert False
if batch_size != image_batch_size:
raise ValueError(
f"original_image batch size: {image_batch_size} must be same as prompt batch size {batch_size}"
)
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.preprocess_image with preprocess_image -> preprocess_original_image
def preprocess_original_image(self, image: PIL.Image.Image) -> torch.Tensor:
if not isinstance(image, list):
image = [image]
def numpy_to_pt(images):
if images.ndim == 3:
images = images[..., None]
images = torch.from_numpy(images.transpose(0, 3, 1, 2))
return images
if isinstance(image[0], PIL.Image.Image):
new_image = []
for image_ in image:
image_ = image_.convert("RGB")
image_ = resize(image_, self.unet.sample_size)
image_ = np.array(image_)
image_ = image_.astype(np.float32)
image_ = image_ / 127.5 - 1
new_image.append(image_)
image = new_image
image = np.stack(image, axis=0) # to np
image = numpy_to_pt(image) # to pt
elif isinstance(image[0], np.ndarray):
image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0)
image = numpy_to_pt(image)
elif isinstance(image[0], torch.Tensor):
image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0)
return image
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_superresolution.IFSuperResolutionPipeline.preprocess_image
def preprocess_image(self, image: PIL.Image.Image, num_images_per_prompt, device) -> torch.Tensor:
if not isinstance(image, torch.Tensor) and not isinstance(image, list):
image = [image]
if isinstance(image[0], PIL.Image.Image):
image = [np.array(i).astype(np.float32) / 127.5 - 1.0 for i in image]
image = np.stack(image, axis=0) # to np
image = torch.from_numpy(image.transpose(0, 3, 1, 2))
elif isinstance(image[0], np.ndarray):
image = np.stack(image, axis=0) # to np
if image.ndim == 5:
image = image[0]
image = torch.from_numpy(image.transpose(0, 3, 1, 2))
elif isinstance(image, list) and isinstance(image[0], torch.Tensor):
dims = image[0].ndim
if dims == 3:
image = torch.stack(image, dim=0)
elif dims == 4:
image = torch.concat(image, dim=0)
else:
raise ValueError(f"Image must have 3 or 4 dimensions, instead got {dims}")
image = image.to(device=device, dtype=self.unet.dtype)
image = image.repeat_interleave(num_images_per_prompt, dim=0)
return image
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.prepare_intermediate_images
def prepare_intermediate_images(
self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None
):
_, channels, height, width = image.shape
batch_size = batch_size * num_images_per_prompt
shape = (batch_size, channels, height, width)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
image = image.repeat_interleave(num_images_per_prompt, dim=0)
image = self.scheduler.add_noise(image, noise, timestep)
return image
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
image: Union[PIL.Image.Image, np.ndarray, torch.FloatTensor],
original_image: Union[
PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray]
] = None,
strength: float = 0.8,
prompt: Union[str, List[str]] = None,
num_inference_steps: int = 50,
timesteps: List[int] = None,
guidance_scale: float = 4.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
noise_level: int = 250,
clean_caption: bool = True,
):
"""
Function invoked when calling the pipeline for generation.
Args:
image (`torch.FloatTensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
original_image (`torch.FloatTensor` or `PIL.Image.Image`):
The original image that `image` was varied from.
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
will be used as a starting point, adding more noise to it the larger the `strength`. The number of
denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
timesteps (`List[int]`, *optional*):
Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
timesteps are used. Must be in descending order.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
noise_level (`int`, *optional*, defaults to 250):
The amount of noise to add to the upscaled image. Must be in the range `[0, 1000)`
clean_caption (`bool`, *optional*, defaults to `True`):
Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
be installed. If the dependencies are not installed, the embeddings will be created from the raw
prompt.
Examples:
Returns:
[`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When
returning a tuple, the first element is a list with the generated images, and the second element is a list
of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw)
or watermarked content, according to the `safety_checker`.
"""
# 1. Check inputs. Raise error if not correct
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
self.check_inputs(
prompt,
image,
original_image,
batch_size,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
)
# 2. Define call parameters
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
device = self._execution_device
# 3. Encode input prompt
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
do_classifier_free_guidance,
num_images_per_prompt=num_images_per_prompt,
device=device,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
clean_caption=clean_caption,
)
if do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
dtype = prompt_embeds.dtype
# 4. Prepare timesteps
if timesteps is not None:
self.scheduler.set_timesteps(timesteps=timesteps, device=device)
timesteps = self.scheduler.timesteps
num_inference_steps = len(timesteps)
else:
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
# 5. prepare original image
original_image = self.preprocess_original_image(original_image)
original_image = original_image.to(device=device, dtype=dtype)
# 6. Prepare intermediate images
noise_timestep = timesteps[0:1]
noise_timestep = noise_timestep.repeat(batch_size * num_images_per_prompt)
intermediate_images = self.prepare_intermediate_images(
original_image,
noise_timestep,
batch_size,
num_images_per_prompt,
dtype,
device,
generator,
)
# 7. Prepare upscaled image and noise level
_, _, height, width = original_image.shape
image = self.preprocess_image(image, num_images_per_prompt, device)
upscaled = F.interpolate(image, (height, width), mode="bilinear", align_corners=True)
noise_level = torch.tensor([noise_level] * upscaled.shape[0], device=upscaled.device)
noise = randn_tensor(upscaled.shape, generator=generator, device=upscaled.device, dtype=upscaled.dtype)
upscaled = self.image_noising_scheduler.add_noise(upscaled, noise, timesteps=noise_level)
if do_classifier_free_guidance:
noise_level = torch.cat([noise_level] * 2)
# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# HACK: see comment in `enable_model_cpu_offload`
if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None:
self.text_encoder_offload_hook.offload()
# 9. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
model_input = torch.cat([intermediate_images, upscaled], dim=1)
model_input = torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input
model_input = self.scheduler.scale_model_input(model_input, t)
# predict the noise residual
noise_pred = self.unet(
model_input,
t,
encoder_hidden_states=prompt_embeds,
class_labels=noise_level,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1] // 2, dim=1)
noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1] // 2, dim=1)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
if self.scheduler.config.variance_type not in ["learned", "learned_range"]:
noise_pred, _ = noise_pred.split(intermediate_images.shape[1], dim=1)
# compute the previous noisy sample x_t -> x_t-1
intermediate_images = self.scheduler.step(
noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False
)[0]
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, intermediate_images)
image = intermediate_images
if output_type == "pil":
# 10. Post-processing
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
# 11. Run safety checker
image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
# 12. Convert to PIL
image = self.numpy_to_pil(image)
# 13. Apply watermark
if self.watermarker is not None:
self.watermarker.apply_watermark(image, self.unet.config.sample_size)
elif output_type == "pt":
nsfw_detected = None
watermark_detected = None
if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
self.unet_offload_hook.offload()
else:
# 10. Post-processing
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
# 11. Run safety checker
image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, nsfw_detected, watermark_detected)
return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py | import html
import inspect
import re
import urllib.parse as ul
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer
from ...loaders import LoraLoaderMixin
from ...models import UNet2DConditionModel
from ...schedulers import DDPMScheduler
from ...utils import (
BACKENDS_MAPPING,
is_accelerate_available,
is_accelerate_version,
is_bs4_available,
is_ftfy_available,
logging,
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline
from . import IFPipelineOutput
from .safety_checker import IFSafetyChecker
from .watermark import IFWatermarker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
if is_bs4_available():
from bs4 import BeautifulSoup
if is_ftfy_available():
import ftfy
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> from diffusers import IFPipeline, IFSuperResolutionPipeline, DiffusionPipeline
>>> from diffusers.utils import pt_to_pil
>>> import torch
>>> pipe = IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
>>> pipe.enable_model_cpu_offload()
>>> prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"'
>>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
>>> image = pipe(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, output_type="pt").images
>>> # save intermediate image
>>> pil_image = pt_to_pil(image)
>>> pil_image[0].save("./if_stage_I.png")
>>> super_res_1_pipe = IFSuperResolutionPipeline.from_pretrained(
... "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16
... )
>>> super_res_1_pipe.enable_model_cpu_offload()
>>> image = super_res_1_pipe(
... image=image, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, output_type="pt"
... ).images
>>> # save intermediate image
>>> pil_image = pt_to_pil(image)
>>> pil_image[0].save("./if_stage_I.png")
>>> safety_modules = {
... "feature_extractor": pipe.feature_extractor,
... "safety_checker": pipe.safety_checker,
... "watermarker": pipe.watermarker,
... }
>>> super_res_2_pipe = DiffusionPipeline.from_pretrained(
... "stabilityai/stable-diffusion-x4-upscaler", **safety_modules, torch_dtype=torch.float16
... )
>>> super_res_2_pipe.enable_model_cpu_offload()
>>> image = super_res_2_pipe(
... prompt=prompt,
... image=image,
... ).images
>>> image[0].save("./if_stage_II.png")
```
"""
class IFPipeline(DiffusionPipeline, LoraLoaderMixin):
tokenizer: T5Tokenizer
text_encoder: T5EncoderModel
unet: UNet2DConditionModel
scheduler: DDPMScheduler
feature_extractor: Optional[CLIPImageProcessor]
safety_checker: Optional[IFSafetyChecker]
watermarker: Optional[IFWatermarker]
bad_punct_regex = re.compile(
r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
) # noqa
_optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor", "watermarker"]
def __init__(
self,
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
unet: UNet2DConditionModel,
scheduler: DDPMScheduler,
safety_checker: Optional[IFSafetyChecker],
feature_extractor: Optional[CLIPImageProcessor],
watermarker: Optional[IFWatermarker],
requires_safety_checker: bool = True,
):
super().__init__()
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the IF license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
self.register_modules(
tokenizer=tokenizer,
text_encoder=text_encoder,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
watermarker=watermarker,
)
self.register_to_config(requires_safety_checker=requires_safety_checker)
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
if self.text_encoder is not None:
_, hook = cpu_offload_with_hook(self.text_encoder, device, prev_module_hook=hook)
# Accelerate will move the next model to the device _before_ calling the offload hook of the
# previous model. This will cause both models to be present on the device at the same time.
# IF uses T5 for its text encoder which is really large. We can manually call the offload
# hook for the text encoder to ensure it's moved to the cpu before the unet is moved to
# the GPU.
self.text_encoder_offload_hook = hook
_, hook = cpu_offload_with_hook(self.unet, device, prev_module_hook=hook)
# if the safety checker isn't called, `unet_offload_hook` will have to be called to manually offload the unet
self.unet_offload_hook = hook
if self.safety_checker is not None:
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
def remove_all_hooks(self):
if is_accelerate_available():
from accelerate.hooks import remove_hook_from_module
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
for model in [self.text_encoder, self.unet, self.safety_checker]:
if model is not None:
remove_hook_from_module(model, recurse=True)
self.unet_offload_hook = None
self.text_encoder_offload_hook = None
self.final_offload_hook = None
@torch.no_grad()
def encode_prompt(
self,
prompt,
do_classifier_free_guidance=True,
num_images_per_prompt=1,
device=None,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
clean_caption: bool = False,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`, *optional*):
torch device to place the resulting embeddings on
num_images_per_prompt (`int`, *optional*, defaults to 1):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
"""
if prompt is not None and negative_prompt is not None:
if type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
if device is None:
device = self._execution_device
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
# while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF
max_length = 77
if prompt_embeds is None:
prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=max_length,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {max_length} tokens: {removed_text}"
)
attention_mask = text_inputs.attention_mask.to(device)
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
if self.text_encoder is not None:
dtype = self.text_encoder.dtype
elif self.unet is not None:
dtype = self.unet.dtype
else:
dtype = None
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
attention_mask = uncond_input.attention_mask.to(device)
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
else:
negative_prompt_embeds = None
return prompt_embeds, negative_prompt_embeds
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
image, nsfw_detected, watermark_detected = self.safety_checker(
images=image,
clip_input=safety_checker_input.pixel_values.to(dtype=dtype),
)
else:
nsfw_detected = None
watermark_detected = None
if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
self.unet_offload_hook.offload()
return image, nsfw_detected, watermark_detected
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self,
prompt,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
def prepare_intermediate_images(self, batch_size, num_channels, height, width, dtype, device, generator):
shape = (batch_size, num_channels, height, width)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
intermediate_images = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
# scale the initial noise by the standard deviation required by the scheduler
intermediate_images = intermediate_images * self.scheduler.init_noise_sigma
return intermediate_images
def _text_preprocessing(self, text, clean_caption=False):
if clean_caption and not is_bs4_available():
logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
logger.warn("Setting `clean_caption` to False...")
clean_caption = False
if clean_caption and not is_ftfy_available():
logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
logger.warn("Setting `clean_caption` to False...")
clean_caption = False
if not isinstance(text, (tuple, list)):
text = [text]
def process(text: str):
if clean_caption:
text = self._clean_caption(text)
text = self._clean_caption(text)
else:
text = text.lower().strip()
return text
return [process(t) for t in text]
def _clean_caption(self, caption):
caption = str(caption)
caption = ul.unquote_plus(caption)
caption = caption.strip().lower()
caption = re.sub("<person>", "person", caption)
# urls:
caption = re.sub(
r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
caption = re.sub(
r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
# html:
caption = BeautifulSoup(caption, features="html.parser").text
# @<nickname>
caption = re.sub(r"@[\w\d]+\b", "", caption)
# 31C0—31EF CJK Strokes
# 31F0—31FF Katakana Phonetic Extensions
# 3200—32FF Enclosed CJK Letters and Months
# 3300—33FF CJK Compatibility
# 3400—4DBF CJK Unified Ideographs Extension A
# 4DC0—4DFF Yijing Hexagram Symbols
# 4E00—9FFF CJK Unified Ideographs
caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
#######################################################
# все виды тире / all types of dash --> "-"
caption = re.sub(
r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa
"-",
caption,
)
# кавычки к одному стандарту
caption = re.sub(r"[`´«»“”¨]", '"', caption)
caption = re.sub(r"[‘’]", "'", caption)
# "
caption = re.sub(r""?", "", caption)
# &
caption = re.sub(r"&", "", caption)
# ip adresses:
caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
# article ids:
caption = re.sub(r"\d:\d\d\s+$", "", caption)
# \n
caption = re.sub(r"\\n", " ", caption)
# "#123"
caption = re.sub(r"#\d{1,3}\b", "", caption)
# "#12345.."
caption = re.sub(r"#\d{5,}\b", "", caption)
# "123456.."
caption = re.sub(r"\b\d{6,}\b", "", caption)
# filenames:
caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)
#
caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT"""
caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT"""
caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT
caption = re.sub(r"\s+\.\s+", r" ", caption) # " . "
# this-is-my-cute-cat / this_is_my_cute_cat
regex2 = re.compile(r"(?:\-|\_)")
if len(re.findall(regex2, caption)) > 3:
caption = re.sub(regex2, " ", caption)
caption = ftfy.fix_text(caption)
caption = html.unescape(html.unescape(caption))
caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640
caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc
caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231
caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
caption = re.sub(r"\bpage\s+\d+\b", "", caption)
caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a...
caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)
caption = re.sub(r"\b\s+\:\s+", r": ", caption)
caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
caption = re.sub(r"\s+", " ", caption)
caption.strip()
caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
caption = re.sub(r"^\.\S+$", "", caption)
return caption.strip()
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
num_inference_steps: int = 100,
timesteps: List[int] = None,
guidance_scale: float = 7.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
height: Optional[int] = None,
width: Optional[int] = None,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
clean_caption: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
timesteps (`List[int]`, *optional*):
Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
timesteps are used. Must be in descending order.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
height (`int`, *optional*, defaults to self.unet.config.sample_size):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size):
The width in pixels of the generated image.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
clean_caption (`bool`, *optional*, defaults to `True`):
Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
be installed. If the dependencies are not installed, the embeddings will be created from the raw
prompt.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
Examples:
Returns:
[`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When
returning a tuple, the first element is a list with the generated images, and the second element is a list
of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw)
or watermarked content, according to the `safety_checker`.
"""
# 1. Check inputs. Raise error if not correct
self.check_inputs(prompt, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)
# 2. Define call parameters
height = height or self.unet.config.sample_size
width = width or self.unet.config.sample_size
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
do_classifier_free_guidance,
num_images_per_prompt=num_images_per_prompt,
device=device,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
clean_caption=clean_caption,
)
if do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
# 4. Prepare timesteps
if timesteps is not None:
self.scheduler.set_timesteps(timesteps=timesteps, device=device)
timesteps = self.scheduler.timesteps
num_inference_steps = len(timesteps)
else:
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare intermediate images
intermediate_images = self.prepare_intermediate_images(
batch_size * num_images_per_prompt,
self.unet.config.in_channels,
height,
width,
prompt_embeds.dtype,
device,
generator,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# HACK: see comment in `enable_model_cpu_offload`
if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None:
self.text_encoder_offload_hook.offload()
# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
model_input = (
torch.cat([intermediate_images] * 2) if do_classifier_free_guidance else intermediate_images
)
model_input = self.scheduler.scale_model_input(model_input, t)
# predict the noise residual
noise_pred = self.unet(
model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1], dim=1)
noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1], dim=1)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
if self.scheduler.config.variance_type not in ["learned", "learned_range"]:
noise_pred, _ = noise_pred.split(model_input.shape[1], dim=1)
# compute the previous noisy sample x_t -> x_t-1
intermediate_images = self.scheduler.step(
noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False
)[0]
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, intermediate_images)
image = intermediate_images
if output_type == "pil":
# 8. Post-processing
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
# 9. Run safety checker
image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
# 10. Convert to PIL
image = self.numpy_to_pil(image)
# 11. Apply watermark
if self.watermarker is not None:
image = self.watermarker.apply_watermark(image, self.unet.config.sample_size)
elif output_type == "pt":
nsfw_detected = None
watermark_detected = None
if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
self.unet_offload_hook.offload()
else:
# 8. Post-processing
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
# 9. Run safety checker
image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, nsfw_detected, watermark_detected)
return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py | import html
import inspect
import re
import urllib.parse as ul
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL
import torch
import torch.nn.functional as F
from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer
from ...loaders import LoraLoaderMixin
from ...models import UNet2DConditionModel
from ...schedulers import DDPMScheduler
from ...utils import (
BACKENDS_MAPPING,
is_accelerate_available,
is_accelerate_version,
is_bs4_available,
is_ftfy_available,
logging,
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline
from . import IFPipelineOutput
from .safety_checker import IFSafetyChecker
from .watermark import IFWatermarker
if is_bs4_available():
from bs4 import BeautifulSoup
if is_ftfy_available():
import ftfy
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> from diffusers import IFPipeline, IFSuperResolutionPipeline, DiffusionPipeline
>>> from diffusers.utils import pt_to_pil
>>> import torch
>>> pipe = IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
>>> pipe.enable_model_cpu_offload()
>>> prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"'
>>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
>>> image = pipe(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, output_type="pt").images
>>> # save intermediate image
>>> pil_image = pt_to_pil(image)
>>> pil_image[0].save("./if_stage_I.png")
>>> super_res_1_pipe = IFSuperResolutionPipeline.from_pretrained(
... "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16
... )
>>> super_res_1_pipe.enable_model_cpu_offload()
>>> image = super_res_1_pipe(
... image=image, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds
... ).images
>>> image[0].save("./if_stage_II.png")
```
"""
class IFSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin):
tokenizer: T5Tokenizer
text_encoder: T5EncoderModel
unet: UNet2DConditionModel
scheduler: DDPMScheduler
image_noising_scheduler: DDPMScheduler
feature_extractor: Optional[CLIPImageProcessor]
safety_checker: Optional[IFSafetyChecker]
watermarker: Optional[IFWatermarker]
bad_punct_regex = re.compile(
r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
) # noqa
_optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor", "watermarker"]
def __init__(
self,
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
unet: UNet2DConditionModel,
scheduler: DDPMScheduler,
image_noising_scheduler: DDPMScheduler,
safety_checker: Optional[IFSafetyChecker],
feature_extractor: Optional[CLIPImageProcessor],
watermarker: Optional[IFWatermarker],
requires_safety_checker: bool = True,
):
super().__init__()
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the IF license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
if unet.config.in_channels != 6:
logger.warn(
"It seems like you have loaded a checkpoint that shall not be used for super resolution from {unet.config._name_or_path} as it accepts {unet.config.in_channels} input channels instead of 6. Please make sure to pass a super resolution checkpoint as the `'unet'`: IFSuperResolutionPipeline.from_pretrained(unet=super_resolution_unet, ...)`."
)
self.register_modules(
tokenizer=tokenizer,
text_encoder=text_encoder,
unet=unet,
scheduler=scheduler,
image_noising_scheduler=image_noising_scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
watermarker=watermarker,
)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.enable_model_cpu_offload
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
if self.text_encoder is not None:
_, hook = cpu_offload_with_hook(self.text_encoder, device, prev_module_hook=hook)
# Accelerate will move the next model to the device _before_ calling the offload hook of the
# previous model. This will cause both models to be present on the device at the same time.
# IF uses T5 for its text encoder which is really large. We can manually call the offload
# hook for the text encoder to ensure it's moved to the cpu before the unet is moved to
# the GPU.
self.text_encoder_offload_hook = hook
_, hook = cpu_offload_with_hook(self.unet, device, prev_module_hook=hook)
# if the safety checker isn't called, `unet_offload_hook` will have to be called to manually offload the unet
self.unet_offload_hook = hook
if self.safety_checker is not None:
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.remove_all_hooks
def remove_all_hooks(self):
if is_accelerate_available():
from accelerate.hooks import remove_hook_from_module
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
for model in [self.text_encoder, self.unet, self.safety_checker]:
if model is not None:
remove_hook_from_module(model, recurse=True)
self.unet_offload_hook = None
self.text_encoder_offload_hook = None
self.final_offload_hook = None
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
def _text_preprocessing(self, text, clean_caption=False):
if clean_caption and not is_bs4_available():
logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
logger.warn("Setting `clean_caption` to False...")
clean_caption = False
if clean_caption and not is_ftfy_available():
logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
logger.warn("Setting `clean_caption` to False...")
clean_caption = False
if not isinstance(text, (tuple, list)):
text = [text]
def process(text: str):
if clean_caption:
text = self._clean_caption(text)
text = self._clean_caption(text)
else:
text = text.lower().strip()
return text
return [process(t) for t in text]
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
def _clean_caption(self, caption):
caption = str(caption)
caption = ul.unquote_plus(caption)
caption = caption.strip().lower()
caption = re.sub("<person>", "person", caption)
# urls:
caption = re.sub(
r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
caption = re.sub(
r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
# html:
caption = BeautifulSoup(caption, features="html.parser").text
# @<nickname>
caption = re.sub(r"@[\w\d]+\b", "", caption)
# 31C0—31EF CJK Strokes
# 31F0—31FF Katakana Phonetic Extensions
# 3200—32FF Enclosed CJK Letters and Months
# 3300—33FF CJK Compatibility
# 3400—4DBF CJK Unified Ideographs Extension A
# 4DC0—4DFF Yijing Hexagram Symbols
# 4E00—9FFF CJK Unified Ideographs
caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
#######################################################
# все виды тире / all types of dash --> "-"
caption = re.sub(
r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa
"-",
caption,
)
# кавычки к одному стандарту
caption = re.sub(r"[`´«»“”¨]", '"', caption)
caption = re.sub(r"[‘’]", "'", caption)
# "
caption = re.sub(r""?", "", caption)
# &
caption = re.sub(r"&", "", caption)
# ip adresses:
caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
# article ids:
caption = re.sub(r"\d:\d\d\s+$", "", caption)
# \n
caption = re.sub(r"\\n", " ", caption)
# "#123"
caption = re.sub(r"#\d{1,3}\b", "", caption)
# "#12345.."
caption = re.sub(r"#\d{5,}\b", "", caption)
# "123456.."
caption = re.sub(r"\b\d{6,}\b", "", caption)
# filenames:
caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)
#
caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT"""
caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT"""
caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT
caption = re.sub(r"\s+\.\s+", r" ", caption) # " . "
# this-is-my-cute-cat / this_is_my_cute_cat
regex2 = re.compile(r"(?:\-|\_)")
if len(re.findall(regex2, caption)) > 3:
caption = re.sub(regex2, " ", caption)
caption = ftfy.fix_text(caption)
caption = html.unescape(html.unescape(caption))
caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640
caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc
caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231
caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
caption = re.sub(r"\bpage\s+\d+\b", "", caption)
caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a...
caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)
caption = re.sub(r"\b\s+\:\s+", r": ", caption)
caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
caption = re.sub(r"\s+", " ", caption)
caption.strip()
caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
caption = re.sub(r"^\.\S+$", "", caption)
return caption.strip()
@torch.no_grad()
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.encode_prompt
def encode_prompt(
self,
prompt,
do_classifier_free_guidance=True,
num_images_per_prompt=1,
device=None,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
clean_caption: bool = False,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`, *optional*):
torch device to place the resulting embeddings on
num_images_per_prompt (`int`, *optional*, defaults to 1):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
"""
if prompt is not None and negative_prompt is not None:
if type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
if device is None:
device = self._execution_device
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
# while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF
max_length = 77
if prompt_embeds is None:
prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=max_length,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {max_length} tokens: {removed_text}"
)
attention_mask = text_inputs.attention_mask.to(device)
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
if self.text_encoder is not None:
dtype = self.text_encoder.dtype
elif self.unet is not None:
dtype = self.unet.dtype
else:
dtype = None
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
attention_mask = uncond_input.attention_mask.to(device)
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
else:
negative_prompt_embeds = None
return prompt_embeds, negative_prompt_embeds
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
image, nsfw_detected, watermark_detected = self.safety_checker(
images=image,
clip_input=safety_checker_input.pixel_values.to(dtype=dtype),
)
else:
nsfw_detected = None
watermark_detected = None
if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
self.unet_offload_hook.offload()
return image, nsfw_detected, watermark_detected
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self,
prompt,
image,
batch_size,
noise_level,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
if noise_level < 0 or noise_level >= self.image_noising_scheduler.config.num_train_timesteps:
raise ValueError(
f"`noise_level`: {noise_level} must be a valid timestep in `self.noising_scheduler`, [0, {self.image_noising_scheduler.config.num_train_timesteps})"
)
if isinstance(image, list):
check_image_type = image[0]
else:
check_image_type = image
if (
not isinstance(check_image_type, torch.Tensor)
and not isinstance(check_image_type, PIL.Image.Image)
and not isinstance(check_image_type, np.ndarray)
):
raise ValueError(
"`image` has to be of type `torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is"
f" {type(check_image_type)}"
)
if isinstance(image, list):
image_batch_size = len(image)
elif isinstance(image, torch.Tensor):
image_batch_size = image.shape[0]
elif isinstance(image, PIL.Image.Image):
image_batch_size = 1
elif isinstance(image, np.ndarray):
image_batch_size = image.shape[0]
else:
assert False
if batch_size != image_batch_size:
raise ValueError(f"image batch size: {image_batch_size} must be same as prompt batch size {batch_size}")
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_intermediate_images
def prepare_intermediate_images(self, batch_size, num_channels, height, width, dtype, device, generator):
shape = (batch_size, num_channels, height, width)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
intermediate_images = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
# scale the initial noise by the standard deviation required by the scheduler
intermediate_images = intermediate_images * self.scheduler.init_noise_sigma
return intermediate_images
def preprocess_image(self, image, num_images_per_prompt, device):
if not isinstance(image, torch.Tensor) and not isinstance(image, list):
image = [image]
if isinstance(image[0], PIL.Image.Image):
image = [np.array(i).astype(np.float32) / 127.5 - 1.0 for i in image]
image = np.stack(image, axis=0) # to np
image = torch.from_numpy(image.transpose(0, 3, 1, 2))
elif isinstance(image[0], np.ndarray):
image = np.stack(image, axis=0) # to np
if image.ndim == 5:
image = image[0]
image = torch.from_numpy(image.transpose(0, 3, 1, 2))
elif isinstance(image, list) and isinstance(image[0], torch.Tensor):
dims = image[0].ndim
if dims == 3:
image = torch.stack(image, dim=0)
elif dims == 4:
image = torch.concat(image, dim=0)
else:
raise ValueError(f"Image must have 3 or 4 dimensions, instead got {dims}")
image = image.to(device=device, dtype=self.unet.dtype)
image = image.repeat_interleave(num_images_per_prompt, dim=0)
return image
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: int = None,
width: int = None,
image: Union[PIL.Image.Image, np.ndarray, torch.FloatTensor] = None,
num_inference_steps: int = 50,
timesteps: List[int] = None,
guidance_scale: float = 4.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
noise_level: int = 250,
clean_caption: bool = True,
):
"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
height (`int`, *optional*, defaults to self.unet.config.sample_size):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size):
The width in pixels of the generated image.
image (`PIL.Image.Image`, `np.ndarray`, `torch.FloatTensor`):
The image to be upscaled.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
timesteps (`List[int]`, *optional*):
Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
timesteps are used. Must be in descending order.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
noise_level (`int`, *optional*, defaults to 250):
The amount of noise to add to the upscaled image. Must be in the range `[0, 1000)`
clean_caption (`bool`, *optional*, defaults to `True`):
Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
be installed. If the dependencies are not installed, the embeddings will be created from the raw
prompt.
Examples:
Returns:
[`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When
returning a tuple, the first element is a list with the generated images, and the second element is a list
of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw)
or watermarked content, according to the `safety_checker`.
"""
# 1. Check inputs. Raise error if not correct
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
self.check_inputs(
prompt,
image,
batch_size,
noise_level,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
)
# 2. Define call parameters
height = height or self.unet.config.sample_size
width = width or self.unet.config.sample_size
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
do_classifier_free_guidance,
num_images_per_prompt=num_images_per_prompt,
device=device,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
clean_caption=clean_caption,
)
if do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
# 4. Prepare timesteps
if timesteps is not None:
self.scheduler.set_timesteps(timesteps=timesteps, device=device)
timesteps = self.scheduler.timesteps
num_inference_steps = len(timesteps)
else:
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare intermediate images
num_channels = self.unet.config.in_channels // 2
intermediate_images = self.prepare_intermediate_images(
batch_size * num_images_per_prompt,
num_channels,
height,
width,
prompt_embeds.dtype,
device,
generator,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Prepare upscaled image and noise level
image = self.preprocess_image(image, num_images_per_prompt, device)
upscaled = F.interpolate(image, (height, width), mode="bilinear", align_corners=True)
noise_level = torch.tensor([noise_level] * upscaled.shape[0], device=upscaled.device)
noise = randn_tensor(upscaled.shape, generator=generator, device=upscaled.device, dtype=upscaled.dtype)
upscaled = self.image_noising_scheduler.add_noise(upscaled, noise, timesteps=noise_level)
if do_classifier_free_guidance:
noise_level = torch.cat([noise_level] * 2)
# HACK: see comment in `enable_model_cpu_offload`
if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None:
self.text_encoder_offload_hook.offload()
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
model_input = torch.cat([intermediate_images, upscaled], dim=1)
model_input = torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input
model_input = self.scheduler.scale_model_input(model_input, t)
# predict the noise residual
noise_pred = self.unet(
model_input,
t,
encoder_hidden_states=prompt_embeds,
class_labels=noise_level,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1] // 2, dim=1)
noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1] // 2, dim=1)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
if self.scheduler.config.variance_type not in ["learned", "learned_range"]:
noise_pred, _ = noise_pred.split(intermediate_images.shape[1], dim=1)
# compute the previous noisy sample x_t -> x_t-1
intermediate_images = self.scheduler.step(
noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False
)[0]
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, intermediate_images)
image = intermediate_images
if output_type == "pil":
# 9. Post-processing
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
# 10. Run safety checker
image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
# 11. Convert to PIL
image = self.numpy_to_pil(image)
# 12. Apply watermark
if self.watermarker is not None:
self.watermarker.apply_watermark(image, self.unet.config.sample_size)
elif output_type == "pt":
nsfw_detected = None
watermark_detected = None
if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
self.unet_offload_hook.offload()
else:
# 9. Post-processing
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
# 10. Run safety checker
image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, nsfw_detected, watermark_detected)
return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/deepfloyd_if/safety_checker.py | import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
logger = logging.get_logger(__name__)
class IFSafetyChecker(PreTrainedModel):
config_class = CLIPConfig
_no_split_modules = ["CLIPEncoderLayer"]
def __init__(self, config: CLIPConfig):
super().__init__(config)
self.vision_model = CLIPVisionModelWithProjection(config.vision_config)
self.p_head = nn.Linear(config.vision_config.projection_dim, 1)
self.w_head = nn.Linear(config.vision_config.projection_dim, 1)
@torch.no_grad()
def forward(self, clip_input, images, p_threshold=0.5, w_threshold=0.5):
image_embeds = self.vision_model(clip_input)[0]
nsfw_detected = self.p_head(image_embeds)
nsfw_detected = nsfw_detected.flatten()
nsfw_detected = nsfw_detected > p_threshold
nsfw_detected = nsfw_detected.tolist()
if any(nsfw_detected):
logger.warning(
"Potential NSFW content was detected in one or more images. A black image will be returned instead."
" Try again with a different prompt and/or seed."
)
for idx, nsfw_detected_ in enumerate(nsfw_detected):
if nsfw_detected_:
images[idx] = np.zeros(images[idx].shape)
watermark_detected = self.w_head(image_embeds)
watermark_detected = watermark_detected.flatten()
watermark_detected = watermark_detected > w_threshold
watermark_detected = watermark_detected.tolist()
if any(watermark_detected):
logger.warning(
"Potential watermarked content was detected in one or more images. A black image will be returned instead."
" Try again with a different prompt and/or seed."
)
for idx, watermark_detected_ in enumerate(watermark_detected):
if watermark_detected_:
images[idx] = np.zeros(images[idx].shape)
return images, nsfw_detected, watermark_detected
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting_superresolution.py | import html
import inspect
import re
import urllib.parse as ul
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL
import torch
import torch.nn.functional as F
from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer
from ...loaders import LoraLoaderMixin
from ...models import UNet2DConditionModel
from ...schedulers import DDPMScheduler
from ...utils import (
BACKENDS_MAPPING,
PIL_INTERPOLATION,
is_accelerate_available,
is_accelerate_version,
is_bs4_available,
is_ftfy_available,
logging,
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline
from . import IFPipelineOutput
from .safety_checker import IFSafetyChecker
from .watermark import IFWatermarker
if is_bs4_available():
from bs4 import BeautifulSoup
if is_ftfy_available():
import ftfy
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.resize
def resize(images: PIL.Image.Image, img_size: int) -> PIL.Image.Image:
w, h = images.size
coef = w / h
w, h = img_size, img_size
if coef >= 1:
w = int(round(img_size / 8 * coef) * 8)
else:
h = int(round(img_size / 8 / coef) * 8)
images = images.resize((w, h), resample=PIL_INTERPOLATION["bicubic"], reducing_gap=None)
return images
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> from diffusers import IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline, DiffusionPipeline
>>> from diffusers.utils import pt_to_pil
>>> import torch
>>> from PIL import Image
>>> import requests
>>> from io import BytesIO
>>> url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/person.png"
>>> response = requests.get(url)
>>> original_image = Image.open(BytesIO(response.content)).convert("RGB")
>>> original_image = original_image
>>> url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/glasses_mask.png"
>>> response = requests.get(url)
>>> mask_image = Image.open(BytesIO(response.content))
>>> mask_image = mask_image
>>> pipe = IFInpaintingPipeline.from_pretrained(
... "DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16
... )
>>> pipe.enable_model_cpu_offload()
>>> prompt = "blue sunglasses"
>>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
>>> image = pipe(
... image=original_image,
... mask_image=mask_image,
... prompt_embeds=prompt_embeds,
... negative_prompt_embeds=negative_embeds,
... output_type="pt",
... ).images
>>> # save intermediate image
>>> pil_image = pt_to_pil(image)
>>> pil_image[0].save("./if_stage_I.png")
>>> super_res_1_pipe = IFInpaintingSuperResolutionPipeline.from_pretrained(
... "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16
... )
>>> super_res_1_pipe.enable_model_cpu_offload()
>>> image = super_res_1_pipe(
... image=image,
... mask_image=mask_image,
... original_image=original_image,
... prompt_embeds=prompt_embeds,
... negative_prompt_embeds=negative_embeds,
... ).images
>>> image[0].save("./if_stage_II.png")
```
"""
class IFInpaintingSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin):
tokenizer: T5Tokenizer
text_encoder: T5EncoderModel
unet: UNet2DConditionModel
scheduler: DDPMScheduler
image_noising_scheduler: DDPMScheduler
feature_extractor: Optional[CLIPImageProcessor]
safety_checker: Optional[IFSafetyChecker]
watermarker: Optional[IFWatermarker]
bad_punct_regex = re.compile(
r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
) # noqa
_optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor", "watermarker"]
def __init__(
self,
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
unet: UNet2DConditionModel,
scheduler: DDPMScheduler,
image_noising_scheduler: DDPMScheduler,
safety_checker: Optional[IFSafetyChecker],
feature_extractor: Optional[CLIPImageProcessor],
watermarker: Optional[IFWatermarker],
requires_safety_checker: bool = True,
):
super().__init__()
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the IF license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
if unet.config.in_channels != 6:
logger.warn(
"It seems like you have loaded a checkpoint that shall not be used for super resolution from {unet.config._name_or_path} as it accepts {unet.config.in_channels} input channels instead of 6. Please make sure to pass a super resolution checkpoint as the `'unet'`: IFSuperResolutionPipeline.from_pretrained(unet=super_resolution_unet, ...)`."
)
self.register_modules(
tokenizer=tokenizer,
text_encoder=text_encoder,
unet=unet,
scheduler=scheduler,
image_noising_scheduler=image_noising_scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
watermarker=watermarker,
)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.enable_model_cpu_offload
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
if self.text_encoder is not None:
_, hook = cpu_offload_with_hook(self.text_encoder, device, prev_module_hook=hook)
# Accelerate will move the next model to the device _before_ calling the offload hook of the
# previous model. This will cause both models to be present on the device at the same time.
# IF uses T5 for its text encoder which is really large. We can manually call the offload
# hook for the text encoder to ensure it's moved to the cpu before the unet is moved to
# the GPU.
self.text_encoder_offload_hook = hook
_, hook = cpu_offload_with_hook(self.unet, device, prev_module_hook=hook)
# if the safety checker isn't called, `unet_offload_hook` will have to be called to manually offload the unet
self.unet_offload_hook = hook
if self.safety_checker is not None:
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.remove_all_hooks
def remove_all_hooks(self):
if is_accelerate_available():
from accelerate.hooks import remove_hook_from_module
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
for model in [self.text_encoder, self.unet, self.safety_checker]:
if model is not None:
remove_hook_from_module(model, recurse=True)
self.unet_offload_hook = None
self.text_encoder_offload_hook = None
self.final_offload_hook = None
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
def _text_preprocessing(self, text, clean_caption=False):
if clean_caption and not is_bs4_available():
logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
logger.warn("Setting `clean_caption` to False...")
clean_caption = False
if clean_caption and not is_ftfy_available():
logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
logger.warn("Setting `clean_caption` to False...")
clean_caption = False
if not isinstance(text, (tuple, list)):
text = [text]
def process(text: str):
if clean_caption:
text = self._clean_caption(text)
text = self._clean_caption(text)
else:
text = text.lower().strip()
return text
return [process(t) for t in text]
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
def _clean_caption(self, caption):
caption = str(caption)
caption = ul.unquote_plus(caption)
caption = caption.strip().lower()
caption = re.sub("<person>", "person", caption)
# urls:
caption = re.sub(
r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
caption = re.sub(
r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
# html:
caption = BeautifulSoup(caption, features="html.parser").text
# @<nickname>
caption = re.sub(r"@[\w\d]+\b", "", caption)
# 31C0—31EF CJK Strokes
# 31F0—31FF Katakana Phonetic Extensions
# 3200—32FF Enclosed CJK Letters and Months
# 3300—33FF CJK Compatibility
# 3400—4DBF CJK Unified Ideographs Extension A
# 4DC0—4DFF Yijing Hexagram Symbols
# 4E00—9FFF CJK Unified Ideographs
caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
#######################################################
# все виды тире / all types of dash --> "-"
caption = re.sub(
r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa
"-",
caption,
)
# кавычки к одному стандарту
caption = re.sub(r"[`´«»“”¨]", '"', caption)
caption = re.sub(r"[‘’]", "'", caption)
# "
caption = re.sub(r""?", "", caption)
# &
caption = re.sub(r"&", "", caption)
# ip adresses:
caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
# article ids:
caption = re.sub(r"\d:\d\d\s+$", "", caption)
# \n
caption = re.sub(r"\\n", " ", caption)
# "#123"
caption = re.sub(r"#\d{1,3}\b", "", caption)
# "#12345.."
caption = re.sub(r"#\d{5,}\b", "", caption)
# "123456.."
caption = re.sub(r"\b\d{6,}\b", "", caption)
# filenames:
caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)
#
caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT"""
caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT"""
caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT
caption = re.sub(r"\s+\.\s+", r" ", caption) # " . "
# this-is-my-cute-cat / this_is_my_cute_cat
regex2 = re.compile(r"(?:\-|\_)")
if len(re.findall(regex2, caption)) > 3:
caption = re.sub(regex2, " ", caption)
caption = ftfy.fix_text(caption)
caption = html.unescape(html.unescape(caption))
caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640
caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc
caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231
caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
caption = re.sub(r"\bpage\s+\d+\b", "", caption)
caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a...
caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)
caption = re.sub(r"\b\s+\:\s+", r": ", caption)
caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
caption = re.sub(r"\s+", " ", caption)
caption.strip()
caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
caption = re.sub(r"^\.\S+$", "", caption)
return caption.strip()
@torch.no_grad()
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.encode_prompt
def encode_prompt(
self,
prompt,
do_classifier_free_guidance=True,
num_images_per_prompt=1,
device=None,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
clean_caption: bool = False,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`, *optional*):
torch device to place the resulting embeddings on
num_images_per_prompt (`int`, *optional*, defaults to 1):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
"""
if prompt is not None and negative_prompt is not None:
if type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
if device is None:
device = self._execution_device
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
# while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF
max_length = 77
if prompt_embeds is None:
prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=max_length,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {max_length} tokens: {removed_text}"
)
attention_mask = text_inputs.attention_mask.to(device)
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
if self.text_encoder is not None:
dtype = self.text_encoder.dtype
elif self.unet is not None:
dtype = self.unet.dtype
else:
dtype = None
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
attention_mask = uncond_input.attention_mask.to(device)
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
else:
negative_prompt_embeds = None
return prompt_embeds, negative_prompt_embeds
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
image, nsfw_detected, watermark_detected = self.safety_checker(
images=image,
clip_input=safety_checker_input.pixel_values.to(dtype=dtype),
)
else:
nsfw_detected = None
watermark_detected = None
if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
self.unet_offload_hook.offload()
return image, nsfw_detected, watermark_detected
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self,
prompt,
image,
original_image,
mask_image,
batch_size,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# image
if isinstance(image, list):
check_image_type = image[0]
else:
check_image_type = image
if (
not isinstance(check_image_type, torch.Tensor)
and not isinstance(check_image_type, PIL.Image.Image)
and not isinstance(check_image_type, np.ndarray)
):
raise ValueError(
"`image` has to be of type `torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is"
f" {type(check_image_type)}"
)
if isinstance(image, list):
image_batch_size = len(image)
elif isinstance(image, torch.Tensor):
image_batch_size = image.shape[0]
elif isinstance(image, PIL.Image.Image):
image_batch_size = 1
elif isinstance(image, np.ndarray):
image_batch_size = image.shape[0]
else:
assert False
if batch_size != image_batch_size:
raise ValueError(f"image batch size: {image_batch_size} must be same as prompt batch size {batch_size}")
# original_image
if isinstance(original_image, list):
check_image_type = original_image[0]
else:
check_image_type = original_image
if (
not isinstance(check_image_type, torch.Tensor)
and not isinstance(check_image_type, PIL.Image.Image)
and not isinstance(check_image_type, np.ndarray)
):
raise ValueError(
"`original_image` has to be of type `torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is"
f" {type(check_image_type)}"
)
if isinstance(original_image, list):
image_batch_size = len(original_image)
elif isinstance(original_image, torch.Tensor):
image_batch_size = original_image.shape[0]
elif isinstance(original_image, PIL.Image.Image):
image_batch_size = 1
elif isinstance(original_image, np.ndarray):
image_batch_size = original_image.shape[0]
else:
assert False
if batch_size != image_batch_size:
raise ValueError(
f"original_image batch size: {image_batch_size} must be same as prompt batch size {batch_size}"
)
# mask_image
if isinstance(mask_image, list):
check_image_type = mask_image[0]
else:
check_image_type = mask_image
if (
not isinstance(check_image_type, torch.Tensor)
and not isinstance(check_image_type, PIL.Image.Image)
and not isinstance(check_image_type, np.ndarray)
):
raise ValueError(
"`mask_image` has to be of type `torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is"
f" {type(check_image_type)}"
)
if isinstance(mask_image, list):
image_batch_size = len(mask_image)
elif isinstance(mask_image, torch.Tensor):
image_batch_size = mask_image.shape[0]
elif isinstance(mask_image, PIL.Image.Image):
image_batch_size = 1
elif isinstance(mask_image, np.ndarray):
image_batch_size = mask_image.shape[0]
else:
assert False
if image_batch_size != 1 and batch_size != image_batch_size:
raise ValueError(
f"mask_image batch size: {image_batch_size} must be `1` or the same as prompt batch size {batch_size}"
)
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.preprocess_image with preprocess_image -> preprocess_original_image
def preprocess_original_image(self, image: PIL.Image.Image) -> torch.Tensor:
if not isinstance(image, list):
image = [image]
def numpy_to_pt(images):
if images.ndim == 3:
images = images[..., None]
images = torch.from_numpy(images.transpose(0, 3, 1, 2))
return images
if isinstance(image[0], PIL.Image.Image):
new_image = []
for image_ in image:
image_ = image_.convert("RGB")
image_ = resize(image_, self.unet.sample_size)
image_ = np.array(image_)
image_ = image_.astype(np.float32)
image_ = image_ / 127.5 - 1
new_image.append(image_)
image = new_image
image = np.stack(image, axis=0) # to np
image = numpy_to_pt(image) # to pt
elif isinstance(image[0], np.ndarray):
image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0)
image = numpy_to_pt(image)
elif isinstance(image[0], torch.Tensor):
image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0)
return image
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_superresolution.IFSuperResolutionPipeline.preprocess_image
def preprocess_image(self, image: PIL.Image.Image, num_images_per_prompt, device) -> torch.Tensor:
if not isinstance(image, torch.Tensor) and not isinstance(image, list):
image = [image]
if isinstance(image[0], PIL.Image.Image):
image = [np.array(i).astype(np.float32) / 127.5 - 1.0 for i in image]
image = np.stack(image, axis=0) # to np
image = torch.from_numpy(image.transpose(0, 3, 1, 2))
elif isinstance(image[0], np.ndarray):
image = np.stack(image, axis=0) # to np
if image.ndim == 5:
image = image[0]
image = torch.from_numpy(image.transpose(0, 3, 1, 2))
elif isinstance(image, list) and isinstance(image[0], torch.Tensor):
dims = image[0].ndim
if dims == 3:
image = torch.stack(image, dim=0)
elif dims == 4:
image = torch.concat(image, dim=0)
else:
raise ValueError(f"Image must have 3 or 4 dimensions, instead got {dims}")
image = image.to(device=device, dtype=self.unet.dtype)
image = image.repeat_interleave(num_images_per_prompt, dim=0)
return image
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_inpainting.IFInpaintingPipeline.preprocess_mask_image
def preprocess_mask_image(self, mask_image) -> torch.Tensor:
if not isinstance(mask_image, list):
mask_image = [mask_image]
if isinstance(mask_image[0], torch.Tensor):
mask_image = torch.cat(mask_image, axis=0) if mask_image[0].ndim == 4 else torch.stack(mask_image, axis=0)
if mask_image.ndim == 2:
# Batch and add channel dim for single mask
mask_image = mask_image.unsqueeze(0).unsqueeze(0)
elif mask_image.ndim == 3 and mask_image.shape[0] == 1:
# Single mask, the 0'th dimension is considered to be
# the existing batch size of 1
mask_image = mask_image.unsqueeze(0)
elif mask_image.ndim == 3 and mask_image.shape[0] != 1:
# Batch of mask, the 0'th dimension is considered to be
# the batching dimension
mask_image = mask_image.unsqueeze(1)
mask_image[mask_image < 0.5] = 0
mask_image[mask_image >= 0.5] = 1
elif isinstance(mask_image[0], PIL.Image.Image):
new_mask_image = []
for mask_image_ in mask_image:
mask_image_ = mask_image_.convert("L")
mask_image_ = resize(mask_image_, self.unet.sample_size)
mask_image_ = np.array(mask_image_)
mask_image_ = mask_image_[None, None, :]
new_mask_image.append(mask_image_)
mask_image = new_mask_image
mask_image = np.concatenate(mask_image, axis=0)
mask_image = mask_image.astype(np.float32) / 255.0
mask_image[mask_image < 0.5] = 0
mask_image[mask_image >= 0.5] = 1
mask_image = torch.from_numpy(mask_image)
elif isinstance(mask_image[0], np.ndarray):
mask_image = np.concatenate([m[None, None, :] for m in mask_image], axis=0)
mask_image[mask_image < 0.5] = 0
mask_image[mask_image >= 0.5] = 1
mask_image = torch.from_numpy(mask_image)
return mask_image
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_inpainting.IFInpaintingPipeline.prepare_intermediate_images
def prepare_intermediate_images(
self, image, timestep, batch_size, num_images_per_prompt, dtype, device, mask_image, generator=None
):
image_batch_size, channels, height, width = image.shape
batch_size = batch_size * num_images_per_prompt
shape = (batch_size, channels, height, width)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
image = image.repeat_interleave(num_images_per_prompt, dim=0)
noised_image = self.scheduler.add_noise(image, noise, timestep)
image = (1 - mask_image) * image + mask_image * noised_image
return image
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
image: Union[PIL.Image.Image, np.ndarray, torch.FloatTensor],
original_image: Union[
PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray]
] = None,
mask_image: Union[
PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray]
] = None,
strength: float = 0.8,
prompt: Union[str, List[str]] = None,
num_inference_steps: int = 100,
timesteps: List[int] = None,
guidance_scale: float = 4.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
noise_level: int = 0,
clean_caption: bool = True,
):
"""
Function invoked when calling the pipeline for generation.
Args:
image (`torch.FloatTensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
original_image (`torch.FloatTensor` or `PIL.Image.Image`):
The original image that `image` was varied from.
mask_image (`PIL.Image.Image`):
`Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
instead of 3, so the expected shape would be `(B, H, W, 1)`.
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
will be used as a starting point, adding more noise to it the larger the `strength`. The number of
denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
timesteps (`List[int]`, *optional*):
Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
timesteps are used. Must be in descending order.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
noise_level (`int`, *optional*, defaults to 0):
The amount of noise to add to the upscaled image. Must be in the range `[0, 1000)`
clean_caption (`bool`, *optional*, defaults to `True`):
Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
be installed. If the dependencies are not installed, the embeddings will be created from the raw
prompt.
Examples:
Returns:
[`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When
returning a tuple, the first element is a list with the generated images, and the second element is a list
of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw)
or watermarked content, according to the `safety_checker`.
"""
# 1. Check inputs. Raise error if not correct
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
self.check_inputs(
prompt,
image,
original_image,
mask_image,
batch_size,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
)
# 2. Define call parameters
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
device = self._execution_device
# 3. Encode input prompt
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
do_classifier_free_guidance,
num_images_per_prompt=num_images_per_prompt,
device=device,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
clean_caption=clean_caption,
)
if do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
dtype = prompt_embeds.dtype
# 4. Prepare timesteps
if timesteps is not None:
self.scheduler.set_timesteps(timesteps=timesteps, device=device)
timesteps = self.scheduler.timesteps
num_inference_steps = len(timesteps)
else:
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
# 5. prepare original image
original_image = self.preprocess_original_image(original_image)
original_image = original_image.to(device=device, dtype=dtype)
# 6. prepare mask image
mask_image = self.preprocess_mask_image(mask_image)
mask_image = mask_image.to(device=device, dtype=dtype)
if mask_image.shape[0] == 1:
mask_image = mask_image.repeat_interleave(batch_size * num_images_per_prompt, dim=0)
else:
mask_image = mask_image.repeat_interleave(num_images_per_prompt, dim=0)
# 6. Prepare intermediate images
noise_timestep = timesteps[0:1]
noise_timestep = noise_timestep.repeat(batch_size * num_images_per_prompt)
intermediate_images = self.prepare_intermediate_images(
original_image,
noise_timestep,
batch_size,
num_images_per_prompt,
dtype,
device,
mask_image,
generator,
)
# 7. Prepare upscaled image and noise level
_, _, height, width = original_image.shape
image = self.preprocess_image(image, num_images_per_prompt, device)
upscaled = F.interpolate(image, (height, width), mode="bilinear", align_corners=True)
noise_level = torch.tensor([noise_level] * upscaled.shape[0], device=upscaled.device)
noise = randn_tensor(upscaled.shape, generator=generator, device=upscaled.device, dtype=upscaled.dtype)
upscaled = self.image_noising_scheduler.add_noise(upscaled, noise, timesteps=noise_level)
if do_classifier_free_guidance:
noise_level = torch.cat([noise_level] * 2)
# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# HACK: see comment in `enable_model_cpu_offload`
if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None:
self.text_encoder_offload_hook.offload()
# 9. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
model_input = torch.cat([intermediate_images, upscaled], dim=1)
model_input = torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input
model_input = self.scheduler.scale_model_input(model_input, t)
# predict the noise residual
noise_pred = self.unet(
model_input,
t,
encoder_hidden_states=prompt_embeds,
class_labels=noise_level,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1] // 2, dim=1)
noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1] // 2, dim=1)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
if self.scheduler.config.variance_type not in ["learned", "learned_range"]:
noise_pred, _ = noise_pred.split(intermediate_images.shape[1], dim=1)
# compute the previous noisy sample x_t -> x_t-1
prev_intermediate_images = intermediate_images
intermediate_images = self.scheduler.step(
noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False
)[0]
intermediate_images = (1 - mask_image) * prev_intermediate_images + mask_image * intermediate_images
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, intermediate_images)
image = intermediate_images
if output_type == "pil":
# 10. Post-processing
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
# 11. Run safety checker
image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
# 12. Convert to PIL
image = self.numpy_to_pil(image)
# 13. Apply watermark
if self.watermarker is not None:
self.watermarker.apply_watermark(image, self.unet.config.sample_size)
elif output_type == "pt":
nsfw_detected = None
watermark_detected = None
if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
self.unet_offload_hook.offload()
else:
# 10. Post-processing
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
# 11. Run safety checker
image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, nsfw_detected, watermark_detected)
return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/deepfloyd_if/watermark.py | from typing import List
import PIL
import torch
from PIL import Image
from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION
class IFWatermarker(ModelMixin, ConfigMixin):
def __init__(self):
super().__init__()
self.register_buffer("watermark_image", torch.zeros((62, 62, 4)))
self.watermark_image_as_pil = None
def apply_watermark(self, images: List[PIL.Image.Image], sample_size=None):
# copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287
h = images[0].height
w = images[0].width
sample_size = sample_size or h
coef = min(h / sample_size, w / sample_size)
img_h, img_w = (int(h / coef), int(w / coef)) if coef < 1 else (h, w)
S1, S2 = 1024**2, img_w * img_h
K = (S2 / S1) ** 0.5
wm_size, wm_x, wm_y = int(K * 62), img_w - int(14 * K), img_h - int(14 * K)
if self.watermark_image_as_pil is None:
watermark_image = self.watermark_image.to(torch.uint8).cpu().numpy()
watermark_image = Image.fromarray(watermark_image, mode="RGBA")
self.watermark_image_as_pil = watermark_image
wm_img = self.watermark_image_as_pil.resize(
(wm_size, wm_size), PIL_INTERPOLATION["bicubic"], reducing_gap=None
)
for pil_img in images:
pil_img.paste(wm_img, box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y), mask=wm_img.split()[-1])
return images
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/deepfloyd_if/timesteps.py | fast27_timesteps = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
smart27_timesteps = [
999,
976,
952,
928,
905,
882,
858,
857,
810,
762,
715,
714,
572,
429,
428,
286,
285,
238,
190,
143,
142,
118,
95,
71,
47,
24,
0,
]
smart50_timesteps = [
999,
988,
977,
966,
955,
944,
933,
922,
911,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
350,
300,
299,
266,
233,
200,
199,
179,
159,
140,
120,
100,
99,
88,
77,
66,
55,
44,
33,
22,
11,
0,
]
smart100_timesteps = [
999,
995,
992,
989,
985,
981,
978,
975,
971,
967,
964,
961,
957,
956,
951,
947,
942,
937,
933,
928,
923,
919,
914,
913,
908,
903,
897,
892,
887,
881,
876,
871,
870,
864,
858,
852,
846,
840,
834,
828,
827,
820,
813,
806,
799,
792,
785,
784,
777,
770,
763,
756,
749,
742,
741,
733,
724,
716,
707,
699,
698,
688,
677,
666,
656,
655,
645,
634,
623,
613,
612,
598,
584,
570,
569,
555,
541,
527,
526,
505,
484,
483,
462,
440,
439,
396,
395,
352,
351,
308,
307,
264,
263,
220,
219,
176,
132,
88,
44,
0,
]
smart185_timesteps = [
999,
997,
995,
992,
990,
988,
986,
984,
981,
979,
977,
975,
972,
970,
968,
966,
964,
961,
959,
957,
956,
954,
951,
949,
946,
944,
941,
939,
936,
934,
931,
929,
926,
924,
921,
919,
916,
914,
913,
910,
907,
905,
902,
899,
896,
893,
891,
888,
885,
882,
879,
877,
874,
871,
870,
867,
864,
861,
858,
855,
852,
849,
846,
843,
840,
837,
834,
831,
828,
827,
824,
821,
817,
814,
811,
808,
804,
801,
798,
795,
791,
788,
785,
784,
780,
777,
774,
770,
766,
763,
760,
756,
752,
749,
746,
742,
741,
737,
733,
730,
726,
722,
718,
714,
710,
707,
703,
699,
698,
694,
690,
685,
681,
677,
673,
669,
664,
660,
656,
655,
650,
646,
641,
636,
632,
627,
622,
618,
613,
612,
607,
602,
596,
591,
586,
580,
575,
570,
569,
563,
557,
551,
545,
539,
533,
527,
526,
519,
512,
505,
498,
491,
484,
483,
474,
466,
457,
449,
440,
439,
428,
418,
407,
396,
395,
381,
366,
352,
351,
330,
308,
307,
286,
264,
263,
242,
220,
219,
176,
175,
132,
131,
88,
44,
0,
]
super27_timesteps = [
999,
991,
982,
974,
966,
958,
950,
941,
933,
925,
916,
908,
900,
899,
874,
850,
825,
800,
799,
700,
600,
500,
400,
300,
200,
100,
0,
]
super40_timesteps = [
999,
992,
985,
978,
971,
964,
957,
949,
942,
935,
928,
921,
914,
907,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
300,
299,
200,
199,
100,
99,
0,
]
super100_timesteps = [
999,
996,
992,
989,
985,
982,
979,
975,
972,
968,
965,
961,
958,
955,
951,
948,
944,
941,
938,
934,
931,
927,
924,
920,
917,
914,
910,
907,
903,
900,
899,
891,
884,
876,
869,
861,
853,
846,
838,
830,
823,
815,
808,
800,
799,
788,
777,
766,
755,
744,
733,
722,
711,
700,
699,
688,
677,
666,
655,
644,
633,
622,
611,
600,
599,
585,
571,
557,
542,
528,
514,
500,
499,
485,
471,
457,
442,
428,
414,
400,
399,
379,
359,
340,
320,
300,
299,
279,
259,
240,
220,
200,
199,
166,
133,
100,
99,
66,
33,
0,
]
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py | import html
import inspect
import re
import urllib.parse as ul
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer
from ...loaders import LoraLoaderMixin
from ...models import UNet2DConditionModel
from ...schedulers import DDPMScheduler
from ...utils import (
BACKENDS_MAPPING,
PIL_INTERPOLATION,
is_accelerate_available,
is_accelerate_version,
is_bs4_available,
is_ftfy_available,
logging,
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline
from . import IFPipelineOutput
from .safety_checker import IFSafetyChecker
from .watermark import IFWatermarker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
if is_bs4_available():
from bs4 import BeautifulSoup
if is_ftfy_available():
import ftfy
def resize(images: PIL.Image.Image, img_size: int) -> PIL.Image.Image:
w, h = images.size
coef = w / h
w, h = img_size, img_size
if coef >= 1:
w = int(round(img_size / 8 * coef) * 8)
else:
h = int(round(img_size / 8 / coef) * 8)
images = images.resize((w, h), resample=PIL_INTERPOLATION["bicubic"], reducing_gap=None)
return images
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> from diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline, DiffusionPipeline
>>> from diffusers.utils import pt_to_pil
>>> import torch
>>> from PIL import Image
>>> import requests
>>> from io import BytesIO
>>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
>>> response = requests.get(url)
>>> original_image = Image.open(BytesIO(response.content)).convert("RGB")
>>> original_image = original_image.resize((768, 512))
>>> pipe = IFImg2ImgPipeline.from_pretrained(
... "DeepFloyd/IF-I-XL-v1.0",
... variant="fp16",
... torch_dtype=torch.float16,
... )
>>> pipe.enable_model_cpu_offload()
>>> prompt = "A fantasy landscape in style minecraft"
>>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
>>> image = pipe(
... image=original_image,
... prompt_embeds=prompt_embeds,
... negative_prompt_embeds=negative_embeds,
... output_type="pt",
... ).images
>>> # save intermediate image
>>> pil_image = pt_to_pil(image)
>>> pil_image[0].save("./if_stage_I.png")
>>> super_res_1_pipe = IFImg2ImgSuperResolutionPipeline.from_pretrained(
... "DeepFloyd/IF-II-L-v1.0",
... text_encoder=None,
... variant="fp16",
... torch_dtype=torch.float16,
... )
>>> super_res_1_pipe.enable_model_cpu_offload()
>>> image = super_res_1_pipe(
... image=image,
... original_image=original_image,
... prompt_embeds=prompt_embeds,
... negative_prompt_embeds=negative_embeds,
... ).images
>>> image[0].save("./if_stage_II.png")
```
"""
class IFImg2ImgPipeline(DiffusionPipeline, LoraLoaderMixin):
tokenizer: T5Tokenizer
text_encoder: T5EncoderModel
unet: UNet2DConditionModel
scheduler: DDPMScheduler
feature_extractor: Optional[CLIPImageProcessor]
safety_checker: Optional[IFSafetyChecker]
watermarker: Optional[IFWatermarker]
bad_punct_regex = re.compile(
r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
) # noqa
_optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor", "watermarker"]
def __init__(
self,
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
unet: UNet2DConditionModel,
scheduler: DDPMScheduler,
safety_checker: Optional[IFSafetyChecker],
feature_extractor: Optional[CLIPImageProcessor],
watermarker: Optional[IFWatermarker],
requires_safety_checker: bool = True,
):
super().__init__()
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the IF license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
self.register_modules(
tokenizer=tokenizer,
text_encoder=text_encoder,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
watermarker=watermarker,
)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.enable_model_cpu_offload
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
if self.text_encoder is not None:
_, hook = cpu_offload_with_hook(self.text_encoder, device, prev_module_hook=hook)
# Accelerate will move the next model to the device _before_ calling the offload hook of the
# previous model. This will cause both models to be present on the device at the same time.
# IF uses T5 for its text encoder which is really large. We can manually call the offload
# hook for the text encoder to ensure it's moved to the cpu before the unet is moved to
# the GPU.
self.text_encoder_offload_hook = hook
_, hook = cpu_offload_with_hook(self.unet, device, prev_module_hook=hook)
# if the safety checker isn't called, `unet_offload_hook` will have to be called to manually offload the unet
self.unet_offload_hook = hook
if self.safety_checker is not None:
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.remove_all_hooks
def remove_all_hooks(self):
if is_accelerate_available():
from accelerate.hooks import remove_hook_from_module
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
for model in [self.text_encoder, self.unet, self.safety_checker]:
if model is not None:
remove_hook_from_module(model, recurse=True)
self.unet_offload_hook = None
self.text_encoder_offload_hook = None
self.final_offload_hook = None
@torch.no_grad()
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.encode_prompt
def encode_prompt(
self,
prompt,
do_classifier_free_guidance=True,
num_images_per_prompt=1,
device=None,
negative_prompt=None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
clean_caption: bool = False,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`, *optional*):
torch device to place the resulting embeddings on
num_images_per_prompt (`int`, *optional*, defaults to 1):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
"""
if prompt is not None and negative_prompt is not None:
if type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
if device is None:
device = self._execution_device
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
# while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF
max_length = 77
if prompt_embeds is None:
prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=max_length,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {max_length} tokens: {removed_text}"
)
attention_mask = text_inputs.attention_mask.to(device)
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
if self.text_encoder is not None:
dtype = self.text_encoder.dtype
elif self.unet is not None:
dtype = self.unet.dtype
else:
dtype = None
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
attention_mask = uncond_input.attention_mask.to(device)
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
else:
negative_prompt_embeds = None
return prompt_embeds, negative_prompt_embeds
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
image, nsfw_detected, watermark_detected = self.safety_checker(
images=image,
clip_input=safety_checker_input.pixel_values.to(dtype=dtype),
)
else:
nsfw_detected = None
watermark_detected = None
if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
self.unet_offload_hook.offload()
return image, nsfw_detected, watermark_detected
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self,
prompt,
image,
batch_size,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
if isinstance(image, list):
check_image_type = image[0]
else:
check_image_type = image
if (
not isinstance(check_image_type, torch.Tensor)
and not isinstance(check_image_type, PIL.Image.Image)
and not isinstance(check_image_type, np.ndarray)
):
raise ValueError(
"`image` has to be of type `torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is"
f" {type(check_image_type)}"
)
if isinstance(image, list):
image_batch_size = len(image)
elif isinstance(image, torch.Tensor):
image_batch_size = image.shape[0]
elif isinstance(image, PIL.Image.Image):
image_batch_size = 1
elif isinstance(image, np.ndarray):
image_batch_size = image.shape[0]
else:
assert False
if batch_size != image_batch_size:
raise ValueError(f"image batch size: {image_batch_size} must be same as prompt batch size {batch_size}")
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
def _text_preprocessing(self, text, clean_caption=False):
if clean_caption and not is_bs4_available():
logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
logger.warn("Setting `clean_caption` to False...")
clean_caption = False
if clean_caption and not is_ftfy_available():
logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
logger.warn("Setting `clean_caption` to False...")
clean_caption = False
if not isinstance(text, (tuple, list)):
text = [text]
def process(text: str):
if clean_caption:
text = self._clean_caption(text)
text = self._clean_caption(text)
else:
text = text.lower().strip()
return text
return [process(t) for t in text]
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
def _clean_caption(self, caption):
caption = str(caption)
caption = ul.unquote_plus(caption)
caption = caption.strip().lower()
caption = re.sub("<person>", "person", caption)
# urls:
caption = re.sub(
r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
caption = re.sub(
r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
# html:
caption = BeautifulSoup(caption, features="html.parser").text
# @<nickname>
caption = re.sub(r"@[\w\d]+\b", "", caption)
# 31C0—31EF CJK Strokes
# 31F0—31FF Katakana Phonetic Extensions
# 3200—32FF Enclosed CJK Letters and Months
# 3300—33FF CJK Compatibility
# 3400—4DBF CJK Unified Ideographs Extension A
# 4DC0—4DFF Yijing Hexagram Symbols
# 4E00—9FFF CJK Unified Ideographs
caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
#######################################################
# все виды тире / all types of dash --> "-"
caption = re.sub(
r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa
"-",
caption,
)
# кавычки к одному стандарту
caption = re.sub(r"[`´«»“”¨]", '"', caption)
caption = re.sub(r"[‘’]", "'", caption)
# "
caption = re.sub(r""?", "", caption)
# &
caption = re.sub(r"&", "", caption)
# ip adresses:
caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
# article ids:
caption = re.sub(r"\d:\d\d\s+$", "", caption)
# \n
caption = re.sub(r"\\n", " ", caption)
# "#123"
caption = re.sub(r"#\d{1,3}\b", "", caption)
# "#12345.."
caption = re.sub(r"#\d{5,}\b", "", caption)
# "123456.."
caption = re.sub(r"\b\d{6,}\b", "", caption)
# filenames:
caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)
#
caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT"""
caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT"""
caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT
caption = re.sub(r"\s+\.\s+", r" ", caption) # " . "
# this-is-my-cute-cat / this_is_my_cute_cat
regex2 = re.compile(r"(?:\-|\_)")
if len(re.findall(regex2, caption)) > 3:
caption = re.sub(regex2, " ", caption)
caption = ftfy.fix_text(caption)
caption = html.unescape(html.unescape(caption))
caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640
caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc
caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231
caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
caption = re.sub(r"\bpage\s+\d+\b", "", caption)
caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a...
caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)
caption = re.sub(r"\b\s+\:\s+", r": ", caption)
caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
caption = re.sub(r"\s+", " ", caption)
caption.strip()
caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
caption = re.sub(r"^\.\S+$", "", caption)
return caption.strip()
def preprocess_image(self, image: PIL.Image.Image) -> torch.Tensor:
if not isinstance(image, list):
image = [image]
def numpy_to_pt(images):
if images.ndim == 3:
images = images[..., None]
images = torch.from_numpy(images.transpose(0, 3, 1, 2))
return images
if isinstance(image[0], PIL.Image.Image):
new_image = []
for image_ in image:
image_ = image_.convert("RGB")
image_ = resize(image_, self.unet.sample_size)
image_ = np.array(image_)
image_ = image_.astype(np.float32)
image_ = image_ / 127.5 - 1
new_image.append(image_)
image = new_image
image = np.stack(image, axis=0) # to np
image = numpy_to_pt(image) # to pt
elif isinstance(image[0], np.ndarray):
image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0)
image = numpy_to_pt(image)
elif isinstance(image[0], torch.Tensor):
image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0)
return image
def get_timesteps(self, num_inference_steps, strength):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def prepare_intermediate_images(
self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None
):
_, channels, height, width = image.shape
batch_size = batch_size * num_images_per_prompt
shape = (batch_size, channels, height, width)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
image = image.repeat_interleave(num_images_per_prompt, dim=0)
image = self.scheduler.add_noise(image, noise, timestep)
return image
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: Union[
PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray]
] = None,
strength: float = 0.7,
num_inference_steps: int = 80,
timesteps: List[int] = None,
guidance_scale: float = 10.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
clean_caption: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
image (`torch.FloatTensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
will be used as a starting point, adding more noise to it the larger the `strength`. The number of
denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
be maximum and the denoising process will run for the full number of iterations specified in
`num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
timesteps (`List[int]`, *optional*):
Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
timesteps are used. Must be in descending order.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
clean_caption (`bool`, *optional*, defaults to `True`):
Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
be installed. If the dependencies are not installed, the embeddings will be created from the raw
prompt.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
Examples:
Returns:
[`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When
returning a tuple, the first element is a list with the generated images, and the second element is a list
of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw)
or watermarked content, according to the `safety_checker`.
"""
# 1. Check inputs. Raise error if not correct
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
self.check_inputs(
prompt, image, batch_size, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
)
# 2. Define call parameters
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
do_classifier_free_guidance,
num_images_per_prompt=num_images_per_prompt,
device=device,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
clean_caption=clean_caption,
)
if do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
dtype = prompt_embeds.dtype
# 4. Prepare timesteps
if timesteps is not None:
self.scheduler.set_timesteps(timesteps=timesteps, device=device)
timesteps = self.scheduler.timesteps
num_inference_steps = len(timesteps)
else:
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
# 5. Prepare intermediate images
image = self.preprocess_image(image)
image = image.to(device=device, dtype=dtype)
noise_timestep = timesteps[0:1]
noise_timestep = noise_timestep.repeat(batch_size * num_images_per_prompt)
intermediate_images = self.prepare_intermediate_images(
image, noise_timestep, batch_size, num_images_per_prompt, dtype, device, generator
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# HACK: see comment in `enable_model_cpu_offload`
if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None:
self.text_encoder_offload_hook.offload()
# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
model_input = (
torch.cat([intermediate_images] * 2) if do_classifier_free_guidance else intermediate_images
)
model_input = self.scheduler.scale_model_input(model_input, t)
# predict the noise residual
noise_pred = self.unet(
model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1], dim=1)
noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1], dim=1)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
if self.scheduler.config.variance_type not in ["learned", "learned_range"]:
noise_pred, _ = noise_pred.split(model_input.shape[1], dim=1)
# compute the previous noisy sample x_t -> x_t-1
intermediate_images = self.scheduler.step(
noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False
)[0]
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, intermediate_images)
image = intermediate_images
if output_type == "pil":
# 8. Post-processing
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
# 9. Run safety checker
image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
# 10. Convert to PIL
image = self.numpy_to_pil(image)
# 11. Apply watermark
if self.watermarker is not None:
self.watermarker.apply_watermark(image, self.unet.config.sample_size)
elif output_type == "pt":
nsfw_detected = None
watermark_detected = None
if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
self.unet_offload_hook.offload()
else:
# 8. Post-processing
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
# 9. Run safety checker
image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, nsfw_detected, watermark_detected)
return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/consistency_models/__init__.py | from .pipeline_consistency_models import ConsistencyModelPipeline
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/consistency_models/pipeline_consistency_models.py | from typing import Callable, List, Optional, Union
import torch
from ...models import UNet2DModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import torch
>>> from diffusers import ConsistencyModelPipeline
>>> device = "cuda"
>>> # Load the cd_imagenet64_l2 checkpoint.
>>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
>>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
>>> pipe.to(device)
>>> # Onestep Sampling
>>> image = pipe(num_inference_steps=1).images[0]
>>> image.save("cd_imagenet64_l2_onestep_sample.png")
>>> # Onestep sampling, class-conditional image generation
>>> # ImageNet-64 class label 145 corresponds to king penguins
>>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
>>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")
>>> # Multistep sampling, class-conditional image generation
>>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
>>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
>>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
>>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
```
"""
class ConsistencyModelPipeline(DiffusionPipeline):
r"""
Pipeline for unconditional or class-conditional image generation.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
unet ([`UNet2DModel`]):
A `UNet2DModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Currently only
compatible with [`CMStochasticIterativeScheduler`].
"""
def __init__(self, unet: UNet2DModel, scheduler: CMStochasticIterativeScheduler) -> None:
super().__init__()
self.register_modules(
unet=unet,
scheduler=scheduler,
)
self.safety_checker = None
def enable_model_cpu_offload(self, gpu_id=0):
r"""
Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
iterative execution of the `unet`.
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
device = torch.device(f"cuda:{gpu_id}")
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
hook = None
for cpu_offloaded_model in [self.unet]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
if self.safety_checker is not None:
_, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
# We'll offload the last model manually.
self.final_offload_hook = hook
def prepare_latents(self, batch_size, num_channels, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels, height, width)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device=device, dtype=dtype)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
# Follows diffusers.VaeImageProcessor.postprocess
def postprocess_image(self, sample: torch.FloatTensor, output_type: str = "pil"):
if output_type not in ["pt", "np", "pil"]:
raise ValueError(
f"output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"
)
# Equivalent to diffusers.VaeImageProcessor.denormalize
sample = (sample / 2 + 0.5).clamp(0, 1)
if output_type == "pt":
return sample
# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
sample = sample.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "np":
return sample
# Output_type must be 'pil'
sample = self.numpy_to_pil(sample)
return sample
def prepare_class_labels(self, batch_size, device, class_labels=None):
if self.unet.config.num_class_embeds is not None:
if isinstance(class_labels, list):
class_labels = torch.tensor(class_labels, dtype=torch.int)
elif isinstance(class_labels, int):
assert batch_size == 1, "Batch size must be 1 if classes is an int"
class_labels = torch.tensor([class_labels], dtype=torch.int)
elif class_labels is None:
# Randomly generate batch_size class labels
# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
class_labels = torch.randint(0, self.unet.config.num_class_embeds, size=(batch_size,))
class_labels = class_labels.to(device)
else:
class_labels = None
return class_labels
def check_inputs(self, num_inference_steps, timesteps, latents, batch_size, img_size, callback_steps):
if num_inference_steps is None and timesteps is None:
raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied.")
if num_inference_steps is not None and timesteps is not None:
logger.warning(
f"Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"
" `timesteps` will be used over `num_inference_steps`."
)
if latents is not None:
expected_shape = (batch_size, 3, img_size, img_size)
if latents.shape != expected_shape:
raise ValueError(f"The shape of latents is {latents.shape} but is expected to be {expected_shape}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
batch_size: int = 1,
class_labels: Optional[Union[torch.Tensor, List[int], int]] = None,
num_inference_steps: int = 1,
timesteps: List[int] = None,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
):
r"""
Args:
batch_size (`int`, *optional*, defaults to 1):
The number of images to generate.
class_labels (`torch.Tensor` or `List[int]` or `int`, *optional*):
Optional class labels for conditioning class-conditional consistency models. Not used if the model is
not class-conditional.
num_inference_steps (`int`, *optional*, defaults to 1):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
timesteps (`List[int]`, *optional*):
Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
timesteps are used. Must be in descending order.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images.
"""
# 0. Prepare call parameters
img_size = self.unet.config.sample_size
device = self._execution_device
# 1. Check inputs
self.check_inputs(num_inference_steps, timesteps, latents, batch_size, img_size, callback_steps)
# 2. Prepare image latents
# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
sample = self.prepare_latents(
batch_size=batch_size,
num_channels=self.unet.config.in_channels,
height=img_size,
width=img_size,
dtype=self.unet.dtype,
device=device,
generator=generator,
latents=latents,
)
# 3. Handle class_labels for class-conditional models
class_labels = self.prepare_class_labels(batch_size, device, class_labels=class_labels)
# 4. Prepare timesteps
if timesteps is not None:
self.scheduler.set_timesteps(timesteps=timesteps, device=device)
timesteps = self.scheduler.timesteps
num_inference_steps = len(timesteps)
else:
self.scheduler.set_timesteps(num_inference_steps)
timesteps = self.scheduler.timesteps
# 5. Denoising loop
# Multistep sampling: implements Algorithm 1 in the paper
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
scaled_sample = self.scheduler.scale_model_input(sample, t)
model_output = self.unet(scaled_sample, t, class_labels=class_labels, return_dict=False)[0]
sample = self.scheduler.step(model_output, t, sample, generator=generator)[0]
# call the callback, if provided
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, sample)
# 6. Post-process image sample
image = self.postprocess_image(sample, output_type=output_type)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/ddpm/__init__.py | from .pipeline_ddpm import DDPMPipeline
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/ddpm/pipeline_ddpm.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional, Tuple, Union
import torch
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class DDPMPipeline(DiffusionPipeline):
r"""
Pipeline for image generation.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Parameters:
unet ([`UNet2DModel`]):
A `UNet2DModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
[`DDPMScheduler`], or [`DDIMScheduler`].
"""
def __init__(self, unet, scheduler):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
num_inference_steps: int = 1000,
output_type: Optional[str] = "pil",
return_dict: bool = True,
) -> Union[ImagePipelineOutput, Tuple]:
r"""
The call function to the pipeline for generation.
Args:
batch_size (`int`, *optional*, defaults to 1):
The number of images to generate.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
num_inference_steps (`int`, *optional*, defaults to 1000):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
Example:
```py
>>> from diffusers import DDPMPipeline
>>> # load model and scheduler
>>> pipe = DDPMPipeline.from_pretrained("google/ddpm-cat-256")
>>> # run pipeline in inference (sample random noise and denoise)
>>> image = pipe().images[0]
>>> # save image
>>> image.save("ddpm_generated_image.png")
```
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images
"""
# Sample gaussian noise to begin loop
if isinstance(self.unet.config.sample_size, int):
image_shape = (
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size,
self.unet.config.sample_size,
)
else:
image_shape = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
if self.device.type == "mps":
# randn does not work reproducibly on mps
image = randn_tensor(image_shape, generator=generator)
image = image.to(self.device)
else:
image = randn_tensor(image_shape, generator=generator, device=self.device)
# set step values
self.scheduler.set_timesteps(num_inference_steps)
for t in self.progress_bar(self.scheduler.timesteps):
# 1. predict noise model_output
model_output = self.unet(image, t).sample
# 2. compute previous image: x_t -> x_t-1
image = self.scheduler.step(model_output, t, image, generator=generator).prev_sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/audio_diffusion/__init__.py | from .mel import Mel
from .pipeline_audio_diffusion import AudioDiffusionPipeline
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class AudioDiffusionPipeline(DiffusionPipeline):
"""
Pipeline for audio diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Parameters:
vqae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
mel ([`Mel`]):
Transform audio into a spectrogram.
scheduler ([`DDIMScheduler`] or [`DDPMScheduler`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`] or [`DDPMScheduler`].
"""
_optional_components = ["vqvae"]
def __init__(
self,
vqvae: AutoencoderKL,
unet: UNet2DConditionModel,
mel: Mel,
scheduler: Union[DDIMScheduler, DDPMScheduler],
):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler, mel=mel, vqvae=vqvae)
def get_default_steps(self) -> int:
"""Returns default number of steps recommended for inference.
Returns:
`int`:
The number of steps.
"""
return 50 if isinstance(self.scheduler, DDIMScheduler) else 1000
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
audio_file: str = None,
raw_audio: np.ndarray = None,
slice: int = 0,
start_step: int = 0,
steps: int = None,
generator: torch.Generator = None,
mask_start_secs: float = 0,
mask_end_secs: float = 0,
step_generator: torch.Generator = None,
eta: float = 0,
noise: torch.Tensor = None,
encoding: torch.Tensor = None,
return_dict=True,
) -> Union[
Union[AudioPipelineOutput, ImagePipelineOutput],
Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
]:
"""
The call function to the pipeline for generation.
Args:
batch_size (`int`):
Number of samples to generate.
audio_file (`str`):
An audio file that must be on disk due to [Librosa](https://librosa.org/) limitation.
raw_audio (`np.ndarray`):
The raw audio file as a NumPy array.
slice (`int`):
Slice number of audio to convert.
start_step (int):
Step to start diffusion from.
steps (`int`):
Number of denoising steps (defaults to `50` for DDIM and `1000` for DDPM).
generator (`torch.Generator`):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
mask_start_secs (`float`):
Number of seconds of audio to mask (not generate) at start.
mask_end_secs (`float`):
Number of seconds of audio to mask (not generate) at end.
step_generator (`torch.Generator`):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) used to denoise.
None
eta (`float`):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
noise (`torch.Tensor`):
A noise tensor of shape `(batch_size, 1, height, width)` or `None`.
encoding (`torch.Tensor`):
A tensor for [`UNet2DConditionModel`] of shape `(batch_size, seq_length, cross_attention_dim)`.
return_dict (`bool`):
Whether or not to return a [`AudioPipelineOutput`], [`ImagePipelineOutput`] or a plain tuple.
Examples:
For audio diffusion:
```py
import torch
from IPython.display import Audio
from diffusers import DiffusionPipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-256").to(device)
output = pipe()
display(output.images[0])
display(Audio(output.audios[0], rate=mel.get_sample_rate()))
```
For latent audio diffusion:
```py
import torch
from IPython.display import Audio
from diffusers import DiffusionPipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained("teticio/latent-audio-diffusion-256").to(device)
output = pipe()
display(output.images[0])
display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
```
For other tasks like variation, inpainting, outpainting, etc:
```py
output = pipe(
raw_audio=output.audios[0, 0],
start_step=int(pipe.get_default_steps() / 2),
mask_start_secs=1,
mask_end_secs=1,
)
display(output.images[0])
display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
```
Returns:
`List[PIL Image]`:
A list of Mel spectrograms (`float`, `List[np.ndarray]`) with the sample rate and raw audio.
"""
steps = steps or self.get_default_steps()
self.scheduler.set_timesteps(steps)
step_generator = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size) == int:
self.unet.config.sample_size = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
noise = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
),
generator=generator,
device=self.device,
)
images = noise
mask = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(audio_file, raw_audio)
input_image = self.mel.audio_slice_to_image(slice)
input_image = np.frombuffer(input_image.tobytes(), dtype="uint8").reshape(
(input_image.height, input_image.width)
)
input_image = (input_image / 255) * 2 - 1
input_images = torch.tensor(input_image[np.newaxis, :, :], dtype=torch.float).to(self.device)
if self.vqvae is not None:
input_images = self.vqvae.encode(torch.unsqueeze(input_images, 0)).latent_dist.sample(
generator=generator
)[0]
input_images = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
images[0, 0] = self.scheduler.add_noise(input_images, noise, self.scheduler.timesteps[start_step - 1])
pixels_per_second = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
mask_start = int(mask_start_secs * pixels_per_second)
mask_end = int(mask_end_secs * pixels_per_second)
mask = self.scheduler.add_noise(input_images, noise, torch.tensor(self.scheduler.timesteps[start_step:]))
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])):
if isinstance(self.unet, UNet2DConditionModel):
model_output = self.unet(images, t, encoding)["sample"]
else:
model_output = self.unet(images, t)["sample"]
if isinstance(self.scheduler, DDIMScheduler):
images = self.scheduler.step(
model_output=model_output,
timestep=t,
sample=images,
eta=eta,
generator=step_generator,
)["prev_sample"]
else:
images = self.scheduler.step(
model_output=model_output,
timestep=t,
sample=images,
generator=step_generator,
)["prev_sample"]
if mask is not None:
if mask_start > 0:
images[:, :, :, :mask_start] = mask[:, step, :, :mask_start]
if mask_end > 0:
images[:, :, :, -mask_end:] = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
images = 1 / self.vqvae.config.scaling_factor * images
images = self.vqvae.decode(images)["sample"]
images = (images / 2 + 0.5).clamp(0, 1)
images = images.cpu().permute(0, 2, 3, 1).numpy()
images = (images * 255).round().astype("uint8")
images = list(
(Image.fromarray(_[:, :, 0]) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(_, mode="RGB").convert("L") for _ in images)
)
audios = [self.mel.image_to_audio(_) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(audios)[:, np.newaxis, :]), **ImagePipelineOutput(images))
@torch.no_grad()
def encode(self, images: List[Image.Image], steps: int = 50) -> np.ndarray:
"""
Reverse the denoising step process to recover a noisy image from the generated image.
Args:
images (`List[PIL Image]`):
List of images to encode.
steps (`int`):
Number of encoding steps to perform (defaults to `50`).
Returns:
`np.ndarray`:
A noise tensor of shape `(batch_size, 1, height, width)`.
"""
# Only works with DDIM as this method is deterministic
assert isinstance(self.scheduler, DDIMScheduler)
self.scheduler.set_timesteps(steps)
sample = np.array(
[np.frombuffer(image.tobytes(), dtype="uint8").reshape((1, image.height, image.width)) for image in images]
)
sample = (sample / 255) * 2 - 1
sample = torch.Tensor(sample).to(self.device)
for t in self.progress_bar(torch.flip(self.scheduler.timesteps, (0,))):
prev_timestep = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
alpha_prod_t = self.scheduler.alphas_cumprod[t]
alpha_prod_t_prev = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
beta_prod_t = 1 - alpha_prod_t
model_output = self.unet(sample, t)["sample"]
pred_sample_direction = (1 - alpha_prod_t_prev) ** (0.5) * model_output
sample = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
sample = sample * alpha_prod_t ** (0.5) + beta_prod_t ** (0.5) * model_output
return sample
@staticmethod
def slerp(x0: torch.Tensor, x1: torch.Tensor, alpha: float) -> torch.Tensor:
"""Spherical Linear intERPolation.
Args:
x0 (`torch.Tensor`):
The first tensor to interpolate between.
x1 (`torch.Tensor`):
Second tensor to interpolate between.
alpha (`float`):
Interpolation between 0 and 1
Returns:
`torch.Tensor`:
The interpolated tensor.
"""
theta = acos(torch.dot(torch.flatten(x0), torch.flatten(x1)) / torch.norm(x0) / torch.norm(x1))
return sin((1 - alpha) * theta) * x0 / sin(theta) + sin(alpha * theta) * x1 / sin(theta)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/audio_diffusion/mel.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np # noqa: E402
from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin
try:
import librosa # noqa: E402
_librosa_can_be_imported = True
_import_error = ""
except Exception as e:
_librosa_can_be_imported = False
_import_error = (
f"Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."
)
from PIL import Image # noqa: E402
class Mel(ConfigMixin, SchedulerMixin):
"""
Parameters:
x_res (`int`):
x resolution of spectrogram (time).
y_res (`int`):
y resolution of spectrogram (frequency bins).
sample_rate (`int`):
Sample rate of audio.
n_fft (`int`):
Number of Fast Fourier Transforms.
hop_length (`int`):
Hop length (a higher number is recommended if `y_res` < 256).
top_db (`int`):
Loudest decibel value.
n_iter (`int`):
Number of iterations for Griffin-Lim Mel inversion.
"""
config_name = "mel_config.json"
@register_to_config
def __init__(
self,
x_res: int = 256,
y_res: int = 256,
sample_rate: int = 22050,
n_fft: int = 2048,
hop_length: int = 512,
top_db: int = 80,
n_iter: int = 32,
):
self.hop_length = hop_length
self.sr = sample_rate
self.n_fft = n_fft
self.top_db = top_db
self.n_iter = n_iter
self.set_resolution(x_res, y_res)
self.audio = None
if not _librosa_can_be_imported:
raise ValueError(_import_error)
def set_resolution(self, x_res: int, y_res: int):
"""Set resolution.
Args:
x_res (`int`):
x resolution of spectrogram (time).
y_res (`int`):
y resolution of spectrogram (frequency bins).
"""
self.x_res = x_res
self.y_res = y_res
self.n_mels = self.y_res
self.slice_size = self.x_res * self.hop_length - 1
def load_audio(self, audio_file: str = None, raw_audio: np.ndarray = None):
"""Load audio.
Args:
audio_file (`str`):
An audio file that must be on disk due to [Librosa](https://librosa.org/) limitation.
raw_audio (`np.ndarray`):
The raw audio file as a NumPy array.
"""
if audio_file is not None:
self.audio, _ = librosa.load(audio_file, mono=True, sr=self.sr)
else:
self.audio = raw_audio
# Pad with silence if necessary.
if len(self.audio) < self.x_res * self.hop_length:
self.audio = np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio),))])
def get_number_of_slices(self) -> int:
"""Get number of slices in audio.
Returns:
`int`:
Number of spectograms audio can be sliced into.
"""
return len(self.audio) // self.slice_size
def get_audio_slice(self, slice: int = 0) -> np.ndarray:
"""Get slice of audio.
Args:
slice (`int`):
Slice number of audio (out of `get_number_of_slices()`).
Returns:
`np.ndarray`:
The audio slice as a NumPy array.
"""
return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]
def get_sample_rate(self) -> int:
"""Get sample rate.
Returns:
`int`:
Sample rate of audio.
"""
return self.sr
def audio_slice_to_image(self, slice: int) -> Image.Image:
"""Convert slice of audio to spectrogram.
Args:
slice (`int`):
Slice number of audio to convert (out of `get_number_of_slices()`).
Returns:
`PIL Image`:
A grayscale image of `x_res x y_res`.
"""
S = librosa.feature.melspectrogram(
y=self.get_audio_slice(slice), sr=self.sr, n_fft=self.n_fft, hop_length=self.hop_length, n_mels=self.n_mels
)
log_S = librosa.power_to_db(S, ref=np.max, top_db=self.top_db)
bytedata = (((log_S + self.top_db) * 255 / self.top_db).clip(0, 255) + 0.5).astype(np.uint8)
image = Image.fromarray(bytedata)
return image
def image_to_audio(self, image: Image.Image) -> np.ndarray:
"""Converts spectrogram to audio.
Args:
image (`PIL Image`):
An grayscale image of `x_res x y_res`.
Returns:
audio (`np.ndarray`):
The audio as a NumPy array.
"""
bytedata = np.frombuffer(image.tobytes(), dtype="uint8").reshape((image.height, image.width))
log_S = bytedata.astype("float") * self.top_db / 255 - self.top_db
S = librosa.db_to_power(log_S)
audio = librosa.feature.inverse.mel_to_audio(
S, sr=self.sr, n_fft=self.n_fft, hop_length=self.hop_length, n_iter=self.n_iter
)
return audio
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/unclip/__init__.py | from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/unclip/text_proj.py | # Copyright 2023 Kakao Brain and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class UnCLIPTextProjModel(ModelMixin, ConfigMixin):
"""
Utility class for CLIP embeddings. Used to combine the image and text embeddings into a format usable by the
decoder.
For more details, see the original paper: https://arxiv.org/abs/2204.06125 section 2.1
"""
@register_to_config
def __init__(
self,
*,
clip_extra_context_tokens: int = 4,
clip_embeddings_dim: int = 768,
time_embed_dim: int,
cross_attention_dim,
):
super().__init__()
self.learned_classifier_free_guidance_embeddings = nn.Parameter(torch.zeros(clip_embeddings_dim))
# parameters for additional clip time embeddings
self.embedding_proj = nn.Linear(clip_embeddings_dim, time_embed_dim)
self.clip_image_embeddings_project_to_time_embeddings = nn.Linear(clip_embeddings_dim, time_embed_dim)
# parameters for encoder hidden states
self.clip_extra_context_tokens = clip_extra_context_tokens
self.clip_extra_context_tokens_proj = nn.Linear(
clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim
)
self.encoder_hidden_states_proj = nn.Linear(clip_embeddings_dim, cross_attention_dim)
self.text_encoder_hidden_states_norm = nn.LayerNorm(cross_attention_dim)
def forward(self, *, image_embeddings, prompt_embeds, text_encoder_hidden_states, do_classifier_free_guidance):
if do_classifier_free_guidance:
# Add the classifier free guidance embeddings to the image embeddings
image_embeddings_batch_size = image_embeddings.shape[0]
classifier_free_guidance_embeddings = self.learned_classifier_free_guidance_embeddings.unsqueeze(0)
classifier_free_guidance_embeddings = classifier_free_guidance_embeddings.expand(
image_embeddings_batch_size, -1
)
image_embeddings = torch.cat([classifier_free_guidance_embeddings, image_embeddings], dim=0)
# The image embeddings batch size and the text embeddings batch size are equal
assert image_embeddings.shape[0] == prompt_embeds.shape[0]
batch_size = prompt_embeds.shape[0]
# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
# adding CLIP embeddings to the existing timestep embedding, ...
time_projected_prompt_embeds = self.embedding_proj(prompt_embeds)
time_projected_image_embeddings = self.clip_image_embeddings_project_to_time_embeddings(image_embeddings)
additive_clip_time_embeddings = time_projected_image_embeddings + time_projected_prompt_embeds
# ... and by projecting CLIP embeddings into four
# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
clip_extra_context_tokens = self.clip_extra_context_tokens_proj(image_embeddings)
clip_extra_context_tokens = clip_extra_context_tokens.reshape(batch_size, -1, self.clip_extra_context_tokens)
clip_extra_context_tokens = clip_extra_context_tokens.permute(0, 2, 1)
text_encoder_hidden_states = self.encoder_hidden_states_proj(text_encoder_hidden_states)
text_encoder_hidden_states = self.text_encoder_hidden_states_norm(text_encoder_hidden_states)
text_encoder_hidden_states = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states], dim=1)
return text_encoder_hidden_states, additive_clip_time_embeddings
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/unclip/pipeline_unclip.py | # Copyright 2023 Kakao Brain and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import List, Optional, Tuple, Union
import torch
from torch.nn import functional as F
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput
from ...models import PriorTransformer, UNet2DConditionModel, UNet2DModel
from ...schedulers import UnCLIPScheduler
from ...utils import logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from .text_proj import UnCLIPTextProjModel
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class UnCLIPPipeline(DiffusionPipeline):
"""
Pipeline for text-to-image generation using unCLIP.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
text_encoder ([`~transformers.CLIPTextModelWithProjection`]):
Frozen text-encoder.
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
prior ([`PriorTransformer`]):
The canonical unCLIP prior to approximate the image embedding from the text embedding.
text_proj ([`UnCLIPTextProjModel`]):
Utility class to prepare and combine the embeddings before they are passed to the decoder.
decoder ([`UNet2DConditionModel`]):
The decoder to invert the image embedding into an image.
super_res_first ([`UNet2DModel`]):
Super resolution UNet. Used in all but the last step of the super resolution diffusion process.
super_res_last ([`UNet2DModel`]):
Super resolution UNet. Used in the last step of the super resolution diffusion process.
prior_scheduler ([`UnCLIPScheduler`]):
Scheduler used in the prior denoising process (a modified [`DDPMScheduler`]).
decoder_scheduler ([`UnCLIPScheduler`]):
Scheduler used in the decoder denoising process (a modified [`DDPMScheduler`]).
super_res_scheduler ([`UnCLIPScheduler`]):
Scheduler used in the super resolution denoising process (a modified [`DDPMScheduler`]).
"""
_exclude_from_cpu_offload = ["prior"]
prior: PriorTransformer
decoder: UNet2DConditionModel
text_proj: UnCLIPTextProjModel
text_encoder: CLIPTextModelWithProjection
tokenizer: CLIPTokenizer
super_res_first: UNet2DModel
super_res_last: UNet2DModel
prior_scheduler: UnCLIPScheduler
decoder_scheduler: UnCLIPScheduler
super_res_scheduler: UnCLIPScheduler
def __init__(
self,
prior: PriorTransformer,
decoder: UNet2DConditionModel,
text_encoder: CLIPTextModelWithProjection,
tokenizer: CLIPTokenizer,
text_proj: UnCLIPTextProjModel,
super_res_first: UNet2DModel,
super_res_last: UNet2DModel,
prior_scheduler: UnCLIPScheduler,
decoder_scheduler: UnCLIPScheduler,
super_res_scheduler: UnCLIPScheduler,
):
super().__init__()
self.register_modules(
prior=prior,
decoder=decoder,
text_encoder=text_encoder,
tokenizer=tokenizer,
text_proj=text_proj,
super_res_first=super_res_first,
super_res_last=super_res_last,
prior_scheduler=prior_scheduler,
decoder_scheduler=decoder_scheduler,
super_res_scheduler=super_res_scheduler,
)
def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
latents = latents * scheduler.init_noise_sigma
return latents
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,
text_attention_mask: Optional[torch.Tensor] = None,
):
if text_model_output is None:
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
text_mask = text_inputs.attention_mask.bool().to(device)
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
text_encoder_output = self.text_encoder(text_input_ids.to(device))
prompt_embeds = text_encoder_output.text_embeds
text_encoder_hidden_states = text_encoder_output.last_hidden_state
else:
batch_size = text_model_output[0].shape[0]
prompt_embeds, text_encoder_hidden_states = text_model_output[0], text_model_output[1]
text_mask = text_attention_mask
prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
uncond_tokens = [""] * batch_size
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
uncond_text_mask = uncond_input.attention_mask.bool().to(device)
negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))
negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds
uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
seq_len = uncond_text_encoder_hidden_states.shape[1]
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt, seq_len, -1
)
uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
text_mask = torch.cat([uncond_text_mask, text_mask])
return prompt_embeds, text_encoder_hidden_states, text_mask
@torch.no_grad()
def __call__(
self,
prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: int = 1,
prior_num_inference_steps: int = 25,
decoder_num_inference_steps: int = 25,
super_res_num_inference_steps: int = 7,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prior_latents: Optional[torch.FloatTensor] = None,
decoder_latents: Optional[torch.FloatTensor] = None,
super_res_latents: Optional[torch.FloatTensor] = None,
text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,
text_attention_mask: Optional[torch.Tensor] = None,
prior_guidance_scale: float = 4.0,
decoder_guidance_scale: float = 8.0,
output_type: Optional[str] = "pil",
return_dict: bool = True,
):
"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide image generation. This can only be left undefined if `text_model_output`
and `text_attention_mask` is passed.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
prior_num_inference_steps (`int`, *optional*, defaults to 25):
The number of denoising steps for the prior. More denoising steps usually lead to a higher quality
image at the expense of slower inference.
decoder_num_inference_steps (`int`, *optional*, defaults to 25):
The number of denoising steps for the decoder. More denoising steps usually lead to a higher quality
image at the expense of slower inference.
super_res_num_inference_steps (`int`, *optional*, defaults to 7):
The number of denoising steps for super resolution. More denoising steps usually lead to a higher
quality image at the expense of slower inference.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
prior_latents (`torch.FloatTensor` of shape (batch size, embeddings dimension), *optional*):
Pre-generated noisy latents to be used as inputs for the prior.
decoder_latents (`torch.FloatTensor` of shape (batch size, channels, height, width), *optional*):
Pre-generated noisy latents to be used as inputs for the decoder.
super_res_latents (`torch.FloatTensor` of shape (batch size, channels, super res height, super res width), *optional*):
Pre-generated noisy latents to be used as inputs for the decoder.
prior_guidance_scale (`float`, *optional*, defaults to 4.0):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
decoder_guidance_scale (`float`, *optional*, defaults to 4.0):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
text_model_output (`CLIPTextModelOutput`, *optional*):
Pre-defined [`CLIPTextModel`] outputs that can be derived from the text encoder. Pre-defined text
outputs can be passed for tasks like text embedding interpolations. Make sure to also pass
`text_attention_mask` in this case. `prompt` can the be left `None`.
text_attention_mask (`torch.Tensor`, *optional*):
Pre-defined CLIP text attention mask that can be derived from the tokenizer. Pre-defined text attention
masks are necessary when passing `text_model_output`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images.
"""
if prompt is not None:
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
else:
batch_size = text_model_output[0].shape[0]
device = self._execution_device
batch_size = batch_size * num_images_per_prompt
do_classifier_free_guidance = prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0
prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(
prompt, device, num_images_per_prompt, do_classifier_free_guidance, text_model_output, text_attention_mask
)
# prior
self.prior_scheduler.set_timesteps(prior_num_inference_steps, device=device)
prior_timesteps_tensor = self.prior_scheduler.timesteps
embedding_dim = self.prior.config.embedding_dim
prior_latents = self.prepare_latents(
(batch_size, embedding_dim),
prompt_embeds.dtype,
device,
generator,
prior_latents,
self.prior_scheduler,
)
for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([prior_latents] * 2) if do_classifier_free_guidance else prior_latents
predicted_image_embedding = self.prior(
latent_model_input,
timestep=t,
proj_embedding=prompt_embeds,
encoder_hidden_states=text_encoder_hidden_states,
attention_mask=text_mask,
).predicted_image_embedding
if do_classifier_free_guidance:
predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)
predicted_image_embedding = predicted_image_embedding_uncond + prior_guidance_scale * (
predicted_image_embedding_text - predicted_image_embedding_uncond
)
if i + 1 == prior_timesteps_tensor.shape[0]:
prev_timestep = None
else:
prev_timestep = prior_timesteps_tensor[i + 1]
prior_latents = self.prior_scheduler.step(
predicted_image_embedding,
timestep=t,
sample=prior_latents,
generator=generator,
prev_timestep=prev_timestep,
).prev_sample
prior_latents = self.prior.post_process_latents(prior_latents)
image_embeddings = prior_latents
# done prior
# decoder
text_encoder_hidden_states, additive_clip_time_embeddings = self.text_proj(
image_embeddings=image_embeddings,
prompt_embeds=prompt_embeds,
text_encoder_hidden_states=text_encoder_hidden_states,
do_classifier_free_guidance=do_classifier_free_guidance,
)
if device.type == "mps":
# HACK: MPS: There is a panic when padding bool tensors,
# so cast to int tensor for the pad and back to bool afterwards
text_mask = text_mask.type(torch.int)
decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=1)
decoder_text_mask = decoder_text_mask.type(torch.bool)
else:
decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=True)
self.decoder_scheduler.set_timesteps(decoder_num_inference_steps, device=device)
decoder_timesteps_tensor = self.decoder_scheduler.timesteps
num_channels_latents = self.decoder.config.in_channels
height = self.decoder.config.sample_size
width = self.decoder.config.sample_size
decoder_latents = self.prepare_latents(
(batch_size, num_channels_latents, height, width),
text_encoder_hidden_states.dtype,
device,
generator,
decoder_latents,
self.decoder_scheduler,
)
for i, t in enumerate(self.progress_bar(decoder_timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([decoder_latents] * 2) if do_classifier_free_guidance else decoder_latents
noise_pred = self.decoder(
sample=latent_model_input,
timestep=t,
encoder_hidden_states=text_encoder_hidden_states,
class_labels=additive_clip_time_embeddings,
attention_mask=decoder_text_mask,
).sample
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred_uncond, _ = noise_pred_uncond.split(latent_model_input.shape[1], dim=1)
noise_pred_text, predicted_variance = noise_pred_text.split(latent_model_input.shape[1], dim=1)
noise_pred = noise_pred_uncond + decoder_guidance_scale * (noise_pred_text - noise_pred_uncond)
noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
if i + 1 == decoder_timesteps_tensor.shape[0]:
prev_timestep = None
else:
prev_timestep = decoder_timesteps_tensor[i + 1]
# compute the previous noisy sample x_t -> x_t-1
decoder_latents = self.decoder_scheduler.step(
noise_pred, t, decoder_latents, prev_timestep=prev_timestep, generator=generator
).prev_sample
decoder_latents = decoder_latents.clamp(-1, 1)
image_small = decoder_latents
# done decoder
# super res
self.super_res_scheduler.set_timesteps(super_res_num_inference_steps, device=device)
super_res_timesteps_tensor = self.super_res_scheduler.timesteps
channels = self.super_res_first.config.in_channels // 2
height = self.super_res_first.config.sample_size
width = self.super_res_first.config.sample_size
super_res_latents = self.prepare_latents(
(batch_size, channels, height, width),
image_small.dtype,
device,
generator,
super_res_latents,
self.super_res_scheduler,
)
if device.type == "mps":
# MPS does not support many interpolations
image_upscaled = F.interpolate(image_small, size=[height, width])
else:
interpolate_antialias = {}
if "antialias" in inspect.signature(F.interpolate).parameters:
interpolate_antialias["antialias"] = True
image_upscaled = F.interpolate(
image_small, size=[height, width], mode="bicubic", align_corners=False, **interpolate_antialias
)
for i, t in enumerate(self.progress_bar(super_res_timesteps_tensor)):
# no classifier free guidance
if i == super_res_timesteps_tensor.shape[0] - 1:
unet = self.super_res_last
else:
unet = self.super_res_first
latent_model_input = torch.cat([super_res_latents, image_upscaled], dim=1)
noise_pred = unet(
sample=latent_model_input,
timestep=t,
).sample
if i + 1 == super_res_timesteps_tensor.shape[0]:
prev_timestep = None
else:
prev_timestep = super_res_timesteps_tensor[i + 1]
# compute the previous noisy sample x_t -> x_t-1
super_res_latents = self.super_res_scheduler.step(
noise_pred, t, super_res_latents, prev_timestep=prev_timestep, generator=generator
).prev_sample
image = super_res_latents
# done super res
# post processing
image = image * 0.5 + 0.5
image = image.clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| 0 |
hf_public_repos/diffusers/src/diffusers/pipelines | hf_public_repos/diffusers/src/diffusers/pipelines/unclip/pipeline_unclip_image_variation.py | # Copyright 2023 Kakao Brain and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import List, Optional, Union
import PIL
import torch
from torch.nn import functional as F
from transformers import (
CLIPImageProcessor,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionModelWithProjection,
)
from ...models import UNet2DConditionModel, UNet2DModel
from ...schedulers import UnCLIPScheduler
from ...utils import logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from .text_proj import UnCLIPTextProjModel
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class UnCLIPImageVariationPipeline(DiffusionPipeline):
"""
Pipeline to generate image variations from an input image using UnCLIP.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
text_encoder ([`~transformers.CLIPTextModelWithProjection`]):
Frozen text-encoder.
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `image_encoder`.
image_encoder ([`~transformers.CLIPVisionModelWithProjection`]):
Frozen CLIP image-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
text_proj ([`UnCLIPTextProjModel`]):
Utility class to prepare and combine the embeddings before they are passed to the decoder.
decoder ([`UNet2DConditionModel`]):
The decoder to invert the image embedding into an image.
super_res_first ([`UNet2DModel`]):
Super resolution UNet. Used in all but the last step of the super resolution diffusion process.
super_res_last ([`UNet2DModel`]):
Super resolution UNet. Used in the last step of the super resolution diffusion process.
decoder_scheduler ([`UnCLIPScheduler`]):
Scheduler used in the decoder denoising process (a modified [`DDPMScheduler`]).
super_res_scheduler ([`UnCLIPScheduler`]):
Scheduler used in the super resolution denoising process (a modified [`DDPMScheduler`]).
"""
decoder: UNet2DConditionModel
text_proj: UnCLIPTextProjModel
text_encoder: CLIPTextModelWithProjection
tokenizer: CLIPTokenizer
feature_extractor: CLIPImageProcessor
image_encoder: CLIPVisionModelWithProjection
super_res_first: UNet2DModel
super_res_last: UNet2DModel
decoder_scheduler: UnCLIPScheduler
super_res_scheduler: UnCLIPScheduler
def __init__(
self,
decoder: UNet2DConditionModel,
text_encoder: CLIPTextModelWithProjection,
tokenizer: CLIPTokenizer,
text_proj: UnCLIPTextProjModel,
feature_extractor: CLIPImageProcessor,
image_encoder: CLIPVisionModelWithProjection,
super_res_first: UNet2DModel,
super_res_last: UNet2DModel,
decoder_scheduler: UnCLIPScheduler,
super_res_scheduler: UnCLIPScheduler,
):
super().__init__()
self.register_modules(
decoder=decoder,
text_encoder=text_encoder,
tokenizer=tokenizer,
text_proj=text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=super_res_first,
super_res_last=super_res_last,
decoder_scheduler=decoder_scheduler,
super_res_scheduler=super_res_scheduler,
)
# Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
latents = latents * scheduler.init_noise_sigma
return latents
def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance):
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
text_mask = text_inputs.attention_mask.bool().to(device)
text_encoder_output = self.text_encoder(text_input_ids.to(device))
prompt_embeds = text_encoder_output.text_embeds
text_encoder_hidden_states = text_encoder_output.last_hidden_state
prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
uncond_tokens = [""] * batch_size
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
uncond_text_mask = uncond_input.attention_mask.bool().to(device)
negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))
negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds
uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
seq_len = uncond_text_encoder_hidden_states.shape[1]
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt, seq_len, -1
)
uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
text_mask = torch.cat([uncond_text_mask, text_mask])
return prompt_embeds, text_encoder_hidden_states, text_mask
def _encode_image(self, image, device, num_images_per_prompt, image_embeddings: Optional[torch.Tensor] = None):
dtype = next(self.image_encoder.parameters()).dtype
if image_embeddings is None:
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(images=image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeddings = self.image_encoder(image).image_embeds
image_embeddings = image_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
return image_embeddings
@torch.no_grad()
def __call__(
self,
image: Optional[Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor]] = None,
num_images_per_prompt: int = 1,
decoder_num_inference_steps: int = 25,
super_res_num_inference_steps: int = 7,
generator: Optional[torch.Generator] = None,
decoder_latents: Optional[torch.FloatTensor] = None,
super_res_latents: Optional[torch.FloatTensor] = None,
image_embeddings: Optional[torch.Tensor] = None,
decoder_guidance_scale: float = 8.0,
output_type: Optional[str] = "pil",
return_dict: bool = True,
):
"""
The call function to the pipeline for generation.
Args:
image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`):
`Image` or tensor representing an image batch to be used as the starting point. If you provide a
tensor, it needs to be compatible with the [`CLIPImageProcessor`]
[configuration](https://huggingface.co/fusing/karlo-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json).
Can be left as `None` only when `image_embeddings` are passed.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
decoder_num_inference_steps (`int`, *optional*, defaults to 25):
The number of denoising steps for the decoder. More denoising steps usually lead to a higher quality
image at the expense of slower inference.
super_res_num_inference_steps (`int`, *optional*, defaults to 7):
The number of denoising steps for super resolution. More denoising steps usually lead to a higher
quality image at the expense of slower inference.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
decoder_latents (`torch.FloatTensor` of shape (batch size, channels, height, width), *optional*):
Pre-generated noisy latents to be used as inputs for the decoder.
super_res_latents (`torch.FloatTensor` of shape (batch size, channels, super res height, super res width), *optional*):
Pre-generated noisy latents to be used as inputs for the decoder.
decoder_guidance_scale (`float`, *optional*, defaults to 4.0):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
image_embeddings (`torch.Tensor`, *optional*):
Pre-defined image embeddings that can be derived from the image encoder. Pre-defined image embeddings
can be passed for tasks like image interpolations. `image` can be left as `None`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images.
"""
if image is not None:
if isinstance(image, PIL.Image.Image):
batch_size = 1
elif isinstance(image, list):
batch_size = len(image)
else:
batch_size = image.shape[0]
else:
batch_size = image_embeddings.shape[0]
prompt = [""] * batch_size
device = self._execution_device
batch_size = batch_size * num_images_per_prompt
do_classifier_free_guidance = decoder_guidance_scale > 1.0
prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(
prompt, device, num_images_per_prompt, do_classifier_free_guidance
)
image_embeddings = self._encode_image(image, device, num_images_per_prompt, image_embeddings)
# decoder
text_encoder_hidden_states, additive_clip_time_embeddings = self.text_proj(
image_embeddings=image_embeddings,
prompt_embeds=prompt_embeds,
text_encoder_hidden_states=text_encoder_hidden_states,
do_classifier_free_guidance=do_classifier_free_guidance,
)
if device.type == "mps":
# HACK: MPS: There is a panic when padding bool tensors,
# so cast to int tensor for the pad and back to bool afterwards
text_mask = text_mask.type(torch.int)
decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=1)
decoder_text_mask = decoder_text_mask.type(torch.bool)
else:
decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=True)
self.decoder_scheduler.set_timesteps(decoder_num_inference_steps, device=device)
decoder_timesteps_tensor = self.decoder_scheduler.timesteps
num_channels_latents = self.decoder.config.in_channels
height = self.decoder.config.sample_size
width = self.decoder.config.sample_size
if decoder_latents is None:
decoder_latents = self.prepare_latents(
(batch_size, num_channels_latents, height, width),
text_encoder_hidden_states.dtype,
device,
generator,
decoder_latents,
self.decoder_scheduler,
)
for i, t in enumerate(self.progress_bar(decoder_timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([decoder_latents] * 2) if do_classifier_free_guidance else decoder_latents
noise_pred = self.decoder(
sample=latent_model_input,
timestep=t,
encoder_hidden_states=text_encoder_hidden_states,
class_labels=additive_clip_time_embeddings,
attention_mask=decoder_text_mask,
).sample
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred_uncond, _ = noise_pred_uncond.split(latent_model_input.shape[1], dim=1)
noise_pred_text, predicted_variance = noise_pred_text.split(latent_model_input.shape[1], dim=1)
noise_pred = noise_pred_uncond + decoder_guidance_scale * (noise_pred_text - noise_pred_uncond)
noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
if i + 1 == decoder_timesteps_tensor.shape[0]:
prev_timestep = None
else:
prev_timestep = decoder_timesteps_tensor[i + 1]
# compute the previous noisy sample x_t -> x_t-1
decoder_latents = self.decoder_scheduler.step(
noise_pred, t, decoder_latents, prev_timestep=prev_timestep, generator=generator
).prev_sample
decoder_latents = decoder_latents.clamp(-1, 1)
image_small = decoder_latents
# done decoder
# super res
self.super_res_scheduler.set_timesteps(super_res_num_inference_steps, device=device)
super_res_timesteps_tensor = self.super_res_scheduler.timesteps
channels = self.super_res_first.config.in_channels // 2
height = self.super_res_first.config.sample_size
width = self.super_res_first.config.sample_size
if super_res_latents is None:
super_res_latents = self.prepare_latents(
(batch_size, channels, height, width),
image_small.dtype,
device,
generator,
super_res_latents,
self.super_res_scheduler,
)
if device.type == "mps":
# MPS does not support many interpolations
image_upscaled = F.interpolate(image_small, size=[height, width])
else:
interpolate_antialias = {}
if "antialias" in inspect.signature(F.interpolate).parameters:
interpolate_antialias["antialias"] = True
image_upscaled = F.interpolate(
image_small, size=[height, width], mode="bicubic", align_corners=False, **interpolate_antialias
)
for i, t in enumerate(self.progress_bar(super_res_timesteps_tensor)):
# no classifier free guidance
if i == super_res_timesteps_tensor.shape[0] - 1:
unet = self.super_res_last
else:
unet = self.super_res_first
latent_model_input = torch.cat([super_res_latents, image_upscaled], dim=1)
noise_pred = unet(
sample=latent_model_input,
timestep=t,
).sample
if i + 1 == super_res_timesteps_tensor.shape[0]:
prev_timestep = None
else:
prev_timestep = super_res_timesteps_tensor[i + 1]
# compute the previous noisy sample x_t -> x_t-1
super_res_latents = self.super_res_scheduler.step(
noise_pred, t, super_res_latents, prev_timestep=prev_timestep, generator=generator
).prev_sample
image = super_res_latents
# done super res
# post processing
image = image * 0.5 + 0.5
image = image.clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
| 0 |
hf_public_repos/diffusers/src/diffusers | hf_public_repos/diffusers/src/diffusers/utils/accelerate_utils.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Accelerate utilities: Utilities related to accelerate
"""
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def apply_forward_hook(method):
"""
Decorator that applies a registered CpuOffload hook to an arbitrary function rather than `forward`. This is useful
for cases where a PyTorch module provides functions other than `forward` that should trigger a move to the
appropriate acceleration device. This is the case for `encode` and `decode` in [`AutoencoderKL`].
This decorator looks inside the internal `_hf_hook` property to find a registered offload hook.
:param method: The method to decorate. This method should be a method of a PyTorch module.
"""
if not is_accelerate_available():
return method
accelerate_version = version.parse(accelerate.__version__).base_version
if version.parse(accelerate_version) < version.parse("0.17.0"):
return method
def wrapper(self, *args, **kwargs):
if hasattr(self, "_hf_hook") and hasattr(self._hf_hook, "pre_forward"):
self._hf_hook.pre_forward(self)
return method(self, *args, **kwargs)
return wrapper
| 0 |
hf_public_repos/diffusers/src/diffusers | hf_public_repos/diffusers/src/diffusers/utils/dummy_transformers_and_torch_and_note_seq_objects.py | # This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
class SpectrogramDiffusionPipeline(metaclass=DummyObject):
_backends = ["transformers", "torch", "note_seq"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["transformers", "torch", "note_seq"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["transformers", "torch", "note_seq"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["transformers", "torch", "note_seq"])
| 0 |
hf_public_repos/diffusers/src/diffusers | hf_public_repos/diffusers/src/diffusers/utils/__init__.py | # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from packaging import version
from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
CONFIG_NAME,
DEPRECATED_REVISION_ARGS,
DIFFUSERS_CACHE,
DIFFUSERS_DYNAMIC_MODULE_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
ONNX_EXTERNAL_WEIGHTS_NAME,
ONNX_WEIGHTS_NAME,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
HF_HUB_OFFLINE,
_add_variant,
_get_model_file,
extract_commit_hash,
http_user_agent,
)
from .import_utils import (
BACKENDS_MAPPING,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
USE_JAX,
USE_TF,
USE_TORCH,
DummyObject,
OptionalDependencyNotAvailable,
is_accelerate_available,
is_accelerate_version,
is_bs4_available,
is_flax_available,
is_ftfy_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_note_seq_available,
is_omegaconf_available,
is_onnx_available,
is_safetensors_available,
is_scipy_available,
is_tensorboard_available,
is_tf_available,
is_torch_available,
is_torch_version,
is_torchsde_available,
is_transformers_available,
is_transformers_version,
is_unidecode_available,
is_wandb_available,
is_xformers_available,
requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor
if is_torch_available():
from .testing_utils import (
floats_tensor,
load_hf_numpy,
load_image,
load_numpy,
load_pt,
nightly,
parse_flag_from_env,
print_tensor_test,
require_torch_2,
require_torch_gpu,
skip_mps,
slow,
torch_all_close,
torch_device,
)
from .torch_utils import maybe_allow_in_graph
from .testing_utils import export_to_gif, export_to_obj, export_to_ply, export_to_video
logger = get_logger(__name__)
def check_min_version(min_version):
if version.parse(__version__) < version.parse(min_version):
if "dev" in min_version:
error_message = (
"This example requires a source install from HuggingFace diffusers (see "
"`https://huggingface.co/docs/diffusers/installation#install-from-source`),"
)
else:
error_message = f"This example requires a minimum version of {min_version},"
error_message += f" but the version found is {__version__}.\n"
raise ImportError(error_message)
| 0 |