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Commit a24862cd authored by Patrick von Platen's avatar Patrick von Platen
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Correct VQDiffusion Pipeline import

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clip_guided_stable_diffusion/72392adcdf265e793b0dc13d166393a9d1367724bb03f6faca8cfb1c91c30827.8d4a13da440f0a37b6d42d3c81220be0a04e4543e6fd9b0f290547749cc06cfb deleted 100644 → 0
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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)
clip_guided_stable_diffusion/72392adcdf265e793b0dc13d166393a9d1367724bb03f6faca8cfb1c91c30827.8d4a13da440f0a37b6d42d3c81220be0a04e4543e6fd9b0f290547749cc06cfb.json deleted 100644 → 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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hf-internal-testing/diffusers-dummy-pipeline/models--hf-internal-testing--diffusers-dummy-pipeline/blobs/bbbcb9f65616524d6199fa3bc16dc0500fb2cbbb deleted 100644 → 0
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# 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"
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