# Copyright 2024 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 Any, Callable, List, Optional, Union import numpy as np import PIL.Image import torch from transformers import CLIPImageProcessor, CLIPTokenizer from ...configuration_utils import FrozenDict from ...schedulers import DDPMScheduler, KarrasDiffusionSchedulers from ...utils import deprecate, logging from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel from ..pipeline_utils import DiffusionPipeline from . import StableDiffusionPipelineOutput logger = logging.get_logger(__name__) def preprocess(image): 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 % 64 for x in (w, h)) # resize to integer multiple of 32 image = [np.array(i.resize((w, h)))[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 class OnnxStableDiffusionUpscalePipeline(DiffusionPipeline): vae: OnnxRuntimeModel text_encoder: OnnxRuntimeModel tokenizer: CLIPTokenizer unet: OnnxRuntimeModel low_res_scheduler: DDPMScheduler scheduler: KarrasDiffusionSchedulers safety_checker: OnnxRuntimeModel feature_extractor: CLIPImageProcessor _optional_components = ["safety_checker", "feature_extractor"] _is_onnx = True def __init__( self, vae: OnnxRuntimeModel, text_encoder: OnnxRuntimeModel, tokenizer: Any, unet: OnnxRuntimeModel, low_res_scheduler: DDPMScheduler, scheduler: KarrasDiffusionSchedulers, safety_checker: Optional[OnnxRuntimeModel] = None, feature_extractor: Optional[CLIPImageProcessor] = None, max_noise_level: int = 350, num_latent_channels=4, num_unet_input_channels=7, 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 hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: deprecation_message = ( f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." " `clip_sample` should be set to False in the configuration file. Please make sure to update the" " config accordingly as not setting `clip_sample` 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 `scheduler/scheduler_config.json` file" ) deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) new_config = dict(scheduler.config) new_config["clip_sample"] = False 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." ) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, low_res_scheduler=low_res_scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) self.register_to_config( max_noise_level=max_noise_level, num_latent_channels=num_latent_channels, num_unet_input_channels=num_unet_input_channels, ) def check_inputs( self, prompt: Union[str, List[str]], image, noise_level, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ): 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}." ) if ( not isinstance(image, torch.Tensor) and not isinstance(image, PIL.Image.Image) and not isinstance(image, np.ndarray) and not isinstance(image, list) ): raise ValueError( f"`image` has to be of type `torch.Tensor`, `np.ndarray`, `PIL.Image.Image` or `list` but is {type(image)}" ) # verify batch size of prompt and image are same if image is a list or tensor or numpy array if isinstance(image, list) or isinstance(image, np.ndarray): 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 isinstance(image, list): image_batch_size = len(image) else: image_batch_size = image.shape[0] if batch_size != image_batch_size: raise ValueError( f"`prompt` has batch size {batch_size} and `image` has batch size {image_batch_size}." " Please make sure that passed `prompt` matches the batch size of `image`." ) # check noise level if noise_level > self.config.max_noise_level: raise ValueError(f"`noise_level` has to be <= {self.config.max_noise_level} but is {noise_level}") 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)}." ) def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, generator, latents=None): shape = (batch_size, num_channels_latents, height, width) if latents is None: latents = generator.randn(*shape).astype(dtype) elif latents.shape != shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") return latents def decode_latents(self, latents): latents = 1 / 0.08333 * latents image = self.vae(latent_sample=latents)[0] image = np.clip(image / 2 + 0.5, 0, 1) image = image.transpose((0, 2, 3, 1)) return image def _encode_prompt( self, prompt: Union[str, List[str]], num_images_per_prompt: Optional[int], do_classifier_free_guidance: bool, negative_prompt: Optional[str], prompt_embeds: Optional[np.ndarray] = None, negative_prompt_embeds: Optional[np.ndarray] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`): prompt to be encoded 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`). prompt_embeds (`np.ndarray`, *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 (`np.ndarray`, *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. """ 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: # get prompt text embeddings text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="np", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="np").input_ids if not np.array_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}" ) prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0] prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0) # 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 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] * batch_size 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 = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="np", ) negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0] if do_classifier_free_guidance: negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0) # 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 prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds]) return prompt_embeds def __call__( self, prompt: Union[str, List[str]], image: Union[np.ndarray, PIL.Image.Image, List[PIL.Image.Image]], num_inference_steps: int = 75, guidance_scale: float = 9.0, noise_level: int = 20, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[np.random.RandomState, List[np.random.RandomState]]] = None, latents: Optional[np.ndarray] = None, prompt_embeds: Optional[np.ndarray] = None, negative_prompt_embeds: Optional[np.ndarray] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, np.ndarray], None]] = None, callback_steps: Optional[int] = 1, ): 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 (`np.ndarray` or `PIL.Image.Image`): `Image`, or tensor representing an image batch, that will be used as the starting point for the process. 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. noise_level (`float`, defaults to 0.2): Deteremines the amount of noise to add to the initial image before performing upscaling. negative_prompt (`str` or `List[str]`, *optional*): 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`). 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 (`np.random.RandomState`, *optional*): A np.random.RandomState 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 (`np.ndarray`, *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 (`np.ndarray`, *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: np.ndarray)`. 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`. """ # 1. Check inputs self.check_inputs( prompt, image, noise_level, 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] if generator is None: generator = np.random # 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 prompt_embeds = self._encode_prompt( prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) latents_dtype = prompt_embeds.dtype image = preprocess(image).cpu().numpy() height, width = image.shape[2:] latents = self.prepare_latents( batch_size * num_images_per_prompt, self.config.num_latent_channels, height, width, latents_dtype, generator, ) image = image.astype(latents_dtype) self.scheduler.set_timesteps(num_inference_steps) timesteps = self.scheduler.timesteps # Scale the initial noise by the standard deviation required by the scheduler latents = latents * np.float64(self.scheduler.init_noise_sigma) # 5. Add noise to image noise_level = np.array([noise_level]).astype(np.int64) noise = generator.randn(*image.shape).astype(latents_dtype) image = self.low_res_scheduler.add_noise( torch.from_numpy(image), torch.from_numpy(noise), torch.from_numpy(noise_level) ) image = image.numpy() batch_multiplier = 2 if do_classifier_free_guidance else 1 image = np.concatenate([image] * batch_multiplier * num_images_per_prompt) noise_level = np.concatenate([noise_level] * image.shape[0]) # 7. Check that sizes of image and latents match num_channels_image = image.shape[1] if self.config.num_latent_channels + num_channels_image != self.config.num_unet_input_channels: raise ValueError( "Incorrect configuration settings! The config of `pipeline.unet` expects" f" {self.config.num_unet_input_channels} but received `num_channels_latents`: {self.config.num_latent_channels} +" f" `num_channels_image`: {num_channels_image} " f" = {self.config.num_latent_channels + num_channels_image}. Please verify the config of" " `pipeline.unet` or your `image` input." ) # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta timestep_dtype = next( (input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)" ) timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype] # 9. 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 = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents # concat latents, mask, masked_image_latents in the channel dimension latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) latent_model_input = np.concatenate([latent_model_input, image], axis=1) # timestep to tensor timestep = np.array([t], dtype=timestep_dtype) # predict the noise residual noise_pred = self.unet( sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds, class_labels=noise_level, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = np.split(noise_pred, 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( torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs ).prev_sample latents = latents.numpy() # 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) # 10. Post-processing image = self.decode_latents(latents) if self.safety_checker is not None: safety_checker_input = self.feature_extractor( self.numpy_to_pil(image), return_tensors="np" ).pixel_values.astype(image.dtype) images, has_nsfw_concept = [], [] for i in range(image.shape[0]): image_i, has_nsfw_concept_i = self.safety_checker( clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1] ) images.append(image_i) has_nsfw_concept.append(has_nsfw_concept_i[0]) image = np.concatenate(images) else: has_nsfw_concept = None 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)