From 5b3eb3b24c2ed33911a7c50b5b1e0f729b86c688 Mon Sep 17 00:00:00 2001 From: Volpeon Date: Sat, 1 Oct 2022 11:40:14 +0200 Subject: Made inference script interactive --- .../clip_guided_stable_diffusion.py | 169 +-------------------- 1 file changed, 3 insertions(+), 166 deletions(-) (limited to 'pipelines/stable_diffusion') diff --git a/pipelines/stable_diffusion/clip_guided_stable_diffusion.py b/pipelines/stable_diffusion/clip_guided_stable_diffusion.py index 306d9a9..ddf7ce1 100644 --- a/pipelines/stable_diffusion/clip_guided_stable_diffusion.py +++ b/pipelines/stable_diffusion/clip_guided_stable_diffusion.py @@ -17,53 +17,14 @@ from schedulers.scheduling_euler_a import EulerAScheduler, CFGDenoiserForward logger = logging.get_logger(__name__) # pylint: disable=invalid-name -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[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], - feature_extractor: CLIPFeatureExtractor, + scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler, EulerAScheduler], **kwargs, ): super().__init__() @@ -85,19 +46,11 @@ class CLIPGuidedStableDiffusion(DiffusionPipeline): 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"): r""" Enable sliced attention computation. @@ -125,87 +78,6 @@ class CLIPGuidedStableDiffusion(DiffusionPipeline): # set slice_size = `None` to disable `attention slicing` 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, @@ -216,10 +88,6 @@ class CLIPGuidedStableDiffusion(DiffusionPipeline): num_inference_steps: Optional[int] = 50, guidance_scale: Optional[float] = 7.5, eta: Optional[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", @@ -305,24 +173,10 @@ class CLIPGuidedStableDiffusion(DiffusionPipeline): "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) + print(f"Too many tokens: {removed_text}") text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length] text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0] - if clip_guidance_scale > 0: - if clip_prompt is not None: - clip_text_inputs = self.tokenizer( - clip_prompt, - padding="max_length", - max_length=self.tokenizer.model_max_length, - truncation=True, - return_tensors="pt", - ) - clip_text_input_ids = clip_text_inputs.input_ids - else: - clip_text_input_ids = text_input_ids - text_embeddings_clip = self.clip_model.get_text_features(clip_text_input_ids.to(self.device)) - 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. @@ -357,7 +211,7 @@ class CLIPGuidedStableDiffusion(DiffusionPipeline): else: if latents.shape != latents_shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}") - latents = latents.to(self.device) + latents = latents.to(self.device) # set timesteps self.scheduler.set_timesteps(num_inference_steps) @@ -414,23 +268,6 @@ class CLIPGuidedStableDiffusion(DiffusionPipeline): 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 if isinstance(self.scheduler, LMSDiscreteScheduler): latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs).prev_sample -- cgit v1.2.3-54-g00ecf