AI-Perfect-Img / pipeline_stable_diffusion_xl_instantid.py
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# Copyright 2024 The InstantX 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 Any, Callable, Dict, List, Optional, Tuple, Union
import cv2
import math
import numpy as np
import PIL.Image
import torch
import torch.nn.functional as F
from transformers import CLIPTokenizer
from diffusers.image_processor import PipelineImageInput
from diffusers.models import ControlNetModel
from diffusers.utils import (
deprecate,
logging,
replace_example_docstring,
)
from diffusers.utils.torch_utils import is_compiled_module, is_torch_version
from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
from diffusers import StableDiffusionXLControlNetPipeline
from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
from diffusers.utils.import_utils import is_xformers_available
from ip_adapter.resampler import Resampler
from ip_adapter.attention_processor import AttnProcessor, IPAttnProcessor
from ip_adapter.attention_processor import region_control
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> # !pip install opencv-python transformers accelerate insightface
>>> import diffusers
>>> from diffusers.utils import load_image
>>> from diffusers.models import ControlNetModel
>>> import cv2
>>> import torch
>>> import numpy as np
>>> from PIL import Image
>>> from insightface.app import FaceAnalysis
>>> from pipeline_stable_diffusion_xl_instantid import StableDiffusionXLInstantIDPipeline, draw_kps
>>> # download 'antelopev2' under ./models
>>> app = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
>>> app.prepare(ctx_id=0, det_size=(640, 640))
>>> # download models under ./checkpoints
>>> face_adapter = f'./checkpoints/ip-adapter.bin'
>>> controlnet_path = f'./checkpoints/ControlNetModel'
>>> # load IdentityNet
>>> controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
>>> pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(
... "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, torch_dtype=torch.float16
... )
>>> pipe.cuda()
>>> # load adapter
>>> pipe.load_ip_adapter_instantid(face_adapter)
>>> prompt = "analog film photo of a man. faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage, masterpiece, best quality"
>>> negative_prompt = "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured (lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch,deformed, mutated, cross-eyed, ugly, disfigured"
>>> # load an image
>>> image = load_image("your-example.jpg")
>>> face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))[-1]
>>> face_emb = face_info['embedding']
>>> face_kps = draw_kps(face_image, face_info['kps'])
>>> pipe.set_ip_adapter_scale(0.8)
>>> # generate image
>>> image = pipe(
... prompt, image_embeds=face_emb, image=face_kps, controlnet_conditioning_scale=0.8
... ).images[0]
```
"""
from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipeline
class LongPromptWeight(object):
"""
Copied from https://github.com/huggingface/diffusers/blob/main/examples/community/lpw_stable_diffusion_xl.py
"""
def __init__(self) -> None:
pass
def parse_prompt_attention(self, text):
"""
Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
Accepted tokens are:
(abc) - increases attention to abc by a multiplier of 1.1
(abc:3.12) - increases attention to abc by a multiplier of 3.12
[abc] - decreases attention to abc by a multiplier of 1.1
\( - literal character '('
\[ - literal character '['
\) - literal character ')'
\] - literal character ']'
\\ - literal character '\'
anything else - just text
>>> parse_prompt_attention('normal text')
[['normal text', 1.0]]
>>> parse_prompt_attention('an (important) word')
[['an ', 1.0], ['important', 1.1], [' word', 1.0]]
>>> parse_prompt_attention('(unbalanced')
[['unbalanced', 1.1]]
>>> parse_prompt_attention('\(literal\]')
[['(literal]', 1.0]]
>>> parse_prompt_attention('(unnecessary)(parens)')
[['unnecessaryparens', 1.1]]
>>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
[['a ', 1.0],
['house', 1.5730000000000004],
[' ', 1.1],
['on', 1.0],
[' a ', 1.1],
['hill', 0.55],
[', sun, ', 1.1],
['sky', 1.4641000000000006],
['.', 1.1]]
"""
import re
re_attention = re.compile(
r"""
\\\(|\\\)|\\\[|\\]|\\\\|\\|\(|\[|:([+-]?[.\d]+)\)|
\)|]|[^\\()\[\]:]+|:
""",
re.X,
)
re_break = re.compile(r"\s*\bBREAK\b\s*", re.S)
res = []
round_brackets = []
square_brackets = []
round_bracket_multiplier = 1.1
square_bracket_multiplier = 1 / 1.1
def multiply_range(start_position, multiplier):
for p in range(start_position, len(res)):
res[p][1] *= multiplier
for m in re_attention.finditer(text):
text = m.group(0)
weight = m.group(1)
if text.startswith("\\"):
res.append([text[1:], 1.0])
elif text == "(":
round_brackets.append(len(res))
elif text == "[":
square_brackets.append(len(res))
elif weight is not None and len(round_brackets) > 0:
multiply_range(round_brackets.pop(), float(weight))
elif text == ")" and len(round_brackets) > 0:
multiply_range(round_brackets.pop(), round_bracket_multiplier)
elif text == "]" and len(square_brackets) > 0:
multiply_range(square_brackets.pop(), square_bracket_multiplier)
else:
parts = re.split(re_break, text)
for i, part in enumerate(parts):
if i > 0:
res.append(["BREAK", -1])
res.append([part, 1.0])
for pos in round_brackets:
multiply_range(pos, round_bracket_multiplier)
for pos in square_brackets:
multiply_range(pos, square_bracket_multiplier)
if len(res) == 0:
res = [["", 1.0]]
# merge runs of identical weights
i = 0
while i + 1 < len(res):
if res[i][1] == res[i + 1][1]:
res[i][0] += res[i + 1][0]
res.pop(i + 1)
else:
i += 1
return res
def get_prompts_tokens_with_weights(self, clip_tokenizer: CLIPTokenizer, prompt: str):
"""
Get prompt token ids and weights, this function works for both prompt and negative prompt
Args:
pipe (CLIPTokenizer)
A CLIPTokenizer
prompt (str)
A prompt string with weights
Returns:
text_tokens (list)
A list contains token ids
text_weight (list)
A list contains the correspodent weight of token ids
Example:
import torch
from transformers import CLIPTokenizer
clip_tokenizer = CLIPTokenizer.from_pretrained(
"stablediffusionapi/deliberate-v2"
, subfolder = "tokenizer"
, dtype = torch.float16
)
token_id_list, token_weight_list = get_prompts_tokens_with_weights(
clip_tokenizer = clip_tokenizer
,prompt = "a (red:1.5) cat"*70
)
"""
texts_and_weights = self.parse_prompt_attention(prompt)
text_tokens, text_weights = [], []
for word, weight in texts_and_weights:
# tokenize and discard the starting and the ending token
token = clip_tokenizer(word, truncation=False).input_ids[1:-1] # so that tokenize whatever length prompt
# the returned token is a 1d list: [320, 1125, 539, 320]
# merge the new tokens to the all tokens holder: text_tokens
text_tokens = [*text_tokens, *token]
# each token chunk will come with one weight, like ['red cat', 2.0]
# need to expand weight for each token.
chunk_weights = [weight] * len(token)
# append the weight back to the weight holder: text_weights
text_weights = [*text_weights, *chunk_weights]
return text_tokens, text_weights
def group_tokens_and_weights(self, token_ids: list, weights: list, pad_last_block=False):
"""
Produce tokens and weights in groups and pad the missing tokens
Args:
token_ids (list)
The token ids from tokenizer
weights (list)
The weights list from function get_prompts_tokens_with_weights
pad_last_block (bool)
Control if fill the last token list to 75 tokens with eos
Returns:
new_token_ids (2d list)
new_weights (2d list)
Example:
token_groups,weight_groups = group_tokens_and_weights(
token_ids = token_id_list
, weights = token_weight_list
)
"""
bos, eos = 49406, 49407
# this will be a 2d list
new_token_ids = []
new_weights = []
while len(token_ids) >= 75:
# get the first 75 tokens
head_75_tokens = [token_ids.pop(0) for _ in range(75)]
head_75_weights = [weights.pop(0) for _ in range(75)]
# extract token ids and weights
temp_77_token_ids = [bos] + head_75_tokens + [eos]
temp_77_weights = [1.0] + head_75_weights + [1.0]
# add 77 token and weights chunk to the holder list
new_token_ids.append(temp_77_token_ids)
new_weights.append(temp_77_weights)
# padding the left
if len(token_ids) >= 0:
padding_len = 75 - len(token_ids) if pad_last_block else 0
temp_77_token_ids = [bos] + token_ids + [eos] * padding_len + [eos]
new_token_ids.append(temp_77_token_ids)
temp_77_weights = [1.0] + weights + [1.0] * padding_len + [1.0]
new_weights.append(temp_77_weights)
return new_token_ids, new_weights
def get_weighted_text_embeddings_sdxl(
self,
pipe: StableDiffusionXLPipeline,
prompt: str = "",
prompt_2: str = None,
neg_prompt: str = "",
neg_prompt_2: str = None,
prompt_embeds=None,
negative_prompt_embeds=None,
pooled_prompt_embeds=None,
negative_pooled_prompt_embeds=None,
extra_emb=None,
extra_emb_alpha=0.6,
):
"""
This function can process long prompt with weights, no length limitation
for Stable Diffusion XL
Args:
pipe (StableDiffusionPipeline)
prompt (str)
prompt_2 (str)
neg_prompt (str)
neg_prompt_2 (str)
Returns:
prompt_embeds (torch.Tensor)
neg_prompt_embeds (torch.Tensor)
"""
#
if prompt_embeds is not None and \
negative_prompt_embeds is not None and \
pooled_prompt_embeds is not None and \
negative_pooled_prompt_embeds is not None:
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
if prompt_2:
prompt = f"{prompt} {prompt_2}"
if neg_prompt_2:
neg_prompt = f"{neg_prompt} {neg_prompt_2}"
eos = pipe.tokenizer.eos_token_id
# tokenizer 1
prompt_tokens, prompt_weights = self.get_prompts_tokens_with_weights(pipe.tokenizer, prompt)
neg_prompt_tokens, neg_prompt_weights = self.get_prompts_tokens_with_weights(pipe.tokenizer, neg_prompt)
# tokenizer 2
# prompt_tokens_2, prompt_weights_2 = self.get_prompts_tokens_with_weights(pipe.tokenizer_2, prompt)
# neg_prompt_tokens_2, neg_prompt_weights_2 = self.get_prompts_tokens_with_weights(pipe.tokenizer_2, neg_prompt)
# tokenizer 2 遇到 !! !!!! 等多感叹号和tokenizer 1的效果不一致
prompt_tokens_2, prompt_weights_2 = self.get_prompts_tokens_with_weights(pipe.tokenizer, prompt)
neg_prompt_tokens_2, neg_prompt_weights_2 = self.get_prompts_tokens_with_weights(pipe.tokenizer, neg_prompt)
# padding the shorter one for prompt set 1
prompt_token_len = len(prompt_tokens)
neg_prompt_token_len = len(neg_prompt_tokens)
if prompt_token_len > neg_prompt_token_len:
# padding the neg_prompt with eos token
neg_prompt_tokens = neg_prompt_tokens + [eos] * abs(prompt_token_len - neg_prompt_token_len)
neg_prompt_weights = neg_prompt_weights + [1.0] * abs(prompt_token_len - neg_prompt_token_len)
else:
# padding the prompt
prompt_tokens = prompt_tokens + [eos] * abs(prompt_token_len - neg_prompt_token_len)
prompt_weights = prompt_weights + [1.0] * abs(prompt_token_len - neg_prompt_token_len)
# padding the shorter one for token set 2
prompt_token_len_2 = len(prompt_tokens_2)
neg_prompt_token_len_2 = len(neg_prompt_tokens_2)
if prompt_token_len_2 > neg_prompt_token_len_2:
# padding the neg_prompt with eos token
neg_prompt_tokens_2 = neg_prompt_tokens_2 + [eos] * abs(prompt_token_len_2 - neg_prompt_token_len_2)
neg_prompt_weights_2 = neg_prompt_weights_2 + [1.0] * abs(prompt_token_len_2 - neg_prompt_token_len_2)
else:
# padding the prompt
prompt_tokens_2 = prompt_tokens_2 + [eos] * abs(prompt_token_len_2 - neg_prompt_token_len_2)
prompt_weights_2 = prompt_weights + [1.0] * abs(prompt_token_len_2 - neg_prompt_token_len_2)
embeds = []
neg_embeds = []
prompt_token_groups, prompt_weight_groups = self.group_tokens_and_weights(prompt_tokens.copy(), prompt_weights.copy())
neg_prompt_token_groups, neg_prompt_weight_groups = self.group_tokens_and_weights(
neg_prompt_tokens.copy(), neg_prompt_weights.copy()
)
prompt_token_groups_2, prompt_weight_groups_2 = self.group_tokens_and_weights(
prompt_tokens_2.copy(), prompt_weights_2.copy()
)
neg_prompt_token_groups_2, neg_prompt_weight_groups_2 = self.group_tokens_and_weights(
neg_prompt_tokens_2.copy(), neg_prompt_weights_2.copy()
)
# get prompt embeddings one by one is not working.
for i in range(len(prompt_token_groups)):
# get positive prompt embeddings with weights
token_tensor = torch.tensor([prompt_token_groups[i]], dtype=torch.long, device=pipe.device)
weight_tensor = torch.tensor(prompt_weight_groups[i], dtype=torch.float16, device=pipe.device)
token_tensor_2 = torch.tensor([prompt_token_groups_2[i]], dtype=torch.long, device=pipe.device)
# use first text encoder
prompt_embeds_1 = pipe.text_encoder(token_tensor.to(pipe.device), output_hidden_states=True)
prompt_embeds_1_hidden_states = prompt_embeds_1.hidden_states[-2]
# use second text encoder
prompt_embeds_2 = pipe.text_encoder_2(token_tensor_2.to(pipe.device), output_hidden_states=True)
prompt_embeds_2_hidden_states = prompt_embeds_2.hidden_states[-2]
pooled_prompt_embeds = prompt_embeds_2[0]
prompt_embeds_list = [prompt_embeds_1_hidden_states, prompt_embeds_2_hidden_states]
token_embedding = torch.concat(prompt_embeds_list, dim=-1).squeeze(0)
for j in range(len(weight_tensor)):
if weight_tensor[j] != 1.0:
token_embedding[j] = (
token_embedding[-1] + (token_embedding[j] - token_embedding[-1]) * weight_tensor[j]
)
token_embedding = token_embedding.unsqueeze(0)
embeds.append(token_embedding)
# get negative prompt embeddings with weights
neg_token_tensor = torch.tensor([neg_prompt_token_groups[i]], dtype=torch.long, device=pipe.device)
neg_token_tensor_2 = torch.tensor([neg_prompt_token_groups_2[i]], dtype=torch.long, device=pipe.device)
neg_weight_tensor = torch.tensor(neg_prompt_weight_groups[i], dtype=torch.float16, device=pipe.device)
# use first text encoder
neg_prompt_embeds_1 = pipe.text_encoder(neg_token_tensor.to(pipe.device), output_hidden_states=True)
neg_prompt_embeds_1_hidden_states = neg_prompt_embeds_1.hidden_states[-2]
# use second text encoder
neg_prompt_embeds_2 = pipe.text_encoder_2(neg_token_tensor_2.to(pipe.device), output_hidden_states=True)
neg_prompt_embeds_2_hidden_states = neg_prompt_embeds_2.hidden_states[-2]
negative_pooled_prompt_embeds = neg_prompt_embeds_2[0]
neg_prompt_embeds_list = [neg_prompt_embeds_1_hidden_states, neg_prompt_embeds_2_hidden_states]
neg_token_embedding = torch.concat(neg_prompt_embeds_list, dim=-1).squeeze(0)
for z in range(len(neg_weight_tensor)):
if neg_weight_tensor[z] != 1.0:
neg_token_embedding[z] = (
neg_token_embedding[-1] + (neg_token_embedding[z] - neg_token_embedding[-1]) * neg_weight_tensor[z]
)
neg_token_embedding = neg_token_embedding.unsqueeze(0)
neg_embeds.append(neg_token_embedding)
prompt_embeds = torch.cat(embeds, dim=1)
negative_prompt_embeds = torch.cat(neg_embeds, dim=1)
if extra_emb is not None:
extra_emb = extra_emb.to(prompt_embeds.device, dtype=prompt_embeds.dtype) * extra_emb_alpha
prompt_embeds = torch.cat([prompt_embeds, extra_emb], 1)
negative_prompt_embeds = torch.cat([negative_prompt_embeds, torch.zeros_like(extra_emb)], 1)
print(f'fix prompt_embeds, extra_emb_alpha={extra_emb_alpha}')
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
def get_prompt_embeds(self, *args, **kwargs):
prompt_embeds, negative_prompt_embeds, _, _ = self.get_weighted_text_embeddings_sdxl(*args, **kwargs)
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
return prompt_embeds
class StableDiffusionXLInstantIDPipeline(StableDiffusionXLControlNetPipeline):
def cuda(self, dtype=torch.float16, use_xformers=False):
self.to('cuda', dtype)
if hasattr(self, 'image_proj_model'):
self.image_proj_model.to(self.unet.device).to(self.unet.dtype)
if use_xformers:
if is_xformers_available():
import xformers
from packaging import version
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
logger.warn(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
self.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
def load_ip_adapter_instantid(self, model_ckpt, image_emb_dim=512, num_tokens=16, scale=0.5):
self.set_image_proj_model(model_ckpt, image_emb_dim, num_tokens)
self.set_ip_adapter(model_ckpt, num_tokens, scale)
def set_image_proj_model(self, model_ckpt, image_emb_dim=512, num_tokens=16):
image_proj_model = Resampler(
dim=1280,
depth=4,
dim_head=64,
heads=20,
num_queries=num_tokens,
embedding_dim=image_emb_dim,
output_dim=self.unet.config.cross_attention_dim,
ff_mult=4,
)
image_proj_model.eval()
self.image_proj_model = image_proj_model.to(self.device, dtype=self.dtype)
state_dict = torch.load(model_ckpt, map_location="cpu")
if 'image_proj' in state_dict:
state_dict = state_dict["image_proj"]
self.image_proj_model.load_state_dict(state_dict)
self.image_proj_model_in_features = image_emb_dim
def set_ip_adapter(self, model_ckpt, num_tokens, scale):
unet = self.unet
attn_procs = {}
for name in unet.attn_processors.keys():
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
if name.startswith("mid_block"):
hidden_size = unet.config.block_out_channels[-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = unet.config.block_out_channels[block_id]
if cross_attention_dim is None:
attn_procs[name] = AttnProcessor().to(unet.device, dtype=unet.dtype)
else:
attn_procs[name] = IPAttnProcessor(hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim,
scale=scale,
num_tokens=num_tokens).to(unet.device, dtype=unet.dtype)
unet.set_attn_processor(attn_procs)
state_dict = torch.load(model_ckpt, map_location="cpu")
ip_layers = torch.nn.ModuleList(self.unet.attn_processors.values())
if 'ip_adapter' in state_dict:
state_dict = state_dict['ip_adapter']
ip_layers.load_state_dict(state_dict)
def set_ip_adapter_scale(self, scale):
unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
for attn_processor in unet.attn_processors.values():
if isinstance(attn_processor, IPAttnProcessor):
attn_processor.scale = scale
def _encode_prompt_image_emb(self, prompt_image_emb, device, dtype, do_classifier_free_guidance):
if isinstance(prompt_image_emb, torch.Tensor):
prompt_image_emb = prompt_image_emb.clone().detach()
else:
prompt_image_emb = torch.tensor(prompt_image_emb)
prompt_image_emb = prompt_image_emb.to(device=device, dtype=dtype)
prompt_image_emb = prompt_image_emb.reshape([1, -1, self.image_proj_model_in_features])
if do_classifier_free_guidance:
prompt_image_emb = torch.cat([torch.zeros_like(prompt_image_emb), prompt_image_emb], dim=0)
else:
prompt_image_emb = torch.cat([prompt_image_emb], dim=0)
prompt_image_emb = self.image_proj_model(prompt_image_emb)
return prompt_image_emb
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
image: PipelineImageInput = None,
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,
negative_prompt_2: 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,
pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
image_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
guess_mode: bool = False,
control_guidance_start: Union[float, List[float]] = 0.0,
control_guidance_end: Union[float, List[float]] = 1.0,
original_size: Tuple[int, int] = None,
crops_coords_top_left: Tuple[int, int] = (0, 0),
target_size: Tuple[int, int] = None,
negative_original_size: Optional[Tuple[int, int]] = None,
negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
negative_target_size: Optional[Tuple[int, int]] = None,
clip_skip: Optional[int] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
control_mask = None,
**kwargs,
):
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`.
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
used in both text-encoders.
image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
`List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
The ControlNet input condition to provide guidance to the `unet` for generation. If the type is
specified as `torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be
accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height
and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in
`init`, images must be passed as a list such that each element of the list can be correctly batched for
input to a single ControlNet.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image. Anything below 512 pixels won't work well for
[stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
and checkpoints that are not specifically fine-tuned on low resolutions.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image. Anything below 512 pixels won't work well for
[stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
and checkpoints that are not specifically fine-tuned on low resolutions.
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`).
negative_prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. This is sent to `tokenizer_2`
and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders.
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.
pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, pooled text embeddings are generated from `prompt` input argument.
negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs (prompt
weighting). If not provided, pooled `negative_prompt_embeds` are generated from `negative_prompt` input
argument.
image_embeds (`torch.FloatTensor`, *optional*):
Pre-generated image embeddings.
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.
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).
controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added
to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set
the corresponding scale as a list.
guess_mode (`bool`, *optional*, defaults to `False`):
The ControlNet encoder tries to recognize the content of the input image even if you remove all
prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.
control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
The percentage of total steps at which the ControlNet starts applying.
control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
The percentage of total steps at which the ControlNet stops applying.
original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
`original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
`crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
`crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
`crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
For most cases, `target_size` should be set to the desired height and width of the generated image. If
not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
To negatively condition the generation process based on a specific image resolution. Part of SDXL's
micro-conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
micro-conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
To negatively condition the generation process based on a target image resolution. It should be as same
as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
callback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeine class.
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned containing the output images.
"""
lpw = LongPromptWeight()
callback = kwargs.pop("callback", None)
callback_steps = kwargs.pop("callback_steps", None)
if callback is not None:
deprecate(
"callback",
"1.0.0",
"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
)
if callback_steps is not None:
deprecate(
"callback_steps",
"1.0.0",
"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
)
controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
# align format for control guidance
if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
control_guidance_start = len(control_guidance_end) * [control_guidance_start]
elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
control_guidance_end = len(control_guidance_start) * [control_guidance_end]
elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
control_guidance_start, control_guidance_end = (
mult * [control_guidance_start],
mult * [control_guidance_end],
)
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
prompt_2,
image,
callback_steps,
negative_prompt,
negative_prompt_2,
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
controlnet_conditioning_scale,
control_guidance_start,
control_guidance_end,
callback_on_step_end_tensor_inputs,
)
self._guidance_scale = guidance_scale
self._clip_skip = clip_skip
self._cross_attention_kwargs = cross_attention_kwargs
# 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
if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)
global_pool_conditions = (
controlnet.config.global_pool_conditions
if isinstance(controlnet, ControlNetModel)
else controlnet.nets[0].config.global_pool_conditions
)
guess_mode = guess_mode or global_pool_conditions
# 3.1 Encode input prompt
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = lpw.get_weighted_text_embeddings_sdxl(
pipe=self,
prompt=prompt,
neg_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
)
# 3.2 Encode image prompt
prompt_image_emb = self._encode_prompt_image_emb(image_embeds,
device,
self.unet.dtype,
self.do_classifier_free_guidance)
# 4. Prepare image
if isinstance(controlnet, ControlNetModel):
image = self.prepare_image(
image=image,
width=width,
height=height,
batch_size=batch_size * num_images_per_prompt,
num_images_per_prompt=num_images_per_prompt,
device=device,
dtype=controlnet.dtype,
do_classifier_free_guidance=self.do_classifier_free_guidance,
guess_mode=guess_mode,
)
height, width = image.shape[-2:]
elif isinstance(controlnet, MultiControlNetModel):
images = []
for image_ in image:
image_ = self.prepare_image(
image=image_,
width=width,
height=height,
batch_size=batch_size * num_images_per_prompt,
num_images_per_prompt=num_images_per_prompt,
device=device,
dtype=controlnet.dtype,
do_classifier_free_guidance=self.do_classifier_free_guidance,
guess_mode=guess_mode,
)
images.append(image_)
image = images
height, width = image[0].shape[-2:]
else:
assert False
# 4.1 Region control
if control_mask is not None:
mask_weight_image = control_mask
mask_weight_image = np.array(mask_weight_image)
mask_weight_image_tensor = torch.from_numpy(mask_weight_image).to(device=device, dtype=prompt_embeds.dtype)
mask_weight_image_tensor = mask_weight_image_tensor[:, :, 0] / 255.
mask_weight_image_tensor = mask_weight_image_tensor[None, None]
h, w = mask_weight_image_tensor.shape[-2:]
control_mask_wight_image_list = []
for scale in [8, 8, 8, 16, 16, 16, 32, 32, 32]:
scale_mask_weight_image_tensor = F.interpolate(
mask_weight_image_tensor,(h // scale, w // scale), mode='bilinear')
control_mask_wight_image_list.append(scale_mask_weight_image_tensor)
region_mask = torch.from_numpy(np.array(control_mask)[:, :, 0]).to(self.unet.device, dtype=self.unet.dtype) / 255.
region_control.prompt_image_conditioning = [dict(region_mask=region_mask)]
else:
control_mask_wight_image_list = None
region_control.prompt_image_conditioning = [dict(region_mask=None)]
# 5. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
self._num_timesteps = len(timesteps)
# 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,
)
# 6.5 Optionally get Guidance Scale Embedding
timestep_cond = None
if self.unet.config.time_cond_proj_dim is not None:
guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
timestep_cond = self.get_guidance_scale_embedding(
guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
).to(device=device, dtype=latents.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)
# 7.1 Create tensor stating which controlnets to keep
controlnet_keep = []
for i in range(len(timesteps)):
keeps = [
1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
for s, e in zip(control_guidance_start, control_guidance_end)
]
controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)
# 7.2 Prepare added time ids & embeddings
if isinstance(image, list):
original_size = original_size or image[0].shape[-2:]
else:
original_size = original_size or image.shape[-2:]
target_size = target_size or (height, width)
add_text_embeds = pooled_prompt_embeds
if self.text_encoder_2 is None:
text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
else:
text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
add_time_ids = self._get_add_time_ids(
original_size,
crops_coords_top_left,
target_size,
dtype=prompt_embeds.dtype,
text_encoder_projection_dim=text_encoder_projection_dim,
)
if negative_original_size is not None and negative_target_size is not None:
negative_add_time_ids = self._get_add_time_ids(
negative_original_size,
negative_crops_coords_top_left,
negative_target_size,
dtype=prompt_embeds.dtype,
text_encoder_projection_dim=text_encoder_projection_dim,
)
else:
negative_add_time_ids = add_time_ids
if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
prompt_embeds = prompt_embeds.to(device)
add_text_embeds = add_text_embeds.to(device)
add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
encoder_hidden_states = torch.cat([prompt_embeds, prompt_image_emb], dim=1)
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
is_unet_compiled = is_compiled_module(self.unet)
is_controlnet_compiled = is_compiled_module(self.controlnet)
is_torch_higher_equal_2_1 = is_torch_version(">=", "2.1")
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# Relevant thread:
# https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428
if (is_unet_compiled and is_controlnet_compiled) and is_torch_higher_equal_2_1:
torch._inductor.cudagraph_mark_step_begin()
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
# controlnet(s) inference
if guess_mode and self.do_classifier_free_guidance:
# Infer ControlNet only for the conditional batch.
control_model_input = latents
control_model_input = self.scheduler.scale_model_input(control_model_input, t)
controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
controlnet_added_cond_kwargs = {
"text_embeds": add_text_embeds.chunk(2)[1],
"time_ids": add_time_ids.chunk(2)[1],
}
else:
control_model_input = latent_model_input
controlnet_prompt_embeds = prompt_embeds
controlnet_added_cond_kwargs = added_cond_kwargs
if isinstance(controlnet_keep[i], list):
cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
else:
controlnet_cond_scale = controlnet_conditioning_scale
if isinstance(controlnet_cond_scale, list):
controlnet_cond_scale = controlnet_cond_scale[0]
cond_scale = controlnet_cond_scale * controlnet_keep[i]
down_block_res_samples, mid_block_res_sample = self.controlnet(
control_model_input,
t,
encoder_hidden_states=prompt_image_emb,
controlnet_cond=image,
conditioning_scale=cond_scale,
guess_mode=guess_mode,
added_cond_kwargs=controlnet_added_cond_kwargs,
return_dict=False,
)
# controlnet mask
if control_mask_wight_image_list is not None:
down_block_res_samples = [
down_block_res_sample * mask_weight
for down_block_res_sample, mask_weight in zip(down_block_res_samples, control_mask_wight_image_list)
]
mid_block_res_sample *= control_mask_wight_image_list[-1]
if guess_mode and self.do_classifier_free_guidance:
# Infered ControlNet only for the conditional batch.
# To apply the output of ControlNet to both the unconditional and conditional batches,
# add 0 to the unconditional batch to keep it unchanged.
down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=encoder_hidden_states,
timestep_cond=timestep_cond,
cross_attention_kwargs=self.cross_attention_kwargs,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
# perform guidance
if self.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_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
# 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:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
if not output_type == "latent":
# make sure the VAE is in float32 mode, as it overflows in float16
needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
if needs_upcasting:
self.upcast_vae()
latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
# cast back to fp16 if needed
if needs_upcasting:
self.vae.to(dtype=torch.float16)
else:
image = latents
if not output_type == "latent":
# apply watermark if available
if self.watermark is not None:
image = self.watermark.apply_watermark(image)
image = self.image_processor.postprocess(image, output_type=output_type)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image,)
return StableDiffusionXLPipelineOutput(images=image)