import inspect import copy, os from safetensors.torch import load_file from typing import Any, Callable, Dict, List, Optional, Tuple, Union import collections import numpy as np import PIL.Image import torch import torch.nn.functional as F from transformers import ( CLIPImageProcessor, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer, ) import gc from diffusers.utils.import_utils import is_xformers_available from diffusers.utils.import_utils import is_invisible_watermark_available from diffusers.image_processor import PipelineImageInput, VaeImageProcessor from diffusers.loaders import FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin from diffusers.models import AutoencoderKL from diffusers.models.attention_processor import ( AttnProcessor2_0, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, XFormersAttnProcessor, ) from diffusers.models.lora import adjust_lora_scale_text_encoder from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.utils import ( USE_PEFT_BACKEND, is_torch_version, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers, delete_adapter_layers, set_adapter_layers, set_weights_and_activate_adapters, ) from diffusers.utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor from diffusers.pipelines.pipeline_utils import DiffusionPipeline from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput from utils.callbacks import MultiPipelineCallbacks, PipelineCallback from utils.controlnet_xs import ControlNetXSAdapter, UNetControlNetXSModel # lora from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel from diffusers.loaders.lora_conversion_utils import _maybe_map_sgm_blocks_to_diffusers, _convert_non_diffusers_lora_to_diffusers from utils.tools import get_module_kohya_state_dict_xs #ipa from utils.resampler import Resampler from utils.utils import is_torch2_available if is_torch2_available(): from utils.attention_processor import IPAttnProcessor2_0 as IPAttnProcessor, AttnProcessor2_0 as AttnProcessor else: from utils.attention_processor import IPAttnProcessor, AttnProcessor from utils.attention_processor import region_control if is_invisible_watermark_available(): from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> # !pip install opencv-python transformers accelerate >>> from diffusers import StableDiffusionXLControlNetXSPipeline, ControlNetXSAdapter, AutoencoderKL >>> from diffusers.utils import load_image >>> import numpy as np >>> import torch >>> import cv2 >>> from PIL import Image >>> prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting" >>> negative_prompt = "low quality, bad quality, sketches" >>> # download an image >>> image = load_image( ... "https://hf.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png" ... ) >>> # initialize the models and pipeline >>> controlnet_conditioning_scale = 0.5 >>> vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) >>> controlnet = ControlNetXSAdapter.from_pretrained( ... "UmerHA/Testing-ConrolNetXS-SDXL-canny", torch_dtype=torch.float16 ... ) >>> pipe = StableDiffusionXLControlNetXSPipeline.from_pretrained( ... "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, torch_dtype=torch.float16 ... ) >>> pipe.enable_model_cpu_offload() >>> # get canny image >>> image = np.array(image) >>> image = cv2.Canny(image, 100, 200) >>> image = image[:, :, None] >>> image = np.concatenate([image, image, image], axis=2) >>> canny_image = Image.fromarray(image) >>> # generate image >>> image = pipe( ... prompt, controlnet_conditioning_scale=controlnet_conditioning_scale, image=canny_image ... ).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 class StableDiffusionXLInstantIDXSPipeline( DiffusionPipeline, TextualInversionLoaderMixin, StableDiffusionXLLoraLoaderMixin, FromSingleFileMixin, ): r""" Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet-XS guidance. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). The pipeline also inherits the following loading methods: - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings - [`loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights - [`loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. text_encoder ([`~transformers.CLIPTextModel`]): Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)). text_encoder_2 ([`~transformers.CLIPTextModelWithProjection`]): Second frozen text-encoder ([laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)). tokenizer ([`~transformers.CLIPTokenizer`]): A `CLIPTokenizer` to tokenize text. tokenizer_2 ([`~transformers.CLIPTokenizer`]): A `CLIPTokenizer` to tokenize text. unet ([`UNet2DConditionModel`]): A [`UNet2DConditionModel`] used to create a UNetControlNetXSModel to denoise the encoded image latents. controlnet ([`ControlNetXSAdapter`]): A [`ControlNetXSAdapter`] to be used in combination with `unet` to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`): Whether the negative prompt embeddings should always be set to 0. Also see the config of `stabilityai/stable-diffusion-xl-base-1-0`. add_watermarker (`bool`, *optional*): Whether to use the [invisible_watermark](https://github.com/ShieldMnt/invisible-watermark/) library to watermark output images. If not defined, it defaults to `True` if the package is installed; otherwise no watermarker is used. """ model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae" _optional_components = [ "tokenizer", "tokenizer_2", "text_encoder", "text_encoder_2", "feature_extractor", ] _callback_tensor_inputs = [ "latents", "prompt_embeds", "negative_prompt_embeds", "add_text_embeds", "add_time_ids", "negative_pooled_prompt_embeds", "negative_add_time_ids", ] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, text_encoder_2: CLIPTextModelWithProjection, tokenizer: CLIPTokenizer, tokenizer_2: CLIPTokenizer, unet: Union[UNet2DConditionModel, UNetControlNetXSModel], controlnet: ControlNetXSAdapter, scheduler: KarrasDiffusionSchedulers, force_zeros_for_empty_prompt: bool = True, add_watermarker: Optional[bool] = None, feature_extractor: CLIPImageProcessor = None, ): super().__init__() # self.org_unet_config = copy.deepcopy(unet.config) if isinstance(unet, UNet2DConditionModel): unet = UNetControlNetXSModel.from_unet(unet, controlnet) self.register_modules( vae=vae, text_encoder=text_encoder, text_encoder_2=text_encoder_2, tokenizer=tokenizer, tokenizer_2=tokenizer_2, unet=unet, controlnet=controlnet, scheduler=scheduler, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True) self.control_image_processor = VaeImageProcessor( vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False ) add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available() if add_watermarker: self.watermark = StableDiffusionXLWatermarker() else: self.watermark = None self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt) def cuda(self, org_unet_config=None, device='cuda', dtype=torch.float16, use_xformers=False): self.org_unet_config = org_unet_config self.to(device, dtype) if hasattr(self, 'image_proj_model'): self.image_proj_model.to(device).to(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 encode_prompt( self, prompt: str, prompt_2: Optional[str] = None, device: Optional[torch.device] = None, num_images_per_prompt: int = 1, do_classifier_free_guidance: bool = True, negative_prompt: Optional[str] = None, negative_prompt_2: Optional[str] = None, prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, pooled_prompt_embeds: Optional[torch.Tensor] = None, negative_pooled_prompt_embeds: Optional[torch.Tensor] = None, lora_scale: Optional[float] = None, clip_skip: Optional[int] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded prompt_2 (`str` or `List[str]`, *optional*): The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is used in both text-encoders device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). negative_prompt_2 (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders prompt_embeds (`torch.Tensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.Tensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. pooled_prompt_embeds (`torch.Tensor`, *optional*): Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled text embeddings will be generated from `prompt` input argument. negative_pooled_prompt_embeds (`torch.Tensor`, *optional*): Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` input argument. lora_scale (`float`, *optional*): A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. 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. """ device = device or self._execution_device # set lora scale so that monkey patched LoRA # function of text encoder can correctly access it if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin): self._lora_scale = lora_scale # dynamically adjust the LoRA scale if self.text_encoder is not None: if not USE_PEFT_BACKEND: adjust_lora_scale_text_encoder(self.text_encoder, lora_scale) else: scale_lora_layers(self.text_encoder, lora_scale) if self.text_encoder_2 is not None: if not USE_PEFT_BACKEND: adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale) else: scale_lora_layers(self.text_encoder_2, lora_scale) prompt = [prompt] if isinstance(prompt, str) else prompt if prompt is not None: batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] # Define tokenizers and text encoders tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2] text_encoders = ( [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2] ) if prompt_embeds is None: prompt_2 = prompt_2 or prompt prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2 # textual inversion: process multi-vector tokens if necessary prompt_embeds_list = [] prompts = [prompt, prompt_2] for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders): if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, tokenizer) text_inputs = tokenizer( prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1]) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {tokenizer.model_max_length} tokens: {removed_text}" ) prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True) # We are only ALWAYS interested in the pooled output of the final text encoder pooled_prompt_embeds = prompt_embeds[0] if clip_skip is None: prompt_embeds = prompt_embeds.hidden_states[-2] else: # "2" because SDXL always indexes from the penultimate layer. prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)] prompt_embeds_list.append(prompt_embeds) prompt_embeds = torch.concat(prompt_embeds_list, dim=-1) # get unconditional embeddings for classifier free guidance zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt: negative_prompt_embeds = torch.zeros_like(prompt_embeds) negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds) elif do_classifier_free_guidance and negative_prompt_embeds is None: negative_prompt = negative_prompt or "" negative_prompt_2 = negative_prompt_2 or negative_prompt # normalize str to list negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt negative_prompt_2 = ( batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2 ) uncond_tokens: List[str] if prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif 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, negative_prompt_2] negative_prompt_embeds_list = [] for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders): if isinstance(self, TextualInversionLoaderMixin): negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer) max_length = prompt_embeds.shape[1] uncond_input = tokenizer( negative_prompt, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) negative_prompt_embeds = text_encoder( uncond_input.input_ids.to(device), output_hidden_states=True, ) # We are only ALWAYS interested in the pooled output of the final text encoder negative_pooled_prompt_embeds = negative_prompt_embeds[0] negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2] negative_prompt_embeds_list.append(negative_prompt_embeds) negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1) if self.text_encoder_2 is not None: prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) else: prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] if self.text_encoder_2 is not None: negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) else: negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( bs_embed * num_images_per_prompt, -1 ) if do_classifier_free_guidance: negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( bs_embed * num_images_per_prompt, -1 ) if self.text_encoder is not None: if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: # Retrieve the original scale by scaling back the LoRA layers unscale_lora_layers(self.text_encoder, lora_scale) if self.text_encoder_2 is not None: if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: # Retrieve the original scale by scaling back the LoRA layers unscale_lora_layers(self.text_encoder_2, lora_scale) return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, prompt_2, image, negative_prompt=None, negative_prompt_2=None, prompt_embeds=None, negative_prompt_embeds=None, pooled_prompt_embeds=None, negative_pooled_prompt_embeds=None, controlnet_conditioning_scale=1.0, control_guidance_start=0.0, control_guidance_end=1.0, callback_on_step_end_tensor_inputs=None, ): if callback_on_step_end_tensor_inputs is not None and not all( k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs ): raise ValueError( f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt_2 is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)): raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) elif negative_prompt_2 is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) if prompt_embeds is not None and pooled_prompt_embeds is None: raise ValueError( "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`." ) if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None: raise ValueError( "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`." ) # Check `image` and ``controlnet_conditioning_scale`` is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance( self.unet, torch._dynamo.eval_frame.OptimizedModule ) if ( isinstance(self.unet, UNetControlNetXSModel) or is_compiled and isinstance(self.unet._orig_mod, UNetControlNetXSModel) ): self.check_image(image, prompt, prompt_embeds) if not isinstance(controlnet_conditioning_scale, float): raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.") else: assert False start, end = control_guidance_start, control_guidance_end if start >= end: raise ValueError( f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}." ) if start < 0.0: raise ValueError(f"control guidance start: {start} can't be smaller than 0.") if end > 1.0: raise ValueError(f"control guidance end: {end} can't be larger than 1.0.") # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image def check_image(self, image, prompt, prompt_embeds): image_is_pil = isinstance(image, PIL.Image.Image) image_is_tensor = isinstance(image, torch.Tensor) image_is_np = isinstance(image, np.ndarray) image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image) image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor) image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray) if ( not image_is_pil and not image_is_tensor and not image_is_np and not image_is_pil_list and not image_is_tensor_list and not image_is_np_list ): raise TypeError( f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}" ) if image_is_pil: image_batch_size = 1 else: image_batch_size = len(image) if prompt is not None and isinstance(prompt, str): prompt_batch_size = 1 elif prompt is not None and isinstance(prompt, list): prompt_batch_size = len(prompt) elif prompt_embeds is not None: prompt_batch_size = prompt_embeds.shape[0] if image_batch_size != 1 and image_batch_size != prompt_batch_size: raise ValueError( f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}" ) def prepare_image( self, image, width, height, batch_size, num_images_per_prompt, device, dtype, do_classifier_free_guidance=False, ): image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32) image_batch_size = image.shape[0] if image_batch_size == 1: repeat_by = batch_size else: # image batch size is the same as prompt batch size repeat_by = num_images_per_prompt image = image.repeat_interleave(repeat_by, dim=0) image = image.to(device=device, dtype=dtype) if do_classifier_free_guidance: image = torch.cat([image] * 2) return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = ( batch_size, num_channels_latents, int(height) // self.vae_scale_factor, int(width) // self.vae_scale_factor, ) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents def _get_add_time_ids( self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None ): add_time_ids = list(original_size + crops_coords_top_left + target_size) passed_add_embed_dim = ( self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim ) expected_add_embed_dim = self.unet.base_add_embedding.linear_1.in_features if expected_add_embed_dim != passed_add_embed_dim: raise ValueError( f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`." ) add_time_ids = torch.tensor([add_time_ids], dtype=dtype) return add_time_ids # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae def upcast_vae(self): dtype = self.vae.dtype self.vae.to(dtype=torch.float32) use_torch_2_0_or_xformers = isinstance( self.vae.decoder.mid_block.attentions[0].processor, ( AttnProcessor2_0, XFormersAttnProcessor, LoRAXFormersAttnProcessor, LoRAAttnProcessor2_0, ), ) # if xformers or torch_2_0 is used attention block does not need # to be in float32 which can save lots of memory if use_torch_2_0_or_xformers: self.vae.post_quant_conv.to(dtype) self.vae.decoder.conv_in.to(dtype) self.vae.decoder.mid_block.to(dtype) @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.guidance_scale def guidance_scale(self): return self._guidance_scale @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.clip_skip def clip_skip(self): return self._clip_skip @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.do_classifier_free_guidance def do_classifier_free_guidance(self): return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.cross_attention_kwargs def cross_attention_kwargs(self): return self._cross_attention_kwargs @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.num_timesteps def num_timesteps(self): return self._num_timesteps def load_ip_adapter(self, image_proj_model, cross_attn_path=None, image_emb_dim=512, num_tokens=16, device='cuda', dtype=torch.float16): self.set_image_proj_model(image_proj_model, image_emb_dim, num_tokens, device=device, dtype=dtype) if cross_attn_path != None: self.set_cross_attn(cross_attn_path, num_tokens) def set_image_proj_model(self, model_ckpt, image_emb_dim=512, num_tokens=16, device='cuda', dtype=torch.float16): 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(device, dtype=dtype) print('**************************** Loading image projection Model ***************************') if isinstance(model_ckpt, collections.OrderedDict): # print('Loading from state dict...') state_dict = model_ckpt elif isinstance(model_ckpt, str): # print(f'Loading state dict from {model_ckpt} ...') # state_dict = torch.load(model_ckpt, map_location="cpu", weights_only=True) state_dict = torch.load(model_ckpt, map_location="cpu", weights_only=True) else: raise TypeError("model_ckpt must be either an OrderedDict or a string (file path).") if isinstance(state_dict, tuple): print("\n\n\n state_dict is a tuple \n\n\n") state_dict = state_dict[0] self.image_proj_model.load_state_dict(state_dict) self.image_proj_model_in_features = image_emb_dim del state_dict gc.collect() def set_cross_attn(self, cross_attn_path, num_tokens): print('**************************** Setting cross attention processors to UNet ***************************') # self.unet # 此时unet就是cnxs datatype = self.unet.dtype state_dict = torch.load(cross_attn_path, map_location="cpu", weights_only=True) attn_state_dict = {} for key, value in state_dict.items(): if 'attn2.processor' in key: attn_state_dict[key] = value attn_procs = {} for name in self.unet.attn_processors.keys(): if 'ctrl' in name: continue cross_attention_dim = None if name.endswith("attn1.processor") else self.unet.config.cross_attention_dim if name.startswith("mid_block"): hidden_size = self.unet.config.block_out_channels[-1] elif name.startswith("up_blocks"): block_id = int(name[len("up_blocks.")]) hidden_size = list(reversed(self.unet.config.block_out_channels))[block_id] elif name.startswith("down_blocks"): block_id = int(name[len("down_blocks.")]) hidden_size = self.unet.config.block_out_channels[block_id] if cross_attention_dim is None: attn_procs[name] = AttnProcessor() else: weights = { "to_k_ip.weight": attn_state_dict[name + ".to_k_ip.weight"], "to_v_ip.weight": attn_state_dict[name + ".to_v_ip.weight"], } attn_procs[name] = IPAttnProcessor(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, num_tokens=num_tokens) attn_procs[name].load_state_dict(weights) # print('length of attn_procs:', len(attn_procs)) # 140 self.unet.set_attn_processor_unet(attn_procs) self.unet.to(dtype=datatype) del attn_state_dict del attn_procs gc.collect() def set_ip_adapter_scale(self, scale): unet = self.unet for attn_processor in unet.attn_processors_unet.values(): # print(attn_processor) ''' Attention( (to_q): Linear(in_features=640, out_features=640, bias=False) (to_k): Linear(in_features=2048, out_features=640, bias=False) (to_v): Linear(in_features=2048, out_features=640, bias=False) (to_out): ModuleList( (0): Linear(in_features=640, out_features=640, bias=True) (1): Dropout(p=0.0, inplace=False) ) (processor): IPAttnProcessor2_0( (to_k_ip): Linear(in_features=2048, out_features=640, bias=False) (to_v_ip): Linear(in_features=2048, out_features=640, bias=False) ) ) ''' if isinstance(attn_processor, IPAttnProcessor): # print('set_ip_adapter_scale: ',scale) 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.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 = prompt_image_emb.to(device=self.image_proj_model.latents.device, dtype=self.image_proj_model.latents.dtype) 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.to(device=device, dtype=dtype) def load_lora_weights( self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], adapter_name=None, **kwargs ): if not USE_PEFT_BACKEND: raise ValueError("PEFT backend is required for this method.") # if a dict is passed, copy it instead of modifying it inplace if isinstance(pretrained_model_name_or_path_or_dict, dict): pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict.copy() # First, ensure that the checkpoint is a compatible one and can be successfully loaded. if isinstance(pretrained_model_name_or_path_or_dict, str): filename = os.path.basename(pretrained_model_name_or_path_or_dict) extension = os.path.splitext(filename)[1] extension = extension[1:] if extension == "safetensors": lora_weight = load_file(pretrained_model_name_or_path_or_dict) else: lora_weight = torch.load(pretrained_model_name_or_path_or_dict, map_location="cpu") if all( ( k.startswith("lora_te_") or k.startswith("lora_unet_") or k.startswith("lora_te1_") or k.startswith("lora_te2_") ) for k in lora_weight.keys() ): state_dict = _maybe_map_sgm_blocks_to_diffusers(lora_weight, self.org_unet_config) state_dict, network_alphas = _convert_non_diffusers_lora_to_diffusers(state_dict) state_dict = get_module_kohya_state_dict_xs(state_dict, torch.float16) state_dict, _ = self.lora_state_dict(state_dict, **kwargs) else: state_dict = get_module_kohya_state_dict_xs(lora_weight, torch.float16) state_dict, network_alphas = self.lora_state_dict(state_dict, **kwargs) else: state_dict, network_alphas = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs) is_correct_format = all("lora" in key for key in state_dict.keys()) if not is_correct_format: raise ValueError("Invalid LoRA checkpoint.") low_cpu_mem_usage = False is_torch_higher_equal_2_1 = is_torch_version(">=", "2.1") if is_torch_higher_equal_2_1: from diffusers.models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT) assert is_torch_higher_equal_2_1 == low_cpu_mem_usage self.load_lora_into_unet( state_dict, network_alphas=network_alphas, unet=getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet, adapter_name=adapter_name, _pipeline=self, low_cpu_mem_usage=low_cpu_mem_usage, ) self.load_lora_into_text_encoder( state_dict, network_alphas=network_alphas, text_encoder=getattr(self, self.text_encoder_name) if not hasattr(self, "text_encoder") else self.text_encoder, lora_scale=self.lora_scale, adapter_name=adapter_name, _pipeline=self, low_cpu_mem_usage=low_cpu_mem_usage, ) def set_adapters( self, adapter_names: Union[List[str], str], adapter_weights: Optional[Union[List[float], float]] = None, ): """ Set the currently active adapters for use in the UNet. Args: adapter_names (`List[str]` or `str`): The names of the adapters to use. adapter_weights (`Union[List[float], float]`, *optional*): The adapter(s) weights to use with the UNet. If `None`, the weights are set to `1.0` for all the adapters. Example: ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16 ).to("cuda") pipeline.load_lora_weights( "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic" ) pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel") pipeline.set_adapters(["cinematic", "pixel"], adapter_weights=[0.5, 0.5]) ``` """ if not USE_PEFT_BACKEND: raise ValueError("PEFT backend is required for `set_adapters()`.") adapter_names = [adapter_names] if isinstance(adapter_names, str) else adapter_names if adapter_weights is None: adapter_weights = [1.0] * len(adapter_names) elif isinstance(adapter_weights, float): adapter_weights = [adapter_weights] * len(adapter_names) if len(adapter_names) != len(adapter_weights): raise ValueError( f"Length of adapter names {len(adapter_names)} is not equal to the length of their weights {len(adapter_weights)}." ) set_weights_and_activate_adapters(self.unet, adapter_names, adapter_weights) ''' def disable_lora(self): """ Disable the UNet's active LoRA layers. Example: ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16 ).to("cuda") pipeline.load_lora_weights( "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic" ) pipeline.disable_lora() ``` """ if not USE_PEFT_BACKEND: raise ValueError("PEFT backend is required for this method.") set_adapter_layers(self.unet, enabled=False) def enable_lora(self): """ Enable the UNet's active LoRA layers. Example: ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16 ).to("cuda") pipeline.load_lora_weights( "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic" ) pipeline.enable_lora() ``` """ if not USE_PEFT_BACKEND: raise ValueError("PEFT backend is required for this method.") set_adapter_layers(self.unet, enabled=True) def delete_adapters(self, adapter_names: Union[List[str], str]): """ Delete an adapter's LoRA layers from the UNet. Args: adapter_names (`Union[List[str], str]`): The names (single string or list of strings) of the adapter to delete. Example: ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16 ).to("cuda") pipeline.load_lora_weights( "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_names="cinematic" ) pipeline.delete_adapters("cinematic") ``` """ if not USE_PEFT_BACKEND: raise ValueError("PEFT backend is required for this method.") if isinstance(adapter_names, str): adapter_names = [adapter_names] for adapter_name in adapter_names: delete_adapter_layers(self.unet, adapter_name) # Pop also the corresponding adapter from the config if hasattr(self.unet, "peft_config"): self.unet.peft_config.pop(adapter_name, None) ''' @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, face_emb: Optional[torch.Tensor] = 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.Tensor] = None, prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, pooled_prompt_embeds: Optional[torch.Tensor] = None, negative_pooled_prompt_embeds: Optional[torch.Tensor] = 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, control_guidance_start: float = 0.0, control_guidance_end: 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[ Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] ] = None, callback_on_step_end_tensor_inputs: List[str] = ["latents"], # IP adapter ip_adapter_scale=None, ): r""" The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`. 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.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,: `List[List[torch.Tensor]]`, `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.Tensor`, 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.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 is generated by sampling using the supplied random `generator`. prompt_embeds (`torch.Tensor`, *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.Tensor`, *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.Tensor`, *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.Tensor`, *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. 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`. control_guidance_start (`float`, *optional*, defaults to 0.0): The percentage of total steps at which the ControlNet starts applying. control_guidance_end (`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 `(width, height)` 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 `(width, height)`. 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`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*): A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of each denoising step during the inference. 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.StableDiffusionXLPipelineOutput`] or `tuple`: If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] is returned, otherwise a `tuple` is returned containing the output images. """ lpw = LongPromptWeight() if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs unet = self.unet._orig_mod if is_compiled_module(self.unet) else self.unet # 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, 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 self._interrupt = False # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt # text_encoder_lora_scale = ( # self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None # ) # ( # prompt_embeds, # negative_prompt_embeds, # pooled_prompt_embeds, # negative_pooled_prompt_embeds, # ) = self.encode_prompt( # prompt, # prompt_2, # device, # num_images_per_prompt, # do_classifier_free_guidance, # negative_prompt, # negative_prompt_2, # prompt_embeds=prompt_embeds, # negative_prompt_embeds=negative_prompt_embeds, # pooled_prompt_embeds=pooled_prompt_embeds, # negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, # lora_scale=text_encoder_lora_scale, # clip_skip=clip_skip, # ) ( 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, ) prompt_image_emb = self._encode_prompt_image_emb( face_emb, device, num_images_per_prompt, unet.dtype, do_classifier_free_guidance ) # 4. Prepare image if isinstance(unet, UNetControlNetXSModel): 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=unet.dtype, do_classifier_free_guidance=do_classifier_free_guidance, ) height, width = image.shape[-2:] else: assert False # 5. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 6. Prepare latent variables num_channels_latents = self.unet.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 7.1 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 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, dtype=unet.dtype) add_text_embeds = add_text_embeds.to(device, dtype=unet.dtype) add_time_ids = add_time_ids.to(device, dtype=unet.dtype).repeat(batch_size * num_images_per_prompt, 1) prompt_image_emb = prompt_image_emb.to(device, dtype=unet.dtype) encoder_hidden_states = torch.cat([prompt_embeds, prompt_image_emb], dim=1) encoder_hidden_states = encoder_hidden_states.to(device, dtype=unet.dtype) # 8. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order self._num_timesteps = len(timesteps) is_controlnet_compiled = is_compiled_module(self.unet) 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_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 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} # predict the noise residual apply_control = ( i / len(timesteps) >= control_guidance_start and (i + 1) / len(timesteps) <= control_guidance_end ) noise_pred = self.unet( sample=latent_model_input, timestep=t, unet_encoder_hidden_states=encoder_hidden_states, cnxs_encoder_hidden_states=prompt_image_emb, controlnet_cond=image, conditioning_scale=controlnet_conditioning_scale, cross_attention_kwargs=self.cross_attention_kwargs, added_cond_kwargs=added_cond_kwargs, return_dict=True, apply_control=apply_control, ).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, 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() # manually for max memory savings if self.vae.dtype == torch.float16 and self.vae.config.force_upcast: self.upcast_vae() latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) 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)