diff --git a/examples/community/README.md b/examples/community/README.md index ce6f57b018cf..aada72163b2e 100644 --- a/examples/community/README.md +++ b/examples/community/README.md @@ -2,5 +2,6 @@ **Community** examples consist of both inference and training examples that have been added by the community. -| Example | Description | Author | | -|:----------|:-------------|:-------------|------:| +| Example | Description | Author | Colab | +|:----------|:----------------------|:-----------------|----------:| +| CLIP Guided Stable Diffusion | Doing CLIP guidance for text to image generation with Stable Diffusion| [Suraj Patil](https://github.com/patil-suraj/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/CLIP_Guided_Stable_diffusion_with_diffusers.ipynb) | diff --git a/examples/community/clip_guided_stable_diffusion.py b/examples/community/clip_guided_stable_diffusion.py new file mode 100644 index 000000000000..08298a74019c --- /dev/null +++ b/examples/community/clip_guided_stable_diffusion.py @@ -0,0 +1,321 @@ +import inspect +from typing import List, Optional, Union + +import torch +from torch import nn +from torch.nn import functional as F + +from diffusers import AutoencoderKL, DiffusionPipeline, LMSDiscreteScheduler, PNDMScheduler, UNet2DConditionModel +from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput +from torchvision import transforms +from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer + + +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], + feature_extractor: CLIPFeatureExtractor, + ): + super().__init__() + scheduler = scheduler.set_format("pt") + 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.make_cutouts = MakeCutouts(feature_extractor.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_() + + if isinstance(self.scheduler, LMSDiscreteScheduler): + sigma = self.scheduler.sigmas[index] + # the model input needs to be scaled to match the continuous ODE formulation in K-LMS + latent_model_input = latents / ((sigma**2 + 1) ** 0.5) + else: + latent_model_input = latents + + # predict the noise residual + noise_pred = self.unet(latent_model_input, timestep, encoder_hidden_states=text_embeddings).sample + + if isinstance(self.scheduler, PNDMScheduler): + 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 / 0.18215 * 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.feature_extractor.size)(image) + image = self.normalize(image) + + image_embeddings_clip = self.clip_model.get_image_features(image).float() + 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, + 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] + + 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", + ).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) + + # 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="pt" + ) + uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[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_device = "cpu" if self.device.type == "mps" else self.device + latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8) + if latents is None: + latents = torch.randn( + latents_shape, + generator=generator, + device=latents_device, + ) + 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) + + # if we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas + if isinstance(self.scheduler, LMSDiscreteScheduler): + latents = latents * self.scheduler.sigmas[0] + + for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)): + # expand the latents if we are doing classifier free guidance + latent_model_input = torch.cat([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(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)[0] 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 + if isinstance(self.scheduler, LMSDiscreteScheduler): + latents = self.scheduler.step(noise_pred, i, latents).prev_sample + else: + latents = self.scheduler.step(noise_pred, t, latents).prev_sample + + # scale and decode the image latents with vae + latents = 1 / 0.18215 * 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)