restore nodes order as it in the V1 version for smaller git diff (2)

comfy_extras/v3/nodes_flux.py

@@ -49,28 +49,6 @@ class CLIPTextEncodeFlux(io.ComfyNode):
 
         return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens, add_dict={"guidance": guidance}))
 
-
-class FluxDisableGuidance(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="FluxDisableGuidance_V3",
-            category="advanced/conditioning/flux",
-            description="This node completely disables the guidance embed on Flux and Flux like models",
-            inputs=[
-                io.Conditioning.Input("conditioning"),
-            ],
-            outputs=[
-                io.Conditioning.Output(),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, conditioning):
-        c = node_helpers.conditioning_set_values(conditioning, {"guidance": None})
-        return io.NodeOutput(c)
-
-
 class FluxGuidance(io.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -91,6 +69,25 @@ class FluxGuidance(io.ComfyNode):
         c = node_helpers.conditioning_set_values(conditioning, {"guidance": guidance})
         return io.NodeOutput(c)
 
+class FluxDisableGuidance(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="FluxDisableGuidance_V3",
+            category="advanced/conditioning/flux",
+            description="This node completely disables the guidance embed on Flux and Flux like models",
+            inputs=[
+                io.Conditioning.Input("conditioning"),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, conditioning):
+        c = node_helpers.conditioning_set_values(conditioning, {"guidance": None})
+        return io.NodeOutput(c)
 
 class FluxKontextImageScale(io.ComfyNode):
     @classmethod
@@ -117,7 +114,7 @@ class FluxKontextImageScale(io.ComfyNode):
         return io.NodeOutput(image)
 
 
-NODES_LIST = [
+NODES_LIST: list[type[io.ComfyNode]] = [
     CLIPTextEncodeFlux,
     FluxDisableGuidance,
     FluxGuidance,

comfy_extras/v3/nodes_freelunch.py

@@ -125,7 +125,7 @@ class FreeU_V2(io.ComfyNode):
         return io.NodeOutput(m)
 
 
-NODES_LIST = [
+NODES_LIST: list[type[io.ComfyNode]] = [
     FreeU,
     FreeU_V2,
 ]

comfy_extras/v3/nodes_hidream.py

@@ -6,33 +6,6 @@ import folder_paths
 from comfy_api.latest import io
 
 
-class CLIPTextEncodeHiDream(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="CLIPTextEncodeHiDream_V3",
-            category="advanced/conditioning",
-            inputs=[
-                io.Clip.Input("clip"),
-                io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
-                io.String.Input("clip_g", multiline=True, dynamic_prompts=True),
-                io.String.Input("t5xxl", multiline=True, dynamic_prompts=True),
-                io.String.Input("llama", multiline=True, dynamic_prompts=True),
-            ],
-            outputs=[
-                io.Conditioning.Output(),
-            ]
-        )
-
-    @classmethod
-    def execute(cls, clip, clip_l, clip_g, t5xxl, llama):
-        tokens = clip.tokenize(clip_g)
-        tokens["l"] = clip.tokenize(clip_l)["l"]
-        tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"]
-        tokens["llama"] = clip.tokenize(llama)["llama"]
-        return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
-
-
 class QuadrupleCLIPLoader(io.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -65,7 +38,34 @@ class QuadrupleCLIPLoader(io.ComfyNode):
         )
 
 
-NODES_LIST = [
+class CLIPTextEncodeHiDream(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPTextEncodeHiDream_V3",
+            category="advanced/conditioning",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
+                io.String.Input("clip_g", multiline=True, dynamic_prompts=True),
+                io.String.Input("t5xxl", multiline=True, dynamic_prompts=True),
+                io.String.Input("llama", multiline=True, dynamic_prompts=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ]
+        )
+
+    @classmethod
+    def execute(cls, clip, clip_l, clip_g, t5xxl, llama):
+        tokens = clip.tokenize(clip_g)
+        tokens["l"] = clip.tokenize(clip_l)["l"]
+        tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"]
+        tokens["llama"] = clip.tokenize(llama)["llama"]
+        return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
+
+
+NODES_LIST: list[type[io.ComfyNode]] = [
     CLIPTextEncodeHiDream,
     QuadrupleCLIPLoader,
 ]

comfy_extras/v3/nodes_hunyuan.py

@@ -7,16 +7,6 @@ import node_helpers
 import nodes
 from comfy_api.latest import io
 
-PROMPT_TEMPLATE_ENCODE_VIDEO_I2V = (
-    "<|start_header_id|>system<|end_header_id|>\n\n<image>\nDescribe the video by detailing the following aspects according to the reference image: "
-    "1. The main content and theme of the video."
-    "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects."
-    "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects."
-    "4. background environment, light, style and atmosphere."
-    "5. camera angles, movements, and transitions used in the video:<|eot_id|>\n\n"
-    "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
-    "<|start_header_id|>assistant<|end_header_id|>\n\n"
-)
 
 class CLIPTextEncodeHunyuanDiT(io.ComfyNode):
     @classmethod
@@ -68,6 +58,51 @@ class EmptyHunyuanLatentVideo(io.ComfyNode):
         return io.NodeOutput({"samples":latent})
 
 
+PROMPT_TEMPLATE_ENCODE_VIDEO_I2V = (
+    "<|start_header_id|>system<|end_header_id|>\n\n<image>\nDescribe the video by detailing the following aspects according to the reference image: "
+    "1. The main content and theme of the video."
+    "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects."
+    "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects."
+    "4. background environment, light, style and atmosphere."
+    "5. camera angles, movements, and transitions used in the video:<|eot_id|>\n\n"
+    "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
+    "<|start_header_id|>assistant<|end_header_id|>\n\n"
+)
+
+
+class TextEncodeHunyuanVideo_ImageToVideo(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="TextEncodeHunyuanVideo_ImageToVideo_V3",
+            category="advanced/conditioning",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.ClipVisionOutput.Input("clip_vision_output"),
+                io.String.Input("prompt", multiline=True, dynamic_prompts=True),
+                io.Int.Input(
+                    "image_interleave",
+                    default=2,
+                    min=1,
+                    max=512,
+                    tooltip="How much the image influences things vs the text prompt. Higher number means more influence from the text prompt.",
+                ),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, clip, clip_vision_output, prompt, image_interleave):
+        tokens = clip.tokenize(
+            prompt, llama_template=PROMPT_TEMPLATE_ENCODE_VIDEO_I2V,
+            image_embeds=clip_vision_output.mm_projected,
+            image_interleave=image_interleave,
+        )
+        return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
+
+
 class HunyuanImageToVideo(io.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -126,40 +161,7 @@ class HunyuanImageToVideo(io.ComfyNode):
         return io.NodeOutput(positive, out_latent)
 
 
-class TextEncodeHunyuanVideo_ImageToVideo(io.ComfyNode):
+NODES_LIST: list[type[io.ComfyNode]] = [
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="TextEncodeHunyuanVideo_ImageToVideo_V3",
-            category="advanced/conditioning",
-            inputs=[
-                io.Clip.Input("clip"),
-                io.ClipVisionOutput.Input("clip_vision_output"),
-                io.String.Input("prompt", multiline=True, dynamic_prompts=True),
-                io.Int.Input(
-                    "image_interleave",
-                    default=2,
-                    min=1,
-                    max=512,
-                    tooltip="How much the image influences things vs the text prompt. Higher number means more influence from the text prompt.",
-                ),
-            ],
-            outputs=[
-                io.Conditioning.Output(),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, clip, clip_vision_output, prompt, image_interleave):
-        tokens = clip.tokenize(
-            prompt, llama_template=PROMPT_TEMPLATE_ENCODE_VIDEO_I2V,
-            image_embeds=clip_vision_output.mm_projected,
-            image_interleave=image_interleave,
-        )
-        return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
-
-
-NODES_LIST = [
     CLIPTextEncodeHunyuanDiT,
     EmptyHunyuanLatentVideo,
     HunyuanImageToVideo,

comfy_extras/v3/nodes_hunyuan3d.py

@@ -661,7 +661,7 @@ class VoxelToMeshBasic(io.ComfyNode):
         return io.NodeOutput(MESH(torch.stack(vertices), torch.stack(faces)))
 
 
-NODES_LIST = [
+NODES_LIST: list[type[io.ComfyNode]] = [
     EmptyLatentHunyuan3Dv2,
     Hunyuan3Dv2Conditioning,
     Hunyuan3Dv2ConditioningMultiView,

comfy_extras/v3/nodes_latent.py

@@ -44,16 +44,15 @@ class LatentAdd(io.ComfyNode):
         return io.NodeOutput(samples_out)
 
 
-class LatentApplyOperation(io.ComfyNode):
+class LatentSubtract(io.ComfyNode):
     @classmethod
     def define_schema(cls):
         return io.Schema(
-            node_id="LatentApplyOperation_V3",
+            node_id="LatentSubtract_V3",
-            category="latent/advanced/operations",
+            category="latent/advanced",
-            is_experimental=True,
             inputs=[
-                io.Latent.Input("samples"),
+                io.Latent.Input("samples1"),
-                io.LatentOperation.Input("operation"),
+                io.Latent.Input("samples2"),
             ],
             outputs=[
                 io.Latent.Output(),
@@ -61,44 +60,78 @@ class LatentApplyOperation(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, samples, operation):
+    def execute(cls, samples1, samples2):
-        samples_out = samples.copy()
+        samples_out = samples1.copy()
 
-        s1 = samples["samples"]
+        s1 = samples1["samples"]
-        samples_out["samples"] = operation(latent=s1)
+        s2 = samples2["samples"]
+
+        s2 = reshape_latent_to(s1.shape, s2)
+        samples_out["samples"] = s1 - s2
         return io.NodeOutput(samples_out)
 
 
-class LatentApplyOperationCFG(io.ComfyNode):
+class LatentMultiply(io.ComfyNode):
     @classmethod
     def define_schema(cls):
         return io.Schema(
-            node_id="LatentApplyOperationCFG_V3",
+            node_id="LatentMultiply_V3",
-            category="latent/advanced/operations",
+            category="latent/advanced",
-            is_experimental=True,
             inputs=[
-                io.Model.Input("model"),
+                io.Latent.Input("samples"),
-                io.LatentOperation.Input("operation"),
+                io.Float.Input("multiplier", default=1.0, min=-10.0, max=10.0, step=0.01),
             ],
             outputs=[
-                io.Model.Output(),
+                io.Latent.Output(),
             ],
         )
 
     @classmethod
-    def execute(cls, model, operation):
+    def execute(cls, samples, multiplier):
-        m = model.clone()
+        samples_out = samples.copy()
 
-        def pre_cfg_function(args):
+        s1 = samples["samples"]
-            conds_out = args["conds_out"]
+        samples_out["samples"] = s1 * multiplier
-            if len(conds_out) == 2:
+        return io.NodeOutput(samples_out)
-                conds_out[0] = operation(latent=(conds_out[0] - conds_out[1])) + conds_out[1]
-            else:
-                conds_out[0] = operation(latent=conds_out[0])
-            return conds_out
 
-        m.set_model_sampler_pre_cfg_function(pre_cfg_function)
+
-        return io.NodeOutput(m)
+class LatentInterpolate(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentInterpolate_V3",
+            category="latent/advanced",
+            inputs=[
+                io.Latent.Input("samples1"),
+                io.Latent.Input("samples2"),
+                io.Float.Input("ratio", default=1.0, min=0.0, max=1.0, step=0.01),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, samples1, samples2, ratio):
+        samples_out = samples1.copy()
+
+        s1 = samples1["samples"]
+        s2 = samples2["samples"]
+
+        s2 = reshape_latent_to(s1.shape, s2)
+
+        m1 = torch.linalg.vector_norm(s1, dim=(1))
+        m2 = torch.linalg.vector_norm(s2, dim=(1))
+
+        s1 = torch.nan_to_num(s1 / m1)
+        s2 = torch.nan_to_num(s2 / m2)
+
+        t = (s1 * ratio + s2 * (1.0 - ratio))
+        mt = torch.linalg.vector_norm(t, dim=(1))
+        st = torch.nan_to_num(t / mt)
+
+        samples_out["samples"] = st * (m1 * ratio + m2 * (1.0 - ratio))
+        return io.NodeOutput(samples_out)
 
 
 class LatentBatch(io.ComfyNode):
@@ -159,54 +192,16 @@ class LatentBatchSeedBehavior(io.ComfyNode):
         return io.NodeOutput(samples_out)
 
 
-class LatentInterpolate(io.ComfyNode):
+class LatentApplyOperation(io.ComfyNode):
     @classmethod
     def define_schema(cls):
         return io.Schema(
-            node_id="LatentInterpolate_V3",
+            node_id="LatentApplyOperation_V3",
-            category="latent/advanced",
+            category="latent/advanced/operations",
-            inputs=[
+            is_experimental=True,
-                io.Latent.Input("samples1"),
-                io.Latent.Input("samples2"),
-                io.Float.Input("ratio", default=1.0, min=0.0, max=1.0, step=0.01),
-            ],
-            outputs=[
-                io.Latent.Output(),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, samples1, samples2, ratio):
-        samples_out = samples1.copy()
-
-        s1 = samples1["samples"]
-        s2 = samples2["samples"]
-
-        s2 = reshape_latent_to(s1.shape, s2)
-
-        m1 = torch.linalg.vector_norm(s1, dim=(1))
-        m2 = torch.linalg.vector_norm(s2, dim=(1))
-
-        s1 = torch.nan_to_num(s1 / m1)
-        s2 = torch.nan_to_num(s2 / m2)
-
-        t = (s1 * ratio + s2 * (1.0 - ratio))
-        mt = torch.linalg.vector_norm(t, dim=(1))
-        st = torch.nan_to_num(t / mt)
-
-        samples_out["samples"] = st * (m1 * ratio + m2 * (1.0 - ratio))
-        return io.NodeOutput(samples_out)
-
-
-class LatentMultiply(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="LatentMultiply_V3",
-            category="latent/advanced",
             inputs=[
                 io.Latent.Input("samples"),
-                io.Float.Input("multiplier", default=1.0, min=-10.0, max=10.0, step=0.01),
+                io.LatentOperation.Input("operation"),
             ],
             outputs=[
                 io.Latent.Output(),
@@ -214,14 +209,81 @@ class LatentMultiply(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, samples, multiplier):
+    def execute(cls, samples, operation):
         samples_out = samples.copy()
 
         s1 = samples["samples"]
-        samples_out["samples"] = s1 * multiplier
+        samples_out["samples"] = operation(latent=s1)
         return io.NodeOutput(samples_out)
 
 
+class LatentApplyOperationCFG(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentApplyOperationCFG_V3",
+            category="latent/advanced/operations",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model"),
+                io.LatentOperation.Input("operation"),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, operation):
+        m = model.clone()
+
+        def pre_cfg_function(args):
+            conds_out = args["conds_out"]
+            if len(conds_out) == 2:
+                conds_out[0] = operation(latent=(conds_out[0] - conds_out[1])) + conds_out[1]
+            else:
+                conds_out[0] = operation(latent=conds_out[0])
+            return conds_out
+
+        m.set_model_sampler_pre_cfg_function(pre_cfg_function)
+        return io.NodeOutput(m)
+
+
+class LatentOperationTonemapReinhard(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentOperationTonemapReinhard_V3",
+            category="latent/advanced/operations",
+            is_experimental=True,
+            inputs=[
+                io.Float.Input("multiplier", default=1.0, min=0.0, max=100.0, step=0.01),
+            ],
+            outputs=[
+                io.LatentOperation.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, multiplier):
+        def tonemap_reinhard(latent, **kwargs):
+            latent_vector_magnitude = (torch.linalg.vector_norm(latent, dim=(1)) + 0.0000000001)[:,None]
+            normalized_latent = latent / latent_vector_magnitude
+
+            mean = torch.mean(latent_vector_magnitude, dim=(1,2,3), keepdim=True)
+            std = torch.std(latent_vector_magnitude, dim=(1,2,3), keepdim=True)
+
+            top = (std * 5 + mean) * multiplier
+
+            #reinhard
+            latent_vector_magnitude *= (1.0 / top)
+            new_magnitude = latent_vector_magnitude / (latent_vector_magnitude + 1.0)
+            new_magnitude *= top
+
+            return normalized_latent * new_magnitude
+        return io.NodeOutput(tonemap_reinhard)
+
+
 class LatentOperationSharpen(io.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -264,69 +326,7 @@ class LatentOperationSharpen(io.ComfyNode):
         return io.NodeOutput(sharpen)
 
 
-class LatentOperationTonemapReinhard(io.ComfyNode):
+NODES_LIST: list[type[io.ComfyNode]] = [
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="LatentOperationTonemapReinhard_V3",
-            category="latent/advanced/operations",
-            is_experimental=True,
-            inputs=[
-                io.Float.Input("multiplier", default=1.0, min=0.0, max=100.0, step=0.01),
-            ],
-            outputs=[
-                io.LatentOperation.Output(),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, multiplier):
-        def tonemap_reinhard(latent, **kwargs):
-            latent_vector_magnitude = (torch.linalg.vector_norm(latent, dim=(1)) + 0.0000000001)[:,None]
-            normalized_latent = latent / latent_vector_magnitude
-
-            mean = torch.mean(latent_vector_magnitude, dim=(1,2,3), keepdim=True)
-            std = torch.std(latent_vector_magnitude, dim=(1,2,3), keepdim=True)
-
-            top = (std * 5 + mean) * multiplier
-
-            #reinhard
-            latent_vector_magnitude *= (1.0 / top)
-            new_magnitude = latent_vector_magnitude / (latent_vector_magnitude + 1.0)
-            new_magnitude *= top
-
-            return normalized_latent * new_magnitude
-        return io.NodeOutput(tonemap_reinhard)
-
-
-class LatentSubtract(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="LatentSubtract_V3",
-            category="latent/advanced",
-            inputs=[
-                io.Latent.Input("samples1"),
-                io.Latent.Input("samples2"),
-            ],
-            outputs=[
-                io.Latent.Output(),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, samples1, samples2):
-        samples_out = samples1.copy()
-
-        s1 = samples1["samples"]
-        s2 = samples2["samples"]
-
-        s2 = reshape_latent_to(s1.shape, s2)
-        samples_out["samples"] = s1 - s2
-        return io.NodeOutput(samples_out)
-
-
-NODES_LIST = [
     LatentAdd,
     LatentApplyOperation,
     LatentApplyOperationCFG,

comfy_extras/v3/nodes_load_3d.py

@@ -172,7 +172,7 @@ class Preview3DAnimation(io.ComfyNode):
         return io.NodeOutput(ui=ui.PreviewUI3D(model_file, camera_info, cls=cls))
 
 
-NODES_LIST = [
+NODES_LIST: list[type[io.ComfyNode]] = [
     Load3D,
     Load3DAnimation,
     Preview3D,

comfy_extras/v3/nodes_lt.py

@@ -516,7 +516,7 @@ class ModelSamplingLTXV(io.ComfyNode):
         return io.NodeOutput(m)
 
 
-NODES_LIST = [
+NODES_LIST: list[type[io.ComfyNode]] = [
     EmptyLTXVLatentVideo,
     LTXVAddGuide,
     LTXVConditioning,

comfy_extras/v3/nodes_optimalsteps.py

@@ -59,4 +59,6 @@ class OptimalStepsScheduler(io.ComfyNode):
         return io.NodeOutput(torch.FloatTensor(sigmas))
 
 
-NODES_LIST = [OptimalStepsScheduler]
+NODES_LIST = [
+    OptimalStepsScheduler,
+]

comfy_extras/v3/nodes_pag.py

@@ -57,4 +57,6 @@ class PerturbedAttentionGuidance(io.ComfyNode):
         return io.NodeOutput(m)
 
 
-NODES_LIST = [PerturbedAttentionGuidance]
+NODES_LIST = [
+    PerturbedAttentionGuidance,
+]

comfy_extras/v3/nodes_perpneg.py

@@ -109,4 +109,6 @@ class PerpNegGuider(io.ComfyNode):
         return io.NodeOutput(guider)
 
 
-NODES_LIST = [PerpNegGuider]
+NODES_LIST = [
+    PerpNegGuider,
+]