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Artiprocher
2024-05-05 22:48:38 +08:00
parent cc37860438
commit 0965477750
15 changed files with 2991 additions and 79 deletions
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diffsynth/schedulers/__init__.py
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@@ -1,65 +1,2 @@
import torch, math
class EnhancedDDIMScheduler():
def __init__(self, num_train_timesteps=1000, beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"):
self.num_train_timesteps = num_train_timesteps
if beta_schedule == "scaled_linear":
betas = torch.square(torch.linspace(math.sqrt(beta_start), math.sqrt(beta_end), num_train_timesteps, dtype=torch.float32))
elif beta_schedule == "linear":
betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
else:
raise NotImplementedError(f"{beta_schedule} is not implemented")
self.alphas_cumprod = torch.cumprod(1.0 - betas, dim=0).tolist()
self.set_timesteps(10)
def set_timesteps(self, num_inference_steps, denoising_strength=1.0):
# The timesteps are aligned to 999...0, which is different from other implementations,
# but I think this implementation is more reasonable in theory.
max_timestep = max(round(self.num_train_timesteps * denoising_strength) - 1, 0)
num_inference_steps = min(num_inference_steps, max_timestep + 1)
if num_inference_steps == 1:
self.timesteps = [max_timestep]
else:
step_length = max_timestep / (num_inference_steps - 1)
self.timesteps = [round(max_timestep - i*step_length) for i in range(num_inference_steps)]
def denoise(self, model_output, sample, alpha_prod_t, alpha_prod_t_prev):
weight_e = math.sqrt(1 - alpha_prod_t_prev) - math.sqrt(alpha_prod_t_prev * (1 - alpha_prod_t) / alpha_prod_t)
weight_x = math.sqrt(alpha_prod_t_prev / alpha_prod_t)
prev_sample = sample * weight_x + model_output * weight_e
weight_e = -math.sqrt((1 - alpha_prod_t) / alpha_prod_t)
weight_x = math.sqrt(1 / alpha_prod_t)
return prev_sample
def step(self, model_output, timestep, sample, to_final=False):
alpha_prod_t = self.alphas_cumprod[timestep]
timestep_id = self.timesteps.index(timestep)
if to_final or timestep_id + 1 >= len(self.timesteps):
alpha_prod_t_prev = 1.0
else:
timestep_prev = self.timesteps[timestep_id + 1]
alpha_prod_t_prev = self.alphas_cumprod[timestep_prev]
return self.denoise(model_output, sample, alpha_prod_t, alpha_prod_t_prev)
def return_to_timestep(self, timestep, sample, sample_stablized):
alpha_prod_t = self.alphas_cumprod[timestep]
noise_pred = (sample - math.sqrt(alpha_prod_t) * sample_stablized) / math.sqrt(1 - alpha_prod_t)
return noise_pred
def add_noise(self, original_samples, noise, timestep):
sqrt_alpha_prod = math.sqrt(self.alphas_cumprod[timestep])
sqrt_one_minus_alpha_prod = math.sqrt(1 - self.alphas_cumprod[timestep])
noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
from .ddim import EnhancedDDIMScheduler
from .continuous_ode import ContinuousODEScheduler