Automated PR - 2026-01-06

2026-01-06 06:06:43 +00:00
parent 08f892eace
commit e1d2cff331
91 changed files with 34231 additions and 65642 deletions
@@ -0,0 +1,57 @@
+import torch
+
+from .nodes_registry import comfy_node
+
+
+@comfy_node(name="DynamicConditioning")
+class DynamicConditioning:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "power": ("FLOAT", {"default": 1.3, "min": 1, "max": 2, "step": 0.01}),
+                "only_first_frame": ("BOOLEAN", {"default": True}),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "apply"
+    CATEGORY = "lightricks/LTXV"
+    INIT = False
+
+    def apply(self, model, power, only_first_frame):
+        self.only_first_frame = only_first_frame
+        self.power = power
+        model = model.clone()
+        model.set_model_denoise_mask_function(self.forward)
+        return (model,)
+
+    def find_step(self, sigma: torch.Tensor, step_sigmas: torch.Tensor):
+        for i, step_sigma in enumerate(step_sigmas):
+            if step_sigma <= sigma:
+                return i
+        return len(step_sigmas) - 1
+
+    def forward(
+        self, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict
+    ):
+        model = extra_options["model"]
+        step_sigmas = extra_options["sigmas"]
+        step = self.find_step(sigma, step_sigmas)
+        # In order to apply power multiple times, this is the same as applying power number of times equal to step
+        power = self.power**step
+        denoise_mask = denoise_mask.clone()
+        if self.only_first_frame:
+            num_channels = model.model_patcher.model.diffusion_model.in_channels
+            denoise_mask[:, :num_channels, :1] **= power
+        else:
+            denoise_mask **= power
+        # make sure to update the denoise mask in the model, to get correct timestep values for all tokens
+        for k in model.conds:
+            if "positive" in k or "negative" in k:
+                for cond in model.conds[k]:
+                    if "model_conds" in cond and "denoise_mask" in cond["model_conds"]:
+                        cond["model_conds"]["denoise_mask"].cond = denoise_mask
+        # print(f"DynamicConditioning: power: {power}, step: {step}, sigma: {sigma}, step_sigmas: {step_sigmas}")
+        return denoise_mask