restore nodes order as it in the V1 version for smaller git diff (1)

comfy_extras/v3/nodes_ace.py

@@ -52,6 +52,6 @@ class EmptyAceStepLatentAudio(io.ComfyNode):
 
 
 NODES_LIST: list[type[io.ComfyNode]] = [
-    TextEncodeAceStepAudio,
     EmptyAceStepLatentAudio,
+    TextEncodeAceStepAudio,
 ]

comfy_extras/v3/nodes_advanced_samplers.py

@@ -122,7 +122,7 @@ class SamplerEulerCFGpp(io.ComfyNode):
         return io.NodeOutput(sampler)
 
 
-NODES_LIST = [
+NODES_LIST: list[type[io.ComfyNode]] = [
-    SamplerLCMUpscale,
     SamplerEulerCFGpp,
+    SamplerLCMUpscale,
 ]

comfy_extras/v3/nodes_align_your_steps.py

@@ -5,6 +5,18 @@ import torch
 
 from comfy_api.latest import io
 
+
+def loglinear_interp(t_steps, num_steps):
+    """Performs log-linear interpolation of a given array of decreasing numbers."""
+    xs = np.linspace(0, 1, len(t_steps))
+    ys = np.log(t_steps[::-1])
+
+    new_xs = np.linspace(0, 1, num_steps)
+    new_ys = np.interp(new_xs, xs, ys)
+
+    return np.exp(new_ys)[::-1].copy()
+
+
 NOISE_LEVELS = {
     "SD1": [
         14.6146412293,
@@ -36,17 +48,6 @@ NOISE_LEVELS = {
 }
 
 
-def loglinear_interp(t_steps, num_steps):
-    """Performs log-linear interpolation of a given array of decreasing numbers."""
-    xs = np.linspace(0, 1, len(t_steps))
-    ys = np.log(t_steps[::-1])
-
-    new_xs = np.linspace(0, 1, num_steps)
-    new_ys = np.interp(new_xs, xs, ys)
-
-    return np.exp(new_ys)[::-1].copy()
-
-
 class AlignYourStepsScheduler(io.ComfyNode):
     @classmethod
     def define_schema(cls) -> io.Schema:
@@ -78,6 +79,6 @@ class AlignYourStepsScheduler(io.ComfyNode):
         return io.NodeOutput(torch.FloatTensor(sigmas))
 
 
-NODES_LIST = [
+NODES_LIST: list[type[io.ComfyNode]] = [
     AlignYourStepsScheduler,
 ]

comfy_extras/v3/nodes_apg.py

@@ -93,6 +93,6 @@ class APG(io.ComfyNode):
         return io.NodeOutput(m)
 
 
-NODES_LIST = [
+NODES_LIST: list[type[io.ComfyNode]] = [
     APG,
 ]

comfy_extras/v3/nodes_attention_multiply.py

@@ -131,9 +131,9 @@ class UNetTemporalAttentionMultiply(io.ComfyNode):
         return io.NodeOutput(m)
 
 
-NODES_LIST = [
+NODES_LIST: list[type[io.ComfyNode]] = [
-    UNetSelfAttentionMultiply,
-    UNetCrossAttentionMultiply,
     CLIPAttentionMultiply,
+    UNetCrossAttentionMultiply,
+    UNetSelfAttentionMultiply,
     UNetTemporalAttentionMultiply,
 ]

comfy_extras/v3/nodes_audio.py

@@ -13,6 +13,28 @@ import node_helpers
 from comfy_api.latest import io, ui
 
 
+class EmptyLatentAudio(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="EmptyLatentAudio_V3",
+            category="latent/audio",
+            inputs=[
+                io.Float.Input("seconds", default=47.6, min=1.0, max=1000.0, step=0.1),
+                io.Int.Input(
+                    "batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."
+                ),
+            ],
+            outputs=[io.Latent.Output()],
+        )
+
+    @classmethod
+    def execute(cls, seconds, batch_size) -> io.NodeOutput:
+        length = round((seconds * 44100 / 2048) / 2) * 2
+        latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device())
+        return io.NodeOutput({"samples": latent, "type": "audio"})
+
+
 class ConditioningStableAudio(io.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -43,26 +65,140 @@ class ConditioningStableAudio(io.ComfyNode):
         )
 
 
-class EmptyLatentAudio(io.ComfyNode):
+class VAEEncodeAudio(io.ComfyNode):
     @classmethod
     def define_schema(cls):
         return io.Schema(
-            node_id="EmptyLatentAudio_V3",
+            node_id="VAEEncodeAudio_V3",
             category="latent/audio",
             inputs=[
-                io.Float.Input("seconds", default=47.6, min=1.0, max=1000.0, step=0.1),
+                io.Audio.Input("audio"),
-                io.Int.Input(
+                io.Vae.Input("vae"),
-                    "batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."
-                ),
             ],
             outputs=[io.Latent.Output()],
         )
 
     @classmethod
-    def execute(cls, seconds, batch_size) -> io.NodeOutput:
+    def execute(cls, vae, audio) -> io.NodeOutput:
-        length = round((seconds * 44100 / 2048) / 2) * 2
+        sample_rate = audio["sample_rate"]
-        latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device())
+        if 44100 != sample_rate:
-        return io.NodeOutput({"samples": latent, "type": "audio"})
+            waveform = torchaudio.functional.resample(audio["waveform"], sample_rate, 44100)
+        else:
+            waveform = audio["waveform"]
+        return io.NodeOutput({"samples": vae.encode(waveform.movedim(1, -1))})
+
+
+class VAEDecodeAudio(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="VAEDecodeAudio_V3",
+            category="latent/audio",
+            inputs=[
+                io.Latent.Input("samples"),
+                io.Vae.Input("vae"),
+            ],
+            outputs=[io.Audio.Output()],
+        )
+
+    @classmethod
+    def execute(cls, vae, samples) -> io.NodeOutput:
+        audio = vae.decode(samples["samples"]).movedim(-1, 1)
+        std = torch.std(audio, dim=[1, 2], keepdim=True) * 5.0
+        std[std < 1.0] = 1.0
+        audio /= std
+        return io.NodeOutput({"waveform": audio, "sample_rate": 44100})
+
+
+class SaveAudio(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SaveAudio_V3",  # frontend expects "SaveAudio" to work
+            display_name="Save Audio _V3",  # frontend ignores "display_name" for this node
+            category="audio",
+            inputs=[
+                io.Audio.Input("audio"),
+                io.String.Input("filename_prefix", default="audio/ComfyUI"),
+            ],
+            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, audio, filename_prefix="ComfyUI", format="flac") -> io.NodeOutput:
+        return io.NodeOutput(
+            ui=ui.AudioSaveHelper.get_save_audio_ui(audio, filename_prefix=filename_prefix, cls=cls, format=format)
+        )
+
+
+class SaveAudioMP3(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SaveAudioMP3_V3",  # frontend expects "SaveAudioMP3" to work
+            display_name="Save Audio(MP3) _V3",  # frontend ignores "display_name" for this node
+            category="audio",
+            inputs=[
+                io.Audio.Input("audio"),
+                io.String.Input("filename_prefix", default="audio/ComfyUI"),
+                io.Combo.Input("quality", options=["V0", "128k", "320k"], default="V0"),
+            ],
+            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, audio, filename_prefix="ComfyUI", format="mp3", quality="V0") -> io.NodeOutput:
+        return io.NodeOutput(
+            ui=ui.AudioSaveHelper.get_save_audio_ui(
+                audio, filename_prefix=filename_prefix, cls=cls, format=format, quality=quality
+            )
+        )
+
+
+class SaveAudioOpus(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SaveAudioOpus_V3",  # frontend expects "SaveAudioOpus" to work
+            display_name="Save Audio(Opus) _V3",  # frontend ignores "display_name" for this node
+            category="audio",
+            inputs=[
+                io.Audio.Input("audio"),
+                io.String.Input("filename_prefix", default="audio/ComfyUI"),
+                io.Combo.Input("quality", options=["64k", "96k", "128k", "192k", "320k"], default="128k"),
+            ],
+            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, audio, filename_prefix="ComfyUI", format="opus", quality="128k") -> io.NodeOutput:
+        return io.NodeOutput(
+            ui=ui.AudioSaveHelper.get_save_audio_ui(
+                audio, filename_prefix=filename_prefix, cls=cls, format=format, quality=quality
+            )
+        )
+
+
+class PreviewAudio(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="PreviewAudio_V3",  # frontend expects "PreviewAudio" to work
+            display_name="Preview Audio _V3",  # frontend ignores "display_name" for this node
+            category="audio",
+            inputs=[
+                io.Audio.Input("audio"),
+            ],
+            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, audio) -> io.NodeOutput:
+        return io.NodeOutput(ui=ui.PreviewAudio(audio, cls=cls))
 
 
 class LoadAudio(io.ComfyNode):
@@ -141,150 +277,14 @@ class LoadAudio(io.ComfyNode):
         return True
 
 
-class PreviewAudio(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="PreviewAudio_V3",  # frontend expects "PreviewAudio" to work
-            display_name="Preview Audio _V3",  # frontend ignores "display_name" for this node
-            category="audio",
-            inputs=[
-                io.Audio.Input("audio"),
-            ],
-            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
-            is_output_node=True,
-        )
-
-    @classmethod
-    def execute(cls, audio) -> io.NodeOutput:
-        return io.NodeOutput(ui=ui.PreviewAudio(audio, cls=cls))
-
-
-class SaveAudioMP3(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="SaveAudioMP3_V3",  # frontend expects "SaveAudioMP3" to work
-            display_name="Save Audio(MP3) _V3",  # frontend ignores "display_name" for this node
-            category="audio",
-            inputs=[
-                io.Audio.Input("audio"),
-                io.String.Input("filename_prefix", default="audio/ComfyUI"),
-                io.Combo.Input("quality", options=["V0", "128k", "320k"], default="V0"),
-            ],
-            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
-            is_output_node=True,
-        )
-
-    @classmethod
-    def execute(cls, audio, filename_prefix="ComfyUI", format="mp3", quality="V0") -> io.NodeOutput:
-        return io.NodeOutput(
-            ui=ui.AudioSaveHelper.get_save_audio_ui(
-                audio, filename_prefix=filename_prefix, cls=cls, format=format, quality=quality
-            )
-        )
-
-
-class SaveAudioOpus(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="SaveAudioOpus_V3",  # frontend expects "SaveAudioOpus" to work
-            display_name="Save Audio(Opus) _V3",  # frontend ignores "display_name" for this node
-            category="audio",
-            inputs=[
-                io.Audio.Input("audio"),
-                io.String.Input("filename_prefix", default="audio/ComfyUI"),
-                io.Combo.Input("quality", options=["64k", "96k", "128k", "192k", "320k"], default="128k"),
-            ],
-            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
-            is_output_node=True,
-        )
-
-    @classmethod
-    def execute(cls, audio, filename_prefix="ComfyUI", format="opus", quality="128k") -> io.NodeOutput:
-        return io.NodeOutput(
-            ui=ui.AudioSaveHelper.get_save_audio_ui(
-                audio, filename_prefix=filename_prefix, cls=cls, format=format, quality=quality
-            )
-        )
-
-
-class SaveAudio(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="SaveAudio_V3",  # frontend expects "SaveAudio" to work
-            display_name="Save Audio _V3",  # frontend ignores "display_name" for this node
-            category="audio",
-            inputs=[
-                io.Audio.Input("audio"),
-                io.String.Input("filename_prefix", default="audio/ComfyUI"),
-            ],
-            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
-            is_output_node=True,
-        )
-
-    @classmethod
-    def execute(cls, audio, filename_prefix="ComfyUI", format="flac") -> io.NodeOutput:
-        return io.NodeOutput(
-            ui=ui.AudioSaveHelper.get_save_audio_ui(audio, filename_prefix=filename_prefix, cls=cls, format=format)
-        )
-
-
-class VAEDecodeAudio(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="VAEDecodeAudio_V3",
-            category="latent/audio",
-            inputs=[
-                io.Latent.Input("samples"),
-                io.Vae.Input("vae"),
-            ],
-            outputs=[io.Audio.Output()],
-        )
-
-    @classmethod
-    def execute(cls, vae, samples) -> io.NodeOutput:
-        audio = vae.decode(samples["samples"]).movedim(-1, 1)
-        std = torch.std(audio, dim=[1, 2], keepdim=True) * 5.0
-        std[std < 1.0] = 1.0
-        audio /= std
-        return io.NodeOutput({"waveform": audio, "sample_rate": 44100})
-
-
-class VAEEncodeAudio(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="VAEEncodeAudio_V3",
-            category="latent/audio",
-            inputs=[
-                io.Audio.Input("audio"),
-                io.Vae.Input("vae"),
-            ],
-            outputs=[io.Latent.Output()],
-        )
-
-    @classmethod
-    def execute(cls, vae, audio) -> io.NodeOutput:
-        sample_rate = audio["sample_rate"]
-        if 44100 != sample_rate:
-            waveform = torchaudio.functional.resample(audio["waveform"], sample_rate, 44100)
-        else:
-            waveform = audio["waveform"]
-        return io.NodeOutput({"samples": vae.encode(waveform.movedim(1, -1))})
-
-
 NODES_LIST: list[type[io.ComfyNode]] = [
     ConditioningStableAudio,
     EmptyLatentAudio,
     LoadAudio,
     PreviewAudio,
+    SaveAudio,
     SaveAudioMP3,
     SaveAudioOpus,
-    SaveAudio,
     VAEDecodeAudio,
     VAEEncodeAudio,
 ]

comfy_extras/v3/nodes_camera_trajectory.py

@@ -212,6 +212,6 @@ class WanCameraEmbedding(io.ComfyNode):
         return io.NodeOutput(control_camera_video, width, height, length)
 
 
-NODES_LIST = [
+NODES_LIST: list[type[io.ComfyNode]] = [
     WanCameraEmbedding,
 ]

comfy_extras/v3/nodes_canny.py

@@ -27,6 +27,6 @@ class Canny(io.ComfyNode):
         return io.NodeOutput(img_out)
 
 
-NODES_LIST = [
+NODES_LIST: list[type[io.ComfyNode]] = [
     Canny,
 ]

comfy_extras/v3/nodes_cfg.py

@@ -5,6 +5,7 @@ import torch
 from comfy_api.latest import io
 
 
+# https://github.com/WeichenFan/CFG-Zero-star
 def optimized_scale(positive, negative):
     positive_flat = positive.reshape(positive.shape[0], -1)
     negative_flat = negative.reshape(negative.shape[0], -1)
@@ -21,6 +22,36 @@ def optimized_scale(positive, negative):
     return st_star.reshape([positive.shape[0]] + [1] * (positive.ndim - 1))
 
 
+class CFGZeroStar(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="CFGZeroStar_V3",
+            category="advanced/guidance",
+            inputs=[
+                io.Model.Input("model"),
+            ],
+            outputs=[io.Model.Output(display_name="patched_model")],
+        )
+
+    @classmethod
+    def execute(cls, model) -> io.NodeOutput:
+        m = model.clone()
+
+        def cfg_zero_star(args):
+            guidance_scale = args['cond_scale']
+            x = args['input']
+            cond_p = args['cond_denoised']
+            uncond_p = args['uncond_denoised']
+            out = args["denoised"]
+            alpha = optimized_scale(x - cond_p, x - uncond_p)
+
+            return out + uncond_p * (alpha - 1.0) + guidance_scale * uncond_p * (1.0 - alpha)
+
+        m.set_model_sampler_post_cfg_function(cfg_zero_star)
+        return io.NodeOutput(m)
+
+
 class CFGNorm(io.ComfyNode):
     @classmethod
     def define_schema(cls) -> io.Schema:
@@ -52,37 +83,7 @@ class CFGNorm(io.ComfyNode):
         return io.NodeOutput(m)
 
 
-class CFGZeroStar(io.ComfyNode):
+NODES_LIST: list[type[io.ComfyNode]] = [
-    @classmethod
-    def define_schema(cls) -> io.Schema:
-        return io.Schema(
-            node_id="CFGZeroStar_V3",
-            category="advanced/guidance",
-            inputs=[
-                io.Model.Input("model"),
-            ],
-            outputs=[io.Model.Output(display_name="patched_model")],
-        )
-
-    @classmethod
-    def execute(cls, model) -> io.NodeOutput:
-        m = model.clone()
-
-        def cfg_zero_star(args):
-            guidance_scale = args['cond_scale']
-            x = args['input']
-            cond_p = args['cond_denoised']
-            uncond_p = args['uncond_denoised']
-            out = args["denoised"]
-            alpha = optimized_scale(x - cond_p, x - uncond_p)
-
-            return out + uncond_p * (alpha - 1.0) + guidance_scale * uncond_p * (1.0 - alpha)
-
-        m.set_model_sampler_post_cfg_function(cfg_zero_star)
-        return io.NodeOutput(m)
-
-
-NODES_LIST = [
     CFGNorm,
     CFGZeroStar,
 ]

comfy_extras/v3/nodes_clip_sdxl.py

@@ -4,6 +4,31 @@ import nodes
 from comfy_api.latest import io
 
 
+class CLIPTextEncodeSDXLRefiner(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPTextEncodeSDXLRefiner_V3",
+            category="advanced/conditioning",
+            inputs=[
+                io.Float.Input("ascore", default=6.0, min=0.0, max=1000.0, step=0.01),
+                io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
+                io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
+                io.String.Input("text", multiline=True, dynamic_prompts=True),
+                io.Clip.Input("clip"),
+            ],
+            outputs=[io.Conditioning.Output()],
+        )
+
+    @classmethod
+    def execute(cls, ascore, width, height, text, clip) -> io.NodeOutput:
+        tokens = clip.tokenize(text)
+        conditioning = clip.encode_from_tokens_scheduled(
+            tokens, add_dict={"aesthetic_score": ascore, "width": width, "height": height}
+        )
+        return io.NodeOutput(conditioning)
+
+
 class CLIPTextEncodeSDXL(io.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -48,32 +73,7 @@ class CLIPTextEncodeSDXL(io.ComfyNode):
         return io.NodeOutput(conditioning)
 
 
-class CLIPTextEncodeSDXLRefiner(io.ComfyNode):
+NODES_LIST: list[type[io.ComfyNode]] = [
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="CLIPTextEncodeSDXLRefiner_V3",
-            category="advanced/conditioning",
-            inputs=[
-                io.Float.Input("ascore", default=6.0, min=0.0, max=1000.0, step=0.01),
-                io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
-                io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
-                io.String.Input("text", multiline=True, dynamic_prompts=True),
-                io.Clip.Input("clip"),
-            ],
-            outputs=[io.Conditioning.Output()],
-        )
-
-    @classmethod
-    def execute(cls, ascore, width, height, text, clip) -> io.NodeOutput:
-        tokens = clip.tokenize(text)
-        conditioning = clip.encode_from_tokens_scheduled(
-            tokens, add_dict={"aesthetic_score": ascore, "width": width, "height": height}
-        )
-        return io.NodeOutput(conditioning)
-
-
-NODES_LIST = [
     CLIPTextEncodeSDXL,
     CLIPTextEncodeSDXLRefiner,
 ]

comfy_extras/v3/nodes_compositing.py

@@ -112,32 +112,6 @@ def porter_duff_composite(
     return out_image, out_alpha
 
 
-class JoinImageWithAlpha(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="JoinImageWithAlpha_V3",
-            display_name="Join Image with Alpha _V3",
-            category="mask/compositing",
-            inputs=[
-                io.Image.Input("image"),
-                io.Mask.Input("alpha"),
-            ],
-            outputs=[io.Image.Output()],
-        )
-
-    @classmethod
-    def execute(cls, image: torch.Tensor, alpha: torch.Tensor) -> io.NodeOutput:
-        batch_size = min(len(image), len(alpha))
-        out_images = []
-
-        alpha = 1.0 - resize_mask(alpha, image.shape[1:])
-        for i in range(batch_size):
-            out_images.append(torch.cat((image[i][:, :, :3], alpha[i].unsqueeze(2)), dim=2))
-
-        return io.NodeOutput(torch.stack(out_images))
-
-
 class PorterDuffImageComposite(io.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -219,7 +193,33 @@ class SplitImageWithAlpha(io.ComfyNode):
         return io.NodeOutput(torch.stack(out_images), 1.0 - torch.stack(out_alphas))
 
 
-NODES_LIST = [
+class JoinImageWithAlpha(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="JoinImageWithAlpha_V3",
+            display_name="Join Image with Alpha _V3",
+            category="mask/compositing",
+            inputs=[
+                io.Image.Input("image"),
+                io.Mask.Input("alpha"),
+            ],
+            outputs=[io.Image.Output()],
+        )
+
+    @classmethod
+    def execute(cls, image: torch.Tensor, alpha: torch.Tensor) -> io.NodeOutput:
+        batch_size = min(len(image), len(alpha))
+        out_images = []
+
+        alpha = 1.0 - resize_mask(alpha, image.shape[1:])
+        for i in range(batch_size):
+            out_images.append(torch.cat((image[i][:, :, :3], alpha[i].unsqueeze(2)), dim=2))
+
+        return io.NodeOutput(torch.stack(out_images))
+
+
+NODES_LIST: list[type[io.ComfyNode]] = [
     JoinImageWithAlpha,
     PorterDuffImageComposite,
     SplitImageWithAlpha,

comfy_extras/v3/nodes_controlnet.py

@@ -3,6 +3,33 @@ from comfy.cldm.control_types import UNION_CONTROLNET_TYPES
 from comfy_api.latest import io
 
 
+class SetUnionControlNetType(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SetUnionControlNetType_V3",
+            category="conditioning/controlnet",
+            inputs=[
+                io.ControlNet.Input("control_net"),
+                io.Combo.Input("type", options=["auto"] + list(UNION_CONTROLNET_TYPES.keys())),
+            ],
+            outputs=[
+                io.ControlNet.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, control_net, type) -> io.NodeOutput:
+        control_net = control_net.copy()
+        type_number = UNION_CONTROLNET_TYPES.get(type, -1)
+        if type_number >= 0:
+            control_net.set_extra_arg("control_type", [type_number])
+        else:
+            control_net.set_extra_arg("control_type", [])
+
+        return io.NodeOutput(control_net)
+
+
 class ControlNetApplyAdvanced(io.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -60,33 +87,6 @@ class ControlNetApplyAdvanced(io.ComfyNode):
         return io.NodeOutput(out[0], out[1])
 
 
-class SetUnionControlNetType(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="SetUnionControlNetType_V3",
-            category="conditioning/controlnet",
-            inputs=[
-                io.ControlNet.Input("control_net"),
-                io.Combo.Input("type", options=["auto"] + list(UNION_CONTROLNET_TYPES.keys())),
-            ],
-            outputs=[
-                io.ControlNet.Output(),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, control_net, type) -> io.NodeOutput:
-        control_net = control_net.copy()
-        type_number = UNION_CONTROLNET_TYPES.get(type, -1)
-        if type_number >= 0:
-            control_net.set_extra_arg("control_type", [type_number])
-        else:
-            control_net.set_extra_arg("control_type", [])
-
-        return io.NodeOutput(control_net)
-
-
 class ControlNetInpaintingAliMamaApply(ControlNetApplyAdvanced):
     @classmethod
     def define_schema(cls):

comfy_extras/v3/nodes_cosmos.py

@@ -9,6 +9,29 @@ import nodes
 from comfy_api.latest import io
 
 
+class EmptyCosmosLatentVideo(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="EmptyCosmosLatentVideo_V3",
+            category="latent/video",
+            inputs=[
+                io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("height", default=704, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("length", default=121, min=1, max=nodes.MAX_RESOLUTION, step=8),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+            ],
+            outputs=[io.Latent.Output()],
+        )
+
+    @classmethod
+    def execute(cls, width, height, length, batch_size) -> io.NodeOutput:
+        latent = torch.zeros(
+            [batch_size, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()
+        )
+        return io.NodeOutput({"samples": latent})
+
+
 def vae_encode_with_padding(vae, image, width, height, length, padding=0):
     pixels = comfy.utils.common_upscale(image[..., :3].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
     pixel_len = min(pixels.shape[0], length)
@@ -116,30 +139,7 @@ class CosmosPredict2ImageToVideoLatent(io.ComfyNode):
         return io.NodeOutput(out_latent)
 
 
-class EmptyCosmosLatentVideo(io.ComfyNode):
+NODES_LIST: list[type[io.ComfyNode]] = [
-    @classmethod
-    def define_schema(cls) -> io.Schema:
-        return io.Schema(
-            node_id="EmptyCosmosLatentVideo_V3",
-            category="latent/video",
-            inputs=[
-                io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16),
-                io.Int.Input("height", default=704, min=16, max=nodes.MAX_RESOLUTION, step=16),
-                io.Int.Input("length", default=121, min=1, max=nodes.MAX_RESOLUTION, step=8),
-                io.Int.Input("batch_size", default=1, min=1, max=4096),
-            ],
-            outputs=[io.Latent.Output()],
-        )
-
-    @classmethod
-    def execute(cls, width, height, length, batch_size) -> io.NodeOutput:
-        latent = torch.zeros(
-            [batch_size, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()
-        )
-        return io.NodeOutput({"samples": latent})
-
-
-NODES_LIST = [
     CosmosImageToVideoLatent,
     CosmosPredict2ImageToVideoLatent,
     EmptyCosmosLatentVideo,