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| # Copyright 2023 The HuggingFace Team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import torch | |
| import PIL.Image | |
| import numpy as np | |
| from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import * | |
| EXAMPLE_DOC_STRING = """ | |
| Examples: | |
| ```py | |
| >>> # !pip install opencv-python transformers accelerate | |
| >>> from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, UniPCMultistepScheduler | |
| >>> from diffusers.utils import load_image | |
| >>> import numpy as np | |
| >>> import torch | |
| >>> import cv2 | |
| >>> from PIL import Image | |
| >>> # download an image | |
| >>> image = load_image( | |
| ... "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" | |
| ... ) | |
| >>> image = np.array(image) | |
| >>> mask_image = load_image( | |
| ... "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" | |
| ... ) | |
| >>> mask_image = np.array(mask_image) | |
| >>> # get canny image | |
| >>> canny_image = cv2.Canny(image, 100, 200) | |
| >>> canny_image = canny_image[:, :, None] | |
| >>> canny_image = np.concatenate([canny_image, canny_image, canny_image], axis=2) | |
| >>> canny_image = Image.fromarray(canny_image) | |
| >>> # load control net and stable diffusion v1-5 | |
| >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16) | |
| >>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained( | |
| ... "runwayml/stable-diffusion-inpainting", controlnet=controlnet, torch_dtype=torch.float16 | |
| ... ) | |
| >>> # speed up diffusion process with faster scheduler and memory optimization | |
| >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) | |
| >>> # remove following line if xformers is not installed | |
| >>> pipe.enable_xformers_memory_efficient_attention() | |
| >>> pipe.enable_model_cpu_offload() | |
| >>> # generate image | |
| >>> generator = torch.manual_seed(0) | |
| >>> image = pipe( | |
| ... "futuristic-looking doggo", | |
| ... num_inference_steps=20, | |
| ... generator=generator, | |
| ... image=image, | |
| ... control_image=canny_image, | |
| ... mask_image=mask_image | |
| ... ).images[0] | |
| ``` | |
| """ | |
| def prepare_mask_and_masked_image(image, mask): | |
| """ | |
| 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 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): | |
| 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 = 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) | |
| return mask, masked_image | |
| class StableDiffusionControlNetInpaintPipeline(StableDiffusionControlNetPipeline): | |
| r""" | |
| Pipeline for text-guided image inpainting using Stable Diffusion with ControlNet guidance. | |
| This model inherits from [`StableDiffusionControlNetPipeline`]. Check the superclass documentation for the generic methods the | |
| library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) | |
| Args: | |
| vae ([`AutoencoderKL`]): | |
| Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. | |
| text_encoder ([`CLIPTextModel`]): | |
| Frozen text-encoder. Stable Diffusion uses the text portion of | |
| [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically | |
| the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. | |
| tokenizer (`CLIPTokenizer`): | |
| Tokenizer of class | |
| [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). | |
| unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. | |
| controlnet ([`ControlNetModel`]): | |
| Provides additional conditioning to the unet during the denoising process | |
| scheduler ([`SchedulerMixin`]): | |
| A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of | |
| [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. | |
| safety_checker ([`StableDiffusionSafetyChecker`]): | |
| Classification module that estimates whether generated images could be considered offensive or harmful. | |
| Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. | |
| feature_extractor ([`CLIPFeatureExtractor`]): | |
| Model that extracts features from generated images to be used as inputs for the `safety_checker`. | |
| """ | |
| def prepare_mask_latents( | |
| self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance | |
| ): | |
| # resize the mask to latents shape as we concatenate the mask to the latents | |
| # we do that before converting to dtype to avoid breaking in case we're using cpu_offload | |
| # and half precision | |
| mask = torch.nn.functional.interpolate( | |
| mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor) | |
| ) | |
| mask = mask.to(device=device, dtype=dtype) | |
| masked_image = masked_image.to(device=device, dtype=dtype) | |
| # encode the mask image into latents space so we can concatenate it to the latents | |
| if isinstance(generator, list): | |
| masked_image_latents = [ | |
| self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i]) | |
| for i in range(batch_size) | |
| ] | |
| masked_image_latents = torch.cat(masked_image_latents, dim=0) | |
| else: | |
| masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator) | |
| masked_image_latents = self.vae.config.scaling_factor * masked_image_latents | |
| # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method | |
| if mask.shape[0] < batch_size: | |
| if not batch_size % mask.shape[0] == 0: | |
| raise ValueError( | |
| "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to" | |
| f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number" | |
| " of masks that you pass is divisible by the total requested batch size." | |
| ) | |
| mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1) | |
| if masked_image_latents.shape[0] < batch_size: | |
| if not batch_size % masked_image_latents.shape[0] == 0: | |
| raise ValueError( | |
| "The passed images and the required batch size don't match. Images are supposed to be duplicated" | |
| f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed." | |
| " Make sure the number of images that you pass is divisible by the total requested batch size." | |
| ) | |
| masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1) | |
| mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask | |
| masked_image_latents = ( | |
| torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents | |
| ) | |
| # aligning device to prevent device errors when concating it with the latent model input | |
| masked_image_latents = masked_image_latents.to(device=device, dtype=dtype) | |
| return mask, masked_image_latents | |
| def __call__( | |
| self, | |
| prompt: Union[str, List[str]] = None, | |
| image: Union[torch.FloatTensor, PIL.Image.Image] = None, | |
| control_image: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] = None, | |
| mask_image: Union[torch.FloatTensor, 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: float = 1.0, | |
| ): | |
| r""" | |
| Function invoked when calling the pipeline for generation. | |
| Args: | |
| prompt (`str` or `List[str]`, *optional*): | |
| The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. | |
| instead. | |
| image (`PIL.Image.Image`): | |
| `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will | |
| be masked out with `mask_image` and repainted according to `prompt`. | |
| control_image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]` or `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 control image is automatically resized to fit the output image. | |
| mask_image (`PIL.Image.Image`): | |
| `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be | |
| repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted | |
| to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L) | |
| instead of 3, so the expected shape would be `(B, H, W, 1)`. | |
| 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. 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 `AttnProcessor` 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`, *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. | |
| 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, control_image) | |
| # 1. Check inputs. Raise error if not correct | |
| self.check_inputs( | |
| prompt, control_image, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds | |
| ) | |
| # 2. Define call parameters | |
| if prompt is not None and isinstance(prompt, str): | |
| batch_size = 1 | |
| elif prompt is not None and isinstance(prompt, list): | |
| batch_size = len(prompt) | |
| else: | |
| batch_size = prompt_embeds.shape[0] | |
| device = self._execution_device | |
| # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) | |
| # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` | |
| # corresponds to doing no classifier free guidance. | |
| do_classifier_free_guidance = guidance_scale > 1.0 | |
| # 3. Encode input prompt | |
| 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 | |
| control_image = self.prepare_image( | |
| control_image, | |
| width, | |
| height, | |
| batch_size * num_images_per_prompt, | |
| num_images_per_prompt, | |
| device, | |
| self.controlnet.dtype, | |
| ) | |
| if do_classifier_free_guidance: | |
| control_image = torch.cat([control_image] * 2) | |
| # 5. Prepare timesteps | |
| self.scheduler.set_timesteps(num_inference_steps, device=device) | |
| timesteps = self.scheduler.timesteps | |
| # 6. Prepare latent variables | |
| num_channels_latents = self.controlnet.config.in_channels | |
| latents = self.prepare_latents( | |
| batch_size * num_images_per_prompt, | |
| num_channels_latents, | |
| height, | |
| width, | |
| prompt_embeds.dtype, | |
| device, | |
| generator, | |
| latents, | |
| ) | |
| # EXTRA: prepare mask latents | |
| mask, masked_image = prepare_mask_and_masked_image(image, mask_image) | |
| mask, masked_image_latents = self.prepare_mask_latents( | |
| mask, | |
| masked_image, | |
| batch_size * num_images_per_prompt, | |
| height, | |
| width, | |
| prompt_embeds.dtype, | |
| device, | |
| generator, | |
| do_classifier_free_guidance, | |
| ) | |
| # 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) | |
| down_block_res_samples, mid_block_res_sample = self.controlnet( | |
| latent_model_input, | |
| t, | |
| encoder_hidden_states=prompt_embeds, | |
| controlnet_cond=control_image, | |
| return_dict=False, | |
| ) | |
| down_block_res_samples = [ | |
| down_block_res_sample * controlnet_conditioning_scale | |
| for down_block_res_sample in down_block_res_samples | |
| ] | |
| mid_block_res_sample *= controlnet_conditioning_scale | |
| # predict the noise residual | |
| latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1) | |
| 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, | |
| ).sample | |
| # 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).prev_sample | |
| # 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) | |