Merge branch 'comfyanonymous:master' into master

comfy_extras/nodes_custom_sampler.py

@@ -1,3 +1,4 @@
+import math
 import comfy.samplers
 import comfy.sample
 from comfy.k_diffusion import sampling as k_diffusion_sampling
@@ -249,6 +250,55 @@ class SetFirstSigma:
         sigmas[0] = sigma
         return (sigmas, )
 
+class ExtendIntermediateSigmas:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"sigmas": ("SIGMAS", ),
+                     "steps": ("INT", {"default": 2, "min": 1, "max": 100}),
+                     "start_at_sigma": ("FLOAT", {"default": -1.0, "min": -1.0, "max": 20000.0, "step": 0.01, "round": False}),
+                     "end_at_sigma": ("FLOAT", {"default": 12.0, "min":  0.0, "max": 20000.0, "step": 0.01, "round": False}),
+                     "spacing": (['linear', 'cosine', 'sine'],),
+                    }
+               }
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "sampling/custom_sampling/sigmas"
+
+    FUNCTION = "extend"
+
+    def extend(self, sigmas: torch.Tensor, steps: int, start_at_sigma: float, end_at_sigma: float, spacing: str):
+        if start_at_sigma < 0:
+            start_at_sigma = float("inf")
+
+        interpolator = {
+            'linear': lambda x: x,
+            'cosine': lambda x: torch.sin(x*math.pi/2),
+            'sine':   lambda x: 1 - torch.cos(x*math.pi/2)
+        }[spacing]
+
+        # linear space for our interpolation function
+        x = torch.linspace(0, 1, steps + 1, device=sigmas.device)[1:-1]
+        computed_spacing = interpolator(x)
+
+        extended_sigmas = []
+        for i in range(len(sigmas) - 1):
+            sigma_current = sigmas[i]
+            sigma_next = sigmas[i+1]
+
+            extended_sigmas.append(sigma_current)
+
+            if end_at_sigma <= sigma_current <= start_at_sigma:
+                interpolated_steps = computed_spacing * (sigma_next - sigma_current) + sigma_current
+                extended_sigmas.extend(interpolated_steps.tolist())
+
+        # Add the last sigma value
+        if len(sigmas) > 0:
+            extended_sigmas.append(sigmas[-1])
+
+        extended_sigmas = torch.FloatTensor(extended_sigmas)
+
+        return (extended_sigmas,)
+
 class KSamplerSelect:
     @classmethod
     def INPUT_TYPES(s):
@@ -735,6 +785,7 @@ NODE_CLASS_MAPPINGS = {
     "SplitSigmasDenoise": SplitSigmasDenoise,
     "FlipSigmas": FlipSigmas,
     "SetFirstSigma": SetFirstSigma,
+    "ExtendIntermediateSigmas": ExtendIntermediateSigmas,
 
     "CFGGuider": CFGGuider,
     "DualCFGGuider": DualCFGGuider,

comfy_extras/nodes_model_merging.py

@@ -276,7 +276,7 @@ class CLIPSave:
         comfy.model_management.load_models_gpu([clip.load_model()], force_patch_weights=True)
         clip_sd = clip.get_sd()
 
-        for prefix in ["clip_l.", "clip_g.", ""]:
+        for prefix in ["clip_l.", "clip_g.", "clip_h.", "t5xxl.", "pile_t5xl.", "mt5xl.", "umt5xxl.", "t5base.", "gemma2_2b.", "llama.", "hydit_clip.", ""]:
             k = list(filter(lambda a: a.startswith(prefix), clip_sd.keys()))
             current_clip_sd = {}
             for x in k:

main.py

@@ -13,7 +13,7 @@ import logging
 import sys
 
 if __name__ == "__main__":
-    #NOTE: These do not do anything on core ComfyUI which should already have no communication with the internet, they are for custom nodes.
+    #NOTE: These do not do anything on core ComfyUI, they are for custom nodes.
     os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1'
     os.environ['DO_NOT_TRACK'] = '1'