mirrors/ComfyUI
1
0
Fork 0
mirror of https://github.com/comfyanonymous/ComfyUI.git synced 2025-07-27 08:16:44 +00:00
Code Issues Packages Projects Releases Wiki Activity

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

Browse Source
This commit is contained in:
bigcat88 2025-07-25 17:59:03 +03:00
parent 675e9fd788
commit 918ca7f2ea
No known key found for this signature in database
GPG Key ID: 1F0BF0EC3CF22721
13 changed files with 305 additions and 303 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
comfy_extras/v3/nodes_ace.py
View File

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

4
comfy_extras/v3/nodes_advanced_samplers.py
View File

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

25
comfy_extras/v3/nodes_align_your_steps.py
View File

@ -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,
]

2
comfy_extras/v3/nodes_apg.py
View File

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

6
comfy_extras/v3/nodes_attention_multiply.py
View File

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

294
comfy_extras/v3/nodes_audio.py
View File

@ -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.Int.Input(
"batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."
),
io.Audio.Input("audio"),
io.Vae.Input("vae"),
],
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"})
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))})
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,
]

2
comfy_extras/v3/nodes_camera_trajectory.py
View File

@ -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,
]

2
comfy_extras/v3/nodes_canny.py
View File

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

63
comfy_extras/v3/nodes_cfg.py
View File

@ -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):
@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 = [
NODES_LIST: list[type[io.ComfyNode]] = [
CFGNorm,
CFGZeroStar,
]

52
comfy_extras/v3/nodes_clip_sdxl.py
View File

@ -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):
@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 = [
NODES_LIST: list[type[io.ComfyNode]] = [
CLIPTextEncodeSDXL,
CLIPTextEncodeSDXLRefiner,
]

54
comfy_extras/v3/nodes_compositing.py
View File

@ -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,

54
comfy_extras/v3/nodes_controlnet.py
View File

@ -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):

48
comfy_extras/v3/nodes_cosmos.py
View File

@ -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):
@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 = [
NODES_LIST: list[type[io.ComfyNode]] = [
CosmosImageToVideoLatent,
CosmosPredict2ImageToVideoLatent,
EmptyCosmosLatentVideo,