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| # Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/ | |
| # From https://raw.githubusercontent.com/huggingface/diffusers/53377ef83c6446033f3ee506e3ef718db817b293/examples/community/stable_diffusion_controlnet_inpaint.py | |
| import inspect | |
| from typing import Any, Callable, Dict, List, Optional, Union, Tuple | |
| import numpy as np | |
| import PIL.Image | |
| import torch | |
| import torch.nn.functional as F | |
| from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer | |
| from diffusers import AutoencoderKL, ControlNetModel, DiffusionPipeline, \ | |
| UNet2DConditionModel, logging, StableDiffusionControlNetPipeline | |
| from diffusers.models.controlnet import ControlNetOutput | |
| from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker | |
| from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel | |
| from diffusers.schedulers import KarrasDiffusionSchedulers | |
| from diffusers.utils import ( | |
| PIL_INTERPOLATION, | |
| is_accelerate_available, | |
| is_accelerate_version, | |
| is_compiled_module, | |
| randn_tensor, | |
| replace_example_docstring, | |
| ) | |
| from diffusers.loaders import LoraLoaderMixin | |
| logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
| EXAMPLE_DOC_STRING = """ | |
| Examples: | |
| ```py | |
| >>> import numpy as np | |
| >>> import torch | |
| >>> from PIL import Image | |
| >>> from stable_diffusion_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline | |
| >>> from transformers import AutoImageProcessor, UperNetForSemanticSegmentation | |
| >>> from diffusers import ControlNetModel, UniPCMultistepScheduler | |
| >>> from diffusers.utils import load_image | |
| >>> def ade_palette(): | |
| return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50], | |
| [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255], | |
| [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7], | |
| [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82], | |
| [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3], | |
| [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255], | |
| [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220], | |
| [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224], | |
| [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255], | |
| [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7], | |
| [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153], | |
| [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255], | |
| [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0], | |
| [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255], | |
| [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255], | |
| [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255], | |
| [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0], | |
| [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0], | |
| [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255], | |
| [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255], | |
| [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20], | |
| [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255], | |
| [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255], | |
| [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255], | |
| [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0], | |
| [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0], | |
| [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255], | |
| [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112], | |
| [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160], | |
| [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163], | |
| [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0], | |
| [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0], | |
| [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255], | |
| [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204], | |
| [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255], | |
| [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255], | |
| [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194], | |
| [102, 255, 0], [92, 0, 255]] | |
| >>> image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small") | |
| >>> image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small") | |
| >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16) | |
| >>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-inpainting", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 | |
| ) | |
| >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) | |
| >>> pipe.enable_xformers_memory_efficient_attention() | |
| >>> pipe.enable_model_cpu_offload() | |
| >>> def image_to_seg(image): | |
| pixel_values = image_processor(image, return_tensors="pt").pixel_values | |
| with torch.no_grad(): | |
| outputs = image_segmentor(pixel_values) | |
| seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] | |
| color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3 | |
| palette = np.array(ade_palette()) | |
| for label, color in enumerate(palette): | |
| color_seg[seg == label, :] = color | |
| color_seg = color_seg.astype(np.uint8) | |
| seg_image = Image.fromarray(color_seg) | |
| return seg_image | |
| >>> image = load_image( | |
| "https://github.com/CompVis/latent-diffusion/raw/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" | |
| ) | |
| >>> mask_image = load_image( | |
| "https://github.com/CompVis/latent-diffusion/raw/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" | |
| ) | |
| >>> controlnet_conditioning_image = image_to_seg(image) | |
| >>> image = pipe( | |
| "Face of a yellow cat, high resolution, sitting on a park bench", | |
| image, | |
| mask_image, | |
| controlnet_conditioning_image, | |
| num_inference_steps=20, | |
| ).images[0] | |
| >>> image.save("out.png") | |
| ``` | |
| """ | |
| def prepare_image(image): | |
| if isinstance(image, torch.Tensor): | |
| # Batch single image | |
| if image.ndim == 3: | |
| image = image.unsqueeze(0) | |
| image = image.to(dtype=torch.float32) | |
| else: | |
| # preprocess image | |
| if isinstance(image, (PIL.Image.Image, np.ndarray)): | |
| image = [image] | |
| if isinstance(image, list) and isinstance(image[0], PIL.Image.Image): | |
| image = [np.array(i.convert("RGB"))[None, :] for i in image] | |
| image = np.concatenate(image, axis=0) | |
| elif isinstance(image, list) and isinstance(image[0], np.ndarray): | |
| image = np.concatenate([i[None, :] for i in image], axis=0) | |
| image = image.transpose(0, 3, 1, 2) | |
| image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0 | |
| return image | |
| # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.prepare_mask_and_masked_image | |
| def prepare_mask_and_masked_image(image, mask, height, width, return_image=False): | |
| """ | |
| Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be | |
| converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the | |
| ``image`` and ``1`` for the ``mask``. | |
| The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be | |
| binarized (``mask > 0.5``) and cast to ``torch.float32`` too. | |
| Args: | |
| image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint. | |
| It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width`` | |
| ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``. | |
| mask (_type_): The mask to apply to the image, i.e. regions to inpaint. | |
| It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width`` | |
| ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``. | |
| Raises: | |
| ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask | |
| should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions. | |
| TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not | |
| (ot the other way around). | |
| Returns: | |
| tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4 | |
| dimensions: ``batch x channels x height x width``. | |
| """ | |
| if image is None: | |
| raise ValueError("`image` input cannot be undefined.") | |
| if mask is None: | |
| raise ValueError("`mask_image` input cannot be undefined.") | |
| if isinstance(image, torch.Tensor): | |
| if not isinstance(mask, torch.Tensor): | |
| raise TypeError(f"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not") | |
| # Batch single image | |
| if image.ndim == 3: | |
| assert image.shape[0] == 3, "Image outside a batch should be of shape (3, H, W)" | |
| image = image.unsqueeze(0) | |
| # Batch and add channel dim for single mask | |
| if mask.ndim == 2: | |
| mask = mask.unsqueeze(0).unsqueeze(0) | |
| # Batch single mask or add channel dim | |
| if mask.ndim == 3: | |
| # Single batched mask, no channel dim or single mask not batched but channel dim | |
| if mask.shape[0] == 1: | |
| mask = mask.unsqueeze(0) | |
| # Batched masks no channel dim | |
| else: | |
| mask = mask.unsqueeze(1) | |
| assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions" | |
| assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions" | |
| assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size" | |
| # Check image is in [-1, 1] | |
| if image.min() < -1 or image.max() > 1: | |
| raise ValueError("Image should be in [-1, 1] range") | |
| # Check mask is in [0, 1] | |
| if mask.min() < 0 or mask.max() > 1: | |
| raise ValueError("Mask should be in [0, 1] range") | |
| # Binarize mask | |
| mask[mask < 0.5] = 0 | |
| mask[mask >= 0.5] = 1 | |
| # Image as float32 | |
| image = image.to(dtype=torch.float32) | |
| elif isinstance(mask, torch.Tensor): | |
| raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not") | |
| else: | |
| # preprocess image | |
| if isinstance(image, (PIL.Image.Image, np.ndarray)): | |
| image = [image] | |
| if isinstance(image, list) and isinstance(image[0], PIL.Image.Image): | |
| # resize all images w.r.t passed height an width | |
| image = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in image] | |
| image = [np.array(i.convert("RGB"))[None, :] for i in image] | |
| image = np.concatenate(image, axis=0) | |
| elif isinstance(image, list) and isinstance(image[0], np.ndarray): | |
| image = np.concatenate([i[None, :] for i in image], axis=0) | |
| image = image.transpose(0, 3, 1, 2) | |
| image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0 | |
| # preprocess mask | |
| if isinstance(mask, (PIL.Image.Image, np.ndarray)): | |
| mask = [mask] | |
| if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image): | |
| mask = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in mask] | |
| mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0) | |
| mask = mask.astype(np.float32) / 255.0 | |
| elif isinstance(mask, list) and isinstance(mask[0], np.ndarray): | |
| mask = np.concatenate([m[None, None, :] for m in mask], axis=0) | |
| mask[mask < 0.5] = 0 | |
| mask[mask >= 0.5] = 1 | |
| mask = torch.from_numpy(mask) | |
| masked_image = image * (mask < 0.5) | |
| # n.b. ensure backwards compatibility as old function does not return image | |
| if return_image: | |
| return mask, masked_image, image | |
| return mask, masked_image | |
| def prepare_mask_image(mask_image): | |
| if isinstance(mask_image, torch.Tensor): | |
| if mask_image.ndim == 2: | |
| # Batch and add channel dim for single mask | |
| mask_image = mask_image.unsqueeze(0).unsqueeze(0) | |
| elif mask_image.ndim == 3 and mask_image.shape[0] == 1: | |
| # Single mask, the 0'th dimension is considered to be | |
| # the existing batch size of 1 | |
| mask_image = mask_image.unsqueeze(0) | |
| elif mask_image.ndim == 3 and mask_image.shape[0] != 1: | |
| # Batch of mask, the 0'th dimension is considered to be | |
| # the batching dimension | |
| mask_image = mask_image.unsqueeze(1) | |
| # Binarize mask | |
| mask_image[mask_image < 0.5] = 0 | |
| mask_image[mask_image >= 0.5] = 1 | |
| else: | |
| # preprocess mask | |
| if isinstance(mask_image, (PIL.Image.Image, np.ndarray)): | |
| mask_image = [mask_image] | |
| if isinstance(mask_image, list) and isinstance(mask_image[0], PIL.Image.Image): | |
| mask_image = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask_image], axis=0) | |
| mask_image = mask_image.astype(np.float32) / 255.0 | |
| elif isinstance(mask_image, list) and isinstance(mask_image[0], np.ndarray): | |
| mask_image = np.concatenate([m[None, None, :] for m in mask_image], axis=0) | |
| mask_image[mask_image < 0.5] = 0 | |
| mask_image[mask_image >= 0.5] = 1 | |
| mask_image = torch.from_numpy(mask_image) | |
| return mask_image | |
| def prepare_controlnet_conditioning_image( | |
| controlnet_conditioning_image, width, height, batch_size, num_images_per_prompt, device, dtype, | |
| do_classifier_free_guidance, | |
| ): | |
| if not isinstance(controlnet_conditioning_image, torch.Tensor): | |
| if isinstance(controlnet_conditioning_image, PIL.Image.Image): | |
| controlnet_conditioning_image = [controlnet_conditioning_image] | |
| if isinstance(controlnet_conditioning_image[0], PIL.Image.Image): | |
| controlnet_conditioning_image = [ | |
| np.array(i.resize((width, height), resample=PIL_INTERPOLATION["lanczos"]))[None, :] | |
| for i in controlnet_conditioning_image | |
| ] | |
| controlnet_conditioning_image = np.concatenate(controlnet_conditioning_image, axis=0) | |
| controlnet_conditioning_image = np.array(controlnet_conditioning_image).astype(np.float32) / 255.0 | |
| controlnet_conditioning_image = controlnet_conditioning_image.transpose(0, 3, 1, 2) | |
| controlnet_conditioning_image = torch.from_numpy(controlnet_conditioning_image) | |
| elif isinstance(controlnet_conditioning_image[0], torch.Tensor): | |
| controlnet_conditioning_image = torch.cat(controlnet_conditioning_image, dim=0) | |
| image_batch_size = controlnet_conditioning_image.shape[0] | |
| if image_batch_size == 1: | |
| repeat_by = batch_size | |
| else: | |
| # image batch size is the same as prompt batch size | |
| repeat_by = num_images_per_prompt | |
| controlnet_conditioning_image = controlnet_conditioning_image.repeat_interleave(repeat_by, dim=0) | |
| controlnet_conditioning_image = controlnet_conditioning_image.to(device=device, dtype=dtype) | |
| if do_classifier_free_guidance: | |
| controlnet_conditioning_image = torch.cat([controlnet_conditioning_image] * 2) | |
| return controlnet_conditioning_image | |
| class StableDiffusionControlNetPipeline2(StableDiffusionControlNetPipeline): | |
| def __call__( | |
| self, | |
| prompt: Union[str, List[str]] = None, | |
| image: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] = None, | |
| height: Optional[int] = None, | |
| width: Optional[int] = None, | |
| num_inference_steps: int = 50, | |
| guidance_scale: float = 7.5, | |
| negative_prompt: Optional[Union[str, List[str]]] = None, | |
| num_images_per_prompt: Optional[int] = 1, | |
| eta: float = 0.0, | |
| generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, | |
| latents: Optional[torch.FloatTensor] = None, | |
| prompt_embeds: Optional[torch.FloatTensor] = None, | |
| negative_prompt_embeds: Optional[torch.FloatTensor] = None, | |
| output_type: Optional[str] = "pil", | |
| return_dict: bool = True, | |
| callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, | |
| callback_steps: int = 1, | |
| cross_attention_kwargs: Optional[Dict[str, Any]] = None, | |
| controlnet_conditioning_scale: Union[float, List[float]] = 1.0, | |
| controlnet_conditioning_scale_map=None, | |
| guess_mode: bool = False, | |
| ): | |
| r""" | |
| Function invoked when calling the pipeline for generation. | |
| Args: | |
| prompt (`str` or `List[str]`, *optional*): | |
| The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. | |
| instead. | |
| image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, | |
| `List[List[torch.FloatTensor]]`, or `List[List[PIL.Image.Image]]`): | |
| The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. 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 according to them. 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. | |
| width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): | |
| The width in pixels of the generated image. | |
| num_inference_steps (`int`, *optional*, defaults to 50): | |
| The number of denoising steps. More denoising steps usually lead to a higher quality image at the | |
| expense of slower inference. | |
| guidance_scale (`float`, *optional*, defaults to 7.5): | |
| Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). | |
| `guidance_scale` is defined as `w` of equation 2. of [Imagen | |
| Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > | |
| 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, | |
| usually at the expense of lower image quality. | |
| negative_prompt (`str` or `List[str]`, *optional*): | |
| The prompt or prompts not to guide the image generation. If not defined, one has to pass | |
| `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is | |
| less than `1`). | |
| num_images_per_prompt (`int`, *optional*, defaults to 1): | |
| The number of images to generate per prompt. | |
| eta (`float`, *optional*, defaults to 0.0): | |
| Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to | |
| [`schedulers.DDIMScheduler`], will be ignored for others. | |
| generator (`torch.Generator` or `List[torch.Generator]`, *optional*): | |
| One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) | |
| to make generation deterministic. | |
| latents (`torch.FloatTensor`, *optional*): | |
| Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image | |
| generation. Can be used to tweak the same generation with different prompts. If not provided, a latents | |
| tensor will ge generated by sampling using the supplied random `generator`. | |
| prompt_embeds (`torch.FloatTensor`, *optional*): | |
| Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not | |
| provided, text embeddings will be generated from `prompt` input argument. | |
| negative_prompt_embeds (`torch.FloatTensor`, *optional*): | |
| Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt | |
| weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input | |
| argument. | |
| output_type (`str`, *optional*, defaults to `"pil"`): | |
| The output format of the generate image. Choose between | |
| [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. | |
| return_dict (`bool`, *optional*, defaults to `True`): | |
| Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a | |
| plain tuple. | |
| callback (`Callable`, *optional*): | |
| A function that will be called every `callback_steps` steps during inference. The function will be | |
| called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. | |
| callback_steps (`int`, *optional*, defaults to 1): | |
| The frequency at which the `callback` function will be called. If not specified, the callback will be | |
| called at every step. | |
| cross_attention_kwargs (`dict`, *optional*): | |
| A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under | |
| `self.processor` in | |
| [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). | |
| controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0): | |
| The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added | |
| to the residual in the original unet. If multiple ControlNets are specified in init, you can set the | |
| corresponding scale as a list. | |
| guess_mode (`bool`, *optional*, defaults to `False`): | |
| In this mode, the ControlNet encoder will try best to recognize the content of the input image even if | |
| you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended. | |
| Examples: | |
| Returns: | |
| [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: | |
| [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. | |
| When returning a tuple, the first element is a list with the generated images, and the second element is a | |
| list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" | |
| (nsfw) content, according to the `safety_checker`. | |
| """ | |
| # 0. Default height and width to unet | |
| height, width = self._default_height_width(height, width, image) | |
| # 1. Check inputs. Raise error if not correct | |
| self.check_inputs( | |
| prompt, | |
| image, | |
| height, | |
| width, | |
| callback_steps, | |
| negative_prompt, | |
| prompt_embeds, | |
| negative_prompt_embeds, | |
| controlnet_conditioning_scale, | |
| ) | |
| # 2. Define call parameters | |
| if prompt is not None and isinstance(prompt, str): | |
| batch_size = 1 | |
| elif prompt is not None and isinstance(prompt, list): | |
| batch_size = len(prompt) | |
| else: | |
| batch_size = prompt_embeds.shape[0] | |
| device = self._execution_device | |
| # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) | |
| # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` | |
| # corresponds to doing no classifier free guidance. | |
| do_classifier_free_guidance = guidance_scale > 1.0 | |
| controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet | |
| if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float): | |
| controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets) | |
| if controlnet_conditioning_scale_map is not None: | |
| if isinstance(controlnet_conditioning_scale, list): | |
| controlnet_conditioning_scale = [scale * controlnet_conditioning_scale_map for scale in | |
| controlnet_conditioning_scale] | |
| else: | |
| controlnet_conditioning_scale = controlnet_conditioning_scale * controlnet_conditioning_scale_map | |
| 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. Encode input prompt | |
| prompt_embeds = self._encode_prompt( | |
| prompt, | |
| device, | |
| num_images_per_prompt, | |
| do_classifier_free_guidance, | |
| negative_prompt, | |
| prompt_embeds=prompt_embeds, | |
| negative_prompt_embeds=negative_prompt_embeds, | |
| ) | |
| # 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=do_classifier_free_guidance, | |
| guess_mode=guess_mode, | |
| ) | |
| 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=do_classifier_free_guidance, | |
| guess_mode=guess_mode, | |
| ) | |
| images.append(image_) | |
| image = images | |
| else: | |
| assert False | |
| # 5. Prepare timesteps | |
| self.scheduler.set_timesteps(num_inference_steps, device=device) | |
| timesteps = self.scheduler.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, | |
| ) | |
| # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline | |
| extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) | |
| # 8. Denoising loop | |
| num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order | |
| with self.progress_bar(total=num_inference_steps) as progress_bar: | |
| for i, t in enumerate(timesteps): | |
| # expand the latents if we are doing classifier free guidance | |
| latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents | |
| latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) | |
| # controlnet(s) inference | |
| if guess_mode and do_classifier_free_guidance: | |
| # Infer ControlNet only for the conditional batch. | |
| controlnet_latent_model_input = latents | |
| controlnet_prompt_embeds = prompt_embeds.chunk(2)[1] | |
| else: | |
| controlnet_latent_model_input = latent_model_input | |
| controlnet_prompt_embeds = prompt_embeds | |
| down_block_res_samples, mid_block_res_sample = self.controlnet( | |
| controlnet_latent_model_input, | |
| t, | |
| encoder_hidden_states=controlnet_prompt_embeds, | |
| controlnet_cond=image, | |
| conditioning_scale=controlnet_conditioning_scale, | |
| guess_mode=guess_mode, | |
| return_dict=False, | |
| ) | |
| if guess_mode and 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]) | |
| # predict the noise residual | |
| noise_pred = self.unet( | |
| latent_model_input, | |
| t, | |
| encoder_hidden_states=prompt_embeds, | |
| cross_attention_kwargs=cross_attention_kwargs, | |
| down_block_additional_residuals=down_block_res_samples, | |
| mid_block_additional_residual=mid_block_res_sample, | |
| return_dict=False, | |
| )[0] | |
| # perform guidance | |
| if do_classifier_free_guidance: | |
| noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) | |
| noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) | |
| # compute the previous noisy sample x_t -> x_t-1 | |
| latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] | |
| # call the callback, if provided | |
| if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): | |
| progress_bar.update() | |
| if callback is not None and i % callback_steps == 0: | |
| callback(i, t, latents) | |
| # If we do sequential model offloading, let's offload unet and controlnet | |
| # manually for max memory savings | |
| if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: | |
| self.unet.to("cpu") | |
| self.controlnet.to("cpu") | |
| torch.cuda.empty_cache() | |
| if output_type == "latent": | |
| image = latents | |
| has_nsfw_concept = None | |
| elif output_type == "pil": | |
| # 8. Post-processing | |
| image = self.decode_latents(latents) | |
| # 9. Run safety checker | |
| image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) | |
| # 10. Convert to PIL | |
| image = self.numpy_to_pil(image) | |
| else: | |
| # 8. Post-processing | |
| image = self.decode_latents(latents) | |
| # 9. Run safety checker | |
| image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) | |
| # Offload last model to CPU | |
| if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: | |
| self.final_offload_hook.offload() | |
| if not return_dict: | |
| return (image, has_nsfw_concept) | |
| return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) | |
| class ControlNetModel2(ControlNetModel): | |
| def forward( | |
| self, | |
| sample: torch.FloatTensor, | |
| timestep: Union[torch.Tensor, float, int], | |
| encoder_hidden_states: torch.Tensor, | |
| controlnet_cond: torch.FloatTensor, | |
| conditioning_scale: float = 1.0, | |
| 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, | |
| guess_mode: bool = False, | |
| return_dict: bool = True, | |
| ) -> Union[ControlNetOutput, Tuple]: | |
| # check channel order | |
| channel_order = self.config.controlnet_conditioning_channel_order | |
| if channel_order == "rgb": | |
| # in rgb order by default | |
| ... | |
| elif channel_order == "bgr": | |
| controlnet_cond = torch.flip(controlnet_cond, dims=[1]) | |
| else: | |
| raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}") | |
| # prepare attention_mask | |
| if attention_mask is not None: | |
| attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 | |
| attention_mask = attention_mask.unsqueeze(1) | |
| # 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) | |
| 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) | |
| class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) | |
| emb = emb + class_emb | |
| # 2. pre-process | |
| sample = self.conv_in(sample) | |
| controlnet_cond = self.controlnet_cond_embedding(controlnet_cond) | |
| sample = sample + controlnet_cond | |
| # 3. down | |
| 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: | |
| 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, | |
| ) | |
| else: | |
| sample, res_samples = downsample_block(hidden_states=sample, temb=emb) | |
| down_block_res_samples += res_samples | |
| # 4. mid | |
| if self.mid_block is not None: | |
| sample = self.mid_block( | |
| sample, | |
| emb, | |
| encoder_hidden_states=encoder_hidden_states, | |
| attention_mask=attention_mask, | |
| cross_attention_kwargs=cross_attention_kwargs, | |
| ) | |
| # 5. Control net blocks | |
| controlnet_down_block_res_samples = () | |
| for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks): | |
| down_block_res_sample = controlnet_block(down_block_res_sample) | |
| controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,) | |
| down_block_res_samples = controlnet_down_block_res_samples | |
| mid_block_res_sample = self.controlnet_mid_block(sample) | |
| # 6. scaling | |
| if guess_mode and not self.config.global_pool_conditions: | |
| scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device) # 0.1 to 1.0 | |
| scales = scales * conditioning_scale | |
| down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)] | |
| mid_block_res_sample = mid_block_res_sample * scales[-1] # last one | |
| else: | |
| if isinstance(conditioning_scale, float): | |
| down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples] | |
| mid_block_res_sample = mid_block_res_sample * conditioning_scale | |
| else: | |
| assert isinstance(conditioning_scale, torch.Tensor) | |
| if len(conditioning_scale.shape) == 2: | |
| conditioning_scale = conditioning_scale[None, None] | |
| elif len(conditioning_scale.shape) == 3: | |
| conditioning_scale = conditioning_scale[None] | |
| down_block_res_samples = [ | |
| sample * F.interpolate(conditioning_scale, sample.shape[-2:], | |
| mode='bilinear', align_corners=True).type(sample.dtype) | |
| for sample in down_block_res_samples | |
| ] | |
| mid_block_res_sample = mid_block_res_sample * F.interpolate( | |
| conditioning_scale, mid_block_res_sample.shape[-2:], | |
| mode='bilinear', align_corners=True | |
| ).type(sample.dtype) | |
| if self.config.global_pool_conditions: | |
| down_block_res_samples = [ | |
| torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples | |
| ] | |
| mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True) | |
| if not return_dict: | |
| return (down_block_res_samples, mid_block_res_sample) | |
| return ControlNetOutput( | |
| down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample | |
| ) | |