From a24862cdaf0dedf3430b2d5cdcdabe2ebc0a7dd8 Mon Sep 17 00:00:00 2001 From: Patrick von Platen Date: Thu, 3 Nov 2022 17:55:14 +0000 Subject: [PATCH] Correct VQDiffusion Pipeline import --- ...3c81220be0a04e4543e6fd9b0f290547749cc06cfb | 324 ------------------ ...20be0a04e4543e6fd9b0f290547749cc06cfb.json | 1 - .../bbbcb9f65616524d6199fa3bc16dc0500fb2cbbb | 102 ------ .../refs/main | 1 - .../pipeline.py | 1 - 5 files changed, 429 deletions(-) delete mode 100644 clip_guided_stable_diffusion/72392adcdf265e793b0dc13d166393a9d1367724bb03f6faca8cfb1c91c30827.8d4a13da440f0a37b6d42d3c81220be0a04e4543e6fd9b0f290547749cc06cfb delete mode 100644 clip_guided_stable_diffusion/72392adcdf265e793b0dc13d166393a9d1367724bb03f6faca8cfb1c91c30827.8d4a13da440f0a37b6d42d3c81220be0a04e4543e6fd9b0f290547749cc06cfb.json delete mode 100644 hf-internal-testing/diffusers-dummy-pipeline/models--hf-internal-testing--diffusers-dummy-pipeline/blobs/bbbcb9f65616524d6199fa3bc16dc0500fb2cbbb delete mode 100644 hf-internal-testing/diffusers-dummy-pipeline/models--hf-internal-testing--diffusers-dummy-pipeline/refs/main delete mode 120000 hf-internal-testing/diffusers-dummy-pipeline/models--hf-internal-testing--diffusers-dummy-pipeline/snapshots/b8fa12635e53eebebc22f95ee863e7af4fc2fb07/pipeline.py diff --git a/clip_guided_stable_diffusion/72392adcdf265e793b0dc13d166393a9d1367724bb03f6faca8cfb1c91c30827.8d4a13da440f0a37b6d42d3c81220be0a04e4543e6fd9b0f290547749cc06cfb b/clip_guided_stable_diffusion/72392adcdf265e793b0dc13d166393a9d1367724bb03f6faca8cfb1c91c30827.8d4a13da440f0a37b6d42d3c81220be0a04e4543e6fd9b0f290547749cc06cfb deleted file mode 100644 index 2c86e913..00000000 --- a/clip_guided_stable_diffusion/72392adcdf265e793b0dc13d166393a9d1367724bb03f6faca8cfb1c91c30827.8d4a13da440f0a37b6d42d3c81220be0a04e4543e6fd9b0f290547749cc06cfb +++ /dev/null @@ -1,324 +0,0 @@ -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__() - 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).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, - 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.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 - - 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).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) diff --git a/clip_guided_stable_diffusion/72392adcdf265e793b0dc13d166393a9d1367724bb03f6faca8cfb1c91c30827.8d4a13da440f0a37b6d42d3c81220be0a04e4543e6fd9b0f290547749cc06cfb.json b/clip_guided_stable_diffusion/72392adcdf265e793b0dc13d166393a9d1367724bb03f6faca8cfb1c91c30827.8d4a13da440f0a37b6d42d3c81220be0a04e4543e6fd9b0f290547749cc06cfb.json deleted file mode 100644 index ebadb907..00000000 --- a/clip_guided_stable_diffusion/72392adcdf265e793b0dc13d166393a9d1367724bb03f6faca8cfb1c91c30827.8d4a13da440f0a37b6d42d3c81220be0a04e4543e6fd9b0f290547749cc06cfb.json +++ /dev/null @@ -1 +0,0 @@ -{"url": "https://raw.githubusercontent.com/huggingface/diffusers/main/examples/community/clip_guided_stable_diffusion.py", "etag": "W/\"3e4886ba6cb31f36f75ec5127cd691e562bb04d1f0ff257edbe1c182fd6a210a\""} \ No newline at end of file diff --git a/hf-internal-testing/diffusers-dummy-pipeline/models--hf-internal-testing--diffusers-dummy-pipeline/blobs/bbbcb9f65616524d6199fa3bc16dc0500fb2cbbb b/hf-internal-testing/diffusers-dummy-pipeline/models--hf-internal-testing--diffusers-dummy-pipeline/blobs/bbbcb9f65616524d6199fa3bc16dc0500fb2cbbb deleted file mode 100644 index bbbcb9f6..00000000 --- a/hf-internal-testing/diffusers-dummy-pipeline/models--hf-internal-testing--diffusers-dummy-pipeline/blobs/bbbcb9f65616524d6199fa3bc16dc0500fb2cbbb +++ /dev/null @@ -1,102 +0,0 @@ -# Copyright 2022 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and - -# limitations under the License. - - -from typing import Optional, Tuple, Union - -import torch - -from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput - - -class CustomPipeline(DiffusionPipeline): - r""" - This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the - library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) - - Parameters: - unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image. - scheduler ([`SchedulerMixin`]): - A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of - [`DDPMScheduler`], or [`DDIMScheduler`]. - """ - - def __init__(self, unet, scheduler): - super().__init__() - self.register_modules(unet=unet, scheduler=scheduler) - - @torch.no_grad() - def __call__( - self, - batch_size: int = 1, - generator: Optional[torch.Generator] = None, - eta: float = 0.0, - num_inference_steps: int = 50, - output_type: Optional[str] = "pil", - return_dict: bool = True, - **kwargs, - ) -> Union[ImagePipelineOutput, Tuple]: - r""" - Args: - batch_size (`int`, *optional*, defaults to 1): - The number of images to generate. - generator (`torch.Generator`, *optional*): - A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation - deterministic. - eta (`float`, *optional*, defaults to 0.0): - The eta parameter which controls the scale of the variance (0 is DDIM and 1 is one type of DDPM). - num_inference_steps (`int`, *optional*, defaults to 50): - The number of denoising steps. More denoising steps usually lead to a higher quality image at the - expense of slower inference. - output_type (`str`, *optional*, defaults to `"pil"`): - The output format of the generate image. Choose between - [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple. - - Returns: - [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if - `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the - generated images. - """ - - # Sample gaussian noise to begin loop - image = torch.randn( - (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size), - generator=generator, - ) - image = image.to(self.device) - - # set step values - self.scheduler.set_timesteps(num_inference_steps) - - for t in self.progress_bar(self.scheduler.timesteps): - # 1. predict noise model_output - model_output = self.unet(image, t).sample - - # 2. predict previous mean of image x_t-1 and add variance depending on eta - # eta corresponds to η in paper and should be between [0, 1] - # do x_t -> x_t-1 - 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" \ No newline at end of file diff --git a/hf-internal-testing/diffusers-dummy-pipeline/models--hf-internal-testing--diffusers-dummy-pipeline/refs/main b/hf-internal-testing/diffusers-dummy-pipeline/models--hf-internal-testing--diffusers-dummy-pipeline/refs/main deleted file mode 100644 index 152c8af6..00000000 --- a/hf-internal-testing/diffusers-dummy-pipeline/models--hf-internal-testing--diffusers-dummy-pipeline/refs/main +++ /dev/null @@ -1 +0,0 @@ -b8fa12635e53eebebc22f95ee863e7af4fc2fb07 \ No newline at end of file diff --git a/hf-internal-testing/diffusers-dummy-pipeline/models--hf-internal-testing--diffusers-dummy-pipeline/snapshots/b8fa12635e53eebebc22f95ee863e7af4fc2fb07/pipeline.py b/hf-internal-testing/diffusers-dummy-pipeline/models--hf-internal-testing--diffusers-dummy-pipeline/snapshots/b8fa12635e53eebebc22f95ee863e7af4fc2fb07/pipeline.py deleted file mode 120000 index 47bb9680..00000000 --- a/hf-internal-testing/diffusers-dummy-pipeline/models--hf-internal-testing--diffusers-dummy-pipeline/snapshots/b8fa12635e53eebebc22f95ee863e7af4fc2fb07/pipeline.py +++ /dev/null @@ -1 +0,0 @@ -../../blobs/bbbcb9f65616524d6199fa3bc16dc0500fb2cbbb \ No newline at end of file