# 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 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.utils import is_torch2_available from ip_adapter.attention_processor import IPAttnProcessor, AttnProcessor 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 transformers import CLIPTokenizer 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 def draw_kps(image_pil, kps, color_list=[(255,0,0), (0,255,0), (0,0,255), (255,255,0), (255,0,255)]): stickwidth = 4 limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]]) kps = np.array(kps) w, h = image_pil.size out_img = np.zeros([h, w, 3]) for i in range(len(limbSeq)): index = limbSeq[i] color = color_list[index[0]] x = kps[index][:, 0] y = kps[index][:, 1] length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5 angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1])) polygon = cv2.ellipse2Poly((int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1) out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color) out_img = (out_img * 0.6).astype(np.uint8) for idx_kp, kp in enumerate(kps): color = color_list[idx_kp] x, y = kp out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1) out_img_pil = PIL.Image.fromarray(out_img.astype(np.uint8)) return out_img_pil 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, num_images_per_prompt, 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) bs_embed, seq_len, _ = prompt_image_emb.shape prompt_image_emb = prompt_image_emb.repeat(1, num_images_per_prompt, 1) prompt_image_emb = prompt_image_emb.view(bs_embed * num_images_per_prompt, seq_len, -1) 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"], # IP adapter ip_adapter_scale=None, # Enhance Face Region 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], ) # 0. set ip_adapter_scale if ip_adapter_scale is not None: self.set_ip_adapter_scale(ip_adapter_scale) # 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, num_images_per_prompt, 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] if isinstance(self.controlnet, MultiControlNetModel): down_block_res_samples_list, mid_block_res_sample_list = [], [] for control_index in range(len(self.controlnet.nets)): controlnet = self.controlnet.nets[control_index] if control_index == 0: # assume fhe first controlnet is IdentityNet controlnet_prompt_embeds = prompt_image_emb else: controlnet_prompt_embeds = prompt_embeds down_block_res_samples, mid_block_res_sample = controlnet(control_model_input, t, encoder_hidden_states=controlnet_prompt_embeds, controlnet_cond=image[control_index], conditioning_scale=cond_scale[control_index], guess_mode=guess_mode, added_cond_kwargs=controlnet_added_cond_kwargs, return_dict=False) # controlnet mask if control_index == 0 and 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] down_block_res_samples_list.append(down_block_res_samples) mid_block_res_sample_list.append(mid_block_res_sample) mid_block_res_sample = torch.stack(mid_block_res_sample_list).sum(dim=0) down_block_res_samples = [torch.stack(down_block_res_samples).sum(dim=0) for down_block_res_samples in zip(*down_block_res_samples_list)] else: 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)