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 (2)

Browse Source
This commit is contained in:
bigcat88 2025-07-25 20:11:08 +03:00
parent 918ca7f2ea
commit e55b540899
No known key found for this signature in database
GPG Key ID: 1F0BF0EC3CF22721
12 changed files with 864 additions and 859 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1242
comfy_extras/v3/nodes_custom_sampler.py
View File

File diff suppressed because it is too large Load Diff

43
comfy_extras/v3/nodes_flux.py
View File

@ -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,

2
comfy_extras/v3/nodes_freelunch.py
View File

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

56
comfy_extras/v3/nodes_hidream.py
View File

@ -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,
]

90
comfy_extras/v3/nodes_hunyuan.py
View File

@ -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):
@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 = [
NODES_LIST: list[type[io.ComfyNode]] = [
CLIPTextEncodeHunyuanDiT,
EmptyHunyuanLatentVideo,
HunyuanImageToVideo,

2
comfy_extras/v3/nodes_hunyuan3d.py
View File

@ -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,

272
comfy_extras/v3/nodes_latent.py
View File

@ -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",
category="latent/advanced/operations",
is_experimental=True,
node_id="LatentSubtract_V3",
category="latent/advanced",
inputs=[
io.Latent.Input("samples"),
io.LatentOperation.Input("operation"),
io.Latent.Input("samples1"),
io.Latent.Input("samples2"),
],
outputs=[
io.Latent.Output(),
@ -61,44 +60,78 @@ class LatentApplyOperation(io.ComfyNode):
)
@classmethod
def execute(cls, samples, operation):
samples_out = samples.copy()
def execute(cls, samples1, samples2):
samples_out = samples1.copy()
s1 = samples["samples"]
samples_out["samples"] = operation(latent=s1)
s1 = samples1["samples"]
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",
category="latent/advanced/operations",
is_experimental=True,
node_id="LatentMultiply_V3",
category="latent/advanced",
inputs=[
io.Model.Input("model"),
io.LatentOperation.Input("operation"),
io.Latent.Input("samples"),
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):
m = model.clone()
def execute(cls, samples, multiplier):
samples_out = samples.copy()
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
s1 = samples["samples"]
samples_out["samples"] = s1 * multiplier
return io.NodeOutput(samples_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",
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 LatentMultiply(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="LatentMultiply_V3",
category="latent/advanced",
node_id="LatentApplyOperation_V3",
category="latent/advanced/operations",
is_experimental=True,
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):
@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 = [
NODES_LIST: list[type[io.ComfyNode]] = [
LatentAdd,
LatentApplyOperation,
LatentApplyOperationCFG,

2
comfy_extras/v3/nodes_load_3d.py
View File

@ -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,

2
comfy_extras/v3/nodes_lt.py
View File

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

4
comfy_extras/v3/nodes_optimalsteps.py
View File

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

4
comfy_extras/v3/nodes_pag.py
View File

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

4
comfy_extras/v3/nodes_perpneg.py
View File

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