Spaces:
Runtime error
Runtime error
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | |
# Copyright 2022 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 paddle | |
import paddle.nn as nn | |
################################################################################ | |
# Code for the text transformer model | |
################################################################################ | |
from paddlenlp.transformers import ( | |
PretrainedModel, | |
PretrainedTokenizer, | |
register_base_model, | |
) | |
from paddlenlp.transformers.model_outputs import ( | |
BaseModelOutputWithPoolingAndCrossAttentions, | |
) | |
from ...configuration_utils import FrozenDict | |
from ...models import AutoencoderKL, UNet2DConditionModel, UNet2DModel, VQModel | |
from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput | |
from ...schedulers import ( | |
DDIMScheduler, | |
DPMSolverMultistepScheduler, | |
EulerAncestralDiscreteScheduler, | |
EulerDiscreteScheduler, | |
LMSDiscreteScheduler, | |
PNDMScheduler, | |
) | |
from ...utils import deprecate, logging | |
logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
class LDMBertPretrainedModel(PretrainedModel): | |
pretrained_init_configuration = {} | |
pretrained_resource_files_map = {} | |
base_model_prefix = "ldmbert" | |
def init_weights(self, layer): | |
if isinstance(layer, (nn.Linear, nn.Embedding)): | |
layer.weight.set_value( | |
paddle.normal( | |
mean=0.0, | |
std=self.initializer_range | |
if hasattr(self, "initializer_range") | |
else self.ldmbert.config["initializer_range"], | |
shape=layer.weight.shape, | |
) | |
) | |
class LDMBertEmbeddings(nn.Layer): | |
def __init__(self, vocab_size, hidden_size=768, hidden_dropout_prob=0.0, max_position_embeddings=512): | |
super().__init__() | |
self.word_embeddings = nn.Embedding(vocab_size, hidden_size) | |
self.position_embeddings = nn.Embedding(max_position_embeddings, hidden_size) | |
self.dropout = nn.Dropout(hidden_dropout_prob) | |
def forward(self, input_ids, position_ids=None): | |
if position_ids is None: | |
ones = paddle.ones_like(input_ids, dtype="int64") | |
seq_length = paddle.cumsum(ones, axis=-1) | |
position_ids = seq_length - ones | |
position_ids.stop_gradient = True | |
input_embedings = self.word_embeddings(input_ids) | |
position_embeddings = self.position_embeddings(position_ids) | |
embeddings = input_embedings + position_embeddings | |
embeddings = self.dropout(embeddings) | |
return embeddings | |
class TransformerEncoderLayer(nn.TransformerEncoderLayer): | |
def __init__( | |
self, | |
d_model, | |
nhead, | |
dim_feedforward, | |
dropout=0.1, | |
activation="gelu", | |
attn_dropout=None, | |
act_dropout=None, | |
normalize_before=False, | |
weight_attr=None, | |
bias_attr=None, | |
head_dim=64, | |
): | |
super().__init__( | |
d_model, | |
nhead, | |
dim_feedforward, | |
dropout, | |
activation, | |
attn_dropout, | |
act_dropout, | |
normalize_before, | |
weight_attr, | |
bias_attr, | |
) | |
# update self attn | |
self.self_attn = LDMBertAttention( | |
d_model, head_dim, nhead, dropout=attn_dropout, weight_attr=weight_attr, bias_attr=False | |
) | |
class LDMBertModel(LDMBertPretrainedModel): | |
_no_split_modules = [] | |
def __init__( | |
self, | |
vocab_size=30522, | |
max_position_embeddings=77, | |
encoder_layers=32, | |
encoder_ffn_dim=5120, | |
encoder_attention_heads=8, | |
head_dim=64, | |
activation_function="gelu", | |
d_model=1280, | |
dropout=0.0, | |
attention_dropout=0.0, | |
activation_dropout=0.0, | |
init_std=0.02, | |
pad_token_id=0, | |
**kwargs | |
): | |
super().__init__() | |
self.pad_token_id = pad_token_id | |
self.initializer_range = init_std | |
self.embeddings = LDMBertEmbeddings(vocab_size, d_model, dropout, max_position_embeddings) | |
encoder_layer = TransformerEncoderLayer( | |
d_model, | |
encoder_attention_heads, | |
encoder_ffn_dim, | |
dropout=dropout, | |
activation=activation_function, | |
attn_dropout=attention_dropout, | |
act_dropout=activation_dropout, | |
normalize_before=True, | |
head_dim=head_dim, | |
) | |
self.encoder = nn.TransformerEncoder(encoder_layer, encoder_layers) | |
self.final_layer_norm = nn.LayerNorm(d_model) | |
self.apply(self.init_weights) | |
def get_input_embeddings(self): | |
return self.embeddings.word_embeddings | |
def set_input_embeddings(self, value): | |
self.embeddings.word_embeddings = value | |
def forward( | |
self, | |
input_ids, | |
position_ids=None, | |
attention_mask=None, | |
output_hidden_states=False, | |
output_attentions=False, | |
return_dict=False, | |
): | |
if attention_mask is not None and attention_mask.ndim == 2: | |
# attention_mask [batch_size, sequence_length] -> [batch_size, 1, 1, sequence_length] | |
attention_mask = attention_mask.unsqueeze(axis=[1, 2]).astype(paddle.get_default_dtype()) | |
attention_mask = (1.0 - attention_mask) * -1e4 | |
embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids) | |
encoder_outputs = self.encoder( | |
embedding_output, | |
src_mask=attention_mask, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
if isinstance(encoder_outputs, type(embedding_output)): | |
sequence_output = self.final_layer_norm(encoder_outputs) | |
return (sequence_output,) | |
else: | |
sequence_output = encoder_outputs[0] | |
sequence_output = self.final_layer_norm(sequence_output) | |
if not return_dict: | |
return (sequence_output,) + encoder_outputs[1:] | |
return BaseModelOutputWithPoolingAndCrossAttentions( | |
last_hidden_state=sequence_output, | |
hidden_states=encoder_outputs.hidden_states, | |
attentions=encoder_outputs.attentions, | |
) | |
class LDMBertAttention(nn.MultiHeadAttention): | |
def __init__( | |
self, | |
embed_dim, | |
head_dim, | |
num_heads, | |
dropout=0.0, | |
kdim=None, | |
vdim=None, | |
need_weights=False, | |
weight_attr=None, | |
bias_attr=None, | |
): | |
super().__init__(embed_dim, num_heads, dropout, kdim, vdim, need_weights, weight_attr, bias_attr) | |
assert embed_dim > 0, "Expected embed_dim to be greater than 0, " "but recieved {}".format(embed_dim) | |
assert num_heads > 0, "Expected num_heads to be greater than 0, " "but recieved {}".format(num_heads) | |
self.embed_dim = embed_dim | |
self.kdim = kdim if kdim is not None else embed_dim | |
self.vdim = vdim if vdim is not None else embed_dim | |
self.num_heads = num_heads | |
self.dropout = dropout | |
self.need_weights = need_weights | |
self.head_dim = head_dim | |
self.inner_dim = head_dim * num_heads | |
self.scaling = self.head_dim**-0.5 | |
self.q_proj = nn.Linear(embed_dim, self.inner_dim, weight_attr, bias_attr=bias_attr) | |
self.k_proj = nn.Linear(self.kdim, self.inner_dim, weight_attr, bias_attr=bias_attr) | |
self.v_proj = nn.Linear(self.vdim, self.inner_dim, weight_attr, bias_attr=bias_attr) | |
self.out_proj = nn.Linear(self.inner_dim, embed_dim, weight_attr) | |
class LDMBertModelForMaskedLM(LDMBertPretrainedModel): | |
def __init__(self, ldmbert): | |
super().__init__() | |
self.ldmbert = ldmbert | |
self.to_logits = nn.Linear(ldmbert.config["hidden_size"], ldmbert.config["vocab_size"]) | |
self.apply(self.init_weights) | |
def forward( | |
self, | |
input_ids=None, | |
attention_mask=None, | |
position_ids=None, | |
output_attentions=None, | |
output_hidden_states=None, | |
return_dict=None, | |
): | |
outputs = self.ldmbert( | |
input_ids, | |
attention_mask=attention_mask, | |
position_ids=position_ids, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
return outputs | |
class LDMTextToImagePipeline(DiffusionPipeline): | |
r""" | |
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 xxxx, 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 [BERT](https://paddlenlp.readthedocs.io/zh/latest/source/paddlenlp.transformers.bert.modeling.html#paddlenlp.transformers.bert.modeling.BertModel) architecture. | |
tokenizer (`paddlenlp.transformers.BertTokenizer`): | |
Tokenizer of class | |
[BertTokenizer](https://paddlenlp.readthedocs.io/zh/latest/source/paddlenlp.transformers.bert.tokenizer.html#paddlenlp.transformers.bert.tokenizer.BertTokenizer). | |
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`], [`PNDMScheduler`], [`EulerDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`] | |
or [`DPMSolverMultistepScheduler`]. | |
""" | |
def __init__( | |
self, | |
vqvae: Union[VQModel, AutoencoderKL], | |
bert: PretrainedModel, | |
tokenizer: PretrainedTokenizer, | |
unet: Union[UNet2DModel, UNet2DConditionModel], | |
scheduler: Union[ | |
DDIMScheduler, | |
PNDMScheduler, | |
LMSDiscreteScheduler, | |
EulerDiscreteScheduler, | |
EulerAncestralDiscreteScheduler, | |
DPMSolverMultistepScheduler, | |
], | |
): | |
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) | |
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) | |
def _encode_prompt(self, prompt, num_images_per_prompt, 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 | |
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`). | |
""" | |
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="pd", | |
) | |
text_input_ids = text_inputs.input_ids | |
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pd").input_ids | |
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not paddle.equal_all( | |
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 LDMBert can only handle sequences up to" | |
f" {self.tokenizer.model_max_length} tokens: {removed_text}" | |
) | |
text_embeddings = self.bert(text_input_ids) | |
text_embeddings = text_embeddings[0] | |
# duplicate text embeddings for each generation per prompt, using mps friendly method | |
bs_embed, seq_len, _ = text_embeddings.shape | |
text_embeddings = text_embeddings.tile([1, num_images_per_prompt, 1]) | |
text_embeddings = text_embeddings.reshape([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="pd", | |
) | |
uncond_embeddings = self.bert(uncond_input.input_ids) | |
uncond_embeddings = uncond_embeddings[0] | |
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method | |
seq_len = uncond_embeddings.shape[1] | |
uncond_embeddings = uncond_embeddings.tile([1, num_images_per_prompt, 1]) | |
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 | |
text_embeddings = paddle.concat([uncond_embeddings, text_embeddings]) | |
return text_embeddings | |
def decode_latents(self, latents): | |
latents = 1 / 0.18215 * latents | |
image = self.vqvae.decode(latents).sample | |
image = (image / 2 + 0.5).clip(0, 1) | |
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 | |
image = image.transpose([0, 2, 3, 1]).cast("float32").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): | |
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 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)}." | |
) | |
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, 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: | |
if isinstance(generator, list): | |
shape = [ | |
1, | |
] + shape[1:] | |
latents = [paddle.randn(shape, generator=generator[i], dtype=dtype) for i in range(batch_size)] | |
latents = paddle.concat(latents, axis=0) | |
else: | |
latents = paddle.randn(shape, generator=generator, dtype=dtype) | |
else: | |
if latents.shape != shape: | |
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") | |
# scale the initial noise by the standard deviation required by the scheduler | |
latents = latents * self.scheduler.init_noise_sigma | |
return latents | |
def __call__( | |
self, | |
prompt: Union[str, List[str]], | |
height: int = 256, | |
width: int = 256, | |
num_inference_steps: int = 50, | |
guidance_scale: float = 1.0, | |
negative_prompt: Optional[Union[str, List[str]]] = None, | |
num_images_per_prompt: Optional[int] = 1, | |
eta: float = 0.0, | |
generator: Optional[Union[paddle.Generator, List[paddle.Generator]]] = None, | |
latents: Optional[paddle.Tensor] = None, | |
output_type: Optional[str] = "pil", | |
return_dict: bool = True, | |
callback: Optional[Callable[[int, int, paddle.Tensor], 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. | |
height (`int`, *optional*, defaults to 256: | |
The height in pixels of the generated image. | |
width (`int`, *optional*, defaults to 256: | |
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): | |
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 (`paddle.Generator`, *optional*): | |
One or a list of paddle generator(s) to make generation deterministic. | |
latents (`paddle.Tensor`, *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](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: paddle.Tensor)`. | |
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. 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) | |
# 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, num_images_per_prompt, do_classifier_free_guidance, negative_prompt | |
) | |
# 4. Prepare timesteps | |
self.scheduler.set_timesteps(num_inference_steps) | |
timesteps = self.scheduler.timesteps | |
# 5. Prepare latent variables | |
num_channels_latents = self.unet.in_channels | |
latents = self.prepare_latents( | |
batch_size * num_images_per_prompt, | |
num_channels_latents, | |
height, | |
width, | |
text_embeddings.dtype, | |
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 = paddle.concat([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=text_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) | |
# 8. Post-processing | |
image = self.decode_latents(latents) | |
# 9. Convert to PIL | |
if output_type == "pil": | |
image = self.numpy_to_pil(image) | |
if not return_dict: | |
return (image,) | |
return ImagePipelineOutput(images=image) | |