initial version

diffsynth/schedulers/__init__.py

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+import torch, math
+
+
+class EnhancedDDIMScheduler():
+
+    def __init__(self, num_train_timesteps=1000, beta_start=0.00085, beta_end=0.012):
+        self.num_train_timesteps = num_train_timesteps
+        betas = torch.square(torch.linspace(math.sqrt(beta_start), math.sqrt(beta_end), num_train_timesteps, dtype=torch.float32))
+        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 = round(self.num_train_timesteps * denoising_strength) - 1
+        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):
+        alpha_prod_t = self.alphas_cumprod[timestep]
+        timestep_id = self.timesteps.index(timestep)
+        if timestep_id + 1 < len(self.timesteps):
+            timestep_prev = self.timesteps[timestep_id + 1]
+            alpha_prod_t_prev = self.alphas_cumprod[timestep_prev]
+        else:
+            alpha_prod_t_prev = 1.0
+
+        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
+