## ---------------------------------------------------------- # A SDXL pipeline can take unlimited weighted prompt # # Author: Andrew Zhu # Github: https://github.com/xhinker # Medium: https://medium.com/@xhinker ## ----------------------------------------------------------- import inspect import os from typing import Any, Callable, Dict, List, Optional, Tuple, Union import torch from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer from diffusers import DiffusionPipeline, StableDiffusionXLPipeline from diffusers.image_processor import VaeImageProcessor from diffusers.loaders import ( FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin, ) from diffusers.models import AutoencoderKL, UNet2DConditionModel from diffusers.models.attention_processor import ( AttnProcessor2_0, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, XFormersAttnProcessor, ) from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.utils import ( is_accelerate_available, is_accelerate_version, is_invisible_watermark_available, logging, randn_tensor, replace_example_docstring, ) if is_invisible_watermark_available(): from diffusers.pipelines.stable_diffusion_xl.watermark import ( StableDiffusionXLWatermarker, ) def parse_prompt_attention(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(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 = 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(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( pipe: StableDiffusionXLPipeline, prompt: str = "", prompt_2: str = None, neg_prompt: str = "", neg_prompt_2: str = None, ): """ 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_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 = get_prompts_tokens_with_weights( pipe.tokenizer, prompt ) neg_prompt_tokens, neg_prompt_weights = get_prompts_tokens_with_weights( pipe.tokenizer, neg_prompt ) # tokenizer 2 prompt_tokens_2, prompt_weights_2 = get_prompts_tokens_with_weights( pipe.tokenizer_2, prompt ) neg_prompt_tokens_2, neg_prompt_weights_2 = get_prompts_tokens_with_weights( pipe.tokenizer_2, 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 = group_tokens_and_weights( prompt_tokens.copy(), prompt_weights.copy() ) neg_prompt_token_groups, neg_prompt_weight_groups = group_tokens_and_weights( neg_prompt_tokens.copy(), neg_prompt_weights.copy() ) prompt_token_groups_2, prompt_weight_groups_2 = group_tokens_and_weights( prompt_tokens_2.copy(), prompt_weights_2.copy() ) neg_prompt_token_groups_2, neg_prompt_weight_groups_2 = 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) return ( prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds, ) # ------------------------------------------------------------------------------------------------------------------------------- # reuse the backbone code from StableDiffusionXLPipeline # ------------------------------------------------------------------------------------------------------------------------------- logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py from diffusers import DiffusionPipeline import torch pipe = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0" , torch_dtype = torch.float16 , use_safetensors = True , variant = "fp16" , custom_pipeline = "lpw_stable_diffusion_xl", ) prompt = "a white cat running on the grass"*20 prompt2 = "play a football"*20 prompt = f"{prompt},{prompt2}" neg_prompt = "blur, low quality" pipe.to("cuda") images = pipe( prompt = prompt , negative_prompt = neg_prompt ).images[0] pipe.to("cpu") torch.cuda.empty_cache() images ``` """ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): """ Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 """ std_text = noise_pred_text.std( dim=list(range(1, noise_pred_text.ndim)), keepdim=True ) std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) # rescale the results from guidance (fixes overexposure) noise_pred_rescaled = noise_cfg * (std_text / std_cfg) # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images noise_cfg = ( guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg ) return noise_cfg class SDXLLongPromptWeightingPipeline( DiffusionPipeline, FromSingleFileMixin, LoraLoaderMixin ): r""" Pipeline for text-to-image generation using Stable Diffusion XL. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) In addition the pipeline inherits the following loading methods: - *LoRA*: [`StableDiffusionXLPipeline.load_lora_weights`] - *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`] as well as the following saving methods: - *LoRA*: [`loaders.StableDiffusionXLPipeline.save_lora_weights`] Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion XL uses the text portion of [CLIP](https://huggingface.co./docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co./openai/clip-vit-large-patch14) variant. text_encoder_2 ([` CLIPTextModelWithProjection`]): Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of [CLIP](https://huggingface.co./docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection), specifically the [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co./laion/CLIP-ViT-bigG-14-laion2B-39B-b160k) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co./docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). tokenizer_2 (`CLIPTokenizer`): Second Tokenizer of class [CLIPTokenizer](https://huggingface.co./docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. """ def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, text_encoder_2: CLIPTextModelWithProjection, tokenizer: CLIPTokenizer, tokenizer_2: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, force_zeros_for_empty_prompt: bool = True, add_watermarker: Optional[bool] = None, ): super().__init__() self.register_modules( vae=vae, text_encoder=text_encoder, text_encoder_2=text_encoder_2, tokenizer=tokenizer, tokenizer_2=tokenizer_2, unet=unet, scheduler=scheduler, ) self.register_to_config( force_zeros_for_empty_prompt=force_zeros_for_empty_prompt ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.default_sample_size = self.unet.config.sample_size 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 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling def enable_vae_tiling(self): r""" Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow processing larger images. """ self.vae.enable_tiling() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling def disable_vae_tiling(self): r""" Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to computing decoding in one step. """ self.vae.disable_tiling() def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError( "`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher." ) device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) model_sequence = ( [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2] ) model_sequence.extend([self.unet, self.vae]) hook = None for cpu_offloaded_model in model_sequence: _, hook = cpu_offload_with_hook( cpu_offloaded_model, device, prev_module_hook=hook ) # We'll offload the last model manually. self.final_offload_hook = hook 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.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, pooled_prompt_embeds: Optional[torch.FloatTensor] = None, negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, lora_scale: Optional[float] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded 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.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. pooled_prompt_embeds (`torch.FloatTensor`, *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.FloatTensor`, *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. """ 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, LoraLoaderMixin): self._lora_scale = lora_scale if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] # 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 # textual inversion: procecss 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] prompt_embeds = prompt_embeds.hidden_states[-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 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 isinstance(negative_prompt, str): uncond_tokens = [negative_prompt, negative_prompt_2] 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) prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.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] negative_prompt_embeds = negative_prompt_embeds.to( dtype=self.text_encoder_2.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) 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, height, width, callback_steps, negative_prompt=None, negative_prompt_2=None, prompt_embeds=None, negative_prompt_embeds=None, pooled_prompt_embeds=None, negative_pooled_prompt_embeds=None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError( f"`height` and `width` have to be divisible by 8 but are {height} and {width}." ) if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt_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`." ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents( self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None, ): shape = ( batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor, ) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor( shape, generator=generator, device=device, dtype=dtype ) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents def _get_add_time_ids( self, original_size, crops_coords_top_left, target_size, dtype ): 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) + self.text_encoder_2.config.projection_dim ) expected_add_embed_dim = self.unet.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) @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: str = None, prompt_2: Optional[str] = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, denoising_end: Optional[float] = None, guidance_scale: float = 5.0, negative_prompt: Optional[str] = None, negative_prompt_2: Optional[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, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, guidance_rescale: float = 0.0, original_size: Optional[Tuple[int, int]] = None, crops_coords_top_left: Tuple[int, int] = (0, 0), target_size: Optional[Tuple[int, int]] = None, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str`): The prompt to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. prompt_2 (`str`): The prompt to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is used in both text-encoders height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. denoising_end (`float`, *optional*): When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be completed before it is intentionally prematurely terminated. As a result, the returned sample will still retain a substantial amount of noise as determined by the discrete timesteps selected by the scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image Output**](https://huggingface.co./docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output) guidance_scale (`float`, *optional*, defaults to 5.0): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str`): The prompt 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`): The prompt 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 num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. pooled_prompt_embeds (`torch.FloatTensor`, *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.FloatTensor`, *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. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). guidance_rescale (`float`, *optional*, defaults to 0.7): Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when using zero terminal SNR. 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). Examples: Returns: [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images. """ # 0. Default height and width to unet height = height or self.default_sample_size * self.vae_scale_factor width = width or self.default_sample_size * self.vae_scale_factor original_size = original_size or (height, width) target_size = target_size or (height, width) # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, prompt_2, height, width, callback_steps, negative_prompt, negative_prompt_2, prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds, ) # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt ( cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None ) negative_prompt = negative_prompt if negative_prompt is not None else "" ( prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds, ) = get_weighted_text_embeddings_sdxl( pipe=self, prompt=prompt, neg_prompt=negative_prompt ) # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 5. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 7. Prepare added time ids & embeddings add_text_embeds = pooled_prompt_embeds add_time_ids = self._get_add_time_ids( original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype ) 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([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 ) # 8. Denoising loop num_warmup_steps = max( len(timesteps) - num_inference_steps * self.scheduler.order, 0 ) # 7.1 Apply denoising_end if ( denoising_end is not None and type(denoising_end) == float and denoising_end > 0 and denoising_end < 1 ): discrete_timestep_cutoff = int( round( self.scheduler.config.num_train_timesteps - (denoising_end * self.scheduler.config.num_train_timesteps) ) ) num_inference_steps = len( list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)) ) timesteps = timesteps[:num_inference_steps] with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = ( torch.cat([latents] * 2) if do_classifier_free_guidance else latents ) latent_model_input = self.scheduler.scale_model_input( latent_model_input, t ) # predict the noise residual added_cond_kwargs = { "text_embeds": add_text_embeds, "time_ids": add_time_ids, } noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs, added_cond_kwargs=added_cond_kwargs, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * ( noise_pred_text - noise_pred_uncond ) if do_classifier_free_guidance and guidance_rescale > 0.0: # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf noise_pred = rescale_noise_cfg( noise_pred, noise_pred_text, guidance_rescale=guidance_rescale ) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step( noise_pred, t, latents, **extra_step_kwargs, return_dict=False )[0] # call the callback, if provided if i == len(timesteps) - 1 or ( (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0 ): progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) # make sure the VAE is in float32 mode, as it overflows in float16 if self.vae.dtype == torch.float16 and self.vae.config.force_upcast: self.upcast_vae() latents = latents.to( next(iter(self.vae.post_quant_conv.parameters())).dtype ) if not output_type == "latent": image = self.vae.decode( latents / self.vae.config.scaling_factor, return_dict=False )[0] else: image = latents return StableDiffusionXLPipelineOutput(images=image) # 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 last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image,) return StableDiffusionXLPipelineOutput(images=image) # Overrride to properly handle the loading and unloading of the additional text encoder. def load_lora_weights( self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs, ): # We could have accessed the unet config from `lora_state_dict()` too. We pass # it here explicitly to be able to tell that it's coming from an SDXL # pipeline. state_dict, network_alphas = self.lora_state_dict( pretrained_model_name_or_path_or_dict, unet_config=self.unet.config, **kwargs, ) self.load_lora_into_unet( state_dict, network_alphas=network_alphas, unet=self.unet ) text_encoder_state_dict = { k: v for k, v in state_dict.items() if "text_encoder." in k } if len(text_encoder_state_dict) > 0: self.load_lora_into_text_encoder( text_encoder_state_dict, network_alphas=network_alphas, text_encoder=self.text_encoder, prefix="text_encoder", lora_scale=self.lora_scale, ) text_encoder_2_state_dict = { k: v for k, v in state_dict.items() if "text_encoder_2." in k } if len(text_encoder_2_state_dict) > 0: self.load_lora_into_text_encoder( text_encoder_2_state_dict, network_alphas=network_alphas, text_encoder=self.text_encoder_2, prefix="text_encoder_2", lora_scale=self.lora_scale, ) @classmethod def save_lora_weights( self, save_directory: Union[str, os.PathLike], unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None, text_encoder_lora_layers: Dict[ str, Union[torch.nn.Module, torch.Tensor] ] = None, text_encoder_2_lora_layers: Dict[ str, Union[torch.nn.Module, torch.Tensor] ] = None, is_main_process: bool = True, weight_name: str = None, save_function: Callable = None, safe_serialization: bool = False, ): state_dict = {} def pack_weights(layers, prefix): layers_weights = ( layers.state_dict() if isinstance(layers, torch.nn.Module) else layers ) layers_state_dict = { f"{prefix}.{module_name}": param for module_name, param in layers_weights.items() } return layers_state_dict state_dict.update(pack_weights(unet_lora_layers, "unet")) if text_encoder_lora_layers and text_encoder_2_lora_layers: state_dict.update(pack_weights(text_encoder_lora_layers, "text_encoder")) state_dict.update( pack_weights(text_encoder_2_lora_layers, "text_encoder_2") ) self.write_lora_layers( state_dict=state_dict, save_directory=save_directory, is_main_process=is_main_process, weight_name=weight_name, save_function=save_function, safe_serialization=safe_serialization, ) def _remove_text_encoder_monkey_patch(self): self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder) self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder_2)