diff --git "a/hidiffusion/hidiffusion.py" "b/hidiffusion/hidiffusion.py" new file mode 100644--- /dev/null +++ "b/hidiffusion/hidiffusion.py" @@ -0,0 +1,1955 @@ +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": 0.0}, + "sd15_2048": {"T1_ratio": 0.7, "T2_ratio": 0.3}, + "sdxl_2048": {"T1_ratio": 0.4, "T2_ratio": 0.0}, + "sdxl_4096": {"T1_ratio": 0.7, "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: + 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" + + diffusion_model.info = { + "size": None, + "hooks": [], + "scheduler": model.scheduler, + "use_controlnet": hasattr(model, "controlnet"), + "is_inpainting_task": "inpainting" in model.name_or_path, + "is_playground": "playground" in model.name_or_path, + } + 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