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| import inspect | |
| from typing import List, Optional, Union | |
| import numpy as np | |
| from transformers import CLIPFeatureExtractor, CLIPTokenizer | |
| from ...onnx_utils import OnnxRuntimeModel | |
| from ...pipeline_utils import DiffusionPipeline | |
| from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler | |
| from . import StableDiffusionPipelineOutput | |
| class StableDiffusionOnnxPipeline(DiffusionPipeline): | |
| vae_decoder: OnnxRuntimeModel | |
| text_encoder: OnnxRuntimeModel | |
| tokenizer: CLIPTokenizer | |
| unet: OnnxRuntimeModel | |
| scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] | |
| safety_checker: OnnxRuntimeModel | |
| feature_extractor: CLIPFeatureExtractor | |
| def __init__( | |
| self, | |
| vae_decoder: OnnxRuntimeModel, | |
| text_encoder: OnnxRuntimeModel, | |
| tokenizer: CLIPTokenizer, | |
| unet: OnnxRuntimeModel, | |
| scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], | |
| safety_checker: OnnxRuntimeModel, | |
| feature_extractor: CLIPFeatureExtractor, | |
| ): | |
| super().__init__() | |
| scheduler = scheduler.set_format("np") | |
| self.register_modules( | |
| vae_decoder=vae_decoder, | |
| text_encoder=text_encoder, | |
| tokenizer=tokenizer, | |
| unet=unet, | |
| scheduler=scheduler, | |
| safety_checker=safety_checker, | |
| feature_extractor=feature_extractor, | |
| ) | |
| def __call__( | |
| self, | |
| prompt: Union[str, List[str]], | |
| height: Optional[int] = 512, | |
| width: Optional[int] = 512, | |
| num_inference_steps: Optional[int] = 50, | |
| guidance_scale: Optional[float] = 7.5, | |
| eta: Optional[float] = 0.0, | |
| latents: Optional[np.ndarray] = None, | |
| output_type: Optional[str] = "pil", | |
| return_dict: bool = True, | |
| **kwargs, | |
| ): | |
| if isinstance(prompt, str): | |
| batch_size = 1 | |
| elif isinstance(prompt, list): | |
| batch_size = len(prompt) | |
| else: | |
| raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") | |
| if height % 8 != 0 or width % 8 != 0: | |
| raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") | |
| # get prompt text embeddings | |
| text_input = self.tokenizer( | |
| prompt, | |
| padding="max_length", | |
| max_length=self.tokenizer.model_max_length, | |
| truncation=True, | |
| return_tensors="np", | |
| ) | |
| text_embeddings = self.text_encoder(input_ids=text_input.input_ids.astype(np.int32))[0] | |
| # 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 | |
| # get unconditional embeddings for classifier free guidance | |
| if do_classifier_free_guidance: | |
| max_length = text_input.input_ids.shape[-1] | |
| uncond_input = self.tokenizer( | |
| [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="np" | |
| ) | |
| uncond_embeddings = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[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 | |
| text_embeddings = np.concatenate([uncond_embeddings, text_embeddings]) | |
| # get the initial random noise unless the user supplied it | |
| latents_shape = (batch_size, 4, height // 8, width // 8) | |
| if latents is None: | |
| latents = np.random.randn(*latents_shape).astype(np.float32) | |
| elif latents.shape != latents_shape: | |
| raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}") | |
| # set timesteps | |
| accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys()) | |
| extra_set_kwargs = {} | |
| if accepts_offset: | |
| extra_set_kwargs["offset"] = 1 | |
| self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs) | |
| # if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas | |
| if isinstance(self.scheduler, LMSDiscreteScheduler): | |
| latents = latents * self.scheduler.sigmas[0] | |
| # 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 | |
| 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 | |
| if isinstance(self.scheduler, LMSDiscreteScheduler): | |
| sigma = self.scheduler.sigmas[i] | |
| # the model input needs to be scaled to match the continuous ODE formulation in K-LMS | |
| latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5) | |
| # predict the noise residual | |
| noise_pred = self.unet( | |
| sample=latent_model_input, timestep=np.array([t]), encoder_hidden_states=text_embeddings | |
| ) | |
| 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 | |
| if isinstance(self.scheduler, LMSDiscreteScheduler): | |
| latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs).prev_sample | |
| else: | |
| latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample | |
| # scale and decode the image latents with vae | |
| latents = 1 / 0.18215 * latents | |
| image = self.vae_decoder(latent_sample=latents)[0] | |
| image = np.clip(image / 2 + 0.5, 0, 1) | |
| image = image.transpose((0, 2, 3, 1)) | |
| # run safety checker | |
| safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="np") | |
| image, has_nsfw_concept = self.safety_checker(clip_input=safety_checker_input.pixel_values, images=image) | |
| 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) | |