fusing
/

ddim-celeba-hq_copy

Inference Endpoints

Model card Files Files and versions Community

ddim-celeba-hq_copy / modeling_ddim.py

patrickvonplaten's picture

patrickvonplaten

Upload modeling_ddim.py

6e970c6 about 2 years ago

raw history blame contribute delete

No virus

3.37 kB

	# 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.


	import torch

	import tqdm
	from diffusers import DiffusionPipeline


	class DDIM(DiffusionPipeline):
	def __init__(self, unet, noise_scheduler):
	super().__init__()
	self.register_modules(unet=unet, noise_scheduler=noise_scheduler)

	def __call__(self, batch_size=1, generator=None, torch_device=None, eta=0.0, num_inference_steps=50):
	# eta corresponds to η in paper and should be between [0, 1]
	if torch_device is None:
	torch_device = "cuda" if torch.cuda.is_available() else "cpu"

	num_trained_timesteps = self.noise_scheduler.num_timesteps
	inference_step_times = range(0, num_trained_timesteps, num_trained_timesteps // num_inference_steps)

	self.unet.to(torch_device)
	image = self.noise_scheduler.sample_noise(
	(batch_size, self.unet.in_channels, self.unet.resolution, self.unet.resolution),
	device=torch_device,
	generator=generator,
	)

	for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_steps):
	# get actual t and t-1
	train_step = inference_step_times[t]
	prev_train_step = inference_step_times[t - 1] if t > 0 else -1

	# compute alphas
	alpha_prod_t = self.noise_scheduler.get_alpha_prod(train_step)
	alpha_prod_t_prev = self.noise_scheduler.get_alpha_prod(prev_train_step)
	alpha_prod_t_rsqrt = 1 / alpha_prod_t.sqrt()
	alpha_prod_t_prev_rsqrt = 1 / alpha_prod_t_prev.sqrt()
	beta_prod_t_sqrt = (1 - alpha_prod_t).sqrt()
	beta_prod_t_prev_sqrt = (1 - alpha_prod_t_prev).sqrt()

	# compute relevant coefficients
	coeff_1 = (
	(alpha_prod_t_prev - alpha_prod_t).sqrt()
	* alpha_prod_t_prev_rsqrt
	* beta_prod_t_prev_sqrt
	/ beta_prod_t_sqrt
	* eta
	)
	coeff_2 = ((1 - alpha_prod_t_prev) - coeff_1**2).sqrt()

	# model forward
	with torch.no_grad():
	noise_residual = self.unet(image, train_step)

	# predict mean of prev image
	pred_mean = alpha_prod_t_rsqrt * (image - beta_prod_t_sqrt * noise_residual)
	pred_mean = torch.clamp(pred_mean, -1, 1)
	pred_mean = (1 / alpha_prod_t_prev_rsqrt) * pred_mean + coeff_2 * noise_residual

	# if eta > 0.0 add noise. Note eta = 1.0 essentially corresponds to DDPM
	if eta > 0.0:
	noise = self.noise_scheduler.sample_noise(image.shape, device=image.device, generator=generator)
	image = pred_mean + coeff_1 * noise
	else:
	image = pred_mean

	return image