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| # Copyright 2022 Microsoft and 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 Callable, List, Optional, Tuple, Union | |
| import torch | |
| from diffusers import Transformer2DModel, VQModel | |
| from diffusers.schedulers.scheduling_vq_diffusion import VQDiffusionScheduler | |
| from transformers import CLIPTextModel, CLIPTokenizer | |
| from ...configuration_utils import ConfigMixin, register_to_config | |
| from ...modeling_utils import ModelMixin | |
| from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput | |
| from ...utils import logging | |
| logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
| class LearnedClassifierFreeSamplingEmbeddings(ModelMixin, ConfigMixin): | |
| """ | |
| Utility class for storing learned text embeddings for classifier free sampling | |
| """ | |
| def __init__(self, learnable: bool, hidden_size: Optional[int] = None, length: Optional[int] = None): | |
| super().__init__() | |
| self.learnable = learnable | |
| if self.learnable: | |
| assert hidden_size is not None, "learnable=True requires `hidden_size` to be set" | |
| assert length is not None, "learnable=True requires `length` to be set" | |
| embeddings = torch.zeros(length, hidden_size) | |
| else: | |
| embeddings = None | |
| self.embeddings = torch.nn.Parameter(embeddings) | |
| class VQDiffusionPipeline(DiffusionPipeline): | |
| r""" | |
| Pipeline for text-to-image generation using VQ Diffusion | |
| 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.) | |
| Args: | |
| vqvae ([`VQModel`]): | |
| Vector Quantized Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent | |
| representations. | |
| text_encoder ([`CLIPTextModel`]): | |
| Frozen text-encoder. VQ Diffusion uses the text portion of | |
| [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically | |
| the [clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) variant. | |
| tokenizer (`CLIPTokenizer`): | |
| Tokenizer of class | |
| [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). | |
| transformer ([`Transformer2DModel`]): | |
| Conditional transformer to denoise the encoded image latents. | |
| scheduler ([`VQDiffusionScheduler`]): | |
| A scheduler to be used in combination with `transformer` to denoise the encoded image latents. | |
| """ | |
| vqvae: VQModel | |
| text_encoder: CLIPTextModel | |
| tokenizer: CLIPTokenizer | |
| transformer: Transformer2DModel | |
| learned_classifier_free_sampling_embeddings: LearnedClassifierFreeSamplingEmbeddings | |
| scheduler: VQDiffusionScheduler | |
| def __init__( | |
| self, | |
| vqvae: VQModel, | |
| text_encoder: CLIPTextModel, | |
| tokenizer: CLIPTokenizer, | |
| transformer: Transformer2DModel, | |
| scheduler: VQDiffusionScheduler, | |
| learned_classifier_free_sampling_embeddings: LearnedClassifierFreeSamplingEmbeddings, | |
| ): | |
| super().__init__() | |
| self.register_modules( | |
| vqvae=vqvae, | |
| transformer=transformer, | |
| text_encoder=text_encoder, | |
| tokenizer=tokenizer, | |
| scheduler=scheduler, | |
| learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings, | |
| ) | |
| def _encode_prompt(self, prompt, num_images_per_prompt, do_classifier_free_guidance): | |
| batch_size = len(prompt) if isinstance(prompt, list) else 1 | |
| # get prompt text embeddings | |
| text_inputs = self.tokenizer( | |
| prompt, | |
| padding="max_length", | |
| max_length=self.tokenizer.model_max_length, | |
| return_tensors="pt", | |
| ) | |
| text_input_ids = text_inputs.input_ids | |
| if text_input_ids.shape[-1] > self.tokenizer.model_max_length: | |
| removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]) | |
| logger.warning( | |
| "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}" | |
| ) | |
| text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length] | |
| text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0] | |
| # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion. | |
| # While CLIP does normalize the pooled output of the text transformer when combining | |
| # the image and text embeddings, CLIP does not directly normalize the last hidden state. | |
| # | |
| # CLIP normalizing the pooled output. | |
| # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053 | |
| text_embeddings = text_embeddings / text_embeddings.norm(dim=-1, keepdim=True) | |
| # duplicate text embeddings for each generation per prompt | |
| text_embeddings = text_embeddings.repeat_interleave(num_images_per_prompt, dim=0) | |
| if do_classifier_free_guidance: | |
| if self.learned_classifier_free_sampling_embeddings.learnable: | |
| uncond_embeddings = self.learned_classifier_free_sampling_embeddings.embeddings | |
| uncond_embeddings = uncond_embeddings.unsqueeze(0).repeat(batch_size, 1, 1) | |
| else: | |
| uncond_tokens = [""] * batch_size | |
| max_length = text_input_ids.shape[-1] | |
| uncond_input = self.tokenizer( | |
| uncond_tokens, | |
| padding="max_length", | |
| max_length=max_length, | |
| truncation=True, | |
| return_tensors="pt", | |
| ) | |
| uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0] | |
| # See comment for normalizing text embeddings | |
| uncond_embeddings = uncond_embeddings / uncond_embeddings.norm(dim=-1, keepdim=True) | |
| # duplicate unconditional embeddings for each generation per prompt, using mps friendly method | |
| seq_len = uncond_embeddings.shape[1] | |
| uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1) | |
| uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1) | |
| # 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]) | |
| return text_embeddings | |
| def __call__( | |
| self, | |
| prompt: Union[str, List[str]], | |
| num_inference_steps: int = 100, | |
| guidance_scale: float = 5.0, | |
| truncation_rate: float = 1.0, | |
| num_images_per_prompt: int = 1, | |
| generator: Optional[torch.Generator] = None, | |
| latents: Optional[torch.FloatTensor] = None, | |
| output_type: Optional[str] = "pil", | |
| return_dict: bool = True, | |
| callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, | |
| callback_steps: Optional[int] = 1, | |
| ) -> Union[ImagePipelineOutput, Tuple]: | |
| """ | |
| Function invoked when calling the pipeline for generation. | |
| Args: | |
| prompt (`str` or `List[str]`): | |
| The prompt or prompts to guide the image generation. | |
| num_inference_steps (`int`, *optional*, defaults to 100): | |
| The number of denoising steps. More denoising steps usually lead to a higher quality image at the | |
| expense of slower inference. | |
| guidance_scale (`float`, *optional*, defaults to 7.5): | |
| Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). | |
| `guidance_scale` is defined as `w` of equation 2. of [Imagen | |
| Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > | |
| 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, | |
| usually at the expense of lower image quality. | |
| truncation_rate (`float`, *optional*, defaults to 1.0 (equivalent to no truncation)): | |
| Used to "truncate" the predicted classes for x_0 such that the cumulative probability for a pixel is at | |
| most `truncation_rate`. The lowest probabilities that would increase the cumulative probability above | |
| `truncation_rate` are set to zero. | |
| num_images_per_prompt (`int`, *optional*, defaults to 1): | |
| The number of images to generate per prompt. | |
| generator (`torch.Generator`, *optional*): | |
| A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation | |
| deterministic. | |
| latents (`torch.FloatTensor` of shape (batch), *optional*): | |
| Pre-generated noisy latents to be used as inputs for image generation. Must be valid embedding indices. | |
| Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will | |
| be generated of completely masked latent pixels. | |
| output_type (`str`, *optional*, defaults to `"pil"`): | |
| The output format of the generated 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. | |
| callback (`Callable`, *optional*): | |
| A function that will be called every `callback_steps` steps during inference. The function will be | |
| called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. | |
| callback_steps (`int`, *optional*, defaults to 1): | |
| The frequency at which the `callback` function will be called. If not specified, the callback will be | |
| called at every step. | |
| Returns: | |
| [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~ pipeline_utils.ImagePipelineOutput `] if | |
| `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the | |
| generated images. | |
| """ | |
| 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)}") | |
| batch_size = batch_size * num_images_per_prompt | |
| do_classifier_free_guidance = guidance_scale > 1.0 | |
| text_embeddings = self._encode_prompt(prompt, num_images_per_prompt, do_classifier_free_guidance) | |
| if (callback_steps is None) or ( | |
| callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) | |
| ): | |
| raise ValueError( | |
| f"`callback_steps` has to be a positive integer but is {callback_steps} of type" | |
| f" {type(callback_steps)}." | |
| ) | |
| # get the initial completely masked latents unless the user supplied it | |
| latents_shape = (batch_size, self.transformer.num_latent_pixels) | |
| if latents is None: | |
| mask_class = self.transformer.num_vector_embeds - 1 | |
| latents = torch.full(latents_shape, mask_class).to(self.device) | |
| else: | |
| if latents.shape != latents_shape: | |
| raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}") | |
| if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any(): | |
| raise ValueError( | |
| "Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0," | |
| f" {self.transformer.num_vector_embeds - 1} (inclusive)." | |
| ) | |
| latents = latents.to(self.device) | |
| # set timesteps | |
| self.scheduler.set_timesteps(num_inference_steps, device=self.device) | |
| timesteps_tensor = self.scheduler.timesteps.to(self.device) | |
| sample = latents | |
| for i, t in enumerate(self.progress_bar(timesteps_tensor)): | |
| # expand the sample if we are doing classifier free guidance | |
| latent_model_input = torch.cat([sample] * 2) if do_classifier_free_guidance else sample | |
| # predict the un-noised image | |
| # model_output == `log_p_x_0` | |
| model_output = self.transformer( | |
| latent_model_input, encoder_hidden_states=text_embeddings, timestep=t | |
| ).sample | |
| if do_classifier_free_guidance: | |
| model_output_uncond, model_output_text = model_output.chunk(2) | |
| model_output = model_output_uncond + guidance_scale * (model_output_text - model_output_uncond) | |
| model_output -= torch.logsumexp(model_output, dim=1, keepdim=True) | |
| model_output = self.truncate(model_output, truncation_rate) | |
| # remove `log(0)`'s (`-inf`s) | |
| model_output = model_output.clamp(-70) | |
| # compute the previous noisy sample x_t -> x_t-1 | |
| sample = self.scheduler.step(model_output, timestep=t, sample=sample, generator=generator).prev_sample | |
| # call the callback, if provided | |
| if callback is not None and i % callback_steps == 0: | |
| callback(i, t, sample) | |
| embedding_channels = self.vqvae.config.vq_embed_dim | |
| embeddings_shape = (batch_size, self.transformer.height, self.transformer.width, embedding_channels) | |
| embeddings = self.vqvae.quantize.get_codebook_entry(sample, shape=embeddings_shape) | |
| image = self.vqvae.decode(embeddings, force_not_quantize=True).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) | |
| def truncate(self, log_p_x_0: torch.FloatTensor, truncation_rate: float) -> torch.FloatTensor: | |
| """ | |
| Truncates log_p_x_0 such that for each column vector, the total cumulative probability is `truncation_rate` The | |
| lowest probabilities that would increase the cumulative probability above `truncation_rate` are set to zero. | |
| """ | |
| sorted_log_p_x_0, indices = torch.sort(log_p_x_0, 1, descending=True) | |
| sorted_p_x_0 = torch.exp(sorted_log_p_x_0) | |
| keep_mask = sorted_p_x_0.cumsum(dim=1) < truncation_rate | |
| # Ensure that at least the largest probability is not zeroed out | |
| all_true = torch.full_like(keep_mask[:, 0:1, :], True) | |
| keep_mask = torch.cat((all_true, keep_mask), dim=1) | |
| keep_mask = keep_mask[:, :-1, :] | |
| keep_mask = keep_mask.gather(1, indices.argsort(1)) | |
| rv = log_p_x_0.clone() | |
| rv[~keep_mask] = -torch.inf # -inf = log(0) | |
| return rv | |