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)