import argparse import torch from baukit import TraceDict from diffusers import AutoencoderKL, UNet2DConditionModel from PIL import Image from tqdm.auto import tqdm from transformers import CLIPTextModel, CLIPTokenizer, CLIPFeatureExtractor from diffusers.schedulers import EulerAncestralDiscreteScheduler from diffusers.schedulers.scheduling_ddim import DDIMScheduler from diffusers.schedulers.scheduling_ddpm import DDPMScheduler from diffusers.schedulers.scheduling_lms_discrete import LMSDiscreteScheduler from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker import util def default_parser(): parser = argparse.ArgumentParser() parser.add_argument('prompts', type=str, nargs='+') parser.add_argument('outpath', type=str) parser.add_argument('--images', type=str, nargs='+', default=None) parser.add_argument('--nsteps', type=int, default=1000) parser.add_argument('--nimgs', type=int, default=1) parser.add_argument('--start_itr', type=int, default=0) parser.add_argument('--return_steps', action='store_true', default=False) parser.add_argument('--pred_x0', action='store_true', default=False) parser.add_argument('--device', type=str, default='cuda:0') parser.add_argument('--seed', type=int, default=42) return parser class StableDiffuser(torch.nn.Module): def __init__(self, scheduler='LMS' ): super().__init__() # Load the autoencoder model which will be used to decode the latents into image space. self.vae = AutoencoderKL.from_pretrained( "CompVis/stable-diffusion-v1-4", subfolder="vae") # Load the tokenizer and text encoder to tokenize and encode the text. self.tokenizer = CLIPTokenizer.from_pretrained( "openai/clip-vit-large-patch14") self.text_encoder = CLIPTextModel.from_pretrained( "openai/clip-vit-large-patch14") # The UNet model for generating the latents. self.unet = UNet2DConditionModel.from_pretrained( "CompVis/stable-diffusion-v1-4", subfolder="unet") self.feature_extractor = CLIPFeatureExtractor.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="feature_extractor") self.safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="safety_checker") if scheduler == 'LMS': self.scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000) elif scheduler == 'DDIM': self.scheduler = DDIMScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler") elif scheduler == 'DDPM': self.scheduler = DDPMScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler") self.eval() def get_noise(self, batch_size, img_size, generator=None): param = list(self.parameters())[0] return torch.randn( (batch_size, self.unet.in_channels, img_size // 8, img_size // 8), generator=generator).type(param.dtype).to(param.device) def add_noise(self, latents, noise, step): return self.scheduler.add_noise(latents, noise, torch.tensor([self.scheduler.timesteps[step]])) def text_tokenize(self, prompts): return self.tokenizer(prompts, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt") def text_detokenize(self, tokens): return [self.tokenizer.decode(token) for token in tokens if token != self.tokenizer.vocab_size - 1] def text_encode(self, tokens): return self.text_encoder(tokens.input_ids.to(self.unet.device))[0] def decode(self, latents): return self.vae.decode(1 / self.vae.config.scaling_factor * latents).sample def encode(self, tensors): return self.vae.encode(tensors).latent_dist.mode() * 0.18215 def to_image(self, image): image = (image / 2 + 0.5).clamp(0, 1) image = image.detach().cpu().permute(0, 2, 3, 1).numpy() images = (image * 255).round().astype("uint8") pil_images = [Image.fromarray(image) for image in images] return pil_images def set_scheduler_timesteps(self, n_steps): self.scheduler.set_timesteps(n_steps, device=self.unet.device) def get_initial_latents(self, n_imgs, img_size, n_prompts, generator=None): noise = self.get_noise(n_imgs, img_size, generator=generator).repeat(n_prompts, 1, 1, 1) latents = noise * self.scheduler.init_noise_sigma return latents def get_text_embeddings(self, prompts, n_imgs): text_tokens = self.text_tokenize(prompts) text_embeddings = self.text_encode(text_tokens) unconditional_tokens = self.text_tokenize([""] * len(prompts)) unconditional_embeddings = self.text_encode(unconditional_tokens) text_embeddings = torch.cat([unconditional_embeddings, text_embeddings]).repeat_interleave(n_imgs, dim=0) return text_embeddings def predict_noise(self, iteration, latents, text_embeddings, guidance_scale=7.5 ): # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes. latents = torch.cat([latents] * 2) latents = self.scheduler.scale_model_input( latents, self.scheduler.timesteps[iteration]) # predict the noise residual noise_prediction = self.unet( latents, self.scheduler.timesteps[iteration], encoder_hidden_states=text_embeddings).sample # perform guidance noise_prediction_uncond, noise_prediction_text = noise_prediction.chunk(2) noise_prediction = noise_prediction_uncond + guidance_scale * \ (noise_prediction_text - noise_prediction_uncond) return noise_prediction @torch.no_grad() def diffusion(self, latents, text_embeddings, end_iteration=1000, start_iteration=0, return_steps=False, pred_x0=False, trace_args=None, show_progress=True, **kwargs): latents_steps = [] trace_steps = [] trace = None for iteration in tqdm(range(start_iteration, end_iteration), disable=not show_progress): if trace_args: trace = TraceDict(self, **trace_args) noise_pred = self.predict_noise( iteration, latents, text_embeddings, **kwargs) # compute the previous noisy sample x_t -> x_t-1 output = self.scheduler.step(noise_pred, self.scheduler.timesteps[iteration], latents) if trace_args: trace.close() trace_steps.append(trace) latents = output.prev_sample if return_steps or iteration == end_iteration - 1: output = output.pred_original_sample if pred_x0 else latents if return_steps: latents_steps.append(output.cpu()) else: latents_steps.append(output) return latents_steps, trace_steps @torch.no_grad() def __call__(self, prompts, img_size=512, n_steps=50, n_imgs=1, end_iteration=None, generator=None, **kwargs ): assert 0 <= n_steps <= 1000 if not isinstance(prompts, list): prompts = [prompts] self.set_scheduler_timesteps(n_steps) latents = self.get_initial_latents(n_imgs, img_size, len(prompts), generator=generator) text_embeddings = self.get_text_embeddings(prompts,n_imgs=n_imgs) end_iteration = end_iteration or n_steps latents_steps, trace_steps = self.diffusion( latents, text_embeddings, end_iteration=end_iteration, **kwargs ) latents_steps = [self.decode(latents.to(self.unet.device)) for latents in latents_steps] images_steps = [self.to_image(latents) for latents in latents_steps] for i in range(len(images_steps)): self.safety_checker = self.safety_checker.float() safety_checker_input = self.feature_extractor(images_steps[i], return_tensors="pt").to(latents_steps[0].device) image, has_nsfw_concept = self.safety_checker( images=latents_steps[i].float().cpu().numpy(), clip_input=safety_checker_input.pixel_values.float() ) images_steps[i][0] = self.to_image(torch.from_numpy(image))[0] images_steps = list(zip(*images_steps)) if trace_steps: return images_steps, trace_steps return images_steps if __name__ == '__main__': parser = default_parser() args = parser.parse_args() diffuser = StableDiffuser(seed=args.seed, scheduler='DDIM').to(torch.device(args.device)).half() images = diffuser(args.prompts, n_steps=args.nsteps, n_imgs=args.nimgs, start_iteration=args.start_itr, return_steps=args.return_steps, pred_x0=args.pred_x0 ) util.image_grid(images, args.outpath)