from typing import Any, Callable, Dict, List, Optional, Tuple, Union import numpy as np import torch from PIL import Image, ImageFilter from diffusers.image_processor import PipelineImageInput from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img import ( StableDiffusionXLImg2ImgPipeline, rescale_noise_cfg, retrieve_latents, retrieve_timesteps, ) from diffusers.utils import ( deprecate, is_torch_xla_available, logging, ) from diffusers.utils.torch_utils import randn_tensor if is_torch_xla_available(): import torch_xla.core.xla_model as xm XLA_AVAILABLE = True else: XLA_AVAILABLE = False logger = logging.get_logger(__name__) # pylint: disable=invalid-name class MaskedStableDiffusionXLImg2ImgPipeline(StableDiffusionXLImg2ImgPipeline): debug_save = 0 @torch.no_grad() def __call__( self, prompt: Union[str, List[str]] = None, prompt_2: Optional[Union[str, List[str]]] = None, image: PipelineImageInput = None, original_image: PipelineImageInput = None, strength: float = 0.3, num_inference_steps: Optional[int] = 50, timesteps: List[int] = None, denoising_start: Optional[float] = None, denoising_end: Optional[float] = None, guidance_scale: Optional[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: Optional[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, ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None, output_type: str | None = "pil", return_dict: bool = True, cross_attention_kwargs: Optional[Dict[str, Any]] = None, guidance_rescale: float = 0.0, 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, aesthetic_score: float = 6.0, negative_aesthetic_score: float = 2.5, 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"], mask: Union[ torch.FloatTensor, Image.Image, np.ndarray, List[torch.FloatTensor], List[Image.Image], List[np.ndarray], ] = None, blur=24, blur_compose=4, sample_mode="sample", **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`. image (`PipelineImageInput`): `Image` or tensor representing an image batch to be used as the starting point. This image might have mask painted on it. original_image (`PipelineImageInput`, *optional*): `Image` or tensor representing an image batch to be used for blending with the result. strength (`float`, *optional*, defaults to 0.8): Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a starting point and more noise is added the higher the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising process runs for the full number of iterations specified in `num_inference_steps`. A value of 1 essentially ignores `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. This parameter is modulated by `strength`. guidance_scale (`float`, *optional*, defaults to 7.5): 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`). 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://huggingface.co/papers/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. 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. 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. callback (`Callable`, *optional*): A function that calls every `callback_steps` steps during inference. The function is 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 is called. If not specified, the callback is called at every step. 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). blur (`int`, *optional*): blur to apply to mask blur_compose (`int`, *optional*): blur to apply for composition of original a mask (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`, *optional*): A mask with non-zero elements for the area to be inpainted. If not specified, no mask is applied. sample_mode (`str`, *optional*): control latents initialisation for the inpaint area, can be one of sample, argmax, random Examples: Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned, otherwise a `tuple` is returned where the first element is a list with the generated images and the second element is a list of `bool`s indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content. """ # code adapted from parent class StableDiffusionXLImg2ImgPipeline 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 use `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 use `callback_on_step_end`", ) # 0. Check inputs. Raise error if not correct self.check_inputs( prompt, prompt_2, strength, num_inference_steps, callback_steps, negative_prompt, negative_prompt_2, prompt_embeds, negative_prompt_embeds, ip_adapter_image, ip_adapter_image_embeds, callback_on_step_end_tensor_inputs, ) self._guidance_scale = guidance_scale self._guidance_rescale = guidance_rescale self._clip_skip = clip_skip self._cross_attention_kwargs = cross_attention_kwargs self._denoising_end = denoising_end self._denoising_start = denoising_start self._interrupt = False # 1. Define call parameters # mask is computed from difference between image and original_image if image is not None: neq = np.any(np.array(original_image) != np.array(image), axis=-1) mask = neq.astype(np.uint8) * 255 else: assert mask is not None if not isinstance(mask, Image.Image): pil_mask = Image.fromarray(mask) if pil_mask.mode != "L": pil_mask = pil_mask.convert("L") mask_blur = self.blur_mask(pil_mask, blur) mask_compose = self.blur_mask(pil_mask, blur_compose) if original_image is None: original_image = image 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 # 2. 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, prompt_2=prompt_2, device=device, num_images_per_prompt=num_images_per_prompt, do_classifier_free_guidance=self.do_classifier_free_guidance, negative_prompt=negative_prompt, negative_prompt_2=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. Preprocess image input_image = image if image is not None else original_image image = self.image_processor.preprocess(input_image) original_image = self.image_processor.preprocess(original_image) # 4. set timesteps def denoising_value_valid(dnv): return isinstance(dnv, float) and 0 < dnv < 1 timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps) timesteps, num_inference_steps = self.get_timesteps( num_inference_steps, strength, device, denoising_start=self.denoising_start if denoising_value_valid(self.denoising_start) else None, ) latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) add_noise = True if self.denoising_start is None else False # 5. Prepare latent variables # It is sampled from the latent distribution of the VAE # that's what we repaint latents = self.prepare_latents( image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator, add_noise, sample_mode=sample_mode, ) # mean of the latent distribution # it is multiplied by self.vae.config.scaling_factor non_paint_latents = self.prepare_latents( original_image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator, add_noise=False, sample_mode="argmax", ) if self.debug_save: init_img_from_latents = self.latents_to_img(non_paint_latents) init_img_from_latents[0].save("non_paint_latents.png") # 6. create latent mask latent_mask = self._make_latent_mask(latents, mask) # 7. Prepare extra step kwargs. extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) height, width = latents.shape[-2:] height = height * self.vae_scale_factor width = width * self.vae_scale_factor original_size = original_size or (height, width) target_size = target_size or (height, width) # 8. Prepare added time ids & embeddings if negative_original_size is None: negative_original_size = original_size if negative_target_size is None: negative_target_size = target_size add_text_embeds = pooled_prompt_embeds if self.text_encoder_2 is None: text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1]) else: text_encoder_projection_dim = self.text_encoder_2.config.projection_dim add_time_ids, add_neg_time_ids = self._get_add_time_ids( original_size, crops_coords_top_left, target_size, aesthetic_score, negative_aesthetic_score, negative_original_size, negative_crops_coords_top_left, negative_target_size, dtype=prompt_embeds.dtype, text_encoder_projection_dim=text_encoder_projection_dim, ) add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1) if self.do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0) add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1) add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0) prompt_embeds = prompt_embeds.to(device) add_text_embeds = add_text_embeds.to(device) add_time_ids = add_time_ids.to(device) if ip_adapter_image is not None or ip_adapter_image_embeds is not None: image_embeds = self.prepare_ip_adapter_image_embeds( ip_adapter_image, ip_adapter_image_embeds, device, batch_size * num_images_per_prompt, self.do_classifier_free_guidance, ) # 10. Denoising loop num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) # 10.1 Apply denoising_end if ( self.denoising_end is not None and self.denoising_start is not None and denoising_value_valid(self.denoising_end) and denoising_value_valid(self.denoising_start) and self.denoising_start >= self.denoising_end ): raise ValueError( f"`denoising_start`: {self.denoising_start} cannot be larger than or equal to `denoising_end`: " + f" {self.denoising_end} when using type float." ) elif self.denoising_end is not None and denoising_value_valid(self.denoising_end): discrete_timestep_cutoff = int( round( self.scheduler.config.num_train_timesteps - (self.denoising_end * self.scheduler.config.num_train_timesteps) ) ) num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps))) timesteps = timesteps[:num_inference_steps] # 10.2 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) self._num_timesteps = len(timesteps) with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): if self.interrupt: continue shape = non_paint_latents.shape noise = randn_tensor(shape, generator=generator, device=device, dtype=latents.dtype) # noisy latent code of input image at current step orig_latents_t = non_paint_latents orig_latents_t = self.scheduler.add_noise(non_paint_latents, noise, t.unsqueeze(0)) # orig_latents_t (1 - latent_mask) + latents * latent_mask latents = torch.lerp(orig_latents_t, latents, latent_mask) if self.debug_save: img1 = self.latents_to_img(latents) t_str = str(t.int().item()) for i in range(3 - len(t_str)): t_str = "0" + t_str img1[0].save(f"step{t_str}.png") # 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) # predict the noise residual added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} if ip_adapter_image is not None or ip_adapter_image_embeds is not None: added_cond_kwargs["image_embeds"] = image_embeds noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, timestep_cond=timestep_cond, cross_attention_kwargs=self.cross_attention_kwargs, 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) if self.do_classifier_free_guidance and self.guidance_rescale > 0.0: # Based on 3.4. in https://huggingface.co/papers/2305.08891 noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale) # compute the previous noisy sample x_t -> x_t-1 latents_dtype = latents.dtype latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] if latents.dtype != latents_dtype: if torch.backends.mps.is_available(): # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272 latents = latents.to(latents_dtype) 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) add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds) negative_pooled_prompt_embeds = callback_outputs.pop( "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds ) add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids) add_neg_time_ids = callback_outputs.pop("add_neg_time_ids", add_neg_time_ids) # 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) if XLA_AVAILABLE: xm.mark_step() 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() elif latents.dtype != self.vae.dtype: if torch.backends.mps.is_available(): # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272 self.vae = self.vae.to(latents.dtype) if self.debug_save: image_gen = self.latents_to_img(latents) image_gen[0].save("from_latent.png") if latent_mask is not None: # interpolate with latent mask latents = torch.lerp(non_paint_latents, latents, latent_mask) latents = self.denormalize(latents) image = self.vae.decode(latents, return_dict=False)[0] m = mask_compose.permute(2, 0, 1).unsqueeze(0).to(image) img_compose = m * image + (1 - m) * original_image.to(image) image = img_compose # cast back to fp16 if needed if needs_upcasting: self.vae.to(dtype=torch.float16) else: image = latents # 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) def _make_latent_mask(self, latents, mask): if mask is not None: latent_mask = [] if not isinstance(mask, list): tmp_mask = [mask] else: tmp_mask = mask _, l_channels, l_height, l_width = latents.shape for m in tmp_mask: if not isinstance(m, Image.Image): if len(m.shape) == 2: m = m[..., np.newaxis] if m.max() > 1: m = m / 255.0 m = self.image_processor.numpy_to_pil(m)[0] if m.mode != "L": m = m.convert("L") resized = self.image_processor.resize(m, l_height, l_width) if self.debug_save: resized.save("latent_mask.png") latent_mask.append(np.repeat(np.array(resized)[np.newaxis, :, :], l_channels, axis=0)) latent_mask = torch.as_tensor(np.stack(latent_mask)).to(latents) latent_mask = latent_mask / max(latent_mask.max(), 1) return latent_mask def prepare_latents( self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None, add_noise=True, sample_mode: str = "sample", ): if not isinstance(image, (torch.Tensor, Image.Image, list)): raise ValueError( f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}" ) # Offload text encoder if `enable_model_cpu_offload` was enabled if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.text_encoder_2.to("cpu") torch.cuda.empty_cache() image = image.to(device=device, dtype=dtype) batch_size = batch_size * num_images_per_prompt if image.shape[1] == 4: init_latents = image elif sample_mode == "random": height, width = image.shape[-2:] num_channels_latents = self.unet.config.in_channels latents = self.random_latents( batch_size, num_channels_latents, height, width, dtype, device, generator, ) return self.vae.config.scaling_factor * latents else: # make sure the VAE is in float32 mode, as it overflows in float16 if self.vae.config.force_upcast: image = image.float() self.vae.to(dtype=torch.float32) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) elif isinstance(generator, list): init_latents = [ retrieve_latents( self.vae.encode(image[i : i + 1]), generator=generator[i], sample_mode=sample_mode ) for i in range(batch_size) ] init_latents = torch.cat(init_latents, dim=0) else: init_latents = retrieve_latents(self.vae.encode(image), generator=generator, sample_mode=sample_mode) if self.vae.config.force_upcast: self.vae.to(dtype) init_latents = init_latents.to(dtype) init_latents = self.vae.config.scaling_factor * init_latents if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0: # expand init_latents for batch_size additional_image_per_prompt = batch_size // init_latents.shape[0] init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0) elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0: raise ValueError( f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts." ) else: init_latents = torch.cat([init_latents], dim=0) if add_noise: shape = init_latents.shape noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) # get latents init_latents = self.scheduler.add_noise(init_latents, noise, timestep) latents = init_latents return latents # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def random_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents def denormalize(self, latents): # unscale/denormalize the latents # denormalize with the mean and std if available and not None has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None if has_latents_mean and has_latents_std: latents_mean = ( torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype) ) latents_std = torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype) latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean else: latents = latents / self.vae.config.scaling_factor return latents def latents_to_img(self, latents): l1 = self.denormalize(latents) img1 = self.vae.decode(l1, return_dict=False)[0] img1 = self.image_processor.postprocess(img1, output_type="pil", do_denormalize=[True]) return img1 def blur_mask(self, pil_mask, blur): mask_blur = pil_mask.filter(ImageFilter.GaussianBlur(radius=blur)) mask_blur = np.array(mask_blur) return torch.from_numpy(np.tile(mask_blur / mask_blur.max(), (3, 1, 1)).transpose(1, 2, 0))