import inspect from typing import List, Optional, Union import torch from torch import nn from torch.nn import functional as F from torchvision import transforms from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer from diffusers import ( AutoencoderKL, DDIMScheduler, DiffusionPipeline, DPMSolverMultistepScheduler, LMSDiscreteScheduler, PNDMScheduler, UNet2DConditionModel, ) from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput class MakeCutouts(nn.Module): def __init__(self, cut_size, cut_power=1.0): super().__init__() self.cut_size = cut_size self.cut_power = cut_power def forward(self, pixel_values, num_cutouts): sideY, sideX = pixel_values.shape[2:4] max_size = min(sideX, sideY) min_size = min(sideX, sideY, self.cut_size) cutouts = [] for _ in range(num_cutouts): size = int(torch.rand([]) ** self.cut_power * (max_size - min_size) + min_size) offsetx = torch.randint(0, sideX - size + 1, ()) offsety = torch.randint(0, sideY - size + 1, ()) cutout = pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size] cutouts.append(F.adaptive_avg_pool2d(cutout, self.cut_size)) return torch.cat(cutouts) def spherical_dist_loss(x, y): x = F.normalize(x, dim=-1) y = F.normalize(y, dim=-1) return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2) def set_requires_grad(model, value): for param in model.parameters(): param.requires_grad = value class CLIPGuidedStableDiffusion(DiffusionPipeline): """CLIP guided stable diffusion based on the amazing repo by @crowsonkb and @Jack000 - https://github.com/Jack000/glid-3-xl - https://github.dev/crowsonkb/k-diffusion """ def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, clip_model: CLIPModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler], feature_extractor: CLIPImageProcessor, ): super().__init__() self.register_modules( vae=vae, text_encoder=text_encoder, clip_model=clip_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler, feature_extractor=feature_extractor, ) self.normalize = transforms.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std) self.cut_out_size = ( feature_extractor.size if isinstance(feature_extractor.size, int) else feature_extractor.size["shortest_edge"] ) self.make_cutouts = MakeCutouts(self.cut_out_size) set_requires_grad(self.text_encoder, False) set_requires_grad(self.clip_model, False) def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"): if slice_size == "auto": # half the attention head size is usually a good trade-off between # speed and memory slice_size = self.unet.config.attention_head_dim // 2 self.unet.set_attention_slice(slice_size) def disable_attention_slicing(self): self.enable_attention_slicing(None) def freeze_vae(self): set_requires_grad(self.vae, False) def unfreeze_vae(self): set_requires_grad(self.vae, True) def freeze_unet(self): set_requires_grad(self.unet, False) def unfreeze_unet(self): set_requires_grad(self.unet, True) @torch.enable_grad() def cond_fn( self, latents, timestep, index, text_embeddings, noise_pred_original, text_embeddings_clip, clip_guidance_scale, num_cutouts, use_cutouts=True, ): latents = latents.detach().requires_grad_() latent_model_input = self.scheduler.scale_model_input(latents, timestep) # predict the noise residual noise_pred = self.unet(latent_model_input, timestep, encoder_hidden_states=text_embeddings).sample if isinstance(self.scheduler, (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)): alpha_prod_t = self.scheduler.alphas_cumprod[timestep] beta_prod_t = 1 - alpha_prod_t # compute predicted original sample from predicted noise also called # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5) fac = torch.sqrt(beta_prod_t) sample = pred_original_sample * (fac) + latents * (1 - fac) elif isinstance(self.scheduler, LMSDiscreteScheduler): sigma = self.scheduler.sigmas[index] sample = latents - sigma * noise_pred else: raise ValueError(f"scheduler type {type(self.scheduler)} not supported") sample = 1 / self.vae.config.scaling_factor * sample image = self.vae.decode(sample).sample image = (image / 2 + 0.5).clamp(0, 1) if use_cutouts: image = self.make_cutouts(image, num_cutouts) else: image = transforms.Resize(self.cut_out_size)(image) image = self.normalize(image).to(latents.dtype) image_embeddings_clip = self.clip_model.get_image_features(image) image_embeddings_clip = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True) if use_cutouts: dists = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip) dists = dists.view([num_cutouts, sample.shape[0], -1]) loss = dists.sum(2).mean(0).sum() * clip_guidance_scale else: loss = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip).mean() * clip_guidance_scale grads = -torch.autograd.grad(loss, latents)[0] if isinstance(self.scheduler, LMSDiscreteScheduler): latents = latents.detach() + grads * (sigma**2) noise_pred = noise_pred_original else: noise_pred = noise_pred_original - torch.sqrt(beta_prod_t) * grads return noise_pred, latents @torch.no_grad() 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, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, clip_guidance_scale: Optional[float] = 100, clip_prompt: Optional[Union[str, List[str]]] = None, num_cutouts: Optional[int] = 4, use_cutouts: Optional[bool] = True, generator: Optional[torch.Generator] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, ): 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="pt", ) text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0] # duplicate text embeddings for each generation per prompt text_embeddings = text_embeddings.repeat_interleave(num_images_per_prompt, dim=0) if clip_guidance_scale > 0: if clip_prompt is not None: clip_text_input = self.tokenizer( clip_prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ).input_ids.to(self.device) else: clip_text_input = text_input.input_ids.to(self.device) text_embeddings_clip = self.clip_model.get_text_features(clip_text_input) text_embeddings_clip = text_embeddings_clip / text_embeddings_clip.norm(p=2, dim=-1, keepdim=True) # duplicate text embeddings clip for each generation per prompt text_embeddings_clip = text_embeddings_clip.repeat_interleave(num_images_per_prompt, dim=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([""], padding="max_length", max_length=max_length, return_tensors="pt") uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0] # duplicate unconditional embeddings for each generation per prompt uncond_embeddings = uncond_embeddings.repeat_interleave(num_images_per_prompt, dim=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 = torch.cat([uncond_embeddings, text_embeddings]) # get the initial random noise unless the user supplied it # Unlike in other pipelines, latents need to be generated in the target device # for 1-to-1 results reproducibility with the CompVis implementation. # However this currently doesn't work in `mps`. latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8) latents_dtype = text_embeddings.dtype if latents is None: if self.device.type == "mps": # randn does not work reproducibly on mps latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to( self.device ) else: latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype) else: if latents.shape != latents_shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}") latents = latents.to(self.device) # 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) # Some schedulers like PNDM have timesteps as arrays # It's more optimized to move all timesteps to correct device beforehand timesteps_tensor = self.scheduler.timesteps.to(self.device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma # 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 # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator for i, t in enumerate(self.progress_bar(timesteps_tensor)): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample # perform classifier free guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # perform clip guidance if clip_guidance_scale > 0: text_embeddings_for_guidance = ( text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings ) noise_pred, latents = self.cond_fn( latents, t, i, text_embeddings_for_guidance, noise_pred, text_embeddings_clip, clip_guidance_scale, num_cutouts, use_cutouts, ) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # scale and decode the image latents with vae latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents).sample image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).numpy() if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image, None) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)