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rebuild base modules

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Artiprocher
2024-07-26 12:15:40 +08:00
parent 9471bff8a4
commit e3f8a576cf
76 changed files with 3253 additions and 3563 deletions
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30
diffsynth/schedulers/ddim.py
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@@ -22,10 +22,10 @@ class EnhancedDDIMScheduler():
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]
self.timesteps = torch.Tensor([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)]
self.timesteps = torch.Tensor([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):
@@ -43,31 +43,37 @@ class EnhancedDDIMScheduler():
def step(self, model_output, timestep, sample, to_final=False):
alpha_prod_t = self.alphas_cumprod[timestep]
timestep_id = self.timesteps.index(timestep)
alpha_prod_t = self.alphas_cumprod[int(timestep.flatten().tolist()[0])]
if isinstance(timestep, torch.Tensor):
timestep = timestep.cpu()
timestep_id = torch.argmin((self.timesteps - timestep).abs())
if to_final or timestep_id + 1 >= len(self.timesteps):
alpha_prod_t_prev = 1.0
else:
timestep_prev = self.timesteps[timestep_id + 1]
timestep_prev = int(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]
alpha_prod_t = self.alphas_cumprod[int(timestep.flatten().tolist()[0])]
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])
sqrt_alpha_prod = math.sqrt(self.alphas_cumprod[int(timestep.flatten().tolist()[0])])
sqrt_one_minus_alpha_prod = math.sqrt(1 - self.alphas_cumprod[int(timestep.flatten().tolist()[0])])
noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def training_target(self, sample, noise, timestep):
sqrt_alpha_prod = math.sqrt(self.alphas_cumprod[timestep])
sqrt_one_minus_alpha_prod = math.sqrt(1 - self.alphas_cumprod[timestep])
target = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return target
if self.prediction_type == "epsilon":
return noise
else:
sqrt_alpha_prod = math.sqrt(self.alphas_cumprod[int(timestep.flatten().tolist()[0])])
sqrt_one_minus_alpha_prod = math.sqrt(1 - self.alphas_cumprod[int(timestep.flatten().tolist()[0])])
target = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return target

4
diffsynth/schedulers/flow_match.py
View File

@@ -20,6 +20,8 @@ class FlowMatchScheduler():
def step(self, model_output, timestep, sample, to_final=False):
if isinstance(timestep, torch.Tensor):
timestep = timestep.cpu()
timestep_id = torch.argmin((self.timesteps - timestep).abs())
sigma = self.sigmas[timestep_id]
if to_final or timestep_id + 1 >= len(self.timesteps):
@@ -36,6 +38,8 @@ class FlowMatchScheduler():
def add_noise(self, original_samples, noise, timestep):
if isinstance(timestep, torch.Tensor):
timestep = timestep.cpu()
timestep_id = torch.argmin((self.timesteps - timestep).abs())
sigma = self.sigmas[timestep_id]
sample = (1 - sigma) * original_samples + sigma * noise