# Copyright © Alibaba, Inc. and its affiliates. # The implementation here is modifed based on diffusers.StableDiffusionPipeline, # originally Apache 2.0 License and public available at # https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py import inspect import re import warnings from typing import Any, Callable, Dict, List, Optional, Union import torch from diffusers import (AutoencoderKL, DiffusionPipeline, StableDiffusionPipeline, UNet2DConditionModel) from diffusers.configuration_utils import FrozenDict from diffusers.image_processor import VaeImageProcessor from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin from diffusers.models.vae import DecoderOutput from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput from diffusers.pipelines.stable_diffusion.safety_checker import \ StableDiffusionSafetyChecker from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.utils import (deprecate, is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring) from packaging import version from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import EulerAncestralDiscreteScheduler >>> modelscope.models.cv.text_to_360panorama_image import StableDiffusionBlendExtendPipeline >>> model_id = "damo/cv_diffusion_text-to-360panorama-image_generation/sd-base" >>> pipe = StableDiffusionBlendExtendPipeline.from_pretrained(model_id, torch_dtype=torch.float16) >>> pipe = pipe.to("cuda") >>> pipe.vae.enable_tiling() >>> pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) >>> # remove following line if xformers is not installed >>> pipe.enable_xformers_memory_efficient_attention() >>> pipe.enable_model_cpu_offload() >>> prompt = "a living room" >>> image = pipe(prompt).images[0] ``` """ re_attention = re.compile( r""" \\\(| \\\)| \\\[| \\]| \\\\| \\| \(| \[| :([+-]?[.\d]+)\)| \)| ]| [^\\()\[\]:]+| : """, re.X, ) 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 """ 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: res.append([text, 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_with_weights(pipe: DiffusionPipeline, prompt: List[str], max_length: int): r""" Tokenize a list of prompts and return its tokens with weights of each token. No padding, starting or ending token is included. """ tokens = [] weights = [] truncated = False for text in prompt: texts_and_weights = parse_prompt_attention(text) text_token = [] text_weight = [] for word, weight in texts_and_weights: # tokenize and discard the starting and the ending token token = pipe.tokenizer(word).input_ids[1:-1] text_token += token # copy the weight by length of token text_weight += [weight] * len(token) # stop if the text is too long (longer than truncation limit) if len(text_token) > max_length: truncated = True break # truncate if len(text_token) > max_length: truncated = True text_token = text_token[:max_length] text_weight = text_weight[:max_length] tokens.append(text_token) weights.append(text_weight) if truncated: logger.warning( 'Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples' ) return tokens, weights def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, pad, no_boseos_middle=True, chunk_length=77): r""" Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length. """ max_embeddings_multiples = (max_length - 2) // (chunk_length - 2) weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length for i in range(len(tokens)): tokens[i] = [ bos ] + tokens[i] + [pad] * (max_length - 1 - len(tokens[i]) - 1) + [eos] if no_boseos_middle: weights[i] = [1.0] + weights[i] + [1.0] * ( max_length - 1 - len(weights[i])) else: w = [] if len(weights[i]) == 0: w = [1.0] * weights_length else: for j in range(max_embeddings_multiples): w.append(1.0) # weight for starting token in this chunk w += weights[i][j * (chunk_length - 2):min( len(weights[i]), (j + 1) * (chunk_length - 2))] w.append(1.0) # weight for ending token in this chunk w += [1.0] * (weights_length - len(w)) weights[i] = w[:] return tokens, weights def get_unweighted_text_embeddings( pipe: DiffusionPipeline, text_input: torch.Tensor, chunk_length: int, no_boseos_middle: Optional[bool] = True, ): """ When the length of tokens is a multiple of the capacity of the text encoder, it should be split into chunks and sent to the text encoder individually. """ max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2) if max_embeddings_multiples > 1: text_embeddings = [] for i in range(max_embeddings_multiples): # extract the i-th chunk text_input_chunk = text_input[:, i * (chunk_length - 2):(i + 1) * (chunk_length - 2) + 2].clone() # cover the head and the tail by the starting and the ending tokens text_input_chunk[:, 0] = text_input[0, 0] text_input_chunk[:, -1] = text_input[0, -1] text_embedding = pipe.text_encoder(text_input_chunk)[0] if no_boseos_middle: if i == 0: # discard the ending token text_embedding = text_embedding[:, :-1] elif i == max_embeddings_multiples - 1: # discard the starting token text_embedding = text_embedding[:, 1:] else: # discard both starting and ending tokens text_embedding = text_embedding[:, 1:-1] text_embeddings.append(text_embedding) text_embeddings = torch.concat(text_embeddings, axis=1) else: text_embeddings = pipe.text_encoder(text_input)[0] return text_embeddings def get_weighted_text_embeddings( pipe: DiffusionPipeline, prompt: Union[str, List[str]], uncond_prompt: Optional[Union[str, List[str]]] = None, max_embeddings_multiples: Optional[int] = 3, no_boseos_middle: Optional[bool] = False, skip_parsing: Optional[bool] = False, skip_weighting: Optional[bool] = False, ): r""" Prompts can be assigned with local weights using brackets. For example, prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful', and the embedding tokens corresponding to the words get multiplied by a constant, 1.1. Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean. Args: pipe (`DiffusionPipeline`): Pipe to provide access to the tokenizer and the text encoder. prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. uncond_prompt (`str` or `List[str]`): The unconditional prompt or prompts for guide the image generation. If unconditional prompt is provided, the embeddings of prompt and uncond_prompt are concatenated. max_embeddings_multiples (`int`, *optional*, defaults to `3`): The max multiple length of prompt embeddings compared to the max output length of text encoder. no_boseos_middle (`bool`, *optional*, defaults to `False`): If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and ending token in each of the chunk in the middle. skip_parsing (`bool`, *optional*, defaults to `False`): Skip the parsing of brackets. skip_weighting (`bool`, *optional*, defaults to `False`): Skip the weighting. When the parsing is skipped, it is forced True. """ max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2 if isinstance(prompt, str): prompt = [prompt] if not skip_parsing: prompt_tokens, prompt_weights = get_prompts_with_weights( pipe, prompt, max_length - 2) if uncond_prompt is not None: if isinstance(uncond_prompt, str): uncond_prompt = [uncond_prompt] uncond_tokens, uncond_weights = get_prompts_with_weights( pipe, uncond_prompt, max_length - 2) else: prompt_tokens = [ token[1:-1] for token in pipe.tokenizer( prompt, max_length=max_length, truncation=True).input_ids ] prompt_weights = [[1.0] * len(token) for token in prompt_tokens] if uncond_prompt is not None: if isinstance(uncond_prompt, str): uncond_prompt = [uncond_prompt] uncond_tokens = [ token[1:-1] for token in pipe.tokenizer( uncond_prompt, max_length=max_length, truncation=True).input_ids ] uncond_weights = [[1.0] * len(token) for token in uncond_tokens] # round up the longest length of tokens to a multiple of (model_max_length - 2) max_length = max([len(token) for token in prompt_tokens]) if uncond_prompt is not None: max_length = max(max_length, max([len(token) for token in uncond_tokens])) max_embeddings_multiples = min( max_embeddings_multiples, (max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1, ) max_embeddings_multiples = max(1, max_embeddings_multiples) max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2 # pad the length of tokens and weights bos = pipe.tokenizer.bos_token_id eos = pipe.tokenizer.eos_token_id pad = getattr(pipe.tokenizer, 'pad_token_id', eos) prompt_tokens, prompt_weights = pad_tokens_and_weights( prompt_tokens, prompt_weights, max_length, bos, eos, pad, no_boseos_middle=no_boseos_middle, chunk_length=pipe.tokenizer.model_max_length, ) prompt_tokens = torch.tensor( prompt_tokens, dtype=torch.long, device=pipe.device) if uncond_prompt is not None: uncond_tokens, uncond_weights = pad_tokens_and_weights( uncond_tokens, uncond_weights, max_length, bos, eos, pad, no_boseos_middle=no_boseos_middle, chunk_length=pipe.tokenizer.model_max_length, ) uncond_tokens = torch.tensor( uncond_tokens, dtype=torch.long, device=pipe.device) # get the embeddings text_embeddings = get_unweighted_text_embeddings( pipe, prompt_tokens, pipe.tokenizer.model_max_length, no_boseos_middle=no_boseos_middle, ) prompt_weights = torch.tensor( prompt_weights, dtype=text_embeddings.dtype, device=text_embeddings.device) if uncond_prompt is not None: uncond_embeddings = get_unweighted_text_embeddings( pipe, uncond_tokens, pipe.tokenizer.model_max_length, no_boseos_middle=no_boseos_middle, ) uncond_weights = torch.tensor( uncond_weights, dtype=uncond_embeddings.dtype, device=uncond_embeddings.device) # assign weights to the prompts and normalize in the sense of mean # TODO: should we normalize by chunk or in a whole (current implementation)? if (not skip_parsing) and (not skip_weighting): previous_mean = text_embeddings.float().mean(axis=[-2, -1]).to( text_embeddings.dtype) text_embeddings *= prompt_weights.unsqueeze(-1) current_mean = text_embeddings.float().mean(axis=[-2, -1]).to( text_embeddings.dtype) text_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1) if uncond_prompt is not None: previous_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to( uncond_embeddings.dtype) uncond_embeddings *= uncond_weights.unsqueeze(-1) current_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to( uncond_embeddings.dtype) uncond_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1) if uncond_prompt is not None: return text_embeddings, uncond_embeddings return text_embeddings, None 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 StableDiffusionBlendExtendPipeline(StableDiffusionPipeline): r""" Pipeline for text-to-image generation using Stable Diffusion. 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: - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`] - *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`] - *Ckpt*: [`loaders.FromCkptMixin.from_ckpt`] as well as the following saving methods: - *LoRA*: [`loaders.LoraLoaderMixin.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 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. tokenizer (`CLIPTokenizer`): 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`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ _optional_components = ['safety_checker', 'feature_extractor'] def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, max_embeddings_multiples=3, prompt_embeds: Optional[torch.FloatTensor] = None, negative_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(int)`): prompt to be encoded 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]`): The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). max_embeddings_multiples (`int`, *optional*, defaults to `3`): The max multiple length of prompt embeddings compared to the max output length of text encoder. """ 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] if negative_prompt_embeds is None: if negative_prompt is None: negative_prompt = [''] * batch_size elif isinstance(negative_prompt, str): negative_prompt = [negative_prompt] * batch_size if 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`.') if prompt_embeds is None or negative_prompt_embeds is None: if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) if do_classifier_free_guidance and negative_prompt_embeds is None: negative_prompt = self.maybe_convert_prompt( negative_prompt, self.tokenizer) prompt_embeds1, negative_prompt_embeds1 = get_weighted_text_embeddings( pipe=self, prompt=prompt, uncond_prompt=negative_prompt if do_classifier_free_guidance else None, max_embeddings_multiples=max_embeddings_multiples, ) if prompt_embeds is None: prompt_embeds = prompt_embeds1 if negative_prompt_embeds is None: negative_prompt_embeds = negative_prompt_embeds1 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: bs_embed, seq_len, _ = negative_prompt_embeds.shape negative_prompt_embeds = negative_prompt_embeds.repeat( 1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view( bs_embed * num_images_per_prompt, seq_len, -1) prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds def blend_v(self, a, b, blend_extent): blend_extent = min(a.shape[2], b.shape[2], blend_extent) for y in range(blend_extent): b[:, :, y, :] = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * ( y / blend_extent) return b def blend_h(self, a, b, blend_extent): blend_extent = min(a.shape[3], b.shape[3], blend_extent) for x in range(blend_extent): b[:, :, :, x] = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * ( x / blend_extent) return b @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, 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, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. 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. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. 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`). 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. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: 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.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.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. Examples: Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ def tiled_decode( self, z: torch.FloatTensor, return_dict: bool = True ) -> Union[DecoderOutput, torch.FloatTensor]: r"""Decode a batch of images using a tiled decoder. Args: When this option is enabled, the VAE will split the input tensor into tiles to compute decoding in several steps. This is useful to keep memory use constant regardless of image size. The end result of tiled decoding is: different from non-tiled decoding due to each tile using a different decoder. To avoid tiling artifacts, the tiles overlap and are blended together to form a smooth output. You may still see tile-sized changes in the look of the output, but they should be much less noticeable. z (`torch.FloatTensor`): Input batch of latent vectors. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`DecoderOutput`] instead of a plain tuple. """ _tile_overlap_factor = 1 - self.tile_overlap_factor overlap_size = int(self.tile_latent_min_size * _tile_overlap_factor) blend_extent = int(self.tile_sample_min_size * self.tile_overlap_factor) row_limit = self.tile_sample_min_size - blend_extent w = z.shape[3] z = torch.cat([z, z[:, :, :, :w // 4]], dim=-1) # Split z into overlapping 64x64 tiles and decode them separately. # The tiles have an overlap to avoid seams between tiles. rows = [] for i in range(0, z.shape[2], overlap_size): row = [] tile = z[:, :, i:i + self.tile_latent_min_size, :] tile = self.post_quant_conv(tile) decoded = self.decoder(tile) vae_scale_factor = decoded.shape[-1] // tile.shape[-1] row.append(decoded) rows.append(row) result_rows = [] for i, row in enumerate(rows): result_row = [] for j, tile in enumerate(row): # blend the above tile and the left tile # to the current tile and add the current tile to the result row if i > 0: tile = self.blend_v(rows[i - 1][j], tile, blend_extent) if j > 0: tile = self.blend_h(row[j - 1], tile, blend_extent) result_row.append( self.blend_h( tile[:, :, :row_limit, w * vae_scale_factor:], tile[:, :, :row_limit, :w * vae_scale_factor], tile.shape[-1] - w * vae_scale_factor)) result_rows.append(torch.cat(result_row, dim=3)) dec = torch.cat(result_rows, dim=2) if not return_dict: return (dec, ) return DecoderOutput(sample=dec) self.vae.tiled_decode = tiled_decode.__get__(self.vae, AutoencoderKL) # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor # 1. Check inputs. Raise error if not correct self.check_inputs(prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds) self.blend_extend = width // self.vae_scale_factor // 32 # 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 = ( cross_attention_kwargs.get('scale', None) if cross_attention_kwargs is not None else None) prompt_embeds = self._encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, lora_scale=text_encoder_lora_scale, ) # 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. Denoising loop num_warmup_steps = len( timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = 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 noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_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 condition_i = i == len(timesteps) - 1 condition_warm = (i + 1) > num_warmup_steps and ( i + 1) % self.scheduler.order == 0 if condition_i or condition_warm: progress_bar.update() if callback is not None and i % callback_steps == 0: callback(i, t, latents) latents = self.blend_h(latents, latents, self.blend_extend) latents = self.blend_h(latents, latents, self.blend_extend) latents = latents[:, :, :, :width // self.vae_scale_factor] if not output_type == 'latent': image = self.vae.decode( latents / self.vae.config.scaling_factor, return_dict=False)[0] image, has_nsfw_concept = self.run_safety_checker( image, device, prompt_embeds.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess( image, output_type=output_type, do_denormalize=do_denormalize) # 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, has_nsfw_concept) return StableDiffusionPipelineOutput( images=image, nsfw_content_detected=has_nsfw_concept)