# Copyright 2025 The DEVAIEXP Team and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect from enum import Enum from typing import Any, Dict, List, Optional, Tuple, Union import numpy as np import torch import torch.nn.functional as F from PIL import Image from transformers import ( CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer, ) from diffusers.image_processor import PipelineImageInput, VaeImageProcessor from diffusers.loaders import ( FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin, ) from diffusers.models import ( AutoencoderKL, ControlNetModel, ControlNetUnionModel, MultiControlNetModel, UNet2DConditionModel, ) from diffusers.models.lora import adjust_lora_scale_text_encoder from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput from diffusers.schedulers import KarrasDiffusionSchedulers, LMSDiscreteScheduler from diffusers.utils import ( USE_PEFT_BACKEND, deprecate, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers, ) from diffusers.utils.import_utils import is_invisible_watermark_available from diffusers.utils.torch_utils import is_compiled_module, randn_tensor if is_invisible_watermark_available(): from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker from diffusers.utils import is_torch_xla_available if is_torch_xla_available(): import torch_xla.core.xla_model as xm XLA_AVAILABLE = True else: XLA_AVAILABLE = False logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py import torch from diffusers import DiffusionPipeline, ControlNetUnionModel, AutoencoderKL, UniPCMultistepScheduler from diffusers.utils import load_image from PIL import Image device = "cuda" # Initialize the models and pipeline controlnet = ControlNetUnionModel.from_pretrained( "brad-twinkl/controlnet-union-sdxl-1.0-promax", torch_dtype=torch.float16 ).to(device=device) vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16).to(device=device) model_id = "SG161222/RealVisXL_V5.0" pipe = StableDiffusionXLControlNetTileSRPipeline.from_pretrained( model_id, controlnet=controlnet, vae=vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16" ).to(device) pipe.enable_model_cpu_offload() # << Enable this if you have limited VRAM pipe.enable_vae_tiling() # << Enable this if you have limited VRAM pipe.enable_vae_slicing() # << Enable this if you have limited VRAM # Set selected scheduler pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) # Load image control_image = load_image("https://huggingface.co/datasets/DEVAIEXP/assets/resolve/main/1.jpg") original_height = control_image.height original_width = control_image.width print(f"Current resolution: H:{original_height} x W:{original_width}") # Pre-upscale image for tiling resolution = 4096 tile_gaussian_sigma = 0.3 max_tile_size = 1024 # or 1280 current_size = max(control_image.size) scale_factor = max(2, resolution / current_size) new_size = (int(control_image.width * scale_factor), int(control_image.height * scale_factor)) image = control_image.resize(new_size, Image.LANCZOS) # Update target height and width target_height = image.height target_width = image.width print(f"Target resolution: H:{target_height} x W:{target_width}") # Calculate overlap size normal_tile_overlap, border_tile_overlap = calculate_overlap(target_width, target_height) # Set other params tile_weighting_method = TileWeightingMethod.COSINE.value guidance_scale = 4 num_inference_steps = 35 denoising_strenght = 0.65 controlnet_strength = 1.0 prompt = "high-quality, noise-free edges, high quality, 4k, hd, 8k" negative_prompt = "blurry, pixelated, noisy, low resolution, artifacts, poor details" # Image generation control_image = pipe( image=image, control_image=control_image, control_mode=[6], controlnet_conditioning_scale=float(controlnet_strength), prompt=prompt, negative_prompt=negative_prompt, normal_tile_overlap=normal_tile_overlap, border_tile_overlap=border_tile_overlap, height=target_height, width=target_width, original_size=(original_width, original_height), target_size=(target_width, target_height), guidance_scale=guidance_scale, strength=float(denoising_strenght), tile_weighting_method=tile_weighting_method, max_tile_size=max_tile_size, tile_gaussian_sigma=float(tile_gaussian_sigma), num_inference_steps=num_inference_steps, )["images"][0] ``` """ # This function was copied and adapted from https://huggingface.co/spaces/gokaygokay/TileUpscalerV2, licensed under Apache 2.0. def _adaptive_tile_size(image_size, base_tile_size=512, max_tile_size=1280): """ Calculate the adaptive tile size based on the image dimensions, ensuring the tile respects the aspect ratio and stays within the specified size limits. """ width, height = image_size aspect_ratio = width / height if aspect_ratio > 1: # Landscape orientation tile_width = min(width, max_tile_size) tile_height = min(int(tile_width / aspect_ratio), max_tile_size) else: # Portrait or square orientation tile_height = min(height, max_tile_size) tile_width = min(int(tile_height * aspect_ratio), max_tile_size) # Ensure the tile size is not smaller than the base_tile_size tile_width = max(tile_width, base_tile_size) tile_height = max(tile_height, base_tile_size) return tile_width, tile_height # Copied and adapted from https://github.com/huggingface/diffusers/blob/main/examples/community/mixture_tiling.py def _tile2pixel_indices( tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap, image_width, image_height ): """Given a tile row and column numbers returns the range of pixels affected by that tiles in the overall image Returns a tuple with: - Starting coordinates of rows in pixel space - Ending coordinates of rows in pixel space - Starting coordinates of columns in pixel space - Ending coordinates of columns in pixel space """ # Calculate initial indices px_row_init = 0 if tile_row == 0 else tile_row * (tile_height - tile_row_overlap) px_col_init = 0 if tile_col == 0 else tile_col * (tile_width - tile_col_overlap) # Calculate end indices px_row_end = px_row_init + tile_height px_col_end = px_col_init + tile_width # Ensure the last tile does not exceed the image dimensions px_row_end = min(px_row_end, image_height) px_col_end = min(px_col_end, image_width) return px_row_init, px_row_end, px_col_init, px_col_end # Copied and adapted from https://github.com/huggingface/diffusers/blob/main/examples/community/mixture_tiling.py def _tile2latent_indices( tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap, image_width, image_height ): """Given a tile row and column numbers returns the range of latents affected by that tiles in the overall image Returns a tuple with: - Starting coordinates of rows in latent space - Ending coordinates of rows in latent space - Starting coordinates of columns in latent space - Ending coordinates of columns in latent space """ # Get pixel indices px_row_init, px_row_end, px_col_init, px_col_end = _tile2pixel_indices( tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap, image_width, image_height ) # Convert to latent space latent_row_init = px_row_init // 8 latent_row_end = px_row_end // 8 latent_col_init = px_col_init // 8 latent_col_end = px_col_end // 8 latent_height = image_height // 8 latent_width = image_width // 8 # Ensure the last tile does not exceed the latent dimensions latent_row_end = min(latent_row_end, latent_height) latent_col_end = min(latent_col_end, latent_width) return latent_row_init, latent_row_end, latent_col_init, latent_col_end # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents def retrieve_latents( encoder_output: torch.Tensor, generator: torch.Generator | None = None, sample_mode: str = "sample" ): if hasattr(encoder_output, "latent_dist") and sample_mode == "sample": return encoder_output.latent_dist.sample(generator) elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax": return encoder_output.latent_dist.mode() elif hasattr(encoder_output, "latents"): return encoder_output.latents else: raise AttributeError("Could not access latents of provided encoder_output") class StableDiffusionXLControlNetTileSRPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionXLLoraLoaderMixin, FromSingleFileMixin, ): r""" Pipeline for image-to-image generation using Stable Diffusion XL with ControlNet guidance. 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.) 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.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving 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. 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. controlnet ([`ControlNetUnionModel`]): Provides additional conditioning to the unet during the denoising process. 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`]. requires_aesthetics_score (`bool`, *optional*, defaults to `"False"`): Whether the `unet` requires an `aesthetic_score` condition to be passed during inference. Also see the config of `stabilityai/stable-diffusion-xl-refiner-1-0`. force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`): Whether the negative prompt embeddings shall be forced to 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 library](https://github.com/ShieldMnt/invisible-watermark/) to watermark output images. If not defined, it will default to True if the package is installed, otherwise no watermarker will be used. """ model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae" _optional_components = [ "tokenizer", "tokenizer_2", "text_encoder", "text_encoder_2", ] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, text_encoder_2: CLIPTextModelWithProjection, tokenizer: CLIPTokenizer, tokenizer_2: CLIPTokenizer, unet: UNet2DConditionModel, controlnet: ControlNetUnionModel, scheduler: KarrasDiffusionSchedulers, requires_aesthetics_score: bool = False, force_zeros_for_empty_prompt: bool = True, add_watermarker: Optional[bool] = None, ): super().__init__() if not isinstance(controlnet, ControlNetUnionModel): raise ValueError("Expected `controlnet` to be of type `ControlNetUnionModel`.") 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, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8 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 ) self.mask_processor = VaeImageProcessor( vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True ) 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) self.register_to_config(requires_aesthetics_score=requires_aesthetics_score) def calculate_overlap(self, width, height, base_overlap=128): """ Calculates dynamic overlap based on the image's aspect ratio. Args: width (int): Width of the image in pixels. height (int): Height of the image in pixels. base_overlap (int, optional): Base overlap value in pixels. Defaults to 128. Returns: tuple: A tuple containing: - row_overlap (int): Overlap between tiles in consecutive rows. - col_overlap (int): Overlap between tiles in consecutive columns. """ ratio = height / width if ratio < 1: # Image is wider than tall return base_overlap // 2, base_overlap else: # Image is taller than wide return base_overlap, base_overlap * 2 class TileWeightingMethod(Enum): """Mode in which the tile weights will be generated""" COSINE = "Cosine" GAUSSIAN = "Gaussian" # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt def encode_prompt( self, prompt: str, prompt_2: str | None = None, device: Optional[torch.device] = None, num_images_per_prompt: int = 1, do_classifier_free_guidance: bool = True, negative_prompt: str | None = None, negative_prompt_2: str | None = 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] ) dtype = text_encoders[0].dtype 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}" ) text_encoder.to(dtype) 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 if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2: 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 if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2: 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://huggingface.co/papers/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, height, width, image, strength, num_inference_steps, normal_tile_overlap, border_tile_overlap, max_tile_size, tile_gaussian_sigma, tile_weighting_method, controlnet_conditioning_scale=1.0, control_guidance_start=0.0, control_guidance_end=1.0, ): 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 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)}") if strength < 0 or strength > 1: raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") if num_inference_steps is None: raise ValueError("`num_inference_steps` cannot be None.") elif not isinstance(num_inference_steps, int) or num_inference_steps <= 0: raise ValueError( f"`num_inference_steps` has to be a positive integer but is {num_inference_steps} of type" f" {type(num_inference_steps)}." ) if normal_tile_overlap is None: raise ValueError("`normal_tile_overlap` cannot be None.") elif not isinstance(normal_tile_overlap, int) or normal_tile_overlap < 64: raise ValueError( f"`normal_tile_overlap` has to be greater than 64 but is {normal_tile_overlap} of type" f" {type(normal_tile_overlap)}." ) if border_tile_overlap is None: raise ValueError("`border_tile_overlap` cannot be None.") elif not isinstance(border_tile_overlap, int) or border_tile_overlap < 128: raise ValueError( f"`border_tile_overlap` has to be greater than 128 but is {border_tile_overlap} of type" f" {type(border_tile_overlap)}." ) if max_tile_size is None: raise ValueError("`max_tile_size` cannot be None.") elif not isinstance(max_tile_size, int) or max_tile_size not in (1024, 1280): raise ValueError( f"`max_tile_size` has to be in 1024 or 1280 but is {max_tile_size} of type {type(max_tile_size)}." ) if tile_gaussian_sigma is None: raise ValueError("`tile_gaussian_sigma` cannot be None.") elif not isinstance(tile_gaussian_sigma, float) or tile_gaussian_sigma <= 0: raise ValueError( f"`tile_gaussian_sigma` has to be a positive float but is {tile_gaussian_sigma} of type" f" {type(tile_gaussian_sigma)}." ) if tile_weighting_method is None: raise ValueError("`tile_weighting_method` cannot be None.") elif not isinstance(tile_weighting_method, str) or tile_weighting_method not in [ t.value for t in self.TileWeightingMethod ]: raise ValueError( f"`tile_weighting_method` has to be a string in ({[t.value for t in self.TileWeightingMethod]}) but is {tile_weighting_method} of type" f" {type(tile_weighting_method)}." ) # Check `image` is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance( self.controlnet, torch._dynamo.eval_frame.OptimizedModule ) if ( isinstance(self.controlnet, ControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, ControlNetModel) ): self.check_image(image, prompt) elif ( isinstance(self.controlnet, ControlNetUnionModel) or is_compiled and isinstance(self.controlnet._orig_mod, ControlNetUnionModel) ): self.check_image(image, prompt) else: assert False # Check `controlnet_conditioning_scale` if ( isinstance(self.controlnet, ControlNetUnionModel) or is_compiled and isinstance(self.controlnet._orig_mod, ControlNetUnionModel) ) or ( isinstance(self.controlnet, MultiControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, MultiControlNetModel) ): if not isinstance(controlnet_conditioning_scale, float): raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.") elif ( isinstance(self.controlnet, MultiControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, MultiControlNetModel) ): if isinstance(controlnet_conditioning_scale, list): if any(isinstance(i, list) for i in controlnet_conditioning_scale): raise ValueError("A single batch of multiple conditionings are supported at the moment.") elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len( self.controlnet.nets ): raise ValueError( "For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have" " the same length as the number of controlnets" ) else: assert False if not isinstance(control_guidance_start, (tuple, list)): control_guidance_start = [control_guidance_start] if not isinstance(control_guidance_end, (tuple, list)): control_guidance_end = [control_guidance_end] if len(control_guidance_start) != len(control_guidance_end): raise ValueError( f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list." ) for start, end in zip(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_sd_xl.StableDiffusionXLControlNetPipeline.check_image def check_image(self, image, prompt): image_is_pil = isinstance(image, 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], 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) 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}" ) # Copied from diffusers.pipelines.controlnet.pipeline_controlnet_sd_xl.StableDiffusionXLControlNetPipeline.prepare_image def prepare_control_image( self, image, width, height, batch_size, num_images_per_prompt, device, dtype, do_classifier_free_guidance=False, guess_mode=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 and not guess_mode: image = torch.cat([image] * 2) return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps def get_timesteps(self, num_inference_steps, strength): # get the original timestep using init_timestep init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] if hasattr(self.scheduler, "set_begin_index"): self.scheduler.set_begin_index(t_start * self.scheduler.order) return timesteps, num_inference_steps - t_start # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline.prepare_latents def prepare_latents( self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None, add_noise=True ): if not isinstance(image, (torch.Tensor, Image.Image, list)): raise ValueError( f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}" ) latents_mean = latents_std = None if hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None: latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1) if hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None: latents_std = torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1) # Offload text encoder if `enable_model_cpu_offload` was enabled if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.text_encoder_2.to("cpu") torch.cuda.empty_cache() image = image.to(device=device, dtype=dtype) batch_size = batch_size * num_images_per_prompt if image.shape[1] == 4: init_latents = image else: # make sure the VAE is in float32 mode, as it overflows in float16 if self.vae.config.force_upcast: image = image.float() self.vae.to(dtype=torch.float32) 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." ) elif isinstance(generator, list): if image.shape[0] < batch_size and batch_size % image.shape[0] == 0: image = torch.cat([image] * (batch_size // image.shape[0]), dim=0) elif image.shape[0] < batch_size and batch_size % image.shape[0] != 0: raise ValueError( f"Cannot duplicate `image` of batch size {image.shape[0]} to effective batch_size {batch_size} " ) init_latents = [ retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i]) for i in range(batch_size) ] init_latents = torch.cat(init_latents, dim=0) else: init_latents = retrieve_latents(self.vae.encode(image), generator=generator) if self.vae.config.force_upcast: self.vae.to(dtype) init_latents = init_latents.to(dtype) if latents_mean is not None and latents_std is not None: latents_mean = latents_mean.to(device=device, dtype=dtype) latents_std = latents_std.to(device=device, dtype=dtype) init_latents = (init_latents - latents_mean) * self.vae.config.scaling_factor / latents_std else: init_latents = self.vae.config.scaling_factor * init_latents if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0: # expand init_latents for batch_size additional_image_per_prompt = batch_size // init_latents.shape[0] init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0) elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0: raise ValueError( f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts." ) else: init_latents = torch.cat([init_latents], dim=0) if add_noise: shape = init_latents.shape noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) # get latents init_latents = self.scheduler.add_noise(init_latents, noise, timestep) latents = init_latents return latents # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline._get_add_time_ids def _get_add_time_ids( self, original_size, crops_coords_top_left, target_size, aesthetic_score, negative_aesthetic_score, negative_original_size, negative_crops_coords_top_left, negative_target_size, dtype, text_encoder_projection_dim=None, ): if self.config.requires_aesthetics_score: add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,)) add_neg_time_ids = list( negative_original_size + negative_crops_coords_top_left + (negative_aesthetic_score,) ) else: add_time_ids = list(original_size + crops_coords_top_left + target_size) add_neg_time_ids = list(negative_original_size + crops_coords_top_left + negative_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.add_embedding.linear_1.in_features if ( expected_add_embed_dim > passed_add_embed_dim and (expected_add_embed_dim - passed_add_embed_dim) == self.unet.config.addition_time_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. Please make sure to enable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=True)` to make sure `aesthetic_score` {aesthetic_score} and `negative_aesthetic_score` {negative_aesthetic_score} is correctly used by the model." ) elif ( expected_add_embed_dim < passed_add_embed_dim and (passed_add_embed_dim - expected_add_embed_dim) == self.unet.config.addition_time_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. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the model." ) elif 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) add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype) return add_time_ids, add_neg_time_ids def _generate_cosine_weights(self, tile_width, tile_height, nbatches, device, dtype): """ Generates cosine weights as a PyTorch tensor for blending tiles. Args: tile_width (int): Width of the tile in pixels. tile_height (int): Height of the tile in pixels. nbatches (int): Number of batches. device (torch.device): Device where the tensor will be allocated (e.g., 'cuda' or 'cpu'). dtype (torch.dtype): Data type of the tensor (e.g., torch.float32). Returns: torch.Tensor: A tensor containing cosine weights for blending tiles, expanded to match batch and channel dimensions. """ # Convert tile dimensions to latent space latent_width = tile_width // 8 latent_height = tile_height // 8 # Generate x and y coordinates in latent space x = np.arange(0, latent_width) y = np.arange(0, latent_height) # Calculate midpoints midpoint_x = (latent_width - 1) / 2 midpoint_y = (latent_height - 1) / 2 # Compute cosine probabilities for x and y x_probs = np.cos(np.pi * (x - midpoint_x) / latent_width) y_probs = np.cos(np.pi * (y - midpoint_y) / latent_height) # Create a 2D weight matrix using the outer product weights_np = np.outer(y_probs, x_probs) # Convert to a PyTorch tensor with the correct device and dtype weights_torch = torch.tensor(weights_np, device=device, dtype=dtype) # Expand for batch and channel dimensions tile_weights_expanded = torch.tile(weights_torch, (nbatches, self.unet.config.in_channels, 1, 1)) return tile_weights_expanded def _generate_gaussian_weights(self, tile_width, tile_height, nbatches, device, dtype, sigma=0.05): """ Generates Gaussian weights as a PyTorch tensor for blending tiles in latent space. Args: tile_width (int): Width of the tile in pixels. tile_height (int): Height of the tile in pixels. nbatches (int): Number of batches. device (torch.device): Device where the tensor will be allocated (e.g., 'cuda' or 'cpu'). dtype (torch.dtype): Data type of the tensor (e.g., torch.float32). sigma (float, optional): Standard deviation of the Gaussian distribution. Controls the smoothness of the weights. Defaults to 0.05. Returns: torch.Tensor: A tensor containing Gaussian weights for blending tiles, expanded to match batch and channel dimensions. """ # Convert tile dimensions to latent space latent_width = tile_width // 8 latent_height = tile_height // 8 # Generate Gaussian weights in latent space x = np.linspace(-1, 1, latent_width) y = np.linspace(-1, 1, latent_height) xx, yy = np.meshgrid(x, y) gaussian_weight = np.exp(-(xx**2 + yy**2) / (2 * sigma**2)) # Convert to a PyTorch tensor with the correct device and dtype weights_torch = torch.tensor(gaussian_weight, device=device, dtype=dtype) # Expand for batch and channel dimensions weights_expanded = weights_torch.unsqueeze(0).unsqueeze(0) # Add batch and channel dimensions weights_expanded = weights_expanded.expand(nbatches, -1, -1, -1) # Expand to the number of batches return weights_expanded def _get_num_tiles(self, height, width, tile_height, tile_width, normal_tile_overlap, border_tile_overlap): """ Calculates the number of tiles needed to cover an image, choosing the appropriate formula based on the ratio between the image size and the tile size. This function automatically selects between two formulas: 1. A universal formula for typical cases (image-to-tile ratio <= 6:1). 2. A specialized formula with border tile overlap for larger or atypical cases (image-to-tile ratio > 6:1). Args: height (int): Height of the image in pixels. width (int): Width of the image in pixels. tile_height (int): Height of each tile in pixels. tile_width (int): Width of each tile in pixels. normal_tile_overlap (int): Overlap between tiles in pixels for normal (non-border) tiles. border_tile_overlap (int): Overlap between tiles in pixels for border tiles. Returns: tuple: A tuple containing: - grid_rows (int): Number of rows in the tile grid. - grid_cols (int): Number of columns in the tile grid. Notes: - The function uses the universal formula (without border_tile_overlap) for typical cases where the image-to-tile ratio is 6:1 or smaller. - For larger or atypical cases (image-to-tile ratio > 6:1), it uses a specialized formula that includes border_tile_overlap to ensure complete coverage of the image, especially at the edges. """ # Calculate the ratio between the image size and the tile size height_ratio = height / tile_height width_ratio = width / tile_width # If the ratio is greater than 6:1, use the formula with border_tile_overlap if height_ratio > 6 or width_ratio > 6: grid_rows = int(np.ceil((height - border_tile_overlap) / (tile_height - normal_tile_overlap))) + 1 grid_cols = int(np.ceil((width - border_tile_overlap) / (tile_width - normal_tile_overlap))) + 1 else: # Otherwise, use the universal formula grid_rows = int(np.ceil((height - normal_tile_overlap) / (tile_height - normal_tile_overlap))) grid_cols = int(np.ceil((width - normal_tile_overlap) / (tile_width - normal_tile_overlap))) return grid_rows, grid_cols def prepare_tiles( self, grid_rows, grid_cols, tile_weighting_method, tile_width, tile_height, normal_tile_overlap, border_tile_overlap, width, height, tile_sigma, batch_size, device, dtype, ): """ Processes image tiles by dynamically adjusting overlap and calculating Gaussian or cosine weights. Args: grid_rows (int): Number of rows in the tile grid. grid_cols (int): Number of columns in the tile grid. tile_weighting_method (str): Method for weighting tiles. Options: "Gaussian" or "Cosine". tile_width (int): Width of each tile in pixels. tile_height (int): Height of each tile in pixels. normal_tile_overlap (int): Overlap between tiles in pixels for normal tiles. border_tile_overlap (int): Overlap between tiles in pixels for border tiles. width (int): Width of the image in pixels. height (int): Height of the image in pixels. tile_sigma (float): Sigma parameter for Gaussian weighting. batch_size (int): Batch size for weight tiles. device (torch.device): Device where tensors will be allocated (e.g., 'cuda' or 'cpu'). dtype (torch.dtype): Data type of the tensors (e.g., torch.float32). Returns: tuple: A tuple containing: - tile_weights (np.ndarray): Array of weights for each tile. - tile_row_overlaps (np.ndarray): Array of row overlaps for each tile. - tile_col_overlaps (np.ndarray): Array of column overlaps for each tile. """ # Create arrays to store dynamic overlaps and weights tile_row_overlaps = np.full((grid_rows, grid_cols), normal_tile_overlap) tile_col_overlaps = np.full((grid_rows, grid_cols), normal_tile_overlap) tile_weights = np.empty((grid_rows, grid_cols), dtype=object) # Stores Gaussian or cosine weights # Iterate over tiles to adjust overlap and calculate weights for row in range(grid_rows): for col in range(grid_cols): # Calculate the size of the current tile px_row_init, px_row_end, px_col_init, px_col_end = _tile2pixel_indices( row, col, tile_width, tile_height, normal_tile_overlap, normal_tile_overlap, width, height ) current_tile_width = px_col_end - px_col_init current_tile_height = px_row_end - px_row_init sigma = tile_sigma # Adjust overlap for smaller tiles if current_tile_width < tile_width: px_row_init, px_row_end, px_col_init, px_col_end = _tile2pixel_indices( row, col, tile_width, tile_height, border_tile_overlap, border_tile_overlap, width, height ) current_tile_width = px_col_end - px_col_init tile_col_overlaps[row, col] = border_tile_overlap sigma = tile_sigma * 1.2 if current_tile_height < tile_height: px_row_init, px_row_end, px_col_init, px_col_end = _tile2pixel_indices( row, col, tile_width, tile_height, border_tile_overlap, border_tile_overlap, width, height ) current_tile_height = px_row_end - px_row_init tile_row_overlaps[row, col] = border_tile_overlap sigma = tile_sigma * 1.2 # Calculate weights for the current tile if tile_weighting_method == self.TileWeightingMethod.COSINE.value: tile_weights[row, col] = self._generate_cosine_weights( tile_width=current_tile_width, tile_height=current_tile_height, nbatches=batch_size, device=device, dtype=torch.float32, ) else: tile_weights[row, col] = self._generate_gaussian_weights( tile_width=current_tile_width, tile_height=current_tile_height, nbatches=batch_size, device=device, dtype=dtype, sigma=sigma, ) return tile_weights, tile_row_overlaps, tile_col_overlaps def upcast_vae(self): deprecate("upcast_vae", "1.0.0", "`upcast_vae` is deprecated. Please use `pipe.vae.to(torch.float32)`") self.vae.to(dtype=torch.float32) @property def guidance_scale(self): return self._guidance_scale @property def clip_skip(self): return self._clip_skip # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://huggingface.co/papers/2205.11487 . `guidance_scale = 1` # corresponds to doing no classifier free guidance. @property def do_classifier_free_guidance(self): return self._guidance_scale > 1 @property def cross_attention_kwargs(self): return self._cross_attention_kwargs @property def num_timesteps(self): return self._num_timesteps @property def interrupt(self): return self._interrupt @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, image: PipelineImageInput = None, control_image: PipelineImageInput = None, height: Optional[int] = None, width: Optional[int] = None, strength: float = 0.9999, num_inference_steps: int = 50, guidance_scale: float = 5.0, 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.Tensor] = None, output_type: str | None = "pil", return_dict: bool = True, cross_attention_kwargs: Optional[Dict[str, Any]] = None, controlnet_conditioning_scale: Union[float, List[float]] = 1.0, guess_mode: bool = False, control_guidance_start: Union[float, List[float]] = 0.0, control_guidance_end: Union[float, List[float]] = 1.0, control_mode: Optional[Union[int, List[int]]] = None, 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, aesthetic_score: float = 6.0, negative_aesthetic_score: float = 2.5, clip_skip: Optional[int] = None, normal_tile_overlap: int = 64, border_tile_overlap: int = 128, max_tile_size: int = 1024, tile_gaussian_sigma: float = 0.05, tile_weighting_method: str = "Cosine", **kwargs, ): 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`. image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`, *optional*): The initial image to be used as the starting point for the image generation process. Can also accept image latents as `image`, if passing latents directly, they will not be encoded again. control_image (`PipelineImageInput`, *optional*): The ControlNet input condition. ControlNet uses this input condition to generate guidance for Unet. 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 default 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*): The height in pixels of the generated image. If not provided, defaults to the height of `control_image`. width (`int`, *optional*): The width in pixels of the generated image. If not provided, defaults to the width of `control_image`. strength (`float`, *optional*, defaults to 0.9999): Indicates the extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a starting point, and more noise is added the higher the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise is maximum, and the denoising process runs for the full number of iterations specified in `num_inference_steps`. 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): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages generating images 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://huggingface.co/papers/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.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 will be generated by sampling using the supplied random `generator`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generated 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. 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). controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0): The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added to the residual in the original UNet. If multiple ControlNets are specified in init, you can set the corresponding scale as a list. guess_mode (`bool`, *optional*, defaults to `False`): In this mode, the ControlNet encoder will try to recognize the content of the input image even if you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended. control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0): The percentage of total steps at which the ControlNet starts applying. control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0): The percentage of total steps at which the ControlNet stops applying. control_mode (`int` or `List[int]`, *optional*): The mode of ControlNet guidance. Can be used to specify different behaviors for multiple ControlNets. original_size (`Tuple[int, int]`, *optional*): If `original_size` is not the same as `target_size`, the image will appear to be down- or upsampled. `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning. crops_coords_top_left (`Tuple[int, 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. target_size (`Tuple[int, int]`, *optional*): For most cases, `target_size` should be set to the desired height and width of the generated image. If not specified, it will default to `(height, width)`. Part of SDXL's micro-conditioning. negative_original_size (`Tuple[int, int]`, *optional*): To negatively condition the generation process based on a specific image resolution. Part of SDXL's micro-conditioning. negative_crops_coords_top_left (`Tuple[int, int]`, *optional*, defaults to (0, 0)): To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's micro-conditioning. negative_target_size (`Tuple[int, int]`, *optional*): To negatively condition the generation process based on a target image resolution. It should be the same as the `target_size` for most cases. Part of SDXL's micro-conditioning. aesthetic_score (`float`, *optional*, defaults to 6.0): Used to simulate an aesthetic score of the generated image by influencing the positive text condition. Part of SDXL's micro-conditioning. negative_aesthetic_score (`float`, *optional*, defaults to 2.5): Used to simulate an aesthetic score of the generated image by influencing the negative text condition. Part of SDXL's micro-conditioning. 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. normal_tile_overlap (`int`, *optional*, defaults to 64): Number of overlapping pixels between tiles in consecutive rows. border_tile_overlap (`int`, *optional*, defaults to 128): Number of overlapping pixels between tiles at the borders. max_tile_size (`int`, *optional*, defaults to 1024): Maximum size of a tile in pixels. tile_gaussian_sigma (`float`, *optional*, defaults to 0.3): Sigma parameter for Gaussian weighting of tiles. tile_weighting_method (`str`, *optional*, defaults to "Cosine"): Method for weighting tiles. Options: "Cosine" or "Gaussian". Examples: Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple` containing the output images. """ controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet # align format for control guidance if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list): control_guidance_start = len(control_guidance_end) * [control_guidance_start] elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list): control_guidance_end = len(control_guidance_start) * [control_guidance_end] if not isinstance(control_image, list): control_image = [control_image] else: control_image = control_image.copy() if control_mode is None or isinstance(control_mode, list) and len(control_mode) == 0: raise ValueError("The value for `control_mode` is expected!") if not isinstance(control_mode, list): control_mode = [control_mode] if len(control_image) != len(control_mode): raise ValueError("Expected len(control_image) == len(control_mode)") num_control_type = controlnet.config.num_control_type # 0. Set internal use parameters height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor original_size = original_size or (height, width) target_size = target_size or (height, width) negative_original_size = negative_original_size or original_size negative_target_size = negative_target_size or target_size control_type = [0 for _ in range(num_control_type)] control_type = torch.Tensor(control_type) self._guidance_scale = guidance_scale self._clip_skip = clip_skip self._cross_attention_kwargs = cross_attention_kwargs self._interrupt = False batch_size = 1 device = self._execution_device global_pool_conditions = controlnet.config.global_pool_conditions guess_mode = guess_mode or global_pool_conditions # 1. Check inputs for _image, control_idx in zip(control_image, control_mode): control_type[control_idx] = 1 self.check_inputs( prompt, height, width, _image, strength, num_inference_steps, normal_tile_overlap, border_tile_overlap, max_tile_size, tile_gaussian_sigma, tile_weighting_method, controlnet_conditioning_scale, control_guidance_start, control_guidance_end, ) # 2 Get tile width and tile height size tile_width, tile_height = _adaptive_tile_size((width, height), max_tile_size=max_tile_size) # 2.1 Calculate the number of tiles needed grid_rows, grid_cols = self._get_num_tiles( height, width, tile_height, tile_width, normal_tile_overlap, border_tile_overlap ) # 2.2 Expand prompt to number of tiles if not isinstance(prompt, list): prompt = [[prompt] * grid_cols] * grid_rows # 2.3 Update height and width tile size by tile size and tile overlap size width = (grid_cols - 1) * (tile_width - normal_tile_overlap) + min( tile_width, width - (grid_cols - 1) * (tile_width - normal_tile_overlap) ) height = (grid_rows - 1) * (tile_height - normal_tile_overlap) + min( tile_height, height - (grid_rows - 1) * (tile_height - normal_tile_overlap) ) # 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 ) text_embeddings = [ [ self.encode_prompt( prompt=col, device=device, num_images_per_prompt=num_images_per_prompt, do_classifier_free_guidance=self.do_classifier_free_guidance, negative_prompt=negative_prompt, prompt_embeds=None, negative_prompt_embeds=None, pooled_prompt_embeds=None, negative_pooled_prompt_embeds=None, lora_scale=text_encoder_lora_scale, clip_skip=self.clip_skip, ) for col in row ] for row in prompt ] # 4. Prepare latent image image_tensor = self.image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32) # 4.1 Prepare controlnet_conditioning_image control_image = self.prepare_control_image( image=image, width=width, height=height, batch_size=batch_size * num_images_per_prompt, num_images_per_prompt=num_images_per_prompt, device=device, dtype=controlnet.dtype, do_classifier_free_guidance=self.do_classifier_free_guidance, guess_mode=guess_mode, ) control_type = ( control_type.reshape(1, -1) .to(device, dtype=controlnet.dtype) .repeat(batch_size * num_images_per_prompt * 2, 1) ) # 5. Prepare timesteps accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys()) extra_set_kwargs = {} if accepts_offset: extra_set_kwargs["offset"] = 1 self.scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength) latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) self._num_timesteps = len(timesteps) # 6. Prepare latent variables dtype = text_embeddings[0][0][0].dtype if latents is None: latents = self.prepare_latents( image_tensor, latent_timestep, batch_size, num_images_per_prompt, dtype, device, generator, True, ) # if we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas if isinstance(self.scheduler, LMSDiscreteScheduler): latents = latents * self.scheduler.sigmas[0] # 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) # 8. Create tensor stating which controlnets to keep controlnet_keep = [] for i in range(len(timesteps)): controlnet_keep.append( 1.0 - float(i / len(timesteps) < control_guidance_start or (i + 1) / len(timesteps) > control_guidance_end) ) # 8.1 Prepare added time ids & embeddings # text_embeddings order: prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds embeddings_and_added_time = [] crops_coords_top_left = negative_crops_coords_top_left = (tile_width, tile_height) for row in range(grid_rows): addition_embed_type_row = [] for col in range(grid_cols): # extract generated values prompt_embeds = text_embeddings[row][col][0] negative_prompt_embeds = text_embeddings[row][col][1] pooled_prompt_embeds = text_embeddings[row][col][2] negative_pooled_prompt_embeds = text_embeddings[row][col][3] if negative_original_size is None: negative_original_size = original_size if negative_target_size is None: negative_target_size = target_size 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, add_neg_time_ids = self._get_add_time_ids( original_size, crops_coords_top_left, target_size, aesthetic_score, negative_aesthetic_score, negative_original_size, negative_crops_coords_top_left, negative_target_size, dtype=prompt_embeds.dtype, text_encoder_projection_dim=text_encoder_projection_dim, ) add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1) if self.do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0) add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1) add_time_ids = torch.cat([add_neg_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) addition_embed_type_row.append((prompt_embeds, add_text_embeds, add_time_ids)) embeddings_and_added_time.append(addition_embed_type_row) # 9. Prepare tiles weights and latent overlaps size to denoising process tile_weights, tile_row_overlaps, tile_col_overlaps = self.prepare_tiles( grid_rows, grid_cols, tile_weighting_method, tile_width, tile_height, normal_tile_overlap, border_tile_overlap, width, height, tile_gaussian_sigma, batch_size, device, dtype, ) # 10. Denoising loop num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # Diffuse each tile noise_preds = [] for row in range(grid_rows): noise_preds_row = [] for col in range(grid_cols): if self.interrupt: continue tile_row_overlap = tile_row_overlaps[row, col] tile_col_overlap = tile_col_overlaps[row, col] px_row_init, px_row_end, px_col_init, px_col_end = _tile2latent_indices( row, col, tile_width, tile_height, tile_row_overlap, tile_col_overlap, width, height ) tile_latents = latents[:, :, px_row_init:px_row_end, px_col_init:px_col_end] # expand the latents if we are doing classifier free guidance latent_model_input = ( torch.cat([tile_latents] * 2) if self.do_classifier_free_guidance else tile_latents # 1, 4, ... ) latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual added_cond_kwargs = { "text_embeds": embeddings_and_added_time[row][col][1], "time_ids": embeddings_and_added_time[row][col][2], } # controlnet(s) inference if guess_mode and self.do_classifier_free_guidance: # Infer ControlNet only for the conditional batch. control_model_input = tile_latents control_model_input = self.scheduler.scale_model_input(control_model_input, t) controlnet_prompt_embeds = embeddings_and_added_time[row][col][0].chunk(2)[1] controlnet_added_cond_kwargs = { "text_embeds": embeddings_and_added_time[row][col][1].chunk(2)[1], "time_ids": embeddings_and_added_time[row][col][2].chunk(2)[1], } else: control_model_input = latent_model_input controlnet_prompt_embeds = embeddings_and_added_time[row][col][0] controlnet_added_cond_kwargs = added_cond_kwargs if isinstance(controlnet_keep[i], list): cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])] else: controlnet_cond_scale = controlnet_conditioning_scale if isinstance(controlnet_cond_scale, list): controlnet_cond_scale = controlnet_cond_scale[0] cond_scale = controlnet_cond_scale * controlnet_keep[i] px_row_init_pixel, px_row_end_pixel, px_col_init_pixel, px_col_end_pixel = _tile2pixel_indices( row, col, tile_width, tile_height, tile_row_overlap, tile_col_overlap, width, height ) tile_control_image = control_image[ :, :, px_row_init_pixel:px_row_end_pixel, px_col_init_pixel:px_col_end_pixel ] down_block_res_samples, mid_block_res_sample = self.controlnet( control_model_input, t, encoder_hidden_states=controlnet_prompt_embeds, controlnet_cond=[tile_control_image], control_type=control_type, control_type_idx=control_mode, conditioning_scale=cond_scale, guess_mode=guess_mode, added_cond_kwargs=controlnet_added_cond_kwargs, return_dict=False, ) if guess_mode and self.do_classifier_free_guidance: # Inferred ControlNet only for the conditional batch. # To apply the output of ControlNet to both the unconditional and conditional batches, # add 0 to the unconditional batch to keep it unchanged. down_block_res_samples = [ torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples ] mid_block_res_sample = torch.cat( [torch.zeros_like(mid_block_res_sample), mid_block_res_sample] ) # predict the noise residual with torch.amp.autocast(device.type, dtype=dtype, enabled=dtype != self.unet.dtype): noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=embeddings_and_added_time[row][col][0], cross_attention_kwargs=self.cross_attention_kwargs, down_block_additional_residuals=down_block_res_samples, mid_block_additional_residual=mid_block_res_sample, added_cond_kwargs=added_cond_kwargs, return_dict=False, )[0] # perform guidance if self.do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred_tile = noise_pred_uncond + guidance_scale * ( noise_pred_text - noise_pred_uncond ) noise_preds_row.append(noise_pred_tile) noise_preds.append(noise_preds_row) # Stitch noise predictions for all tiles noise_pred = torch.zeros(latents.shape, device=device) contributors = torch.zeros(latents.shape, device=device) # Add each tile contribution to overall latents for row in range(grid_rows): for col in range(grid_cols): tile_row_overlap = tile_row_overlaps[row, col] tile_col_overlap = tile_col_overlaps[row, col] px_row_init, px_row_end, px_col_init, px_col_end = _tile2latent_indices( row, col, tile_width, tile_height, tile_row_overlap, tile_col_overlap, width, height ) tile_weights_resized = tile_weights[row, col] noise_pred[:, :, px_row_init:px_row_end, px_col_init:px_col_end] += ( noise_preds[row][col] * tile_weights_resized ) contributors[:, :, px_row_init:px_row_end, px_col_init:px_col_end] += tile_weights_resized # Average overlapping areas with more than 1 contributor noise_pred /= contributors noise_pred = noise_pred.to(dtype) # compute the previous noisy sample x_t -> x_t-1 latents_dtype = latents.dtype latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] if latents.dtype != latents_dtype: if torch.backends.mps.is_available(): # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272 latents = latents.to(latents_dtype) # update progress bar if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if XLA_AVAILABLE: xm.mark_step() # If we do sequential model offloading, let's offload unet and controlnet # manually for max memory savings if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.unet.to("cpu") self.controlnet.to("cpu") torch.cuda.empty_cache() 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) # unscale/denormalize the latents # denormalize with the mean and std if available and not None has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None if has_latents_mean and has_latents_std: latents_mean = ( torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype) ) latents_std = ( torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype) ) latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean else: latents = latents / self.vae.config.scaling_factor image = self.vae.decode(latents, return_dict=False)[0] # cast back to fp16 if needed if needs_upcasting: self.vae.to(dtype=torch.float16) # 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) else: image = latents # Offload all models self.maybe_free_model_hooks() result = StableDiffusionXLPipelineOutput(images=image) if not return_dict: return (image,) return result