import torch import math import os from typing import Type, Dict, Any, Tuple, Callable, Optional, Union, List import torch.nn.functional as F from .utils import isinstance_str from dataclasses import dataclass import diffusers from diffusers.utils import USE_PEFT_BACKEND, replace_example_docstring from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput from diffusers.image_processor import PipelineImageInput, VaeImageProcessor from diffusers.utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel from diffusers.models import ControlNetModel diffusers_version = diffusers.__version__ if diffusers_version < "0.27.0": from diffusers.models.unet_2d_condition import UNet2DConditionOutput old_diffusers = True else: from diffusers.models.unets.unet_2d_condition import UNet2DConditionOutput old_diffusers = False def sd15_hidiffusion_key(): modified_key = dict() modified_key['down_module_key'] = ['down_blocks.0.downsamplers.0.conv'] modified_key['down_module_key_extra'] = ['down_blocks.1'] modified_key['up_module_key'] = ['up_blocks.2.upsamplers.0.conv'] modified_key['up_module_key_extra'] = ['up_blocks.2'] modified_key['windown_attn_module_key'] = ['down_blocks.0.attentions.0.transformer_blocks.0', 'down_blocks.0.attentions.1.transformer_blocks.0', 'up_blocks.3.attentions.0.transformer_blocks.0', 'up_blocks.3.attentions.1.transformer_blocks.0', 'up_blocks.3.attentions.2.transformer_blocks.0'] return modified_key def sdxl_hidiffusion_key(): modified_key = dict() modified_key['down_module_key'] = ['down_blocks.1'] modified_key['down_module_key_extra'] = ['down_blocks.1.downsamplers.0.conv'] modified_key['up_module_key'] = ['up_blocks.1'] modified_key['up_module_key_extra'] = ['up_blocks.0.upsamplers.0.conv'] modified_key['windown_attn_module_key'] = ['down_blocks.1.attentions.0.transformer_blocks.0', 'down_blocks.1.attentions.0.transformer_blocks.1', 'down_blocks.1.attentions.1.transformer_blocks.0', 'down_blocks.1.attentions.1.transformer_blocks.1', 'up_blocks.1.attentions.0.transformer_blocks.0', 'up_blocks.1.attentions.0.transformer_blocks.1', 'up_blocks.1.attentions.1.transformer_blocks.0', 'up_blocks.1.attentions.1.transformer_blocks.1', 'up_blocks.1.attentions.2.transformer_blocks.0', 'up_blocks.1.attentions.2.transformer_blocks.1'] return modified_key def sdxl_turbo_hidiffusion_key(): modified_key = dict() modified_key['down_module_key'] = ['down_blocks.1'] modified_key['up_module_key'] = ['up_blocks.1'] modified_key['windown_attn_module_key'] = ['down_blocks.1.attentions.0.transformer_blocks.0', 'down_blocks.1.attentions.0.transformer_blocks.1', 'down_blocks.1.attentions.1.transformer_blocks.0', 'down_blocks.1.attentions.1.transformer_blocks.1', 'up_blocks.1.attentions.0.transformer_blocks.0', 'up_blocks.1.attentions.0.transformer_blocks.1', 'up_blocks.1.attentions.1.transformer_blocks.0', 'up_blocks.1.attentions.1.transformer_blocks.1', 'up_blocks.1.attentions.2.transformer_blocks.0', 'up_blocks.1.attentions.2.transformer_blocks.1'] return modified_key # supported official model. If you use non-official model based on the following models/pipelines, hidiffusion will automatically select the best strategy to fit it. surppoted_official_model = [ 'runwayml/stable-diffusion-v1-5', 'stabilityai/stable-diffusion-2-1-base', 'stabilityai/stable-diffusion-xl-base-1.0', 'diffusers/stable-diffusion-xl-1.0-inpainting-0.1', 'stabilityai/sdxl-turbo' ] # T1_ratio: see T1 introduced in the main paper. T1 = number_inference_step * T1_ratio. A higher T1_ratio can better mitigate object duplication. We set T1_ratio=0.4 by default. You'd better adjust it to fit your prompt. Only active when apply_raunet=True. # T2_ratio: see T2 introduced in the appendix, used in extreme resolution image generation. T2 = number_inference_step * T2_ratio. A higher T2_ratio can better mitigate object duplication. Only active when apply_raunet=True switching_threshold_ratio_dict = { 'sd15_1024': {'T1_ratio': 0.4, 'T2_ratio': 0.1}, 'sd15_2048': {'T1_ratio': 0.7, 'T2_ratio': 0.3}, 'sdxl_2048': {'T1_ratio': 0.5, 'T2_ratio': 0.1}, 'sdxl_4096': {'T1_ratio': 0.9, 'T2_ratio': 0.3}, 'sdxl_turbo_1024': {'T1_ratio': 0.5, 'T2_ratio': 0.0}, } controlnet_switching_threshold_ratio_dict = { 'sdxl_2048': {'T1_ratio': 0.5, 'T2_ratio': 0.0}, } controlnet_apply_steps_rate = 0.6 is_aggressive_raunet = True aggressive_step = 8 inpainting_is_aggressive_raunet = False playground_is_aggressive_raunet = False current_path = os.path.dirname(__file__) module_key_path = os.path.join(current_path, "sd_module_key") with open(os.path.join(module_key_path, 'sd15_module_key.txt'), 'r') as f: sd15_module_key = f.read().splitlines() with open(os.path.join(module_key_path, 'sdxl_module_key.txt'), 'r') as f: sdxl_module_key = f.read().splitlines() def make_diffusers_sdxl_contrtolnet_ppl(block_class): EXAMPLE_DOC_STRING = """ Examples: ```py >>> # !pip install opencv-python transformers accelerate >>> from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL >>> from diffusers.utils import load_image >>> import numpy as np >>> import torch >>> import cv2 >>> from PIL import Image >>> prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting" >>> negative_prompt = "low quality, bad quality, sketches" >>> # download an image >>> image = load_image( ... "https://hf.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png" ... ) >>> # initialize the models and pipeline >>> controlnet_conditioning_scale = 0.5 # recommended for good generalization >>> controlnet = ControlNetModel.from_pretrained( ... "diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16 ... ) >>> vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) >>> pipe = StableDiffusionXLControlNetPipeline.from_pretrained( ... "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, vae=vae, torch_dtype=torch.float16 ... ) >>> pipe.enable_model_cpu_offload() >>> # get canny image >>> image = np.array(image) >>> image = cv2.Canny(image, 100, 200) >>> image = image[:, :, None] >>> image = np.concatenate([image, image, image], axis=2) >>> canny_image = Image.fromarray(image) >>> # generate image >>> image = pipe( ... prompt, controlnet_conditioning_scale=controlnet_conditioning_scale, image=canny_image ... ).images[0] ``` """ class sdxl_contrtolnet_ppl(block_class): # Save for unpatching later _parent = block_class @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, prompt_2: Optional[Union[str, List[str]]] = None, image: PipelineImageInput = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 5.0, negative_prompt: Optional[Union[str, List[str]]] = None, negative_prompt_2: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, pooled_prompt_embeds: Optional[torch.FloatTensor] = None, negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, ip_adapter_image: Optional[PipelineImageInput] = None, output_type: Optional[str] = "pil", return_dict: bool = True, cross_attention_kwargs: Optional[Dict[str, Any]] = None, controlnet_conditioning_scale: Union[float, List[float]] = 1.0, guess_mode: bool = False, control_guidance_start: Union[float, List[float]] = 0.0, control_guidance_end: Union[float, List[float]] = 1.0, original_size: Tuple[int, int] = None, crops_coords_top_left: Tuple[int, int] = (0, 0), target_size: Tuple[int, int] = None, negative_original_size: Optional[Tuple[int, int]] = None, negative_crops_coords_top_left: Tuple[int, int] = (0, 0), negative_target_size: Optional[Tuple[int, int]] = None, clip_skip: Optional[int] = None, callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, callback_on_step_end_tensor_inputs: List[str] = ["latents"], **kwargs, ): r""" The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`. prompt_2 (`str` or `List[str]`, *optional*): The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is used in both text-encoders. image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,: `List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`): The ControlNet input condition to provide guidance to the `unet` for generation. If the type is specified as `torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in `init`, images must be passed as a list such that each element of the list can be correctly batched for input to a single ControlNet. height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): The height in pixels of the generated image. Anything below 512 pixels won't work well for [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and checkpoints that are not specifically fine-tuned on low resolutions. width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): The width in pixels of the generated image. Anything below 512 pixels won't work well for [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and checkpoints that are not specifically fine-tuned on low resolutions. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 5.0): A higher guidance scale value encourages the model to generate images closely linked to the text `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide what to not include in image generation. If not defined, you need to pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`). negative_prompt_2 (`str` or `List[str]`, *optional*): The prompt or prompts to guide what to not include in image generation. This is sent to `tokenizer_2` and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders. num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor is generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, text embeddings are generated from the `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. pooled_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, pooled text embeddings are generated from `prompt` input argument. negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, pooled `negative_prompt_embeds` are generated from `negative_prompt` input argument. ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generated image. Choose between `PIL.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0): The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set the corresponding scale as a list. guess_mode (`bool`, *optional*, defaults to `False`): The ControlNet encoder tries to recognize the content of the input image even if you remove all prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended. control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0): The percentage of total steps at which the ControlNet starts applying. control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0): The percentage of total steps at which the ControlNet stops applying. original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled. `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): For most cases, `target_size` should be set to the desired height and width of the generated image. If not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): To negatively condition the generation process based on a specific image resolution. Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): To negatively condition the generation process based on a target image resolution. It should be as same as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. clip_skip (`int`, *optional*): Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings. callback_on_step_end (`Callable`, *optional*): A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by `callback_on_step_end_tensor_inputs`. callback_on_step_end_tensor_inputs (`List`, *optional*): The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the `._callback_tensor_inputs` attribute of your pipeine class. Examples: Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned, otherwise a `tuple` is returned containing the output images. """ callback = kwargs.pop("callback", None) callback_steps = kwargs.pop("callback_steps", None) if callback is not None: deprecate( "callback", "1.0.0", "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`", ) if callback_steps is not None: deprecate( "callback_steps", "1.0.0", "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`", ) controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet # align format for control guidance if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list): control_guidance_start = len(control_guidance_end) * [control_guidance_start] elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list): control_guidance_end = len(control_guidance_start) * [control_guidance_end] elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list): mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1 control_guidance_start, control_guidance_end = ( mult * [control_guidance_start], mult * [control_guidance_end], ) # 1. Check inputs. Raise error if not correct if old_diffusers: self.check_inputs( prompt, prompt_2, image, callback_steps, negative_prompt, negative_prompt_2, prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds, controlnet_conditioning_scale, control_guidance_start, control_guidance_end, callback_on_step_end_tensor_inputs, ) else: self.check_inputs( prompt, prompt_2, image, callback_steps, negative_prompt, negative_prompt_2, prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, None, None, negative_pooled_prompt_embeds, controlnet_conditioning_scale, control_guidance_start, control_guidance_end, callback_on_step_end_tensor_inputs, ) self._guidance_scale = guidance_scale self._clip_skip = clip_skip self._cross_attention_kwargs = cross_attention_kwargs # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float): controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets) global_pool_conditions = ( controlnet.config.global_pool_conditions if isinstance(controlnet, ControlNetModel) else controlnet.nets[0].config.global_pool_conditions ) guess_mode = guess_mode or global_pool_conditions # 3.1 Encode input prompt text_encoder_lora_scale = ( self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None ) ( prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds, ) = self.encode_prompt( prompt, prompt_2, device, num_images_per_prompt, self.do_classifier_free_guidance, negative_prompt, negative_prompt_2, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, pooled_prompt_embeds=pooled_prompt_embeds, negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, lora_scale=text_encoder_lora_scale, clip_skip=self.clip_skip, ) # 3.2 Encode ip_adapter_image if ip_adapter_image is not None: output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True image_embeds, negative_image_embeds = self.encode_image( ip_adapter_image, device, num_images_per_prompt, output_hidden_state ) if self.do_classifier_free_guidance: image_embeds = torch.cat([negative_image_embeds, image_embeds]) # 4. Prepare image if isinstance(controlnet, ControlNetModel): image = self.prepare_image( image=image, width=width, height=height, batch_size=batch_size * num_images_per_prompt, num_images_per_prompt=num_images_per_prompt, device=device, dtype=controlnet.dtype, do_classifier_free_guidance=self.do_classifier_free_guidance, guess_mode=guess_mode, ) height, width = image.shape[-2:] elif isinstance(controlnet, MultiControlNetModel): images = [] for image_ in image: image_ = self.prepare_image( image=image_, width=width, height=height, batch_size=batch_size * num_images_per_prompt, num_images_per_prompt=num_images_per_prompt, device=device, dtype=controlnet.dtype, do_classifier_free_guidance=self.do_classifier_free_guidance, guess_mode=guess_mode, ) images.append(image_) image = images height, width = image[0].shape[-2:] else: assert False # 5. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps self._num_timesteps = len(timesteps) # 6. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # 6.5 Optionally get Guidance Scale Embedding timestep_cond = None if self.unet.config.time_cond_proj_dim is not None: guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) timestep_cond = self.get_guidance_scale_embedding( guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim ).to(device=device, dtype=latents.dtype) # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 7.1 Create tensor stating which controlnets to keep controlnet_keep = [] for i in range(len(timesteps)): keeps = [ 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e) for s, e in zip(control_guidance_start, control_guidance_end) ] controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps) # 7.2 Prepare added time ids & embeddings if isinstance(image, list): original_size = original_size or image[0].shape[-2:] else: original_size = original_size or image.shape[-2:] target_size = target_size or (height, width) add_text_embeds = pooled_prompt_embeds if self.text_encoder_2 is None: text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1]) else: text_encoder_projection_dim = self.text_encoder_2.config.projection_dim add_time_ids = self._get_add_time_ids( original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype, text_encoder_projection_dim=text_encoder_projection_dim, ) if negative_original_size is not None and negative_target_size is not None: negative_add_time_ids = self._get_add_time_ids( negative_original_size, negative_crops_coords_top_left, negative_target_size, dtype=prompt_embeds.dtype, text_encoder_projection_dim=text_encoder_projection_dim, ) else: negative_add_time_ids = add_time_ids if self.do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0) add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0) prompt_embeds = prompt_embeds.to(device) add_text_embeds = add_text_embeds.to(device) add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1) # 8. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order is_unet_compiled = is_compiled_module(self.unet) is_controlnet_compiled = is_compiled_module(self.controlnet) is_torch_higher_equal_2_1 = is_torch_version(">=", "2.1") with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # Relevant thread: # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428 if (is_unet_compiled and is_controlnet_compiled) and is_torch_higher_equal_2_1: torch._inductor.cudagraph_mark_step_begin() # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} # controlnet(s) inference if guess_mode and self.do_classifier_free_guidance: # Infer ControlNet only for the conditional batch. control_model_input = latents control_model_input = self.scheduler.scale_model_input(control_model_input, t) controlnet_prompt_embeds = prompt_embeds.chunk(2)[1] controlnet_added_cond_kwargs = { "text_embeds": add_text_embeds.chunk(2)[1], "time_ids": add_time_ids.chunk(2)[1], } else: control_model_input = latent_model_input controlnet_prompt_embeds = prompt_embeds controlnet_added_cond_kwargs = added_cond_kwargs if isinstance(controlnet_keep[i], list): cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])] else: controlnet_cond_scale = controlnet_conditioning_scale if isinstance(controlnet_cond_scale, list): controlnet_cond_scale = controlnet_cond_scale[0] cond_scale = controlnet_cond_scale * controlnet_keep[i] if i < controlnet_apply_steps_rate * num_inference_steps: original_h, original_w = (128,128) _, _, model_input_h, model_input_w = control_model_input.shape downsample_factor = math.ceil(max(model_input_h/original_h, model_input_w/original_w)) downsample_size = (model_input_h//downsample_factor, model_input_w//downsample_factor) original_pixel_h, original_pixel_w = (1024,1024) _, _, pixel_h, pixel_w = image.shape downsample_pixel_factor = math.ceil(max(pixel_h/original_pixel_h, pixel_w/original_pixel_w)) downsample_pixel_size = (pixel_h//downsample_pixel_factor, pixel_w//downsample_pixel_factor) down_block_res_samples, mid_block_res_sample = self.controlnet( F.interpolate(control_model_input, downsample_size), # control_model_input, t, encoder_hidden_states=controlnet_prompt_embeds, # controlnet_cond=image, controlnet_cond=F.interpolate(image, downsample_pixel_size), 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: # Infered ControlNet only for the conditional batch. # To apply the output of ControlNet to both the unconditional and conditional batches, # add 0 to the unconditional batch to keep it unchanged. down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples] mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample]) if ip_adapter_image is not None: added_cond_kwargs["image_embeds"] = image_embeds # predict the noise residual if i < controlnet_apply_steps_rate * num_inference_steps: noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, timestep_cond=timestep_cond, cross_attention_kwargs=self.cross_attention_kwargs, down_block_additional_residuals=down_block_res_samples, mid_block_additional_residual=mid_block_res_sample, added_cond_kwargs=added_cond_kwargs, return_dict=False, )[0] else: noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, timestep_cond=timestep_cond, cross_attention_kwargs=self.cross_attention_kwargs, down_block_additional_residuals=None, mid_block_additional_residual=None, added_cond_kwargs=added_cond_kwargs, return_dict=False, )[0] # perform guidance if self.do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] if callback_on_step_end is not None: callback_kwargs = {} for k in callback_on_step_end_tensor_inputs: callback_kwargs[k] = locals()[k] callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) latents = callback_outputs.pop("latents", latents) prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: step_idx = i // getattr(self.scheduler, "order", 1) callback(step_idx, t, latents) # manually for max memory savings if self.vae.dtype == torch.float16 and self.vae.config.force_upcast: self.upcast_vae() latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) if not output_type == "latent": # make sure the VAE is in float32 mode, as it overflows in float16 needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast if needs_upcasting: self.upcast_vae() latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] # cast back to fp16 if needed if needs_upcasting: self.vae.to(dtype=torch.float16) else: image = latents if not output_type == "latent": # apply watermark if available if self.watermark is not None: image = self.watermark.apply_watermark(image) image = self.image_processor.postprocess(image, output_type=output_type) # Offload all models self.maybe_free_model_hooks() if not return_dict: return (image,) return StableDiffusionXLPipelineOutput(images=image) return sdxl_contrtolnet_ppl def make_diffusers_unet_2d_condition(block_class): class unet_2d_condition(block_class): # Save for unpatching later _parent = block_class def forward( self, sample: torch.FloatTensor, timestep: Union[torch.Tensor, float, int], encoder_hidden_states: torch.Tensor, class_labels: Optional[torch.Tensor] = None, timestep_cond: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, mid_block_additional_residual: Optional[torch.Tensor] = None, down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None, encoder_attention_mask: Optional[torch.Tensor] = None, return_dict: bool = True, ) -> Union[UNet2DConditionOutput, Tuple]: r""" The [`UNet2DConditionModel`] forward method. Args: sample (`torch.FloatTensor`): The noisy input tensor with the following shape `(batch, channel, height, width)`. timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input. encoder_hidden_states (`torch.FloatTensor`): The encoder hidden states with shape `(batch, sequence_length, feature_dim)`. class_labels (`torch.Tensor`, *optional*, defaults to `None`): Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings. timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`): Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed through the `self.time_embedding` layer to obtain the timestep embeddings. attention_mask (`torch.Tensor`, *optional*, defaults to `None`): An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large negative values to the attention scores corresponding to "discard" tokens. 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). added_cond_kwargs: (`dict`, *optional*): A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that are passed along to the UNet blocks. down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*): A tuple of tensors that if specified are added to the residuals of down unet blocks. mid_block_additional_residual: (`torch.Tensor`, *optional*): A tensor that if specified is added to the residual of the middle unet block. encoder_attention_mask (`torch.Tensor`): A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias, which adds large negative values to the attention scores corresponding to "discard" tokens. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the [`AttnProcessor`]. added_cond_kwargs: (`dict`, *optional*): A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that are passed along to the UNet blocks. down_block_additional_residuals (`tuple` of `torch.Tensor`, *optional*): additional residuals to be added to UNet long skip connections from down blocks to up blocks for example from ControlNet side model(s) mid_block_additional_residual (`torch.Tensor`, *optional*): additional residual to be added to UNet mid block output, for example from ControlNet side model down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*): additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s) Returns: [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise a `tuple` is returned where the first element is the sample tensor. """ # By default samples have to be AT least a multiple of the overall upsampling factor. # The overall upsampling factor is equal to 2 ** (# num of upsampling layers). # However, the upsampling interpolation output size can be forced to fit any upsampling size # on the fly if necessary. default_overall_up_factor = 2**self.num_upsamplers # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` forward_upsample_size = False upsample_size = None for dim in sample.shape[-2:]: if dim % default_overall_up_factor != 0: # Forward upsample size to force interpolation output size. forward_upsample_size = True break # ensure attention_mask is a bias, and give it a singleton query_tokens dimension # expects mask of shape: # [batch, key_tokens] # adds singleton query_tokens dimension: # [batch, 1, key_tokens] # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) if attention_mask is not None: # assume that mask is expressed as: # (1 = keep, 0 = discard) # convert mask into a bias that can be added to attention scores: # (keep = +0, discard = -10000.0) attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 attention_mask = attention_mask.unsqueeze(1) # convert encoder_attention_mask to a bias the same way we do for attention_mask if encoder_attention_mask is not None: encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0 encoder_attention_mask = encoder_attention_mask.unsqueeze(1) # 0. center input if necessary if self.config.center_input_sample: sample = 2 * sample - 1.0 # 1. time timesteps = timestep if not torch.is_tensor(timesteps): # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can # This would be a good case for the `match` statement (Python 3.10+) is_mps = sample.device.type == "mps" if isinstance(timestep, float): dtype = torch.float32 if is_mps else torch.float64 else: dtype = torch.int32 if is_mps else torch.int64 timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) elif len(timesteps.shape) == 0: timesteps = timesteps[None].to(sample.device) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timesteps = timesteps.expand(sample.shape[0]) t_emb = self.time_proj(timesteps) # `Timesteps` does not contain any weights and will always return f32 tensors # but time_embedding might actually be running in fp16. so we need to cast here. # there might be better ways to encapsulate this. t_emb = t_emb.to(dtype=sample.dtype) emb = self.time_embedding(t_emb, timestep_cond) aug_emb = None if self.class_embedding is not None: if class_labels is None: raise ValueError("class_labels should be provided when num_class_embeds > 0") if self.config.class_embed_type == "timestep": class_labels = self.time_proj(class_labels) # `Timesteps` does not contain any weights and will always return f32 tensors # there might be better ways to encapsulate this. class_labels = class_labels.to(dtype=sample.dtype) class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype) if self.config.class_embeddings_concat: emb = torch.cat([emb, class_emb], dim=-1) else: emb = emb + class_emb if self.config.addition_embed_type == "text": aug_emb = self.add_embedding(encoder_hidden_states) elif self.config.addition_embed_type == "text_image": # Kandinsky 2.1 - style if "image_embeds" not in added_cond_kwargs: raise ValueError( f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" ) image_embs = added_cond_kwargs.get("image_embeds") text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states) aug_emb = self.add_embedding(text_embs, image_embs) elif self.config.addition_embed_type == "text_time": # SDXL - style if "text_embeds" not in added_cond_kwargs: raise ValueError( f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`" ) text_embeds = added_cond_kwargs.get("text_embeds") if "time_ids" not in added_cond_kwargs: raise ValueError( f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`" ) time_ids = added_cond_kwargs.get("time_ids") time_embeds = self.add_time_proj(time_ids.flatten()) time_embeds = time_embeds.reshape((text_embeds.shape[0], -1)) add_embeds = torch.concat([text_embeds, time_embeds], dim=-1) add_embeds = add_embeds.to(emb.dtype) aug_emb = self.add_embedding(add_embeds) elif self.config.addition_embed_type == "image": # Kandinsky 2.2 - style if "image_embeds" not in added_cond_kwargs: raise ValueError( f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" ) image_embs = added_cond_kwargs.get("image_embeds") aug_emb = self.add_embedding(image_embs) elif self.config.addition_embed_type == "image_hint": # Kandinsky 2.2 - style if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs: raise ValueError( f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`" ) image_embs = added_cond_kwargs.get("image_embeds") hint = added_cond_kwargs.get("hint") aug_emb, hint = self.add_embedding(image_embs, hint) sample = torch.cat([sample, hint], dim=1) emb = emb + aug_emb if aug_emb is not None else emb if self.time_embed_act is not None: emb = self.time_embed_act(emb) if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj": encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states) elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj": # Kadinsky 2.1 - style if "image_embeds" not in added_cond_kwargs: raise ValueError( f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" ) image_embeds = added_cond_kwargs.get("image_embeds") encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds) elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj": # Kandinsky 2.2 - style if "image_embeds" not in added_cond_kwargs: raise ValueError( f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" ) image_embeds = added_cond_kwargs.get("image_embeds") encoder_hidden_states = self.encoder_hid_proj(image_embeds) elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj": if "image_embeds" not in added_cond_kwargs: raise ValueError( f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" ) image_embeds = added_cond_kwargs.get("image_embeds") image_embeds = self.encoder_hid_proj(image_embeds).to(encoder_hidden_states.dtype) encoder_hidden_states = torch.cat([encoder_hidden_states, image_embeds], dim=1) # 2. pre-process sample = self.conv_in(sample) # 2.5 GLIGEN position net if cross_attention_kwargs is not None and cross_attention_kwargs.get("gligen", None) is not None: cross_attention_kwargs = cross_attention_kwargs.copy() gligen_args = cross_attention_kwargs.pop("gligen") cross_attention_kwargs["gligen"] = {"objs": self.position_net(**gligen_args)} # 3. down lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 if USE_PEFT_BACKEND: # weight the lora layers by setting `lora_scale` for each PEFT layer scale_lora_layers(self, lora_scale) is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None # using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets is_adapter = down_intrablock_additional_residuals is not None # maintain backward compatibility for legacy usage, where # T2I-Adapter and ControlNet both use down_block_additional_residuals arg # but can only use one or the other if not is_adapter and mid_block_additional_residual is None and down_block_additional_residuals is not None: deprecate( "T2I should not use down_block_additional_residuals", "1.3.0", "Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \ and will be removed in diffusers 1.3.0. `down_block_additional_residuals` should only be used \ for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ", standard_warn=False, ) down_intrablock_additional_residuals = down_block_additional_residuals is_adapter = True down_block_res_samples = (sample,) for downsample_block in self.down_blocks: if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: # For t2i-adapter CrossAttnDownBlock2D additional_residuals = {} if is_adapter and len(down_intrablock_additional_residuals) > 0: additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0) sample, res_samples = downsample_block( hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs, encoder_attention_mask=encoder_attention_mask, **additional_residuals, ) else: # sample, res_samples = downsample_block(hidden_states=sample, temb=emb, scale=lora_scale) sample, res_samples = downsample_block(hidden_states=sample, temb=emb) if is_adapter and len(down_intrablock_additional_residuals) > 0: sample += down_intrablock_additional_residuals.pop(0) down_block_res_samples += res_samples if is_controlnet: new_down_block_res_samples = () for down_block_res_sample, down_block_additional_residual in zip( down_block_res_samples, down_block_additional_residuals ): _, _, ori_H, ori_W = down_block_res_sample.shape down_block_additional_residual = F.interpolate(down_block_additional_residual, (ori_H, ori_W), mode='bicubic') down_block_res_sample = down_block_res_sample + down_block_additional_residual new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,) down_block_res_samples = new_down_block_res_samples # 4. mid if self.mid_block is not None: if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention: sample = self.mid_block( sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs, encoder_attention_mask=encoder_attention_mask, ) else: sample = self.mid_block(sample, emb) # To support T2I-Adapter-XL if ( is_adapter and len(down_intrablock_additional_residuals) > 0 and sample.shape == down_intrablock_additional_residuals[0].shape ): sample += down_intrablock_additional_residuals.pop(0) if is_controlnet: _, _, ori_H, ori_W = sample.shape mid_block_additional_residual = F.interpolate(mid_block_additional_residual, (ori_H, ori_W), mode='bicubic') sample = sample + mid_block_additional_residual # 5. up for i, upsample_block in enumerate(self.up_blocks): is_final_block = i == len(self.up_blocks) - 1 res_samples = down_block_res_samples[-len(upsample_block.resnets) :] down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] # if we have not reached the final block and need to forward the # upsample size, we do it here if not is_final_block and forward_upsample_size: upsample_size = down_block_res_samples[-1].shape[2:] if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: sample = upsample_block( hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, upsample_size=upsample_size, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, ) else: sample = upsample_block( hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size, # scale=lora_scale, ) # sample = upsample_block( # hidden_states=sample, # temb=emb, # res_hidden_states_tuple=res_samples, # upsample_size=upsample_size, # scale=lora_scale, # ) # 6. post-process if self.conv_norm_out: sample = self.conv_norm_out(sample) sample = self.conv_act(sample) sample = self.conv_out(sample) if USE_PEFT_BACKEND: # remove `lora_scale` from each PEFT layer unscale_lora_layers(self, lora_scale) if not return_dict: return (sample,) return UNet2DConditionOutput(sample=sample) return unet_2d_condition def make_diffusers_transformer_block(block_class: Type[torch.nn.Module]) -> Type[torch.nn.Module]: # replace global self-attention with MSW-MSA class transformer_block(block_class): # Save for unpatching later _parent = block_class def forward( self, hidden_states: torch.FloatTensor, attention_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, timestep: Optional[torch.LongTensor] = None, cross_attention_kwargs: Dict[str, Any] = None, class_labels: Optional[torch.LongTensor] = None, added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, ) -> torch.FloatTensor: # reference: https://github.com/microsoft/Swin-Transformer def window_partition(x, window_size, shift_size, H, W): """ Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C) """ B, N, C = x.shape # H, W = int(N**0.5), int(N**0.5) x = x.view(B,H,W,C) if type(shift_size) == list or type(shift_size) == tuple: if shift_size[0] > 0: x = torch.roll(x, shifts=(-shift_size[0], -shift_size[1]), dims=(1, 2)) else: if shift_size > 0: x = torch.roll(x, shifts=(-shift_size, -shift_size), dims=(1, 2)) x = x.view(B, H // window_size[0], window_size[0], W // window_size[1], window_size[1], C) windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0], window_size[1], C) windows = windows.view(-1, window_size[0] * window_size[1], C) return windows def window_reverse(windows, window_size, H, W, shift_size): """ Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image W (int): Width of image Returns: x: (B, H, W, C) """ B, N, C = windows.shape windows = windows.view(-1, window_size[0], window_size[1], C) B = int(windows.shape[0] / (H * W / window_size[0] / window_size[1])) x = windows.view(B, H // window_size[0], W // window_size[1], window_size[0], window_size[1], -1) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) if type(shift_size) == list or type(shift_size) == tuple: if shift_size[0] > 0: x = torch.roll(x, shifts=(shift_size[0], shift_size[1]), dims=(1, 2)) else: if shift_size > 0: x = torch.roll(x, shifts=(shift_size, shift_size), dims=(1, 2)) x = x.view(B, H*W, C) return x # Notice that normalization is always applied before the real computation in the following blocks. # 0. Self-Attention batch_size = hidden_states.shape[0] if self.use_ada_layer_norm: norm_hidden_states = self.norm1(hidden_states, timestep) elif self.use_ada_layer_norm_zero: norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype ) elif self.use_layer_norm: norm_hidden_states = self.norm1(hidden_states) elif self.use_ada_layer_norm_continuous: norm_hidden_states = self.norm1(hidden_states, added_cond_kwargs["pooled_text_emb"]) elif self.use_ada_layer_norm_single: shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1) ).chunk(6, dim=1) norm_hidden_states = self.norm1(hidden_states) norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa norm_hidden_states = norm_hidden_states.squeeze(1) else: raise ValueError("Incorrect norm used") if self.pos_embed is not None: norm_hidden_states = self.pos_embed(norm_hidden_states) # MSW-MSA rand_num = torch.rand(1) B, N, C = hidden_states.shape ori_H, ori_W = self.info['size'] downsample_ratio = int(((ori_H*ori_W) // N)**0.5) H, W = (ori_H//downsample_ratio, ori_W//downsample_ratio) widow_size = (H//2, W//2) if rand_num <= 0.25: shift_size = (0,0) if rand_num > 0.25 and rand_num <= 0.5: shift_size = (widow_size[0]//4, widow_size[1]//4) if rand_num > 0.5 and rand_num <= 0.75: shift_size = (widow_size[0]//4*2, widow_size[1]//4*2) if rand_num > 0.75 and rand_num <= 1: shift_size = (widow_size[0]//4*3, widow_size[1]//4*3) norm_hidden_states = window_partition(norm_hidden_states, widow_size, shift_size, H, W) # 1. Retrieve lora scale. lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 # 2. Prepare GLIGEN inputs cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {} gligen_kwargs = cross_attention_kwargs.pop("gligen", None) attn_output = self.attn1( norm_hidden_states, encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, attention_mask=attention_mask, **cross_attention_kwargs, ) if self.use_ada_layer_norm_zero: attn_output = gate_msa.unsqueeze(1) * attn_output elif self.use_ada_layer_norm_single: attn_output = gate_msa * attn_output attn_output = window_reverse(attn_output, widow_size, H, W, shift_size) hidden_states = attn_output + hidden_states if hidden_states.ndim == 4: hidden_states = hidden_states.squeeze(1) # 2.5 GLIGEN Control if gligen_kwargs is not None: hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"]) # 3. Cross-Attention if self.attn2 is not None: if self.use_ada_layer_norm: norm_hidden_states = self.norm2(hidden_states, timestep) elif self.use_ada_layer_norm_zero or self.use_layer_norm: norm_hidden_states = self.norm2(hidden_states) elif self.use_ada_layer_norm_single: # For PixArt norm2 isn't applied here: # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103 norm_hidden_states = hidden_states elif self.use_ada_layer_norm_continuous: norm_hidden_states = self.norm2(hidden_states, added_cond_kwargs["pooled_text_emb"]) else: raise ValueError("Incorrect norm") if self.pos_embed is not None and self.use_ada_layer_norm_single is False: norm_hidden_states = self.pos_embed(norm_hidden_states) attn_output = self.attn2( norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=encoder_attention_mask, **cross_attention_kwargs, ) hidden_states = attn_output + hidden_states # 4. Feed-forward if self.use_ada_layer_norm_continuous: norm_hidden_states = self.norm3(hidden_states, added_cond_kwargs["pooled_text_emb"]) elif not self.use_ada_layer_norm_single: norm_hidden_states = self.norm3(hidden_states) if self.use_ada_layer_norm_zero: norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] if self.use_ada_layer_norm_single: norm_hidden_states = self.norm2(hidden_states) norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp if self._chunk_size is not None: # "feed_forward_chunk_size" can be used to save memory ff_output = _chunked_feed_forward( self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size ) # ff_output = _chunked_feed_forward( # self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size, lora_scale=lora_scale # ) else: ff_output = self.ff(norm_hidden_states) # ff_output = self.ff(norm_hidden_states, scale=lora_scale) if self.use_ada_layer_norm_zero: ff_output = gate_mlp.unsqueeze(1) * ff_output elif self.use_ada_layer_norm_single: ff_output = gate_mlp * ff_output hidden_states = ff_output + hidden_states if hidden_states.ndim == 4: hidden_states = hidden_states.squeeze(1) return hidden_states return transformer_block def make_diffusers_cross_attn_down_block(block_class: Type[torch.nn.Module]) -> Type[torch.nn.Module]: # replace conventional downsampler with resolution-aware downsampler class cross_attn_down_block(block_class): # Save for unpatching later _parent = block_class timestep = 0 aggressive_raunet = False T1_ratio = 0 T1_start = 0 T1_end = 0 aggressive_raunet = False T1 = 0 # to avoid confict with sdxl-turbo max_timestep = 50 def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, additional_residuals: Optional[torch.FloatTensor] = None, ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: self.max_timestep = len(self.info['scheduler'].timesteps) ori_H, ori_W = self.info['size'] if self.model == 'sd15': if ori_H < 256 or ori_W < 256: self.T1_ratio = switching_threshold_ratio_dict['sd15_1024'][self.switching_threshold_ratio] else: self.T1_ratio = switching_threshold_ratio_dict['sd15_2048'][self.switching_threshold_ratio] elif self.model == 'sdxl': if ori_H < 512 or ori_W < 512: if self.info['use_controlnet']: self.T1_ratio = controlnet_switching_threshold_ratio_dict['sdxl_2048'][self.switching_threshold_ratio] else: self.T1_ratio = switching_threshold_ratio_dict['sdxl_2048'][self.switching_threshold_ratio] if self.info['is_inpainting_task']: self.aggressive_raunet = inpainting_is_aggressive_raunet elif self.info['is_playground']: self.aggressive_raunet = playground_is_aggressive_raunet else: self.aggressive_raunet = is_aggressive_raunet else: self.T1_ratio = switching_threshold_ratio_dict['sdxl_4096'][self.switching_threshold_ratio] elif self.model == 'sdxl_turbo': self.T1_ratio = switching_threshold_ratio_dict['sdxl_turbo_1024'][self.switching_threshold_ratio] else: raise Exception(f'Error model. HiDiffusion now only supports sd15, sd21, sdxl, sdxl-turbo.') if self.aggressive_raunet: # self.T1_start = min(int(self.max_timestep * self.T1_ratio * 0.4), int(8/50 * self.max_timestep)) self.T1_start = int(aggressive_step/50 * self.max_timestep) self.T1_end = int(self.max_timestep * self.T1_ratio) self.T1 = 0 # to avoid confict with sdxl-turbo else: self.T1 = int(self.max_timestep * self.T1_ratio) output_states = () lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 blocks = list(zip(self.resnets, self.attentions)) for i, (resnet, attn) in enumerate(blocks): if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, **ckpt_kwargs, ) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] else: # hidden_states = resnet(hidden_states, temb, scale=lora_scale) hidden_states = resnet(hidden_states, temb) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] # apply additional residuals to the output of the last pair of resnet and attention blocks if i == len(blocks) - 1 and additional_residuals is not None: hidden_states = hidden_states + additional_residuals if i == 0: if self.aggressive_raunet and self.timestep >= self.T1_start and self.timestep < self.T1_end: hidden_states = F.avg_pool2d(hidden_states, kernel_size=(2,2)) elif self.timestep < self.T1: hidden_states = F.avg_pool2d(hidden_states, kernel_size=(2,2)) output_states = output_states + (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states) # hidden_states = downsampler(hidden_states, scale=lora_scale) output_states = output_states + (hidden_states,) self.timestep += 1 if self.timestep == self.max_timestep: self.timestep = 0 return hidden_states, output_states return cross_attn_down_block def make_diffusers_cross_attn_up_block(block_class: Type[torch.nn.Module]) -> Type[torch.nn.Module]: # replace conventional downsampler with resolution-aware downsampler class cross_attn_up_block(block_class): # Save for unpatching later _parent = block_class timestep = 0 aggressive_raunet = False T1_ratio = 0 T1_start = 0 T1_end = 0 aggressive_raunet = False T1 = 0 # to avoid confict with sdxl-turbo max_timestep = 50 def forward( self, hidden_states: torch.FloatTensor, res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], temb: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, upsample_size: Optional[int] = None, attention_mask: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: self.max_timestep = len(self.info['scheduler'].timesteps) ori_H, ori_W = self.info['size'] if self.model == 'sd15': if ori_H < 256 or ori_W < 256: self.T1_ratio = switching_threshold_ratio_dict['sd15_1024'][self.switching_threshold_ratio] else: self.T1_ratio = switching_threshold_ratio_dict['sd15_2048'][self.switching_threshold_ratio] elif self.model == 'sdxl': if ori_H < 512 or ori_W < 512: if self.info['use_controlnet']: self.T1_ratio = controlnet_switching_threshold_ratio_dict['sdxl_2048'][self.switching_threshold_ratio] else: self.T1_ratio = switching_threshold_ratio_dict['sdxl_2048'][self.switching_threshold_ratio] if self.info['is_inpainting_task']: self.aggressive_raunet = inpainting_is_aggressive_raunet elif self.info['is_playground']: self.aggressive_raunet = playground_is_aggressive_raunet else: self.aggressive_raunet = is_aggressive_raunet else: self.T1_ratio = switching_threshold_ratio_dict['sdxl_4096'][self.switching_threshold_ratio] elif self.model == 'sdxl_turbo': self.T1_ratio = switching_threshold_ratio_dict['sdxl_turbo_1024'][self.switching_threshold_ratio] else: raise Exception(f'Error model. HiDiffusion now only supports sd15, sd21, sdxl, sdxl-turbo.') if self.aggressive_raunet: # self.T1_start = min(int(self.max_timestep * self.T1_ratio * 0.4), int(8/50 * self.max_timestep)) self.T1_start = int(aggressive_step/50 * self.max_timestep) self.T1_end = int(self.max_timestep * self.T1_ratio) self.T1 = 0 # to avoid confict with sdxl-turbo else: self.T1 = int(self.max_timestep * self.T1_ratio) lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 is_freeu_enabled = ( getattr(self, "s1", None) and getattr(self, "s2", None) and getattr(self, "b1", None) and getattr(self, "b2", None) ) for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)): # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] # FreeU: Only operate on the first two stages if is_freeu_enabled: hidden_states, res_hidden_states = apply_freeu( self.resolution_idx, hidden_states, res_hidden_states, s1=self.s1, s2=self.s2, b1=self.b1, b2=self.b2, ) hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, **ckpt_kwargs, ) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] else: hidden_states = resnet(hidden_states, temb) # hidden_states = resnet(hidden_states, temb, scale=lora_scale) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] if i == 1: if self.aggressive_raunet and self.timestep >= self.T1_start and self.timestep < self.T1_end: re_size = (int(hidden_states.shape[-2] * 2), int(hidden_states.shape[-1] * 2)) hidden_states = F.interpolate(hidden_states, size=re_size, mode='bicubic') elif self.timestep < self.T1: re_size = (int(hidden_states.shape[-2] * 2), int(hidden_states.shape[-1] * 2)) hidden_states = F.interpolate(hidden_states, size=re_size, mode='bicubic') if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, upsample_size) # hidden_states = upsampler(hidden_states, upsample_size, scale=lora_scale) self.timestep += 1 if self.timestep == self.max_timestep: self.timestep = 0 return hidden_states return cross_attn_up_block def make_diffusers_downsampler_block(block_class: Type[torch.nn.Module]) -> Type[torch.nn.Module]: # replace conventional downsampler with resolution-aware downsampler class downsampler_block(block_class): # Save for unpatching later _parent = block_class T1_ratio = 0 T1 = 0 timestep = 0 aggressive_raunet = False max_timestep = 50 def forward(self, hidden_states: torch.Tensor, scale = 1.0) -> torch.Tensor: self.max_timestep = len(self.info['scheduler'].timesteps) ori_H, ori_W = self.info['size'] if self.model == 'sd15': if ori_H < 256 or ori_W < 256: self.T1_ratio = switching_threshold_ratio_dict['sd15_1024'][self.switching_threshold_ratio] else: self.T1_ratio = switching_threshold_ratio_dict['sd15_2048'][self.switching_threshold_ratio] elif self.model == 'sdxl': if ori_H < 512 or ori_W < 512: if self.info['use_controlnet']: self.T1_ratio = controlnet_switching_threshold_ratio_dict['sdxl_2048'][self.switching_threshold_ratio] else: self.T1_ratio = switching_threshold_ratio_dict['sdxl_2048'][self.switching_threshold_ratio] if self.info['is_inpainting_task']: self.aggressive_raunet = inpainting_is_aggressive_raunet elif self.info['is_playground']: self.aggressive_raunet = playground_is_aggressive_raunet else: self.aggressive_raunet = is_aggressive_raunet else: self.T1_ratio = switching_threshold_ratio_dict['sdxl_4096'][self.switching_threshold_ratio] elif self.model == 'sdxl_turbo': self.T1_ratio = switching_threshold_ratio_dict['sdxl_turbo_1024'][self.switching_threshold_ratio] else: raise Exception(f'Error model. HiDiffusion now only supports sd15, sd21, sdxl, sdxl-turbo.') if self.aggressive_raunet: # self.T1 = min(int(self.max_timestep * self.T1_ratio), int(8/50 * self.max_timestep)) self.T1 = int(aggressive_step/50 * self.max_timestep) else: self.T1 = int(self.max_timestep * self.T1_ratio) if self.timestep < self.T1: self.ori_stride = self.stride self.ori_padding = self.padding self.ori_dilation = self.dilation self.stride = (4,4) self.padding = (2,2) self.dilation = (2,2) if old_diffusers: if self.lora_layer is None: # make sure to the functional Conv2D function as otherwise torch.compile's graph will break # see: https://github.com/huggingface/diffusers/pull/4315 hidden_states = F.conv2d( hidden_states, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups ) if self.timestep < self.T1: self.stride = self.ori_stride self.padding = self.ori_padding self.dilation = self.ori_dilation self.timestep += 1 if self.timestep == self.max_timestep: self.timestep = 0 return hidden_states else: original_outputs = F.conv2d( hidden_states, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups ) return original_outputs + (scale * self.lora_layer(hidden_states)) else: hidden_states = F.conv2d( hidden_states, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups ) if self.timestep < self.T1: self.stride = self.ori_stride self.padding = self.ori_padding self.dilation = self.ori_dilation self.timestep += 1 if self.timestep == self.max_timestep: self.timestep = 0 return hidden_states return downsampler_block def make_diffusers_upsampler_block(block_class: Type[torch.nn.Module]) -> Type[torch.nn.Module]: # replace conventional upsampler with resolution-aware downsampler class upsampler_block(block_class): # Save for unpatching later _parent = block_class T1_ratio = 0 T1 = 0 timestep = 0 aggressive_raunet = False max_timestep = 50 def forward(self, hidden_states: torch.Tensor, scale = 1.0) -> torch.Tensor: self.max_timestep = len(self.info['scheduler'].timesteps) ori_H, ori_W = self.info['size'] if self.model == 'sd15': if ori_H < 256 or ori_W < 256: self.T1_ratio = switching_threshold_ratio_dict['sd15_1024'][self.switching_threshold_ratio] else: self.T1_ratio = switching_threshold_ratio_dict['sd15_2048'][self.switching_threshold_ratio] elif self.model == 'sdxl': if ori_H < 512 or ori_W < 512: if self.info['use_controlnet']: self.T1_ratio = controlnet_switching_threshold_ratio_dict['sdxl_2048'][self.switching_threshold_ratio] else: self.T1_ratio = switching_threshold_ratio_dict['sdxl_2048'][self.switching_threshold_ratio] if self.info['is_inpainting_task']: self.aggressive_raunet = inpainting_is_aggressive_raunet elif self.info['is_playground']: self.aggressive_raunet = playground_is_aggressive_raunet else: self.aggressive_raunet = is_aggressive_raunet else: self.T1_ratio = switching_threshold_ratio_dict['sdxl_4096'][self.switching_threshold_ratio] elif self.model == 'sdxl_turbo': self.T1_ratio = switching_threshold_ratio_dict['sdxl_turbo_1024'][self.switching_threshold_ratio] else: raise Exception(f'Error model. HiDiffusion now only supports sd15, sd21, sdxl, sdxl-turbo.') if self.aggressive_raunet: # self.T1 = min(int(self.max_timestep * self.T1_ratio), int(8/50 * self.max_timestep)) self.T1 = int(aggressive_step/50 * self.max_timestep) else: self.T1 = int(self.max_timestep * self.T1_ratio) if self.timestep < self.T1: if ori_H != hidden_states.shape[2] and ori_W != hidden_states.shape[3]: hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode='bicubic') self.timestep += 1 if self.timestep == self.max_timestep: self.timestep = 0 if old_diffusers: if self.lora_layer is None: # make sure to the functional Conv2D function as otherwise torch.compile's graph will break # see: https://github.com/huggingface/diffusers/pull/4315 return F.conv2d( hidden_states, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups ) else: original_outputs = F.conv2d( hidden_states, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups ) return original_outputs + (scale * self.lora_layer(hidden_states)) else: return F.conv2d( hidden_states, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups ) return upsampler_block def hook_diffusion_model(model: torch.nn.Module): """ Adds a forward pre hook to get the image size. This hook can be removed with remove_hidiffusion. """ def hook(module, args): module.info["size"] = (args[0].shape[2], args[0].shape[3]) return None model.info["hooks"].append(model.register_forward_pre_hook(hook)) def apply_hidiffusion( model: torch.nn.Module, apply_raunet: bool = True, apply_window_attn: bool = True): """ model: diffusers model. We support SD 1.5, 2.1, XL, XL Turbo. apply_raunet: whether to apply RAU-Net apply_window_attn: whether to apply MSW-MSA. """ # Make sure the module is not currently patched remove_hidiffusion(model) is_diffusers = isinstance_str(model, "DiffusionPipeline") or isinstance_str(model, "ModelMixin") if not is_diffusers: # if not hasattr(model, "model") or not hasattr(model.model, "diffusion_model"): # # Provided model not supported # raise RuntimeError("Provided model was not a Stable Diffusion / Latent Diffusion model, as expected.") # diffusion_model = model.model.diffusion_model raise RuntimeError("Provided model was not a diffusers model/pipeline, as expected.") else: if hasattr(model, 'controlnet'): make_ppl_fn = make_diffusers_sdxl_contrtolnet_ppl model.__class__ = make_ppl_fn(model.__class__) make_block_fn = make_diffusers_unet_2d_condition model.unet.__class__ = make_block_fn(model.unet.__class__) diffusion_model = model.unet if hasattr(model, "unet") else model name_or_path = model.name_or_path diffusion_model_module_key = [] if name_or_path not in surppoted_official_model: for key, module in diffusion_model.named_modules(): diffusion_model_module_key.append(key) if set(sd15_module_key) < set(diffusion_model_module_key): name_or_path = 'runwayml/stable-diffusion-v1-5' elif set(sdxl_module_key) < set(diffusion_model_module_key): name_or_path = 'stabilityai/stable-diffusion-xl-base-1.0' from diffusers.pipelines import auto_pipeline is_inpainting_task = model.__class__ in auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING.values() diffusion_model.info = {'size': None, 'hooks': [], 'scheduler': model.scheduler, 'use_controlnet': hasattr(model, 'controlnet'), 'is_inpainting_task': is_inpainting_task, 'is_playground': False} hook_diffusion_model(diffusion_model) if name_or_path in ['runwayml/stable-diffusion-v1-5', 'stabilityai/stable-diffusion-2-1-base']: modified_key = sd15_hidiffusion_key() for key, module in diffusion_model.named_modules(): if apply_raunet and key in modified_key['down_module_key']: make_block_fn = make_diffusers_downsampler_block module.__class__ = make_block_fn(module.__class__) module.switching_threshold_ratio = 'T1_ratio' if apply_raunet and key in modified_key['down_module_key_extra']: make_block_fn = make_diffusers_cross_attn_down_block module.__class__ = make_block_fn(module.__class__) module.switching_threshold_ratio = 'T2_ratio' if apply_raunet and key in modified_key['up_module_key']: make_block_fn = make_diffusers_upsampler_block module.__class__ = make_block_fn(module.__class__) module.switching_threshold_ratio = 'T1_ratio' if apply_raunet and key in modified_key['up_module_key_extra']: make_block_fn = make_diffusers_cross_attn_up_block module.__class__ = make_block_fn(module.__class__) module.switching_threshold_ratio = 'T2_ratio' if apply_window_attn and key in modified_key['windown_attn_module_key']: make_block_fn = make_diffusers_transformer_block module.__class__ = make_block_fn(module.__class__) module.model = 'sd15' module.info = diffusion_model.info elif name_or_path in ['stabilityai/stable-diffusion-xl-base-1.0', 'diffusers/stable-diffusion-xl-1.0-inpainting-0.1']: modified_key = sdxl_hidiffusion_key() for key, module in diffusion_model.named_modules(): if apply_raunet and key in modified_key['down_module_key']: make_block_fn = make_diffusers_cross_attn_down_block module.__class__ = make_block_fn(module.__class__) module.switching_threshold_ratio = 'T1_ratio' if apply_raunet and key in modified_key['down_module_key_extra']: make_block_fn = make_diffusers_downsampler_block module.__class__ = make_block_fn(module.__class__) module.switching_threshold_ratio = 'T2_ratio' if apply_raunet and key in modified_key['up_module_key']: make_block_fn = make_diffusers_cross_attn_up_block module.__class__ = make_block_fn(module.__class__) module.switching_threshold_ratio = 'T1_ratio' if apply_raunet and key in modified_key['up_module_key_extra']: make_block_fn = make_diffusers_upsampler_block module.__class__ = make_block_fn(module.__class__) module.switching_threshold_ratio = 'T2_ratio' if apply_window_attn and key in modified_key['windown_attn_module_key']: make_block_fn = make_diffusers_transformer_block module.__class__ = make_block_fn(module.__class__) module.model = 'sdxl' module.info = diffusion_model.info elif name_or_path == 'stabilityai/sdxl-turbo': modified_key = sdxl_turbo_hidiffusion_key() for key, module in diffusion_model.named_modules(): if apply_raunet and key in modified_key['down_module_key']: make_block_fn = make_diffusers_cross_attn_down_block module.__class__ = make_block_fn(module.__class__) module.switching_threshold_ratio = 'T1_ratio' if apply_raunet and key in modified_key['up_module_key']: make_block_fn = make_diffusers_cross_attn_up_block module.__class__ = make_block_fn(module.__class__) module.switching_threshold_ratio = 'T1_ratio' if apply_window_attn and key in modified_key['windown_attn_module_key']: make_block_fn = make_diffusers_transformer_block module.__class__ = make_block_fn(module.__class__) module.model = 'sdxl_turbo' module.info = diffusion_model.info else: raise Exception(f'{model.name_or_path} is not a supported model. HiDiffusion now only supports runwayml/stable-diffusion-v1-5, stabilityai/stable-diffusion-2-1-base, stabilityai/stable-diffusion-xl-base-1.0, stabilityai/sdxl-turbo, diffusers/stable-diffusion-xl-1.0-inpainting-0.1 and their derivative models/pipelines.') return model def remove_hidiffusion(model: torch.nn.Module): """ Removes hidiffusion from a Diffusion module if it was already patched. """ # For diffusers model = model.unet if hasattr(model, "unet") else model for _, module in model.named_modules(): if hasattr(module, "info"): for hook in module.info["hooks"]: hook.remove() module.info["hooks"].clear() if hasattr(module, "_parent"): module.__class__ = module._parent return model