336 lines
16 KiB
Python
336 lines
16 KiB
Python
# 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
|
|
"""
|
|
|
|
@register_to_config
|
|
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
|
|
|
|
@torch.no_grad()
|
|
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[Union[torch.Generator, List[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*):
|
|
One or a list of [torch generator(s)](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
|