Stable Video Diffusion

scripts/demo/discretization.py

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+import torch
+
+from sgm.modules.diffusionmodules.discretizer import Discretization
+
+
+class Img2ImgDiscretizationWrapper:
+    """
+    wraps a discretizer, and prunes the sigmas
+    params:
+        strength: float between 0.0 and 1.0. 1.0 means full sampling (all sigmas are returned)
+    """
+
+    def __init__(self, discretization: Discretization, strength: float = 1.0):
+        self.discretization = discretization
+        self.strength = strength
+        assert 0.0 <= self.strength <= 1.0
+
+    def __call__(self, *args, **kwargs):
+        # sigmas start large first, and decrease then
+        sigmas = self.discretization(*args, **kwargs)
+        print(f"sigmas after discretization, before pruning img2img: ", sigmas)
+        sigmas = torch.flip(sigmas, (0,))
+        sigmas = sigmas[: max(int(self.strength * len(sigmas)), 1)]
+        print("prune index:", max(int(self.strength * len(sigmas)), 1))
+        sigmas = torch.flip(sigmas, (0,))
+        print(f"sigmas after pruning: ", sigmas)
+        return sigmas
+
+
+class Txt2NoisyDiscretizationWrapper:
+    """
+    wraps a discretizer, and prunes the sigmas
+    params:
+        strength: float between 0.0 and 1.0. 0.0 means full sampling (all sigmas are returned)
+    """
+
+    def __init__(
+        self, discretization: Discretization, strength: float = 0.0, original_steps=None
+    ):
+        self.discretization = discretization
+        self.strength = strength
+        self.original_steps = original_steps
+        assert 0.0 <= self.strength <= 1.0
+
+    def __call__(self, *args, **kwargs):
+        # sigmas start large first, and decrease then
+        sigmas = self.discretization(*args, **kwargs)
+        print(f"sigmas after discretization, before pruning img2img: ", sigmas)
+        sigmas = torch.flip(sigmas, (0,))
+        if self.original_steps is None:
+            steps = len(sigmas)
+        else:
+            steps = self.original_steps + 1
+        prune_index = max(min(int(self.strength * steps) - 1, steps - 1), 0)
+        sigmas = sigmas[prune_index:]
+        print("prune index:", prune_index)
+        sigmas = torch.flip(sigmas, (0,))
+        print(f"sigmas after pruning: ", sigmas)
+        return sigmas