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| import inspect | |
| from typing import Any, Callable, Dict, List, Optional, Union | |
| import numpy as np | |
| import PIL | |
| import torch | |
| from packaging import version | |
| from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer | |
| from diffusers.configuration_utils import FrozenDict | |
| from diffusers.image_processor import PipelineImageInput, VaeImageProcessor | |
| from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin | |
| from diffusers.models import AutoencoderKL, UNet2DConditionModel | |
| from diffusers.models.lora import adjust_lora_scale_text_encoder | |
| from diffusers.schedulers import LCMScheduler | |
| from diffusers.utils import PIL_INTERPOLATION, deprecate, logging | |
| from diffusers.utils.torch_utils import randn_tensor | |
| from diffusers.pipelines.pipeline_utils import DiffusionPipeline | |
| from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput | |
| from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker | |
| logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
| # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess | |
| def preprocess(image): | |
| deprecation_message = "The preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead" | |
| deprecate("preprocess", "1.0.0", deprecation_message, standard_warn=False) | |
| if isinstance(image, torch.Tensor): | |
| return image | |
| elif isinstance(image, PIL.Image.Image): | |
| image = [image] | |
| if isinstance(image[0], PIL.Image.Image): | |
| w, h = image[0].size | |
| w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 | |
| image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] | |
| image = np.concatenate(image, axis=0) | |
| image = np.array(image).astype(np.float32) / 255.0 | |
| image = image.transpose(0, 3, 1, 2) | |
| image = 2.0 * image - 1.0 | |
| image = torch.from_numpy(image) | |
| elif isinstance(image[0], torch.Tensor): | |
| image = torch.cat(image, dim=0) | |
| return image | |
| def ddcm_sampler(scheduler, x_s, x_t, timestep, e_s, e_t, x_0, noise, eta, to_next=True): | |
| if scheduler.num_inference_steps is None: | |
| raise ValueError( | |
| "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" | |
| ) | |
| if scheduler.step_index is None: | |
| scheduler._init_step_index(timestep) | |
| prev_step_index = scheduler.step_index + 1 | |
| if prev_step_index < len(scheduler.timesteps): | |
| prev_timestep = scheduler.timesteps[prev_step_index] | |
| else: | |
| prev_timestep = timestep | |
| alpha_prod_t = scheduler.alphas_cumprod[timestep] | |
| alpha_prod_t_prev = ( | |
| scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else scheduler.final_alpha_cumprod | |
| ) | |
| beta_prod_t = 1 - alpha_prod_t | |
| beta_prod_t_prev = 1 - alpha_prod_t_prev | |
| variance = beta_prod_t_prev | |
| std_dev_t = eta * variance | |
| noise = std_dev_t ** (0.5) * noise | |
| e_c = (x_s - alpha_prod_t ** (0.5) * x_0) / (1 - alpha_prod_t) ** (0.5) | |
| pred_x0 = x_0 + ((x_t - x_s) - beta_prod_t ** (0.5) * (e_t - e_s)) / alpha_prod_t ** (0.5) | |
| eps = (e_t - e_s) + e_c | |
| dir_xt = (beta_prod_t_prev - std_dev_t) ** (0.5) * eps | |
| # Noise is not used for one-step sampling. | |
| if len(scheduler.timesteps) > 1: | |
| prev_xt = alpha_prod_t_prev ** (0.5) * pred_x0 + dir_xt + noise | |
| prev_xs = alpha_prod_t_prev ** (0.5) * x_0 + dir_xt + noise | |
| else: | |
| prev_xt = pred_x0 | |
| prev_xs = x_0 | |
| if to_next: | |
| scheduler._step_index += 1 | |
| return prev_xs, prev_xt, pred_x0 | |
| class EditPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin): | |
| model_cpu_offload_seq = "text_encoder->unet->vae" | |
| _optional_components = ["safety_checker", "feature_extractor"] | |
| def __init__( | |
| self, | |
| vae: AutoencoderKL, | |
| text_encoder: CLIPTextModel, | |
| tokenizer: CLIPTokenizer, | |
| unet: UNet2DConditionModel, | |
| scheduler: LCMScheduler, | |
| safety_checker: StableDiffusionSafetyChecker, | |
| feature_extractor: CLIPImageProcessor, | |
| requires_safety_checker: bool = True, | |
| ): | |
| super().__init__() | |
| if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: | |
| deprecation_message = ( | |
| f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" | |
| f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " | |
| "to update the config accordingly as leaving `steps_offset` might led to incorrect results" | |
| " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," | |
| " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" | |
| " file" | |
| ) | |
| deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) | |
| new_config = dict(scheduler.config) | |
| new_config["steps_offset"] = 1 | |
| scheduler._internal_dict = FrozenDict(new_config) | |
| if safety_checker is None and requires_safety_checker: | |
| logger.warning( | |
| f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" | |
| " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" | |
| " results in services or applications open to the public. Both the diffusers team and Hugging Face" | |
| " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" | |
| " it only for use-cases that involve analyzing network behavior or auditing its results. For more" | |
| " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." | |
| ) | |
| if safety_checker is not None and feature_extractor is None: | |
| raise ValueError( | |
| "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" | |
| " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." | |
| ) | |
| is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( | |
| version.parse(unet.config._diffusers_version).base_version | |
| ) < version.parse("0.9.0.dev0") | |
| is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 | |
| if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: | |
| deprecation_message = ( | |
| "The configuration file of the unet has set the default `sample_size` to smaller than" | |
| " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the" | |
| " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" | |
| " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" | |
| " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" | |
| " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" | |
| " in the config might lead to incorrect results in future versions. If you have downloaded this" | |
| " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" | |
| " the `unet/config.json` file" | |
| ) | |
| deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) | |
| new_config = dict(unet.config) | |
| new_config["sample_size"] = 64 | |
| unet._internal_dict = FrozenDict(new_config) | |
| self.register_modules( | |
| vae=vae, | |
| text_encoder=text_encoder, | |
| tokenizer=tokenizer, | |
| unet=unet, | |
| scheduler=scheduler, | |
| safety_checker=safety_checker, | |
| feature_extractor=feature_extractor, | |
| ) | |
| self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) | |
| self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) | |
| self.register_to_config(requires_safety_checker=requires_safety_checker) | |
| # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt | |
| def _encode_prompt( | |
| self, | |
| prompt, | |
| device, | |
| num_images_per_prompt, | |
| do_classifier_free_guidance, | |
| negative_prompt=None, | |
| prompt_embeds: Optional[torch.FloatTensor] = None, | |
| negative_prompt_embeds: Optional[torch.FloatTensor] = None, | |
| lora_scale: Optional[float] = None, | |
| ): | |
| deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple." | |
| deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False) | |
| prompt_embeds_tuple = self.encode_prompt( | |
| prompt=prompt, | |
| device=device, | |
| num_images_per_prompt=num_images_per_prompt, | |
| do_classifier_free_guidance=do_classifier_free_guidance, | |
| negative_prompt=negative_prompt, | |
| prompt_embeds=prompt_embeds, | |
| negative_prompt_embeds=negative_prompt_embeds, | |
| lora_scale=lora_scale, | |
| ) | |
| # concatenate for backwards comp | |
| prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]]) | |
| return prompt_embeds | |
| # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt | |
| def encode_prompt( | |
| self, | |
| prompt, | |
| device, | |
| num_images_per_prompt, | |
| do_classifier_free_guidance, | |
| negative_prompt=None, | |
| prompt_embeds: Optional[torch.FloatTensor] = None, | |
| negative_prompt_embeds: Optional[torch.FloatTensor] = None, | |
| lora_scale: Optional[float] = None, | |
| ): | |
| # set lora scale so that monkey patched LoRA | |
| # function of text encoder can correctly access it | |
| if lora_scale is not None and isinstance(self, LoraLoaderMixin): | |
| self._lora_scale = lora_scale | |
| # dynamically adjust the LoRA scale | |
| adjust_lora_scale_text_encoder(self.text_encoder, lora_scale) | |
| 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] | |
| if prompt_embeds is None: | |
| # textual inversion: procecss multi-vector tokens if necessary | |
| if isinstance(self, TextualInversionLoaderMixin): | |
| prompt = self.maybe_convert_prompt(prompt, self.tokenizer) | |
| text_inputs = self.tokenizer( | |
| prompt, | |
| padding="max_length", | |
| max_length=self.tokenizer.model_max_length, | |
| truncation=True, | |
| return_tensors="pt", | |
| ) | |
| text_input_ids = text_inputs.input_ids | |
| untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids | |
| if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( | |
| text_input_ids, untruncated_ids | |
| ): | |
| removed_text = self.tokenizer.batch_decode( | |
| untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] | |
| ) | |
| logger.warning( | |
| "The following part of your input was truncated because CLIP can only handle sequences up to" | |
| f" {self.tokenizer.model_max_length} tokens: {removed_text}" | |
| ) | |
| if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: | |
| attention_mask = text_inputs.attention_mask.to(device) | |
| else: | |
| attention_mask = None | |
| prompt_embeds = self.text_encoder( | |
| text_input_ids.to(device), | |
| attention_mask=attention_mask, | |
| ) | |
| prompt_embeds = prompt_embeds[0] | |
| if self.text_encoder is not None: | |
| prompt_embeds_dtype = self.text_encoder.dtype | |
| elif self.unet is not None: | |
| prompt_embeds_dtype = self.unet.dtype | |
| else: | |
| prompt_embeds_dtype = prompt_embeds.dtype | |
| prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device) | |
| bs_embed, seq_len, _ = prompt_embeds.shape | |
| # duplicate text embeddings for each generation per prompt, using mps friendly method | |
| prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) | |
| prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) | |
| # get unconditional embeddings for classifier free guidance | |
| if do_classifier_free_guidance and negative_prompt_embeds is None: | |
| uncond_tokens: List[str] | |
| if negative_prompt is None: | |
| uncond_tokens = [""] * batch_size | |
| elif prompt is not None and type(prompt) is not type(negative_prompt): | |
| raise TypeError( | |
| f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" | |
| f" {type(prompt)}." | |
| ) | |
| elif isinstance(negative_prompt, str): | |
| uncond_tokens = [negative_prompt] | |
| elif batch_size != len(negative_prompt): | |
| raise ValueError( | |
| f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" | |
| f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" | |
| " the batch size of `prompt`." | |
| ) | |
| else: | |
| uncond_tokens = negative_prompt | |
| # textual inversion: procecss multi-vector tokens if necessary | |
| if isinstance(self, TextualInversionLoaderMixin): | |
| uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) | |
| max_length = prompt_embeds.shape[1] | |
| uncond_input = self.tokenizer( | |
| uncond_tokens, | |
| padding="max_length", | |
| max_length=max_length, | |
| truncation=True, | |
| return_tensors="pt", | |
| ) | |
| if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: | |
| attention_mask = uncond_input.attention_mask.to(device) | |
| else: | |
| attention_mask = None | |
| negative_prompt_embeds = self.text_encoder( | |
| uncond_input.input_ids.to(device), | |
| attention_mask=attention_mask, | |
| ) | |
| negative_prompt_embeds = negative_prompt_embeds[0] | |
| if do_classifier_free_guidance: | |
| # duplicate unconditional embeddings for each generation per prompt, using mps friendly method | |
| seq_len = negative_prompt_embeds.shape[1] | |
| negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device) | |
| negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) | |
| negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) | |
| return prompt_embeds, negative_prompt_embeds | |
| # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.check_inputs | |
| def check_inputs( | |
| self, prompt, strength, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None | |
| ): | |
| if strength < 0 or strength > 1: | |
| raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") | |
| if (callback_steps is None) or ( | |
| callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) | |
| ): | |
| raise ValueError( | |
| f"`callback_steps` has to be a positive integer but is {callback_steps} of type" | |
| f" {type(callback_steps)}." | |
| ) | |
| if prompt is not None and prompt_embeds is not None: | |
| raise ValueError( | |
| f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" | |
| " only forward one of the two." | |
| ) | |
| elif prompt is None and prompt_embeds is None: | |
| raise ValueError( | |
| "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." | |
| ) | |
| elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): | |
| raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") | |
| if negative_prompt is not None and negative_prompt_embeds is not None: | |
| raise ValueError( | |
| f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" | |
| f" {negative_prompt_embeds}. Please make sure to only forward one of the two." | |
| ) | |
| if prompt_embeds is not None and negative_prompt_embeds is not None: | |
| if prompt_embeds.shape != negative_prompt_embeds.shape: | |
| raise ValueError( | |
| "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" | |
| f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" | |
| f" {negative_prompt_embeds.shape}." | |
| ) | |
| # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs | |
| def prepare_extra_step_kwargs(self, generator, eta): | |
| # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature | |
| # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. | |
| # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 | |
| # and should be between [0, 1] | |
| accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) | |
| extra_step_kwargs = {} | |
| if accepts_eta: | |
| extra_step_kwargs["eta"] = eta | |
| # check if the scheduler accepts generator | |
| accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) | |
| if accepts_generator: | |
| extra_step_kwargs["generator"] = generator | |
| return extra_step_kwargs | |
| # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker | |
| def run_safety_checker(self, image, device, dtype): | |
| if self.safety_checker is None: | |
| has_nsfw_concept = None | |
| else: | |
| if torch.is_tensor(image): | |
| feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") | |
| else: | |
| feature_extractor_input = self.image_processor.numpy_to_pil(image) | |
| safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) | |
| image, has_nsfw_concept = self.safety_checker( | |
| images=image, clip_input=safety_checker_input.pixel_values.to(dtype) | |
| ) | |
| return image, has_nsfw_concept | |
| # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents | |
| def decode_latents(self, latents): | |
| deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead" | |
| deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False) | |
| latents = 1 / self.vae.config.scaling_factor * latents | |
| image = self.vae.decode(latents, return_dict=False)[0] | |
| image = (image / 2 + 0.5).clamp(0, 1) | |
| # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 | |
| image = image.cpu().permute(0, 2, 3, 1).float().numpy() | |
| return image | |
| # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps | |
| def get_timesteps(self, num_inference_steps, strength, device): | |
| # get the original timestep using init_timestep | |
| init_timestep = min(int(num_inference_steps * strength), num_inference_steps) | |
| t_start = max(num_inference_steps - init_timestep, 0) | |
| timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] | |
| return timesteps, num_inference_steps - t_start | |
| def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, denoise_model, generator=None): | |
| image = image.to(device=device, dtype=dtype) | |
| batch_size = image.shape[0] | |
| if image.shape[1] == 4: | |
| init_latents = image | |
| else: | |
| 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 isinstance(generator, list): | |
| init_latents = [ | |
| self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size) | |
| ] | |
| init_latents = torch.cat(init_latents, dim=0) | |
| else: | |
| init_latents = self.vae.encode(image).latent_dist.sample(generator) | |
| 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 | |
| deprecation_message = ( | |
| f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial" | |
| " images (`image`). Initial images are now duplicating to match the number of text prompts. Note" | |
| " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update" | |
| " your script to pass as many initial images as text prompts to suppress this warning." | |
| ) | |
| deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False) | |
| additional_image_per_prompt = batch_size // init_latents.shape[0] | |
| init_latents = torch.cat([init_latents] * additional_image_per_prompt * num_images_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] * num_images_per_prompt, dim=0) | |
| # add noise to latents using the timestep | |
| shape = init_latents.shape | |
| noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) | |
| # get latents | |
| clean_latents = init_latents | |
| if denoise_model: | |
| init_latents = self.scheduler.add_noise(init_latents, noise, timestep) | |
| latents = init_latents | |
| else: | |
| latents = noise | |
| return latents, clean_latents | |
| def __call__( | |
| self, | |
| prompt: Union[str, List[str]], | |
| source_prompt: Union[str, List[str]], | |
| negative_prompt: Union[str, List[str]]=None, | |
| positive_prompt: Union[str, List[str]]=None, | |
| image: PipelineImageInput = None, | |
| strength: float = 0.8, | |
| num_inference_steps: Optional[int] = 50, | |
| original_inference_steps: Optional[int] = 50, | |
| guidance_scale: Optional[float] = 7.5, | |
| source_guidance_scale: Optional[float] = 1, | |
| num_images_per_prompt: Optional[int] = 1, | |
| eta: Optional[float] = 1.0, | |
| generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, | |
| 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, | |
| denoise_model: Optional[bool] = True, | |
| ): | |
| # 1. Check inputs | |
| self.check_inputs(prompt, strength, callback_steps) | |
| # 2. Define call parameters | |
| batch_size = 1 if isinstance(prompt, str) else len(prompt) | |
| 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 | |
| text_encoder_lora_scale = ( | |
| cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None | |
| ) | |
| prompt_embeds_tuple = self.encode_prompt( | |
| prompt, | |
| device, | |
| num_images_per_prompt, | |
| do_classifier_free_guidance, | |
| negative_prompt=negative_prompt, | |
| prompt_embeds=prompt_embeds, | |
| lora_scale=text_encoder_lora_scale, | |
| ) | |
| source_prompt_embeds_tuple = self.encode_prompt( | |
| source_prompt, device, num_images_per_prompt, do_classifier_free_guidance, positive_prompt, None | |
| ) | |
| if prompt_embeds_tuple[1] is not None: | |
| prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]]) | |
| else: | |
| prompt_embeds = prompt_embeds_tuple[0] | |
| if source_prompt_embeds_tuple[1] is not None: | |
| source_prompt_embeds = torch.cat([source_prompt_embeds_tuple[1], source_prompt_embeds_tuple[0]]) | |
| else: | |
| source_prompt_embeds = source_prompt_embeds_tuple[0] | |
| # 4. Preprocess image | |
| image = self.image_processor.preprocess(image) | |
| # 5. Prepare timesteps | |
| self.scheduler.set_timesteps( | |
| num_inference_steps=num_inference_steps, | |
| device=device, | |
| original_inference_steps=original_inference_steps) | |
| timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device) | |
| latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) | |
| # 6. Prepare latent variables | |
| latents, clean_latents = self.prepare_latents( | |
| image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, denoise_model, generator | |
| ) | |
| source_latents = latents | |
| mutual_latents = 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) | |
| generator = extra_step_kwargs.pop("generator", None) | |
| # 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 | |
| source_latent_model_input = ( | |
| torch.cat([source_latents] * 2) if do_classifier_free_guidance else source_latents | |
| ) | |
| mutual_latent_model_input = ( | |
| torch.cat([mutual_latents] * 2) if do_classifier_free_guidance else mutual_latents | |
| ) | |
| latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) | |
| source_latent_model_input = self.scheduler.scale_model_input(source_latent_model_input, t) | |
| mutual_latent_model_input = self.scheduler.scale_model_input(mutual_latent_model_input, t) | |
| # predict the noise residual | |
| if do_classifier_free_guidance: | |
| concat_latent_model_input = torch.stack( | |
| [ | |
| source_latent_model_input[0], | |
| latent_model_input[0], | |
| mutual_latent_model_input[0], | |
| source_latent_model_input[1], | |
| latent_model_input[1], | |
| mutual_latent_model_input[1], | |
| ], | |
| dim=0, | |
| ) | |
| concat_prompt_embeds = torch.stack( | |
| [ | |
| source_prompt_embeds[0], | |
| prompt_embeds[0], | |
| source_prompt_embeds[0], | |
| source_prompt_embeds[1], | |
| prompt_embeds[1], | |
| source_prompt_embeds[1], | |
| ], | |
| dim=0, | |
| ) | |
| else: | |
| concat_latent_model_input = torch.cat( | |
| [ | |
| source_latent_model_input, | |
| latent_model_input, | |
| mutual_latent_model_input, | |
| ], | |
| dim=0, | |
| ) | |
| concat_prompt_embeds = torch.cat( | |
| [ | |
| source_prompt_embeds, | |
| prompt_embeds, | |
| source_prompt_embeds, | |
| ], | |
| dim=0, | |
| ) | |
| concat_noise_pred = self.unet( | |
| concat_latent_model_input, | |
| t, | |
| cross_attention_kwargs=cross_attention_kwargs, | |
| encoder_hidden_states=concat_prompt_embeds, | |
| ).sample | |
| # perform guidance | |
| if do_classifier_free_guidance: | |
| ( | |
| source_noise_pred_uncond, | |
| noise_pred_uncond, | |
| mutual_noise_pred_uncond, | |
| source_noise_pred_text, | |
| noise_pred_text, | |
| mutual_noise_pred_text | |
| ) = concat_noise_pred.chunk(6, dim=0) | |
| noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) | |
| source_noise_pred = source_noise_pred_uncond + source_guidance_scale * ( | |
| source_noise_pred_text - source_noise_pred_uncond | |
| ) | |
| mutual_noise_pred = mutual_noise_pred_uncond + source_guidance_scale * ( | |
| mutual_noise_pred_text - mutual_noise_pred_uncond | |
| ) | |
| else: | |
| (source_noise_pred, noise_pred, mutual_noise_pred) = concat_noise_pred.chunk(3, dim=0) | |
| noise = torch.randn( | |
| latents.shape, dtype=latents.dtype, device=latents.device, generator=generator | |
| ) | |
| _, latents, pred_x0 = ddcm_sampler( | |
| self.scheduler, source_latents, | |
| latents, t, | |
| source_noise_pred, noise_pred, | |
| clean_latents, noise=noise, | |
| eta=eta, to_next=False, | |
| **extra_step_kwargs | |
| ) | |
| source_latents, mutual_latents, pred_xm = ddcm_sampler( | |
| self.scheduler, source_latents, | |
| mutual_latents, t, | |
| source_noise_pred, mutual_noise_pred, | |
| clean_latents, noise=noise, | |
| eta=eta, **extra_step_kwargs | |
| ) | |
| # 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: | |
| alpha_prod_t = self.scheduler.alphas_cumprod[t] | |
| mutual_latents, latents = callback(i, t, source_latents, latents, mutual_latents, alpha_prod_t) | |
| # 9. Post-processing | |
| if not output_type == "latent": | |
| image = self.vae.decode(pred_x0 / self.vae.config.scaling_factor, return_dict=False)[0] | |
| image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) | |
| else: | |
| image = pred_x0 | |
| has_nsfw_concept = None | |
| if has_nsfw_concept is None: | |
| do_denormalize = [True] * image.shape[0] | |
| else: | |
| do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] | |
| image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) | |
| if not return_dict: | |
| return (image, has_nsfw_concept) | |
| return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) | |