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Merge branch 'main' of https://github.com/huggingface/diffusers into main

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patil-suraj
2022-06-09 16:40:45 +02:00
parent 302ac73b74 27039cd37c
commit e3820fa3f6
3 changed files with 84 additions and 63 deletions
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69
models/vision/ddim/modeling_ddim.py
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@@ -34,49 +34,68 @@ class DDIM(DiffusionPipeline):
inference_step_times = range(0, num_trained_timesteps, num_trained_timesteps // num_inference_steps)
self.unet.to(torch_device)
# Sample gaussian noise to begin loop
image = self.noise_scheduler.sample_noise(
(batch_size, self.unet.in_channels, self.unet.resolution, self.unet.resolution),
device=torch_device,
generator=generator,
)
# See formulas (9), (10) and (7) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
# Ideally, read DDIM paper in-detail understanding
# Notation (<variable name> -> <name in paper>
# - pred_noise_t -> e_theta(x_t, t)
# - pred_original_image -> f_theta(x_t, t) or x_0
# - std_dev_t -> sigma_t
# - eta -> η
# - pred_image_direction -> "direction pointingc to x_t"
# - pred_prev_image -> "x_t-1"
for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_steps):
# get actual t and t-1
# 1. predict noise residual
with torch.no_grad():
pred_noise_t = self.unet(image, inference_step_times[t])
# 2. 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
# 3. compute alphas, betas
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()
beta_prod_t = (1 - alpha_prod_t)
beta_prod_t_prev = (1 - alpha_prod_t_prev)
# 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()
# 4. Compute predicted previous image from predicted noise
# model forward
with torch.no_grad():
noise_residual = self.unet(image, train_step)
# First: compute predicted original image from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_original_image = (image - beta_prod_t.sqrt() * pred_noise_t) / alpha_prod_t.sqrt()
# 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
# Second: Clip "predicted x_0"
pred_original_image = torch.clamp(pred_original_image, -1, 1)
# if eta > 0.0 add noise. Note eta = 1.0 essentially corresponds to DDPM
# Third: Compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
std_dev_t = (beta_prod_t_prev / beta_prod_t).sqrt() * (1 - alpha_prod_t / alpha_prod_t_prev).sqrt()
std_dev_t = eta * std_dev_t
# Fourth: Compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_image_direction = (1 - alpha_prod_t_prev - std_dev_t**2).sqrt() * pred_noise_t
# Fifth: Compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_prev_image = alpha_prod_t_prev.sqrt() * pred_original_image + pred_image_direction
# 5. Sample x_t-1 image optionally if η > 0.0 by adding noise to pred_prev_image
# 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
prev_image = pred_prev_image + std_dev_t * noise
else:
image = pred_mean
prev_image = pred_prev_image
# 6. Set current image to prev_image: x_t -> x_t-1
image = prev_image
return image

46
models/vision/ddpm/modeling_ddpm.py
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@@ -30,43 +30,43 @@ class DDPM(DiffusionPipeline):
torch_device = "cuda" if torch.cuda.is_available() else "cpu"
self.unet.to(torch_device)
# 1. Sample gaussian noise
# Sample gaussian noise to begin loop
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(len(self.noise_scheduler))), total=len(self.noise_scheduler)):
# i) define coefficients for time step t
clipped_image_coeff = 1 / torch.sqrt(self.noise_scheduler.get_alpha_prod(t))
clipped_noise_coeff = torch.sqrt(1 / self.noise_scheduler.get_alpha_prod(t) - 1)
image_coeff = (
(1 - self.noise_scheduler.get_alpha_prod(t - 1))
* torch.sqrt(self.noise_scheduler.get_alpha(t))
/ (1 - self.noise_scheduler.get_alpha_prod(t))
)
clipped_coeff = (
torch.sqrt(self.noise_scheduler.get_alpha_prod(t - 1))
* self.noise_scheduler.get_beta(t)
/ (1 - self.noise_scheduler.get_alpha_prod(t))
)
# ii) predict noise residual
for t in tqdm.tqdm(reversed(range(len(self.noise_scheduler))), total=len(self.noise_scheduler)):
# 1. predict noise residual
with torch.no_grad():
noise_residual = self.unet(image, t)
# iii) compute predicted image from residual
# See 2nd formula at https://github.com/hojonathanho/diffusion/issues/5#issue-896554416 for comparison
pred_mean = clipped_image_coeff * image - clipped_noise_coeff * noise_residual
pred_mean = torch.clamp(pred_mean, -1, 1)
prev_image = clipped_coeff * pred_mean + image_coeff * image
# 2. compute alphas, betas
alpha_prod_t = self.noise_scheduler.get_alpha_prod(t)
alpha_prod_t_prev = self.noise_scheduler.get_alpha_prod(t - 1)
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev
# iv) sample variance
# 3. compute predicted image from residual
# See 2nd formula at https://github.com/hojonathanho/diffusion/issues/5#issue-896554416 for comparison
# First: Compute inner formula
pred_mean = (1 / alpha_prod_t.sqrt()) * (image - beta_prod_t.sqrt() * noise_residual)
# Second: Clip
pred_mean = torch.clamp(pred_mean, -1, 1)
# Third: Compute outer coefficients
pred_mean_coeff = (alpha_prod_t_prev.sqrt() * self.noise_scheduler.get_beta(t)) / beta_prod_t
image_coeff = (beta_prod_t_prev * self.noise_scheduler.get_alpha(t).sqrt()) / beta_prod_t
# Fourth: Compute outer formula
prev_image = pred_mean_coeff * pred_mean + image_coeff * image
# 4. sample variance
prev_variance = self.noise_scheduler.sample_variance(
t, prev_image.shape, device=torch_device, generator=generator
)
# v) sample x_{t-1} ~ N(prev_image, prev_variance)
# 5. sample x_{t-1} ~ N(prev_image, prev_variance) = add variance to predicted image
sampled_prev_image = prev_image + prev_variance
image = sampled_prev_image

32
src/diffusers/pipeline_utils.py
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@@ -40,6 +40,7 @@ LOADABLE_CLASSES = {
},
"transformers": {
"PreTrainedTokenizer": ["save_pretrained", "from_pretrained"],
"PreTrainedModel": ["save_pretrained", "from_pretrained"],
},
}
@@ -82,24 +83,25 @@ class DiffusionPipeline(ConfigMixin):
model_index_dict.pop("_diffusers_version")
model_index_dict.pop("_module")
for name, (library_name, class_name) in model_index_dict.items():
importable_classes = LOADABLE_CLASSES[library_name]
# TODO: Suraj
if library_name == self.__module__:
library_name = self
library = importlib.import_module(library_name)
class_obj = getattr(library, class_name)
class_candidates = {c: getattr(library, c) for c in importable_classes.keys()}
for pipeline_component_name in model_index_dict.keys():
sub_model = getattr(self, pipeline_component_name)
model_cls = sub_model.__class__
save_method_name = None
for class_name, class_candidate in class_candidates.items():
if issubclass(class_obj, class_candidate):
save_method_name = importable_classes[class_name][0]
# search for the model's base class in LOADABLE_CLASSES
for library_name, library_classes in LOADABLE_CLASSES.items():
library = importlib.import_module(library_name)
for base_class, save_load_methods in library_classes.items():
class_candidate = getattr(library, base_class)
if issubclass(model_cls, class_candidate):
# if we found a suitable base class in LOADABLE_CLASSES then grab its save method
save_method_name = save_load_methods[0]
break
if save_method_name is not None:
break
save_method = getattr(getattr(self, name), save_method_name)
save_method(os.path.join(save_directory, name))
save_method = getattr(sub_model, save_method_name)
save_method(os.path.join(save_directory, pipeline_component_name))
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):