import inspect from typing import Union, Optional, Callable, List, Any import numpy as np import torch import diffusers from diffusers.pipelines.onnx_utils import ORT_TO_NP_TYPE from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput from modules.onnx_impl.pipelines import CallablePipelineBase from modules.onnx_impl.pipelines.utils import prepare_latents class OnnxStableDiffusionPipeline(diffusers.OnnxStableDiffusionPipeline, CallablePipelineBase): __module__ = 'diffusers' __name__ = 'OnnxStableDiffusionPipeline' def __init__( self, vae_encoder: diffusers.OnnxRuntimeModel, vae_decoder: diffusers.OnnxRuntimeModel, text_encoder: diffusers.OnnxRuntimeModel, tokenizer: Any, unet: diffusers.OnnxRuntimeModel, scheduler: Any, safety_checker: diffusers.OnnxRuntimeModel, feature_extractor: Any, requires_safety_checker: bool = True ): super().__init__(vae_encoder, vae_decoder, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor, requires_safety_checker) def __call__( self, prompt: Union[str, List[str]] = None, height: Optional[int] = 512, width: Optional[int] = 512, num_inference_steps: Optional[int] = 50, guidance_scale: Optional[float] = 7.5, negative_prompt: 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[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: int = 1, ): # check inputs. Raise error if not correct self.check_inputs( prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds ) # 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 = torch.Generator("cpu") # 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, ) # get the initial random noise unless the user supplied it latents = prepare_latents( self.scheduler.init_noise_sigma, batch_size * num_images_per_prompt, height, width, prompt_embeds.dtype, generator, latents ) # set timesteps self.scheduler.set_timesteps(num_inference_steps) # 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 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] for i, t in enumerate(self.progress_bar(self.scheduler.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 latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t) latent_model_input = latent_model_input.cpu().numpy() # predict the noise residual timestep = np.array([t], dtype=timestep_dtype) noise_pred = self.unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds) noise_pred = noise_pred[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 scheduler_output = self.scheduler.step( torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs ) latents = scheduler_output.prev_sample.numpy() # call the callback, if provided if callback is not None and i % callback_steps == 0: step_idx = i // getattr(self.scheduler, "order", 1) callback(step_idx, t, latents) has_nsfw_concept = None if output_type != "latent": latents /= self.vae_decoder.config.get("scaling_factor", 0.18215) # image = self.vae_decoder(latent_sample=latents)[0] # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1 image = np.concatenate( [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])] ) image = np.clip(image / 2 + 0.5, 0, 1) image = image.transpose((0, 2, 3, 1)) 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) if output_type == "pil": image = self.numpy_to_pil(image) else: image = latents if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)