Correct VQDiffusion Pipeline import
This commit is contained in:
Patrick von Platen
parent
9eb389f298
commit
a24862cdaf
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import inspect
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from typing import List, Optional, Union
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import torch
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from torch import nn
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from torch.nn import functional as F
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from diffusers import AutoencoderKL, DiffusionPipeline, LMSDiscreteScheduler, PNDMScheduler, UNet2DConditionModel
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from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
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from torchvision import transforms
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from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
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class MakeCutouts(nn.Module):
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def __init__(self, cut_size, cut_power=1.0):
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super().__init__()
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self.cut_size = cut_size
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self.cut_power = cut_power
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def forward(self, pixel_values, num_cutouts):
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sideY, sideX = pixel_values.shape[2:4]
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max_size = min(sideX, sideY)
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min_size = min(sideX, sideY, self.cut_size)
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cutouts = []
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for _ in range(num_cutouts):
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size = int(torch.rand([]) ** self.cut_power * (max_size - min_size) + min_size)
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offsetx = torch.randint(0, sideX - size + 1, ())
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offsety = torch.randint(0, sideY - size + 1, ())
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cutout = pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
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cutouts.append(F.adaptive_avg_pool2d(cutout, self.cut_size))
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return torch.cat(cutouts)
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def spherical_dist_loss(x, y):
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x = F.normalize(x, dim=-1)
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y = F.normalize(y, dim=-1)
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return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
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def set_requires_grad(model, value):
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for param in model.parameters():
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param.requires_grad = value
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class CLIPGuidedStableDiffusion(DiffusionPipeline):
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"""CLIP guided stable diffusion based on the amazing repo by @crowsonkb and @Jack000
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- https://github.com/Jack000/glid-3-xl
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- https://github.dev/crowsonkb/k-diffusion
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"""
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def __init__(
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self,
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vae: AutoencoderKL,
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text_encoder: CLIPTextModel,
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clip_model: CLIPModel,
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tokenizer: CLIPTokenizer,
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unet: UNet2DConditionModel,
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scheduler: Union[PNDMScheduler, LMSDiscreteScheduler],
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feature_extractor: CLIPFeatureExtractor,
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):
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super().__init__()
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self.register_modules(
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vae=vae,
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text_encoder=text_encoder,
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clip_model=clip_model,
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tokenizer=tokenizer,
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unet=unet,
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scheduler=scheduler,
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feature_extractor=feature_extractor,
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)
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self.normalize = transforms.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
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self.make_cutouts = MakeCutouts(feature_extractor.size)
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set_requires_grad(self.text_encoder, False)
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set_requires_grad(self.clip_model, False)
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def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
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if slice_size == "auto":
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# half the attention head size is usually a good trade-off between
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# speed and memory
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slice_size = self.unet.config.attention_head_dim // 2
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self.unet.set_attention_slice(slice_size)
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def disable_attention_slicing(self):
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self.enable_attention_slicing(None)
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def freeze_vae(self):
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set_requires_grad(self.vae, False)
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def unfreeze_vae(self):
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set_requires_grad(self.vae, True)
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def freeze_unet(self):
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set_requires_grad(self.unet, False)
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def unfreeze_unet(self):
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set_requires_grad(self.unet, True)
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@torch.enable_grad()
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def cond_fn(
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self,
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latents,
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timestep,
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index,
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text_embeddings,
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noise_pred_original,
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text_embeddings_clip,
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clip_guidance_scale,
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num_cutouts,
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use_cutouts=True,
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):
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latents = latents.detach().requires_grad_()
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if isinstance(self.scheduler, LMSDiscreteScheduler):
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sigma = self.scheduler.sigmas[index]
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# the model input needs to be scaled to match the continuous ODE formulation in K-LMS
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latent_model_input = latents / ((sigma**2 + 1) ** 0.5)
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else:
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latent_model_input = latents
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# predict the noise residual
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noise_pred = self.unet(latent_model_input, timestep, encoder_hidden_states=text_embeddings).sample
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if isinstance(self.scheduler, PNDMScheduler):
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alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
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beta_prod_t = 1 - alpha_prod_t
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# compute predicted original sample from predicted noise also called
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# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
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pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)
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fac = torch.sqrt(beta_prod_t)
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sample = pred_original_sample * (fac) + latents * (1 - fac)
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elif isinstance(self.scheduler, LMSDiscreteScheduler):
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sigma = self.scheduler.sigmas[index]
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sample = latents - sigma * noise_pred
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else:
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raise ValueError(f"scheduler type {type(self.scheduler)} not supported")
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sample = 1 / 0.18215 * sample
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image = self.vae.decode(sample).sample
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image = (image / 2 + 0.5).clamp(0, 1)
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if use_cutouts:
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image = self.make_cutouts(image, num_cutouts)
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else:
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image = transforms.Resize(self.feature_extractor.size)(image)
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image = self.normalize(image).to(latents.dtype)
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image_embeddings_clip = self.clip_model.get_image_features(image)
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image_embeddings_clip = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
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if use_cutouts:
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dists = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip)
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dists = dists.view([num_cutouts, sample.shape[0], -1])
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loss = dists.sum(2).mean(0).sum() * clip_guidance_scale
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else:
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loss = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip).mean() * clip_guidance_scale
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grads = -torch.autograd.grad(loss, latents)[0]
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if isinstance(self.scheduler, LMSDiscreteScheduler):
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latents = latents.detach() + grads * (sigma**2)
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noise_pred = noise_pred_original
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else:
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noise_pred = noise_pred_original - torch.sqrt(beta_prod_t) * grads
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return noise_pred, latents
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@torch.no_grad()
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def __call__(
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self,
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prompt: Union[str, List[str]],
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height: Optional[int] = 512,
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width: Optional[int] = 512,
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num_inference_steps: Optional[int] = 50,
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guidance_scale: Optional[float] = 7.5,
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num_images_per_prompt: Optional[int] = 1,
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clip_guidance_scale: Optional[float] = 100,
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clip_prompt: Optional[Union[str, List[str]]] = None,
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num_cutouts: Optional[int] = 4,
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use_cutouts: Optional[bool] = True,
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generator: Optional[torch.Generator] = None,
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latents: Optional[torch.FloatTensor] = None,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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):
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if isinstance(prompt, str):
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batch_size = 1
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elif isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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if height % 8 != 0 or width % 8 != 0:
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raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
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# get prompt text embeddings
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text_input = self.tokenizer(
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prompt,
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padding="max_length",
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max_length=self.tokenizer.model_max_length,
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truncation=True,
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return_tensors="pt",
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)
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text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
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# duplicate text embeddings for each generation per prompt
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text_embeddings = text_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
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if clip_guidance_scale > 0:
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if clip_prompt is not None:
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clip_text_input = self.tokenizer(
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clip_prompt,
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padding="max_length",
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max_length=self.tokenizer.model_max_length,
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truncation=True,
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return_tensors="pt",
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).input_ids.to(self.device)
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else:
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clip_text_input = text_input.input_ids.to(self.device)
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text_embeddings_clip = self.clip_model.get_text_features(clip_text_input)
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text_embeddings_clip = text_embeddings_clip / text_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
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# duplicate text embeddings clip for each generation per prompt
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text_embeddings_clip = text_embeddings_clip.repeat_interleave(num_images_per_prompt, dim=0)
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# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
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# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
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# corresponds to doing no classifier free guidance.
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do_classifier_free_guidance = guidance_scale > 1.0
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# get unconditional embeddings for classifier free guidance
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if do_classifier_free_guidance:
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max_length = text_input.input_ids.shape[-1]
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uncond_input = self.tokenizer([""], padding="max_length", max_length=max_length, return_tensors="pt")
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uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
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# duplicate unconditional embeddings for each generation per prompt
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uncond_embeddings = uncond_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
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# For classifier free guidance, we need to do two forward passes.
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# Here we concatenate the unconditional and text embeddings into a single batch
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# to avoid doing two forward passes
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text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
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# get the initial random noise unless the user supplied it
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# Unlike in other pipelines, latents need to be generated in the target device
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# for 1-to-1 results reproducibility with the CompVis implementation.
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# However this currently doesn't work in `mps`.
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latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
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latents_dtype = text_embeddings.dtype
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if latents is None:
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if self.device.type == "mps":
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# randn does not work reproducibly on mps
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latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
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self.device
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)
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else:
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latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
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else:
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if latents.shape != latents_shape:
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raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
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latents = latents.to(self.device)
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# set timesteps
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accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
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extra_set_kwargs = {}
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if accepts_offset:
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extra_set_kwargs["offset"] = 1
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self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
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# Some schedulers like PNDM have timesteps as arrays
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# It's more optimized to move all timesteps to correct device beforehand
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timesteps_tensor = self.scheduler.timesteps.to(self.device)
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# scale the initial noise by the standard deviation required by the scheduler
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latents = latents * self.scheduler.init_noise_sigma
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for i, t in enumerate(self.progress_bar(timesteps_tensor)):
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# expand the latents if we are doing classifier free guidance
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latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
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latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
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# predict the noise residual
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noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
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# perform classifier free guidance
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if do_classifier_free_guidance:
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noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
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noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
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# perform clip guidance
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if clip_guidance_scale > 0:
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text_embeddings_for_guidance = (
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text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
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)
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noise_pred, latents = self.cond_fn(
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latents,
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t,
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i,
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text_embeddings_for_guidance,
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noise_pred,
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text_embeddings_clip,
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clip_guidance_scale,
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num_cutouts,
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use_cutouts,
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)
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# compute the previous noisy sample x_t -> x_t-1
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latents = self.scheduler.step(noise_pred, t, latents).prev_sample
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# scale and decode the image latents with vae
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latents = 1 / 0.18215 * latents
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image = self.vae.decode(latents).sample
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image = (image / 2 + 0.5).clamp(0, 1)
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image = image.cpu().permute(0, 2, 3, 1).numpy()
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if output_type == "pil":
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image = self.numpy_to_pil(image)
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if not return_dict:
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return (image, None)
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return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)
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@ -1 +0,0 @@
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{"url": "https://raw.githubusercontent.com/huggingface/diffusers/main/examples/community/clip_guided_stable_diffusion.py", "etag": "W/\"3e4886ba6cb31f36f75ec5127cd691e562bb04d1f0ff257edbe1c182fd6a210a\""}
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@ -1,102 +0,0 @@
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# Copyright 2022 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Optional, Tuple, Union
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import torch
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from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
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class CustomPipeline(DiffusionPipeline):
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r"""
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
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library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
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Parameters:
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unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
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scheduler ([`SchedulerMixin`]):
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A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
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[`DDPMScheduler`], or [`DDIMScheduler`].
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"""
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def __init__(self, unet, scheduler):
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super().__init__()
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self.register_modules(unet=unet, scheduler=scheduler)
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@torch.no_grad()
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def __call__(
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self,
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batch_size: int = 1,
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generator: Optional[torch.Generator] = None,
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eta: float = 0.0,
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num_inference_steps: int = 50,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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**kwargs,
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) -> Union[ImagePipelineOutput, Tuple]:
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r"""
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Args:
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batch_size (`int`, *optional*, defaults to 1):
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The number of images to generate.
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generator (`torch.Generator`, *optional*):
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A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
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deterministic.
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eta (`float`, *optional*, defaults to 0.0):
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The eta parameter which controls the scale of the variance (0 is DDIM and 1 is one type of DDPM).
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num_inference_steps (`int`, *optional*, defaults to 50):
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the
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expense of slower inference.
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output_type (`str`, *optional*, defaults to `"pil"`):
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The output format of the generate image. Choose between
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[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
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Returns:
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[`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
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`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
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generated images.
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"""
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# Sample gaussian noise to begin loop
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image = torch.randn(
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(batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
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generator=generator,
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)
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image = image.to(self.device)
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# set step values
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self.scheduler.set_timesteps(num_inference_steps)
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for t in self.progress_bar(self.scheduler.timesteps):
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# 1. predict noise model_output
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model_output = self.unet(image, t).sample
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# 2. predict previous mean of image x_t-1 and add variance depending on eta
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# eta corresponds to η in paper and should be between [0, 1]
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# do x_t -> x_t-1
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image = self.scheduler.step(model_output, t, image, eta).prev_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,)
|
||||
|
||||
return ImagePipelineOutput(images=image), "This is a test"
|
||||
|
|
@ -1 +0,0 @@
|
|||
b8fa12635e53eebebc22f95ee863e7af4fc2fb07
|
||||
|
|
@ -1 +0,0 @@
|
|||
../../blobs/bbbcb9f65616524d6199fa3bc16dc0500fb2cbbb
|
||||
Loading…
Reference in New Issue