Spaces:
Runtime error
Runtime error
| # Copyright 2022 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 inspect | |
| from typing import Callable, List, Optional, Union | |
| import numpy as np | |
| import torch | |
| import PIL | |
| from diffusers.utils import is_accelerate_available | |
| from packaging import version | |
| from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer | |
| from ...configuration_utils import FrozenDict | |
| from ...models import AutoencoderKL, UNet2DConditionModel | |
| from ...pipeline_utils import DiffusionPipeline | |
| from ...schedulers import DDIMScheduler | |
| from ...utils import PIL_INTERPOLATION, deprecate, logging | |
| from . import StableDiffusionPipelineOutput | |
| from .safety_checker import StableDiffusionSafetyChecker | |
| logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
| def preprocess(image): | |
| w, h = image.size | |
| w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 | |
| image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]) | |
| image = np.array(image).astype(np.float32) / 255.0 | |
| image = image[None].transpose(0, 3, 1, 2) | |
| image = torch.from_numpy(image) | |
| return 2.0 * image - 1.0 | |
| def posterior_sample(scheduler, latents, timestep, clean_latents, generator, eta): | |
| # 1. get previous step value (=t-1) | |
| prev_timestep = timestep - scheduler.config.num_train_timesteps // scheduler.num_inference_steps | |
| if prev_timestep <= 0: | |
| return clean_latents | |
| # 2. compute alphas, betas | |
| 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 | |
| ) | |
| variance = scheduler._get_variance(timestep, prev_timestep) | |
| std_dev_t = eta * variance ** (0.5) | |
| # direction pointing to x_t | |
| e_t = (latents - alpha_prod_t ** (0.5) * clean_latents) / (1 - alpha_prod_t) ** (0.5) | |
| dir_xt = (1.0 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * e_t | |
| noise = std_dev_t * torch.randn( | |
| clean_latents.shape, dtype=clean_latents.dtype, device=clean_latents.device, generator=generator | |
| ) | |
| prev_latents = alpha_prod_t_prev ** (0.5) * clean_latents + dir_xt + noise | |
| return prev_latents | |
| def compute_noise(scheduler, prev_latents, latents, timestep, noise_pred, eta): | |
| # 1. get previous step value (=t-1) | |
| prev_timestep = timestep - scheduler.config.num_train_timesteps // scheduler.num_inference_steps | |
| # 2. compute alphas, betas | |
| 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 | |
| # 3. compute predicted original sample from predicted noise also called | |
| # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf | |
| pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5) | |
| # 4. Clip "predicted x_0" | |
| if scheduler.config.clip_sample: | |
| pred_original_sample = torch.clamp(pred_original_sample, -1, 1) | |
| # 5. compute variance: "sigma_t(η)" -> see formula (16) | |
| # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1) | |
| variance = scheduler._get_variance(timestep, prev_timestep) | |
| std_dev_t = eta * variance ** (0.5) | |
| # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf | |
| pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * noise_pred | |
| noise = (prev_latents - (alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction)) / ( | |
| variance ** (0.5) * eta | |
| ) | |
| return noise | |
| class CycleDiffusionPipeline(DiffusionPipeline): | |
| r""" | |
| Pipeline for text-guided image to image generation using Stable Diffusion. | |
| This model inherits from [`DiffusionPipeline`]. 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. | |
| 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/CompVis/stable-diffusion-v1-4) for details. | |
| feature_extractor ([`CLIPFeatureExtractor`]): | |
| Model that extracts features from generated images to be used as inputs for the `safety_checker`. | |
| """ | |
| _optional_components = ["safety_checker", "feature_extractor"] | |
| def __init__( | |
| self, | |
| vae: AutoencoderKL, | |
| text_encoder: CLIPTextModel, | |
| tokenizer: CLIPTokenizer, | |
| unet: UNet2DConditionModel, | |
| scheduler: DDIMScheduler, | |
| safety_checker: StableDiffusionSafetyChecker, | |
| feature_extractor: CLIPFeatureExtractor, | |
| 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.register_to_config(requires_safety_checker=requires_safety_checker) | |
| # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_attention_slicing | |
| def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"): | |
| r""" | |
| Enable sliced attention computation. | |
| When this option is enabled, the attention module will split the input tensor in slices, to compute attention | |
| in several steps. This is useful to save some memory in exchange for a small speed decrease. | |
| Args: | |
| slice_size (`str` or `int`, *optional*, defaults to `"auto"`): | |
| When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If | |
| a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, | |
| `attention_head_dim` must be a multiple of `slice_size`. | |
| """ | |
| if slice_size == "auto": | |
| if isinstance(self.unet.config.attention_head_dim, int): | |
| # half the attention head size is usually a good trade-off between | |
| # speed and memory | |
| slice_size = self.unet.config.attention_head_dim // 2 | |
| else: | |
| # if `attention_head_dim` is a list, take the smallest head size | |
| slice_size = min(self.unet.config.attention_head_dim) | |
| self.unet.set_attention_slice(slice_size) | |
| # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_attention_slicing | |
| def disable_attention_slicing(self): | |
| r""" | |
| Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go | |
| back to computing attention in one step. | |
| """ | |
| # set slice_size = `None` to disable `attention slicing` | |
| self.enable_attention_slicing(None) | |
| # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload | |
| def enable_sequential_cpu_offload(self, gpu_id=0): | |
| r""" | |
| Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, | |
| text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a | |
| `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. | |
| """ | |
| if is_accelerate_available(): | |
| from accelerate import cpu_offload | |
| else: | |
| raise ImportError("Please install accelerate via `pip install accelerate`") | |
| device = torch.device(f"cuda:{gpu_id}") | |
| for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: | |
| if cpu_offloaded_model is not None: | |
| cpu_offload(cpu_offloaded_model, device) | |
| if self.safety_checker is not None: | |
| # TODO(Patrick) - there is currently a bug with cpu offload of nn.Parameter in accelerate | |
| # fix by only offloading self.safety_checker for now | |
| cpu_offload(self.safety_checker.vision_model, device) | |
| # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device | |
| def _execution_device(self): | |
| r""" | |
| Returns the device on which the pipeline's models will be executed. After calling | |
| `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module | |
| hooks. | |
| """ | |
| if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"): | |
| return self.device | |
| for module in self.unet.modules(): | |
| if ( | |
| hasattr(module, "_hf_hook") | |
| and hasattr(module._hf_hook, "execution_device") | |
| and module._hf_hook.execution_device is not None | |
| ): | |
| return torch.device(module._hf_hook.execution_device) | |
| return self.device | |
| # 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): | |
| r""" | |
| Encodes the prompt into text encoder hidden states. | |
| Args: | |
| prompt (`str` or `list(int)`): | |
| prompt to be encoded | |
| device: (`torch.device`): | |
| torch device | |
| num_images_per_prompt (`int`): | |
| number of images that should be generated per prompt | |
| do_classifier_free_guidance (`bool`): | |
| whether to use classifier free guidance or not | |
| negative_prompt (`str` or `List[str]`): | |
| The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored | |
| if `guidance_scale` is less than `1`). | |
| """ | |
| batch_size = len(prompt) if isinstance(prompt, list) else 1 | |
| 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="max_length", return_tensors="pt").input_ids | |
| if 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 | |
| text_embeddings = self.text_encoder( | |
| text_input_ids.to(device), | |
| attention_mask=attention_mask, | |
| ) | |
| text_embeddings = text_embeddings[0] | |
| # duplicate text embeddings for each generation per prompt, using mps friendly method | |
| bs_embed, seq_len, _ = text_embeddings.shape | |
| text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1) | |
| text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1) | |
| # get unconditional embeddings for classifier free guidance | |
| if do_classifier_free_guidance: | |
| uncond_tokens: List[str] | |
| if negative_prompt is None: | |
| uncond_tokens = [""] * batch_size | |
| elif 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 | |
| max_length = text_input_ids.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 | |
| uncond_embeddings = self.text_encoder( | |
| uncond_input.input_ids.to(device), | |
| attention_mask=attention_mask, | |
| ) | |
| uncond_embeddings = uncond_embeddings[0] | |
| # duplicate unconditional embeddings for each generation per prompt, using mps friendly method | |
| seq_len = uncond_embeddings.shape[1] | |
| uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1) | |
| uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1) | |
| # For classifier free guidance, we need to do two forward passes. | |
| # Here we concatenate the unconditional and text embeddings into a single batch | |
| # to avoid doing two forward passes | |
| text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) | |
| return text_embeddings | |
| # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.check_inputs | |
| def check_inputs(self, prompt, strength, callback_steps): | |
| if 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 strength < 0 or strength > 1: | |
| raise ValueError(f"The value of strength should in [1.0, 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)}." | |
| ) | |
| # 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 not None: | |
| safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device) | |
| image, has_nsfw_concept = self.safety_checker( | |
| images=image, clip_input=safety_checker_input.pixel_values.to(dtype) | |
| ) | |
| else: | |
| has_nsfw_concept = None | |
| return image, has_nsfw_concept | |
| # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents | |
| def decode_latents(self, latents): | |
| latents = 1 / 0.18215 * latents | |
| image = self.vae.decode(latents).sample | |
| image = (image / 2 + 0.5).clamp(0, 1) | |
| # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 | |
| 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 | |
| offset = self.scheduler.config.get("steps_offset", 0) | |
| init_timestep = int(num_inference_steps * strength) + offset | |
| init_timestep = min(init_timestep, num_inference_steps) | |
| t_start = max(num_inference_steps - init_timestep + offset, 0) | |
| timesteps = self.scheduler.timesteps[t_start:] | |
| return timesteps, num_inference_steps - t_start | |
| def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None): | |
| image = image.to(device=device, dtype=dtype) | |
| init_latent_dist = self.vae.encode(image).latent_dist | |
| init_latents = init_latent_dist.sample(generator=generator) | |
| init_latents = 0.18215 * 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 | |
| noise = torch.randn(init_latents.shape, generator=generator, device=device, dtype=dtype) | |
| # get latents | |
| clean_latents = init_latents | |
| init_latents = self.scheduler.add_noise(init_latents, noise, timestep) | |
| latents = init_latents | |
| return latents, clean_latents | |
| def __call__( | |
| self, | |
| prompt: Union[str, List[str]], | |
| source_prompt: Union[str, List[str]], | |
| image: Union[torch.FloatTensor, PIL.Image.Image], | |
| strength: float = 0.8, | |
| num_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] = 0.1, | |
| generator: Optional[torch.Generator] = None, | |
| output_type: Optional[str] = "pil", | |
| return_dict: bool = True, | |
| callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, | |
| callback_steps: Optional[int] = 1, | |
| **kwargs, | |
| ): | |
| r""" | |
| Function invoked when calling the pipeline for generation. | |
| Args: | |
| prompt (`str` or `List[str]`): | |
| The prompt or prompts to guide the image generation. | |
| image (`torch.FloatTensor` or `PIL.Image.Image`): | |
| `Image`, or tensor representing an image batch, that will be used as the starting point for the | |
| process. | |
| strength (`float`, *optional*, defaults to 0.8): | |
| Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image` | |
| will be used as a starting point, adding more noise to it the larger the `strength`. The number of | |
| denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will | |
| be maximum and the denoising process will run for the full number of iterations specified in | |
| `num_inference_steps`. A value of 1, therefore, 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 will be modulated by `strength`. | |
| 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. | |
| source_guidance_scale (`float`, *optional*, defaults to 1): | |
| Guidance scale for the source prompt. This is useful to control the amount of influence the source | |
| prompt for encoding. | |
| num_images_per_prompt (`int`, *optional*, defaults to 1): | |
| The number of images to generate per prompt. | |
| eta (`float`, *optional*, defaults to 0.1): | |
| 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`, *optional*): | |
| A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation | |
| deterministic. | |
| 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. | |
| 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`. | |
| """ | |
| message = "Please use `image` instead of `init_image`." | |
| init_image = deprecate("init_image", "0.12.0", message, take_from=kwargs) | |
| image = init_image or image | |
| # 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_embeddings = self._encode_prompt(prompt, device, num_images_per_prompt, do_classifier_free_guidance, None) | |
| source_text_embeddings = self._encode_prompt( | |
| source_prompt, device, num_images_per_prompt, do_classifier_free_guidance, None | |
| ) | |
| # 4. Preprocess image | |
| if isinstance(image, PIL.Image.Image): | |
| image = preprocess(image) | |
| # 5. Prepare timesteps | |
| self.scheduler.set_timesteps(num_inference_steps, device=device) | |
| 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, text_embeddings.dtype, device, generator | |
| ) | |
| source_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) | |
| source_latent_model_input = torch.cat([source_latents] * 2) | |
| 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) | |
| # predict the noise residual | |
| concat_latent_model_input = torch.stack( | |
| [ | |
| source_latent_model_input[0], | |
| latent_model_input[0], | |
| source_latent_model_input[1], | |
| latent_model_input[1], | |
| ], | |
| dim=0, | |
| ) | |
| concat_text_embeddings = torch.stack( | |
| [ | |
| source_text_embeddings[0], | |
| text_embeddings[0], | |
| source_text_embeddings[1], | |
| text_embeddings[1], | |
| ], | |
| dim=0, | |
| ) | |
| concat_noise_pred = self.unet( | |
| concat_latent_model_input, t, encoder_hidden_states=concat_text_embeddings | |
| ).sample | |
| # perform guidance | |
| ( | |
| source_noise_pred_uncond, | |
| noise_pred_uncond, | |
| source_noise_pred_text, | |
| noise_pred_text, | |
| ) = concat_noise_pred.chunk(4, 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 | |
| ) | |
| # Sample source_latents from the posterior distribution. | |
| prev_source_latents = posterior_sample( | |
| self.scheduler, source_latents, t, clean_latents, generator=generator, **extra_step_kwargs | |
| ) | |
| # Compute noise. | |
| noise = compute_noise( | |
| self.scheduler, prev_source_latents, source_latents, t, source_noise_pred, **extra_step_kwargs | |
| ) | |
| source_latents = prev_source_latents | |
| # compute the previous noisy sample x_t -> x_t-1 | |
| latents = self.scheduler.step( | |
| noise_pred, t, latents, variance_noise=noise, **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) | |
| # 9. Post-processing | |
| image = self.decode_latents(latents) | |
| # 10. Run safety checker | |
| image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype) | |
| # 11. Convert to PIL | |
| if output_type == "pil": | |
| image = self.numpy_to_pil(image) | |
| if not return_dict: | |
| return (image, has_nsfw_concept) | |
| return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) | |