mirror of
https://github.com/comfyanonymous/ComfyUI.git
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Merge branch 'alexis/add_output_save_nodes' of https://github.com/Comfy-Org/ComfyUI into alexis/add_output_save_nodes
This commit is contained in:
Alexis Rolland
commit
2da3353399
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blueprints/Character Replacement (SCAIL-2 Base).json
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blueprints/Character Replacement (SCAIL-2 Base).json
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blueprints/Character Replacement (SCAIL-2 Extend).json
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blueprints/Character Replacement (SCAIL-2 Extend).json
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blueprints/Image Depth Estimation (Depth Anything 3).json
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blueprints/Image Depth Estimation (Depth Anything 3).json
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"description": "This subgraph takes an input image and produces a depth map using the Depth Anything 3 model, which recovers spatially consistent geometry from any number of views. It is ideal for single or multi-view images, videos, and 3D scenes where accurate depth estimation is needed for tasks like SLAM, novel view synthesis, or spatial perception. The model uses a plain transformer backbone and supports both monocular and multi-view inputs without."
|
||||
}
|
||||
]
|
||||
},
|
||||
"extra": {
|
||||
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|
||||
}
|
||||
}
|
||||
3549
blueprints/Image Edit (Bernini-R).json
Normal file
3549
blueprints/Image Edit (Bernini-R).json
Normal file
File diff suppressed because it is too large
Load Diff
1983
blueprints/Image to Gaussian Splat (TripoSplat).json
Normal file
1983
blueprints/Image to Gaussian Splat (TripoSplat).json
Normal file
File diff suppressed because it is too large
Load Diff
1088
blueprints/Text to Image (Anima Base 1.0).json
Normal file
1088
blueprints/Text to Image (Anima Base 1.0).json
Normal file
File diff suppressed because it is too large
Load Diff
@ -1077,9 +1077,12 @@
|
||||
}
|
||||
],
|
||||
"extra": {},
|
||||
"category": "Image generation and editing/Text to image"
|
||||
"category": "Image generation and editing/Text to image",
|
||||
"description": "This subgraph converts text prompts into non-photorealistic illustrations using a 2-billion-parameter model optimized for anime and artistic styles. It is ideal for generating concept art, character designs, or stylized illustrations where photorealism is not required. The model excels with anime and artistic content but performs poorly on realistic subjects."
|
||||
}
|
||||
]
|
||||
},
|
||||
"extra": {}
|
||||
"extra": {
|
||||
"BlueprintDescription": "This subgraph converts text prompts into non-photorealistic illustrations using a 2-billion-parameter model optimized for anime and artistic styles. It is ideal for generating concept art, character designs, or stylized illustrations where photorealism is not required. The model excels with anime and artistic content but performs poorly on realistic subjects."
|
||||
}
|
||||
}
|
||||
2473
blueprints/Text to Image (Ideogram v4).json
Normal file
2473
blueprints/Text to Image (Ideogram v4).json
Normal file
File diff suppressed because it is too large
Load Diff
825
blueprints/Video Depth Estimation (Depth Anything 3).json
Normal file
825
blueprints/Video Depth Estimation (Depth Anything 3).json
Normal file
@ -0,0 +1,825 @@
|
||||
{
|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
],
|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
"name": "DA3_MODEL",
|
||||
"type": "DA3_MODEL",
|
||||
"links": [
|
||||
107
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "LoadDA3Model",
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.24.0",
|
||||
"models": [
|
||||
{
|
||||
"name": "depth_anything_3_mono_large.safetensors",
|
||||
"url": "https://huggingface.co/Comfy-Org/Depth-Anything-3/resolve/main/geometry_estimation/depth_anything_3_mono_large.safetensors",
|
||||
"directory": "geometry_estimation"
|
||||
}
|
||||
]
|
||||
},
|
||||
"widgets_values": [
|
||||
"depth_anything_3_mono_large.safetensors",
|
||||
"default"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 95,
|
||||
"type": "GetVideoComponents",
|
||||
"pos": [
|
||||
70,
|
||||
-140
|
||||
],
|
||||
"size": [
|
||||
260,
|
||||
120
|
||||
],
|
||||
"flags": {},
|
||||
"order": 3,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "video",
|
||||
"name": "video",
|
||||
"type": "VIDEO",
|
||||
"link": 120
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "images",
|
||||
"name": "images",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
111
|
||||
]
|
||||
},
|
||||
{
|
||||
"localized_name": "audio",
|
||||
"name": "audio",
|
||||
"type": "AUDIO",
|
||||
"links": [
|
||||
112
|
||||
]
|
||||
},
|
||||
{
|
||||
"localized_name": "fps",
|
||||
"name": "fps",
|
||||
"type": "FLOAT",
|
||||
"links": [
|
||||
113
|
||||
]
|
||||
},
|
||||
{
|
||||
"localized_name": "bit_depth",
|
||||
"name": "bit_depth",
|
||||
"type": "INT",
|
||||
"links": null
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "GetVideoComponents",
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.24.0"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 96,
|
||||
"type": "Video Slice",
|
||||
"pos": [
|
||||
70,
|
||||
-360
|
||||
],
|
||||
"size": [
|
||||
270,
|
||||
170
|
||||
],
|
||||
"flags": {},
|
||||
"order": 4,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "video",
|
||||
"name": "video",
|
||||
"type": "VIDEO",
|
||||
"link": 119
|
||||
},
|
||||
{
|
||||
"localized_name": "start_time",
|
||||
"name": "start_time",
|
||||
"type": "FLOAT",
|
||||
"widget": {
|
||||
"name": "start_time"
|
||||
},
|
||||
"link": 121
|
||||
},
|
||||
{
|
||||
"localized_name": "duration",
|
||||
"name": "duration",
|
||||
"type": "FLOAT",
|
||||
"widget": {
|
||||
"name": "duration"
|
||||
},
|
||||
"link": 122
|
||||
},
|
||||
{
|
||||
"localized_name": "strict_duration",
|
||||
"name": "strict_duration",
|
||||
"type": "BOOLEAN",
|
||||
"widget": {
|
||||
"name": "strict_duration"
|
||||
},
|
||||
"link": null
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "VIDEO",
|
||||
"name": "VIDEO",
|
||||
"type": "VIDEO",
|
||||
"links": [
|
||||
120
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "Video Slice",
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.24.0"
|
||||
},
|
||||
"widgets_values": [
|
||||
0,
|
||||
5,
|
||||
false
|
||||
]
|
||||
}
|
||||
],
|
||||
"groups": [],
|
||||
"links": [
|
||||
{
|
||||
"id": 12,
|
||||
"origin_id": 93,
|
||||
"origin_slot": 0,
|
||||
"target_id": 92,
|
||||
"target_slot": 0,
|
||||
"type": "DA3_GEOMETRY"
|
||||
},
|
||||
{
|
||||
"id": 7,
|
||||
"origin_id": 92,
|
||||
"origin_slot": 0,
|
||||
"target_id": -20,
|
||||
"target_slot": 0,
|
||||
"type": "IMAGE"
|
||||
},
|
||||
{
|
||||
"id": 107,
|
||||
"origin_id": 94,
|
||||
"origin_slot": 0,
|
||||
"target_id": 93,
|
||||
"target_slot": 0,
|
||||
"type": "DA3_MODEL"
|
||||
},
|
||||
{
|
||||
"id": 111,
|
||||
"origin_id": 95,
|
||||
"origin_slot": 0,
|
||||
"target_id": 93,
|
||||
"target_slot": 1,
|
||||
"type": "IMAGE"
|
||||
},
|
||||
{
|
||||
"id": 112,
|
||||
"origin_id": 95,
|
||||
"origin_slot": 1,
|
||||
"target_id": -20,
|
||||
"target_slot": 1,
|
||||
"type": "AUDIO"
|
||||
},
|
||||
{
|
||||
"id": 113,
|
||||
"origin_id": 95,
|
||||
"origin_slot": 2,
|
||||
"target_id": -20,
|
||||
"target_slot": 2,
|
||||
"type": "FLOAT"
|
||||
},
|
||||
{
|
||||
"id": 119,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 0,
|
||||
"target_id": 96,
|
||||
"target_slot": 0,
|
||||
"type": "VIDEO"
|
||||
},
|
||||
{
|
||||
"id": 120,
|
||||
"origin_id": 96,
|
||||
"origin_slot": 0,
|
||||
"target_id": 95,
|
||||
"target_slot": 0,
|
||||
"type": "VIDEO"
|
||||
},
|
||||
{
|
||||
"id": 121,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 1,
|
||||
"target_id": 96,
|
||||
"target_slot": 1,
|
||||
"type": "FLOAT"
|
||||
},
|
||||
{
|
||||
"id": 122,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 2,
|
||||
"target_id": 96,
|
||||
"target_slot": 2,
|
||||
"type": "FLOAT"
|
||||
},
|
||||
{
|
||||
"id": 124,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 3,
|
||||
"target_id": 93,
|
||||
"target_slot": 2,
|
||||
"type": "INT"
|
||||
},
|
||||
{
|
||||
"id": 125,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 4,
|
||||
"target_id": 93,
|
||||
"target_slot": 3,
|
||||
"type": "COMBO"
|
||||
},
|
||||
{
|
||||
"id": 126,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 5,
|
||||
"target_id": 92,
|
||||
"target_slot": 1,
|
||||
"type": "COMFY_DYNAMICCOMBO_V3"
|
||||
},
|
||||
{
|
||||
"id": 127,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 6,
|
||||
"target_id": 92,
|
||||
"target_slot": 2,
|
||||
"type": "COMBO"
|
||||
},
|
||||
{
|
||||
"id": 128,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 7,
|
||||
"target_id": 92,
|
||||
"target_slot": 3,
|
||||
"type": "BOOLEAN"
|
||||
},
|
||||
{
|
||||
"id": 129,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 8,
|
||||
"target_id": 94,
|
||||
"target_slot": 0,
|
||||
"type": "COMBO"
|
||||
}
|
||||
],
|
||||
"extra": {},
|
||||
"category": "Conditioning & Preprocessors/Depth",
|
||||
"description": "This subgraph processes a video input through Depth Anything 3 to produce temporally consistent depth maps for each frame, outputting a depth video. It is ideal for video content requiring spatial geometry estimation, such as 3D reconstruction, SLAM, or novel view synthesis from moving cameras. The model uses a plain transformer backbone trained with a depth-ray representation, supporting any number of views without requiring known camera poses."
|
||||
}
|
||||
]
|
||||
},
|
||||
"extra": {
|
||||
"BlueprintDescription": "This subgraph processes a video input through Depth Anything 3 to produce temporally consistent depth maps for each frame, outputting a depth video. It is ideal for video content requiring spatial geometry estimation, such as 3D reconstruction, SLAM, or novel view synthesis from moving cameras. The model uses a plain transformer backbone trained with a depth-ray representation, supporting any number of views without requiring known camera poses."
|
||||
}
|
||||
}
|
||||
3732
blueprints/Video Edit (Bernini-R).json
Normal file
3732
blueprints/Video Edit (Bernini-R).json
Normal file
File diff suppressed because it is too large
Load Diff
15
comfy/sd.py
15
comfy/sd.py
@ -67,6 +67,7 @@ import comfy.text_encoders.anima
|
||||
import comfy.text_encoders.ace15
|
||||
import comfy.text_encoders.longcat_image
|
||||
import comfy.text_encoders.qwen35
|
||||
import comfy.text_encoders.qwen3vl
|
||||
import comfy.text_encoders.ernie
|
||||
import comfy.text_encoders.gemma4
|
||||
import comfy.text_encoders.cogvideo
|
||||
@ -1353,6 +1354,8 @@ class TEModel(Enum):
|
||||
GEMMA_4_31B = 31
|
||||
T5_GEMMA = 32
|
||||
GPT_OSS_20B = 33
|
||||
QWEN3VL_4B = 34
|
||||
QWEN3VL_8B = 35
|
||||
|
||||
|
||||
def detect_te_model(sd):
|
||||
@ -1414,6 +1417,8 @@ def detect_te_model(sd):
|
||||
if weight.shape[0] == 5120:
|
||||
return TEModel.QWEN35_27B
|
||||
return TEModel.QWEN35_2B
|
||||
if "model.visual.deepstack_merger_list.0.norm.weight" in sd: # DeepStack is unique to Qwen3-VL
|
||||
return TEModel.QWEN3VL_4B if sd["model.visual.merger.linear_fc2.weight"].shape[0] == 2560 else TEModel.QWEN3VL_8B
|
||||
if "model.layers.0.post_attention_layernorm.weight" in sd:
|
||||
weight = sd['model.layers.0.post_attention_layernorm.weight']
|
||||
if 'model.layers.0.self_attn.q_norm.weight' in sd:
|
||||
@ -1612,6 +1617,16 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
|
||||
qwen35_type = {TEModel.QWEN35_08B: "qwen35_08b", TEModel.QWEN35_2B: "qwen35_2b", TEModel.QWEN35_4B: "qwen35_4b", TEModel.QWEN35_9B: "qwen35_9b", TEModel.QWEN35_27B: "qwen35_27b"}[te_model]
|
||||
clip_target.clip = comfy.text_encoders.qwen35.te(**llama_detect(clip_data), model_type=qwen35_type)
|
||||
clip_target.tokenizer = comfy.text_encoders.qwen35.tokenizer(model_type=qwen35_type)
|
||||
elif te_model in (TEModel.QWEN3VL_4B, TEModel.QWEN3VL_8B):
|
||||
if clip_type == CLIPType.IDEOGRAM4 and te_model == TEModel.QWEN3VL_8B: # Ideogram4 reuses the full Qwen3-VL-8B (13-layer tap for conditioning + multimodal generate).
|
||||
clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
|
||||
clip_target.clip = comfy.text_encoders.ideogram4.te_qwen3vl(**llama_detect(clip_data))
|
||||
clip_target.tokenizer = comfy.text_encoders.ideogram4.Ideogram4Qwen3VLTokenizer
|
||||
else:
|
||||
clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
|
||||
qwen3vl_type = {TEModel.QWEN3VL_4B: "qwen3vl_4b", TEModel.QWEN3VL_8B: "qwen3vl_8b"}[te_model]
|
||||
clip_target.clip = comfy.text_encoders.qwen3vl.te(**llama_detect(clip_data), model_type=qwen3vl_type)
|
||||
clip_target.tokenizer = comfy.text_encoders.qwen3vl.tokenizer(model_type=qwen3vl_type)
|
||||
elif te_model == TEModel.QWEN3_06B:
|
||||
clip_target.clip = comfy.text_encoders.anima.te(**llama_detect(clip_data))
|
||||
clip_target.tokenizer = comfy.text_encoders.anima.AnimaTokenizer
|
||||
|
||||
@ -9,6 +9,7 @@ import os
|
||||
from transformers import Qwen2Tokenizer
|
||||
|
||||
import comfy.text_encoders.llama
|
||||
import comfy.text_encoders.qwen3vl
|
||||
from comfy import sd1_clip
|
||||
|
||||
# Reference taps outputs of layers (0,3,...,35); comfy captures layer inputs, offset by +1.
|
||||
@ -77,3 +78,43 @@ def te(dtype_llama=None, llama_quantization_metadata=None):
|
||||
model_options["quantization_metadata"] = llama_quantization_metadata
|
||||
super().__init__(device=device, dtype=dtype, model_options=model_options)
|
||||
return Ideogram4TEModel_
|
||||
|
||||
|
||||
# Full Qwen3-VL-8B variant with vision
|
||||
|
||||
class Ideogram4Qwen3VLClipModel(comfy.text_encoders.qwen3vl.Qwen3VLClipModel):
|
||||
def __init__(self, device="cpu", dtype=None, attention_mask=True, model_options={}):
|
||||
super().__init__(device=device, layer=IDEOGRAM4_TAP_LAYERS, layer_idx=None, dtype=dtype,
|
||||
attention_mask=attention_mask, model_options=model_options, model_type="qwen3vl_8b")
|
||||
|
||||
|
||||
class Ideogram4Qwen3VLTEModel(sd1_clip.SD1ClipModel):
|
||||
def __init__(self, device="cpu", dtype=None, model_options={}):
|
||||
super().__init__(device=device, dtype=dtype, name="qwen3vl_8b", clip_model=Ideogram4Qwen3VLClipModel, model_options=model_options)
|
||||
|
||||
def encode_token_weights(self, token_weight_pairs):
|
||||
out, pooled, extra = super().encode_token_weights(token_weight_pairs)
|
||||
b, n, seq, h = out.shape # (B, n_taps=13, seq, 4096), ascending layer order.
|
||||
out = out.permute(0, 2, 3, 1).reshape(b, seq, h * n) # (B, seq, 4096*13 = 53248).
|
||||
return out, pooled, extra
|
||||
|
||||
|
||||
class Ideogram4Qwen3VLTokenizer(comfy.text_encoders.qwen3vl.Qwen3VLTokenizer):
|
||||
def __init__(self, embedding_directory=None, tokenizer_data={}):
|
||||
super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, model_type="qwen3vl_8b")
|
||||
|
||||
def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, thinking=True, **kwargs):
|
||||
# Ideogram 4 conditions on the no-think template; default thinking=True drops the empty think block qwen3vl adds.
|
||||
return super().tokenize_with_weights(text, return_word_ids=return_word_ids, llama_template=llama_template, images=images, prevent_empty_text=prevent_empty_text, thinking=thinking, **kwargs)
|
||||
|
||||
|
||||
def te_qwen3vl(dtype_llama=None, llama_quantization_metadata=None):
|
||||
class Ideogram4Qwen3VLTEModel_(Ideogram4Qwen3VLTEModel):
|
||||
def __init__(self, device="cpu", dtype=None, model_options={}):
|
||||
if dtype_llama is not None:
|
||||
dtype = dtype_llama
|
||||
if llama_quantization_metadata is not None:
|
||||
model_options = model_options.copy()
|
||||
model_options["quantization_metadata"] = llama_quantization_metadata
|
||||
super().__init__(device=device, dtype=dtype, model_options=model_options)
|
||||
return Ideogram4Qwen3VLTEModel_
|
||||
|
||||
@ -251,6 +251,19 @@ class Qwen3_8BConfig:
|
||||
lm_head: bool = True
|
||||
stop_tokens = [151643, 151645]
|
||||
|
||||
@dataclass
|
||||
class Qwen3VL_8BConfig(Qwen3_8BConfig):
|
||||
max_position_embeddings: int = 262144
|
||||
rope_theta: float = 5000000.0
|
||||
rope_dims = [24, 20, 20]
|
||||
interleaved_mrope = True
|
||||
|
||||
@dataclass
|
||||
class Qwen3VL_4BConfig(Qwen3VL_8BConfig):
|
||||
hidden_size: int = 2560
|
||||
intermediate_size: int = 9728
|
||||
lm_head: bool = False # 4B ties word embeddings
|
||||
|
||||
@dataclass
|
||||
class Ovis25_2BConfig:
|
||||
vocab_size: int = 151936
|
||||
@ -703,7 +716,8 @@ class Llama2_(nn.Module):
|
||||
interleaved_mrope=getattr(self.config, "interleaved_mrope", False),
|
||||
device=device)
|
||||
|
||||
def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=[], past_key_values=None, input_ids=None):
|
||||
def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True,
|
||||
dtype=None, position_ids=None, embeds_info=[], past_key_values=None, input_ids=None,deepstack_embeds=None, visual_pos_masks=None):
|
||||
if embeds is not None:
|
||||
x = embeds
|
||||
else:
|
||||
@ -767,6 +781,10 @@ class Llama2_(nn.Module):
|
||||
if current_kv is not None:
|
||||
next_key_values.append(current_kv)
|
||||
|
||||
# DeepStack: add per-layer visual features into the first len() decoder layers at image positions (Qwen3-VL)
|
||||
if deepstack_embeds is not None and i < len(deepstack_embeds):
|
||||
x[visual_pos_masks] = x[visual_pos_masks] + deepstack_embeds[i].to(x)
|
||||
|
||||
if i == intermediate_output:
|
||||
intermediate = x.clone()
|
||||
|
||||
@ -860,7 +878,7 @@ class BaseGenerate:
|
||||
torch.empty([batch, model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype), 0))
|
||||
return past_key_values
|
||||
|
||||
def generate(self, embeds=None, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.9, min_p=0.0, repetition_penalty=1.0, seed=42, stop_tokens=None, initial_tokens=[], execution_dtype=None, min_tokens=0, presence_penalty=0.0, initial_input_ids=None):
|
||||
def generate(self, embeds=None, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.9, min_p=0.0, repetition_penalty=1.0, seed=42, stop_tokens=None, initial_tokens=[], execution_dtype=None, min_tokens=0, presence_penalty=0.0, initial_input_ids=None, position_ids=None, deepstack_embeds=None, visual_pos_masks=None):
|
||||
device = embeds.device
|
||||
|
||||
if stop_tokens is None:
|
||||
@ -884,10 +902,18 @@ class BaseGenerate:
|
||||
generated_token_ids = []
|
||||
pbar = comfy.utils.ProgressBar(max_length)
|
||||
|
||||
# MRoPE: prefill uses explicit 3D position_ids, decode continues from the last position
|
||||
next_pos = int(position_ids[:, -1].max()) + 1 if position_ids is not None else None
|
||||
|
||||
# Generation loop
|
||||
current_input_ids = initial_input_ids
|
||||
for step in tqdm(range(max_length), desc="Generating tokens"):
|
||||
x, _, past_key_values = self.model.forward(None, embeds=embeds, attention_mask=None, past_key_values=past_key_values, input_ids=current_input_ids)
|
||||
# DeepStack visual features are injected on the prefill only; gemma4's forward lacks these kwargs.
|
||||
extra = {}
|
||||
if step == 0 and deepstack_embeds is not None:
|
||||
extra["deepstack_embeds"] = deepstack_embeds
|
||||
extra["visual_pos_masks"] = visual_pos_masks
|
||||
x, _, past_key_values = self.model.forward(None, embeds=embeds, attention_mask=None, past_key_values=past_key_values, input_ids=current_input_ids, position_ids=position_ids, **extra)
|
||||
logits = self.logits(x)[:, -1]
|
||||
next_token = self.sample_token(logits, temperature, top_k, top_p, min_p, repetition_penalty, initial_tokens + generated_token_ids, generator, do_sample=do_sample, presence_penalty=presence_penalty)
|
||||
token_id = next_token[0].item()
|
||||
@ -895,6 +921,9 @@ class BaseGenerate:
|
||||
|
||||
embeds = self.model.embed_tokens(next_token).to(execution_dtype)
|
||||
current_input_ids = next_token if initial_input_ids is not None else None
|
||||
if next_pos is not None: # advance MRoPE position for the next (decode) step
|
||||
position_ids = torch.tensor([[next_pos]], device=device)
|
||||
next_pos += 1
|
||||
pbar.update(1)
|
||||
|
||||
if token_id in stop_tokens:
|
||||
|
||||
@ -3,7 +3,6 @@ import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from dataclasses import dataclass, field
|
||||
import os
|
||||
import math
|
||||
|
||||
import comfy.model_management
|
||||
from comfy.ldm.modules.attention import optimized_attention_for_device
|
||||
@ -563,6 +562,8 @@ class Qwen35VisionModel(nn.Module):
|
||||
for _ in range(config["depth"])
|
||||
])
|
||||
self.merger = Qwen35VisionPatchMerger(self.hidden_size, self.spatial_merge_size, config["out_hidden_size"], device=device, dtype=dtype, ops=ops)
|
||||
self.deepstack_visual_indexes = [] # DeepStack, per-layer visual features (Qwen3-VL)
|
||||
self.deepstack_merger_list = None
|
||||
|
||||
def rot_pos_emb(self, grid_thw):
|
||||
merge_size = self.spatial_merge_size
|
||||
@ -664,9 +665,14 @@ class Qwen35VisionModel(nn.Module):
|
||||
).cumsum(dim=0, dtype=torch.int32)
|
||||
cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
|
||||
optimized_attention = optimized_attention_for_device(x.device, mask=False, small_input=True)
|
||||
for blk in self.blocks:
|
||||
deepstack_features = []
|
||||
for layer_num, blk in enumerate(self.blocks):
|
||||
x = blk(x, cu_seqlens=cu_seqlens, position_embeddings=position_embeddings, optimized_attention=optimized_attention)
|
||||
if self.deepstack_merger_list is not None and layer_num in self.deepstack_visual_indexes:
|
||||
deepstack_features.append(self.deepstack_merger_list[self.deepstack_visual_indexes.index(layer_num)](x))
|
||||
merged = self.merger(x)
|
||||
if self.deepstack_merger_list is not None:
|
||||
return merged, deepstack_features
|
||||
return merged
|
||||
|
||||
# Model Wrapper
|
||||
@ -690,30 +696,7 @@ class Qwen35(BaseLlama, BaseGenerate, torch.nn.Module):
|
||||
return None, None
|
||||
|
||||
def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[], past_key_values=None):
|
||||
grid = None
|
||||
position_ids = None
|
||||
offset = 0
|
||||
for e in embeds_info:
|
||||
if e.get("type") == "image":
|
||||
grid = e.get("extra", None)
|
||||
start = e.get("index")
|
||||
if position_ids is None:
|
||||
position_ids = torch.zeros((3, embeds.shape[1]), device=embeds.device)
|
||||
position_ids[:, :start] = torch.arange(0, start, device=embeds.device)
|
||||
end = e.get("size") + start
|
||||
len_max = int(grid.max()) // 2
|
||||
start_next = len_max + start
|
||||
position_ids[:, end:] = torch.arange(start_next + offset, start_next + (embeds.shape[1] - end) + offset, device=embeds.device)
|
||||
position_ids[0, start:end] = start + offset
|
||||
max_d = int(grid[0][1]) // 2
|
||||
position_ids[1, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start]
|
||||
max_d = int(grid[0][2]) // 2
|
||||
position_ids[2, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(0).repeat(math.ceil((end - start) / max_d), 1).flatten(0)[:end - start]
|
||||
offset += len_max - (end - start)
|
||||
|
||||
if grid is None:
|
||||
position_ids = None
|
||||
|
||||
position_ids = comfy.text_encoders.qwen_vl.qwen2vl_mrope_position_ids(embeds_info, embeds.shape[1], embeds.device)
|
||||
return super().forward(x, attention_mask=attention_mask, embeds=embeds, num_tokens=num_tokens, intermediate_output=intermediate_output, final_layer_norm_intermediate=final_layer_norm_intermediate, dtype=dtype, position_ids=position_ids, past_key_values=past_key_values)
|
||||
|
||||
def init_kv_cache(self, batch, max_cache_len, device, execution_dtype):
|
||||
|
||||
193
comfy/text_encoders/qwen3vl.py
Normal file
193
comfy/text_encoders/qwen3vl.py
Normal file
@ -0,0 +1,193 @@
|
||||
import os
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from transformers import Qwen2Tokenizer
|
||||
|
||||
from comfy import sd1_clip
|
||||
import comfy.text_encoders.qwen_vl
|
||||
from .qwen35 import Qwen35VisionModel
|
||||
from .llama import BaseLlama, BaseQwen3, BaseGenerate, Llama2_, Qwen3VL_4BConfig, Qwen3VL_8BConfig
|
||||
|
||||
|
||||
QWEN3VL_VISION = {
|
||||
"qwen3vl_4b": dict(hidden_size=1024, intermediate_size=4096, depth=24, deepstack_visual_indexes=[5, 11, 17]),
|
||||
"qwen3vl_8b": dict(hidden_size=1152, intermediate_size=4304, depth=27, deepstack_visual_indexes=[8, 16, 24]),
|
||||
}
|
||||
QWEN3VL_VISION_COMMON = dict(num_heads=16, patch_size=16, temporal_patch_size=2, in_channels=3,
|
||||
spatial_merge_size=2, num_position_embeddings=2304)
|
||||
|
||||
QWEN3VL_CONFIGS = {"qwen3vl_4b": Qwen3VL_4BConfig, "qwen3vl_8b": Qwen3VL_8BConfig}
|
||||
|
||||
|
||||
class Qwen3VLDeepstackMerger(nn.Module):
|
||||
# DeepStack merger: postshuffle LayerNorm (applied after spatial merge), unlike the main merger.
|
||||
def __init__(self, hidden_size, spatial_merge_size, out_hidden_size, device=None, dtype=None, ops=None):
|
||||
super().__init__()
|
||||
self.merge_dim = hidden_size * (spatial_merge_size ** 2)
|
||||
self.norm = ops.LayerNorm(self.merge_dim, eps=1e-6, device=device, dtype=dtype)
|
||||
self.linear_fc1 = ops.Linear(self.merge_dim, self.merge_dim, device=device, dtype=dtype)
|
||||
self.linear_fc2 = ops.Linear(self.merge_dim, out_hidden_size, device=device, dtype=dtype)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.norm(x.view(-1, self.merge_dim))
|
||||
return self.linear_fc2(F.gelu(self.linear_fc1(x)))
|
||||
|
||||
|
||||
class Qwen3VLVisionModel(Qwen35VisionModel):
|
||||
# Qwen3.5 vision + DeepStack
|
||||
def __init__(self, config, device=None, dtype=None, ops=None):
|
||||
super().__init__(config, device=device, dtype=dtype, ops=ops)
|
||||
self.deepstack_visual_indexes = config["deepstack_visual_indexes"]
|
||||
self.deepstack_merger_list = nn.ModuleList([
|
||||
Qwen3VLDeepstackMerger(self.hidden_size, self.spatial_merge_size, config["out_hidden_size"], device=device, dtype=dtype, ops=ops)
|
||||
for _ in self.deepstack_visual_indexes
|
||||
])
|
||||
|
||||
|
||||
class Qwen3VL(BaseLlama, BaseQwen3, BaseGenerate, torch.nn.Module):
|
||||
model_type = "qwen3vl_8b"
|
||||
|
||||
def __init__(self, config_dict, dtype, device, operations):
|
||||
super().__init__()
|
||||
config = QWEN3VL_CONFIGS[self.model_type](**config_dict)
|
||||
self.num_layers = config.num_hidden_layers
|
||||
self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
|
||||
vision_config = {**QWEN3VL_VISION_COMMON, **QWEN3VL_VISION[self.model_type], "out_hidden_size": config.hidden_size}
|
||||
self.visual = Qwen3VLVisionModel(vision_config, device=device, dtype=dtype, ops=operations)
|
||||
self.dtype = dtype
|
||||
|
||||
def preprocess_embed(self, embed, device):
|
||||
if embed["type"] == "image":
|
||||
# Qwen3-VL normalizes to [-1, 1] (mean/std 0.5), unlike Qwen2.5-VL's CLIP normalization.
|
||||
image, grid = comfy.text_encoders.qwen_vl.process_qwen2vl_images(embed["data"], patch_size=16, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5])
|
||||
merged, deepstack = self.visual(image.to(device, dtype=torch.float32), grid)
|
||||
return merged, {"grid": grid, "deepstack": deepstack}
|
||||
return None, None
|
||||
|
||||
def build_image_inputs(self, embeds, embeds_info):
|
||||
# Returns (position_ids, visual_pos_masks, deepstack) for the prompt
|
||||
images = sorted([e for e in embeds_info if e.get("type") == "image"], key=lambda e: e["index"])
|
||||
if len(images) == 0:
|
||||
return None, None, None
|
||||
|
||||
device = embeds.device
|
||||
seq = embeds.shape[1]
|
||||
position_ids = comfy.text_encoders.qwen_vl.qwen2vl_mrope_position_ids(embeds_info, seq, device)
|
||||
|
||||
# DeepStack: mask of image positions + per-vision-layer features to inject there.
|
||||
visual_pos_masks = torch.zeros((1, seq), dtype=torch.bool, device=device)
|
||||
deepstack = None
|
||||
for e in images:
|
||||
start = e["index"]
|
||||
end = e["size"] + start
|
||||
visual_pos_masks[0, start:end] = True
|
||||
ds = e["extra"]["deepstack"]
|
||||
if deepstack is None:
|
||||
deepstack = [d for d in ds]
|
||||
else:
|
||||
deepstack = [torch.cat([deepstack[i], ds[i]], dim=0) for i in range(len(ds))]
|
||||
return position_ids, visual_pos_masks, deepstack
|
||||
|
||||
|
||||
def _make_qwen3vl_model(model_type):
|
||||
class Qwen3VL_(Qwen3VL):
|
||||
pass
|
||||
Qwen3VL_.model_type = model_type
|
||||
return Qwen3VL_
|
||||
|
||||
|
||||
class Qwen3VLClipModel(sd1_clip.SDClipModel):
|
||||
def __init__(self, device="cpu", layer="hidden", layer_idx=-1, dtype=None, attention_mask=True, model_options={}, model_type="qwen3vl_8b"):
|
||||
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={},
|
||||
dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False,
|
||||
model_class=_make_qwen3vl_model(model_type), enable_attention_masks=attention_mask,
|
||||
return_attention_masks=attention_mask, model_options=model_options)
|
||||
|
||||
def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, presence_penalty=0.0):
|
||||
if isinstance(tokens, dict):
|
||||
tokens = next(iter(tokens.values()))
|
||||
tokens_only = [[t[0] for t in b] for b in tokens]
|
||||
embeds, _, _, embeds_info = self.process_tokens(tokens_only, self.execution_device)
|
||||
position_ids, visual_pos_masks, deepstack = self.transformer.build_image_inputs(embeds, embeds_info)
|
||||
return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed,
|
||||
presence_penalty=presence_penalty, position_ids=position_ids,
|
||||
visual_pos_masks=visual_pos_masks, deepstack_embeds=deepstack)
|
||||
|
||||
|
||||
class Qwen3VLTEModel(sd1_clip.SD1ClipModel):
|
||||
def __init__(self, device="cpu", dtype=None, model_options={}, model_type="qwen3vl_8b"):
|
||||
clip_model = lambda **kw: Qwen3VLClipModel(**kw, model_type=model_type)
|
||||
super().__init__(device=device, dtype=dtype, name=model_type, clip_model=clip_model, model_options=model_options)
|
||||
|
||||
|
||||
class Qwen3VLSDTokenizer(sd1_clip.SDTokenizer):
|
||||
def __init__(self, embedding_directory=None, tokenizer_data={}, embedding_size=4096, embedding_key="qwen3vl_8b"):
|
||||
tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer")
|
||||
super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=embedding_size, embedding_key=embedding_key, tokenizer_class=Qwen2Tokenizer,
|
||||
has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data)
|
||||
|
||||
|
||||
class Qwen3VLTokenizer(sd1_clip.SD1Tokenizer):
|
||||
def __init__(self, embedding_directory=None, tokenizer_data={}, model_type="qwen3vl_8b"):
|
||||
embedding_size = 2560 if model_type == "qwen3vl_4b" else 4096
|
||||
tokenizer = lambda *a, **kw: Qwen3VLSDTokenizer(*a, **kw, embedding_size=embedding_size, embedding_key=model_type)
|
||||
super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name=model_type, tokenizer=tokenizer)
|
||||
self.llama_template = "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
|
||||
self.llama_template_images = "<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n"
|
||||
|
||||
def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, thinking=False, **kwargs):
|
||||
image = kwargs.get("image", None)
|
||||
if image is not None and len(images) == 0:
|
||||
images = [image[i:i + 1] for i in range(image.shape[0])]
|
||||
|
||||
skip_template = text.startswith('<|im_start|>')
|
||||
if prevent_empty_text and text == '':
|
||||
text = ' '
|
||||
|
||||
if skip_template:
|
||||
llama_text = text
|
||||
else:
|
||||
if llama_template is not None:
|
||||
template = llama_template
|
||||
elif len(images) == 0:
|
||||
template = self.llama_template
|
||||
else:
|
||||
template = self.llama_template_images
|
||||
if len(images) > 1:
|
||||
vision_block = "<|vision_start|><|image_pad|><|vision_end|>"
|
||||
template = template.replace(vision_block, vision_block * len(images), 1)
|
||||
llama_text = template.format(text)
|
||||
if not thinking: # Qwen3 convention: empty think block suppresses reasoning
|
||||
llama_text += "<think>\n\n</think>\n\n"
|
||||
|
||||
tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)
|
||||
key_name = next(iter(tokens))
|
||||
embed_count = 0
|
||||
for r in tokens[key_name]:
|
||||
for i in range(len(r)):
|
||||
if r[i][0] == 151655: # <|image_pad|>
|
||||
if len(images) > embed_count:
|
||||
r[i] = ({"type": "image", "data": images[embed_count], "original_type": "image"},) + r[i][1:]
|
||||
embed_count += 1
|
||||
return tokens
|
||||
|
||||
|
||||
def tokenizer(model_type="qwen3vl_8b"):
|
||||
class Qwen3VLTokenizer_(Qwen3VLTokenizer):
|
||||
def __init__(self, embedding_directory=None, tokenizer_data={}):
|
||||
super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, model_type=model_type)
|
||||
return Qwen3VLTokenizer_
|
||||
|
||||
|
||||
def te(dtype_llama=None, llama_quantization_metadata=None, model_type="qwen3vl_8b"):
|
||||
class Qwen3VLTEModel_(Qwen3VLTEModel):
|
||||
def __init__(self, device="cpu", dtype=None, model_options={}):
|
||||
if dtype_llama is not None:
|
||||
dtype = dtype_llama
|
||||
if llama_quantization_metadata is not None:
|
||||
model_options = model_options.copy()
|
||||
model_options["quantization_metadata"] = llama_quantization_metadata
|
||||
super().__init__(device=device, dtype=dtype, model_options=model_options, model_type=model_type)
|
||||
return Qwen3VLTEModel_
|
||||
@ -88,6 +88,32 @@ def process_qwen2vl_images(
|
||||
return flatten_patches, image_grid_thw
|
||||
|
||||
|
||||
def qwen2vl_mrope_position_ids(embeds_info, seq_len, device):
|
||||
# (3, seq_len) T/H/W MRoPE position ids: text runs sequentially, each image span gets its grid positions.
|
||||
# Returns None when there are no image embeds. `extra` is the image grid_thw, or a dict carrying it under "grid".
|
||||
position_ids = None
|
||||
offset = 0
|
||||
for e in embeds_info:
|
||||
if e.get("type") == "image":
|
||||
extra = e.get("extra", None)
|
||||
grid = extra["grid"] if isinstance(extra, dict) else extra
|
||||
start = e.get("index")
|
||||
if position_ids is None:
|
||||
position_ids = torch.zeros((3, seq_len), device=device)
|
||||
position_ids[:, :start] = torch.arange(0, start, device=device)
|
||||
end = e.get("size") + start
|
||||
len_max = int(grid.max()) // 2
|
||||
start_next = len_max + start
|
||||
position_ids[:, end:] = torch.arange(start_next + offset, start_next + (seq_len - end) + offset, device=device)
|
||||
position_ids[0, start:end] = start + offset
|
||||
max_d = int(grid[0][1]) // 2
|
||||
position_ids[1, start:end] = torch.arange(start + offset, start + max_d + offset, device=device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start]
|
||||
max_d = int(grid[0][2]) // 2
|
||||
position_ids[2, start:end] = torch.arange(start + offset, start + max_d + offset, device=device).unsqueeze(0).repeat(math.ceil((end - start) / max_d), 1).flatten(0)[:end - start]
|
||||
offset += len_max - (end - start)
|
||||
return position_ids
|
||||
|
||||
|
||||
class VisionPatchEmbed(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
|
||||
@ -11,7 +11,7 @@ class TextEncodeAceStepAudio(IO.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="TextEncodeAceStepAudio",
|
||||
category="model/conditioning",
|
||||
category="model/conditioning/ace",
|
||||
inputs=[
|
||||
IO.Clip.Input("clip"),
|
||||
IO.String.Input("tags", multiline=True, dynamic_prompts=True),
|
||||
@ -33,7 +33,7 @@ class TextEncodeAceStepAudio15(IO.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="TextEncodeAceStepAudio1.5",
|
||||
category="model/conditioning",
|
||||
category="model/conditioning/ace",
|
||||
inputs=[
|
||||
IO.Clip.Input("clip"),
|
||||
IO.String.Input("tags", multiline=True, dynamic_prompts=True),
|
||||
@ -67,7 +67,7 @@ class EmptyAceStepLatentAudio(IO.ComfyNode):
|
||||
return IO.Schema(
|
||||
node_id="EmptyAceStepLatentAudio",
|
||||
display_name="Empty Ace Step 1.0 Latent Audio",
|
||||
category="model/latent/audio",
|
||||
category="model/latent/ace",
|
||||
inputs=[
|
||||
IO.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.1),
|
||||
IO.Int.Input(
|
||||
@ -90,7 +90,7 @@ class EmptyAceStep15LatentAudio(IO.ComfyNode):
|
||||
return IO.Schema(
|
||||
node_id="EmptyAceStep1.5LatentAudio",
|
||||
display_name="Empty Ace Step 1.5 Latent Audio",
|
||||
category="model/latent/audio",
|
||||
category="model/latent/ace",
|
||||
inputs=[
|
||||
IO.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.01),
|
||||
IO.Int.Input(
|
||||
@ -111,8 +111,8 @@ class ReferenceAudio(IO.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="ReferenceTimbreAudio",
|
||||
display_name="Reference Audio",
|
||||
category="advanced/conditioning/audio",
|
||||
display_name="Set Reference Audio",
|
||||
category="model/conditioning",
|
||||
is_experimental=True,
|
||||
description="This node sets the reference audio for ace step 1.5",
|
||||
inputs=[
|
||||
|
||||
@ -16,7 +16,7 @@ class APG(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="APG",
|
||||
display_name="Adaptive Projected Guidance",
|
||||
category="model/sampling/custom_sampling",
|
||||
category="model/sampling/custom",
|
||||
inputs=[
|
||||
io.Model.Input("model"),
|
||||
io.Float.Input(
|
||||
|
||||
@ -19,7 +19,7 @@ class EmptyARVideoLatent(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="EmptyARVideoLatent",
|
||||
category="model/latent/video",
|
||||
category="model/latent/autoregressive",
|
||||
inputs=[
|
||||
io.Int.Input("width", default=832, min=16, max=8192, step=16),
|
||||
io.Int.Input("height", default=480, min=16, max=8192, step=16),
|
||||
@ -85,7 +85,7 @@ class ARVideoI2V(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="ARVideoI2V",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/autoregressive",
|
||||
inputs=[
|
||||
io.Model.Input("model"),
|
||||
io.Vae.Input("vae"),
|
||||
|
||||
@ -16,7 +16,7 @@ class EmptyLatentAudio(IO.ComfyNode):
|
||||
return IO.Schema(
|
||||
node_id="EmptyLatentAudio",
|
||||
display_name="Empty Latent Audio",
|
||||
category="model/latent/audio",
|
||||
category="model/latent",
|
||||
essentials_category="Audio",
|
||||
inputs=[
|
||||
IO.Float.Input("seconds", default=47.6, min=1.0, max=1000.0, step=0.1),
|
||||
@ -41,7 +41,7 @@ class ConditioningStableAudio(IO.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="ConditioningStableAudio",
|
||||
category="model/conditioning",
|
||||
category="model/conditioning/stable audio",
|
||||
inputs=[
|
||||
IO.Conditioning.Input("positive"),
|
||||
IO.Conditioning.Input("negative"),
|
||||
@ -70,7 +70,7 @@ class VAEEncodeAudio(IO.ComfyNode):
|
||||
node_id="VAEEncodeAudio",
|
||||
search_aliases=["audio to latent"],
|
||||
display_name="VAE Encode Audio",
|
||||
category="model/latent/audio",
|
||||
category="model/latent",
|
||||
inputs=[
|
||||
IO.Audio.Input("audio"),
|
||||
IO.Vae.Input("vae"),
|
||||
@ -115,7 +115,7 @@ class VAEDecodeAudio(IO.ComfyNode):
|
||||
node_id="VAEDecodeAudio",
|
||||
search_aliases=["latent to audio"],
|
||||
display_name="VAE Decode Audio",
|
||||
category="model/latent/audio",
|
||||
category="model/latent",
|
||||
inputs=[
|
||||
IO.Latent.Input("samples"),
|
||||
IO.Vae.Input("vae"),
|
||||
@ -137,7 +137,7 @@ class VAEDecodeAudioTiled(IO.ComfyNode):
|
||||
node_id="VAEDecodeAudioTiled",
|
||||
search_aliases=["latent to audio"],
|
||||
display_name="VAE Decode Audio (Tiled)",
|
||||
category="model/latent/audio",
|
||||
category="model/latent",
|
||||
inputs=[
|
||||
IO.Latent.Input("samples"),
|
||||
IO.Vae.Input("vae"),
|
||||
|
||||
@ -39,9 +39,9 @@ class BerniniConditioning(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="BerniniConditioning",
|
||||
display_name="Bernini Conditioning",
|
||||
category="conditioning/video_models",
|
||||
category="model/conditioning/bernini",
|
||||
description="Conditioning node for Bernini in-context video/image conditioning. It can be used for the following tasks: t2v (text-to-video), v2v (video-to-video), rv2v (reference-guided video editing), r2v (reference-to-video), ads2v (insert image/video into video)."
|
||||
"Reference images injected as in-context tokens (r2v, rv2v) are encoded independently at their own native aspect ratio (long edge capped at ref_max_size)",
|
||||
"Reference images injected as in-context tokens (r2v, rv2v) are encoded independently at their own native aspect ratio (long edge capped at ref_max_size)",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -50,14 +50,11 @@ class BerniniConditioning(io.ComfyNode):
|
||||
io.Int.Input("height", default=480, min=16, max=8192, step=16),
|
||||
io.Int.Input("length", default=81, min=1, max=8192, step=4),
|
||||
io.Int.Input("batch_size", default=1, min=1, max=4096),
|
||||
io.Image.Input("source_video", optional=True, tooltip=(
|
||||
"Source video to edit or restyle (v2v, rv2v). Resized to width/height and trimmed to length.")),
|
||||
io.Image.Input("reference_video", optional=True, tooltip=(
|
||||
"Video to insert into the source video (ads2v).")),
|
||||
io.Image.Input("source_video", optional=True, tooltip=("Source video to edit or restyle (v2v, rv2v). Resized to width/height and trimmed to length.")),
|
||||
io.Image.Input("reference_video", optional=True, tooltip=("Video to insert into the source video (ads2v).")),
|
||||
io.Autogrow.Input("reference_images", optional=True,
|
||||
template=io.Autogrow.TemplatePrefix(
|
||||
input=io.Image.Input("reference_image", tooltip=(
|
||||
"Reference image injected as an in-context token (r2v, rv2v).")),
|
||||
input=io.Image.Input("reference_image", tooltip=("Reference image injected as an in-context token (r2v, rv2v).")),
|
||||
prefix="reference_image_", min=0, max=8)),
|
||||
io.Int.Input("ref_max_size", default=848, min=16, max=8192, step=16, optional=True, tooltip=(
|
||||
"Max size for the long edge of reference_video and reference_images. Resized with preserved aspect ratio and snapped to 16px.")),
|
||||
@ -70,10 +67,8 @@ class BerniniConditioning(io.ComfyNode):
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, positive, negative, vae, width, height, length, batch_size,
|
||||
source_video=None, reference_video=None, reference_images=None, ref_max_size=848) -> io.NodeOutput:
|
||||
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8],
|
||||
device=comfy.model_management.intermediate_device())
|
||||
def execute(cls, positive, negative, vae, width, height, length, batch_size, source_video=None, reference_video=None, reference_images=None, ref_max_size=848) -> io.NodeOutput:
|
||||
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
|
||||
|
||||
# source_video (1), reference_video (2), reference_images (3, 4, ...).
|
||||
context = []
|
||||
@ -106,9 +101,7 @@ class BerniniConditioning(io.ComfyNode):
|
||||
class BerniniExtension(ComfyExtension):
|
||||
@override
|
||||
async def get_node_list(self) -> list[type[io.ComfyNode]]:
|
||||
return [
|
||||
BerniniConditioning,
|
||||
]
|
||||
return [BerniniConditioning,]
|
||||
|
||||
|
||||
async def comfy_entrypoint() -> BerniniExtension:
|
||||
|
||||
@ -153,7 +153,7 @@ class WanCameraEmbedding(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanCameraEmbedding",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/wan/camera",
|
||||
inputs=[
|
||||
io.Combo.Input(
|
||||
"camera_pose",
|
||||
|
||||
@ -13,7 +13,7 @@ class EmptyChromaRadianceLatentImage(io.ComfyNode):
|
||||
def define_schema(cls) -> io.Schema:
|
||||
return io.Schema(
|
||||
node_id="EmptyChromaRadianceLatentImage",
|
||||
category="model/latent/chroma_radiance",
|
||||
category="model/latent/chroma radiance",
|
||||
inputs=[
|
||||
io.Int.Input(id="width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
io.Int.Input(id="height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
@ -33,7 +33,7 @@ class ChromaRadianceOptions(io.ComfyNode):
|
||||
def define_schema(cls) -> io.Schema:
|
||||
return io.Schema(
|
||||
node_id="ChromaRadianceOptions",
|
||||
category="model/patch/chroma_radiance",
|
||||
category="model/patch/chroma radiance",
|
||||
description="Allows setting advanced options for the Chroma Radiance model.",
|
||||
inputs=[
|
||||
io.Model.Input(id="model"),
|
||||
|
||||
@ -9,7 +9,8 @@ class CLIPTextEncodeSDXLRefiner(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="CLIPTextEncodeSDXLRefiner",
|
||||
category="advanced/conditioning",
|
||||
display_name="CLIP Text Encode (SDXL Refiner)",
|
||||
category="model/conditioning/stable diffusion",
|
||||
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),
|
||||
@ -30,7 +31,8 @@ class CLIPTextEncodeSDXL(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="CLIPTextEncodeSDXL",
|
||||
category="advanced/conditioning",
|
||||
display_name="CLIP Text Encode (SDXL)",
|
||||
category="model/conditioning/stable diffusion",
|
||||
inputs=[
|
||||
io.Clip.Input("clip"),
|
||||
io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
|
||||
|
||||
@ -66,6 +66,7 @@ class WanContextWindowsManualNode(ContextWindowsManualNode):
|
||||
schema.node_id = "WanContextWindowsManual"
|
||||
schema.display_name = "WAN Context Windows (Manual)"
|
||||
schema.description = "Manually set context windows for WAN-like models (dim=2)."
|
||||
schema.category="model/patch/wan"
|
||||
schema.inputs = [
|
||||
io.Model.Input("model", tooltip="The model to apply context windows to during sampling."),
|
||||
io.Int.Input("context_length", min=1, max=nodes.MAX_RESOLUTION, step=4, default=81, tooltip="The length of the context window.", advanced=True),
|
||||
|
||||
@ -9,6 +9,8 @@ class SetUnionControlNetType(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="SetUnionControlNetType",
|
||||
search_aliases=["set controlnet type", "union controlnet type"],
|
||||
display_name="Set Union ControlNet Type",
|
||||
category="model/conditioning/controlnet",
|
||||
inputs=[
|
||||
io.ControlNet.Input("control_net"),
|
||||
@ -39,6 +41,7 @@ class ControlNetInpaintingAliMamaApply(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="ControlNetInpaintingAliMamaApply",
|
||||
search_aliases=["masked controlnet"],
|
||||
display_name="Apply ControlNet Inpainting (AliMama)",
|
||||
category="model/conditioning/controlnet",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
|
||||
@ -13,7 +13,7 @@ class EmptyCosmosLatentVideo(io.ComfyNode):
|
||||
def define_schema(cls) -> io.Schema:
|
||||
return io.Schema(
|
||||
node_id="EmptyCosmosLatentVideo",
|
||||
category="model/latent/video",
|
||||
category="model/latent/cosmos",
|
||||
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),
|
||||
@ -45,7 +45,7 @@ class CosmosImageToVideoLatent(io.ComfyNode):
|
||||
def define_schema(cls) -> io.Schema:
|
||||
return io.Schema(
|
||||
node_id="CosmosImageToVideoLatent",
|
||||
category="model/conditioning/inpaint",
|
||||
category="model/conditioning/cosmos",
|
||||
inputs=[
|
||||
io.Vae.Input("vae"),
|
||||
io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
@ -88,7 +88,7 @@ class CosmosPredict2ImageToVideoLatent(io.ComfyNode):
|
||||
def define_schema(cls) -> io.Schema:
|
||||
return io.Schema(
|
||||
node_id="CosmosPredict2ImageToVideoLatent",
|
||||
category="model/conditioning/inpaint",
|
||||
category="model/conditioning/cosmos",
|
||||
inputs=[
|
||||
io.Vae.Input("vae"),
|
||||
io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
|
||||
@ -729,7 +729,7 @@ class SamplerCustom(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="SamplerCustom",
|
||||
category="model/sampling/custom_sampling",
|
||||
category="model/sampling/custom",
|
||||
inputs=[
|
||||
io.Model.Input("model"),
|
||||
io.Boolean.Input("add_noise", default=True, advanced=True),
|
||||
@ -1015,7 +1015,7 @@ class SamplerCustomAdvanced(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="SamplerCustomAdvanced",
|
||||
category="model/sampling/custom_sampling",
|
||||
category="model/sampling/custom",
|
||||
inputs=[
|
||||
io.Noise.Input("noise"),
|
||||
io.Guider.Input("guider"),
|
||||
@ -1143,7 +1143,7 @@ class CFGOverride(io.ComfyNode):
|
||||
display_name="CFG Override",
|
||||
description="Override cfg to a fixed value over a [start, end] percent (sigma) range. "
|
||||
"With multiple overrides, the one nearest the sampler wins on overlap.",
|
||||
category="sampling/custom_sampling",
|
||||
category="model/sampling/guiders",
|
||||
inputs=[
|
||||
io.Model.Input("model"),
|
||||
io.Float.Input("cfg", default=1.0, min=0.0, max=100.0, step=0.1, round=0.01),
|
||||
|
||||
@ -363,7 +363,7 @@ class EasyCacheNode(io.ComfyNode):
|
||||
node_id="EasyCache",
|
||||
display_name="EasyCache",
|
||||
description="Native EasyCache implementation.",
|
||||
category="advanced/debug/model",
|
||||
category="advanced/debug",
|
||||
is_experimental=True,
|
||||
inputs=[
|
||||
io.Model.Input("model", tooltip="The model to add EasyCache to."),
|
||||
@ -496,7 +496,7 @@ class LazyCacheNode(io.ComfyNode):
|
||||
node_id="LazyCache",
|
||||
display_name="LazyCache",
|
||||
description="A homebrew version of EasyCache - even 'easier' version of EasyCache to implement. Overall works worse than EasyCache, but better in some rare cases AND universal compatibility with everything in ComfyUI.",
|
||||
category="advanced/debug/model",
|
||||
category="advanced/debug",
|
||||
is_experimental=True,
|
||||
inputs=[
|
||||
io.Model.Input("model", tooltip="The model to add LazyCache to."),
|
||||
|
||||
@ -8,7 +8,8 @@ class ReferenceLatent(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="ReferenceLatent",
|
||||
category="advanced/conditioning/edit_models",
|
||||
display_name="Set Reference Latent",
|
||||
category="model/conditioning",
|
||||
description="This node sets the guiding latent for an edit model. If the model supports it you can chain multiple to set multiple reference images.",
|
||||
inputs=[
|
||||
io.Conditioning.Input("conditioning"),
|
||||
|
||||
@ -13,7 +13,7 @@ class CLIPTextEncodeFlux(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="CLIPTextEncodeFlux",
|
||||
category="advanced/conditioning/flux",
|
||||
category="model/conditioning/flux",
|
||||
inputs=[
|
||||
io.Clip.Input("clip"),
|
||||
io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
|
||||
@ -40,7 +40,7 @@ class EmptyFlux2LatentImage(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="EmptyFlux2LatentImage",
|
||||
display_name="Empty Flux 2 Latent",
|
||||
category="model/latent",
|
||||
category="model/latent/flux",
|
||||
inputs=[
|
||||
io.Int.Input("width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
io.Int.Input("height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
@ -61,7 +61,7 @@ class FluxGuidance(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="FluxGuidance",
|
||||
category="advanced/conditioning/flux",
|
||||
category="model/conditioning/flux",
|
||||
inputs=[
|
||||
io.Conditioning.Input("conditioning"),
|
||||
io.Float.Input("guidance", default=3.5, min=0.0, max=100.0, step=0.1),
|
||||
@ -84,7 +84,7 @@ class FluxDisableGuidance(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="FluxDisableGuidance",
|
||||
category="advanced/conditioning/flux",
|
||||
category="model/conditioning/flux",
|
||||
description="This node completely disables the guidance embed on Flux and Flux like models",
|
||||
inputs=[
|
||||
io.Conditioning.Input("conditioning"),
|
||||
@ -128,7 +128,7 @@ class FluxKontextImageScale(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="FluxKontextImageScale",
|
||||
category="advanced/conditioning/flux",
|
||||
category="model/conditioning/flux",
|
||||
description="This node resizes the image to one that is more optimal for flux kontext.",
|
||||
inputs=[
|
||||
io.Image.Input("image"),
|
||||
@ -156,7 +156,7 @@ class FluxKontextMultiReferenceLatentMethod(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="FluxKontextMultiReferenceLatentMethod",
|
||||
display_name="Edit Model Reference Method",
|
||||
category="advanced/conditioning/flux",
|
||||
category="model/conditioning/flux",
|
||||
inputs=[
|
||||
io.Conditioning.Input("conditioning"),
|
||||
io.Combo.Input(
|
||||
|
||||
@ -11,8 +11,9 @@ class QuadrupleCLIPLoader(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="QuadrupleCLIPLoader",
|
||||
category="advanced/loaders",
|
||||
description="[Recipes]\n\nhidream: long clip-l, long clip-g, t5xxl, llama_8b_3.1_instruct",
|
||||
display_name="Load CLIP (Quadruple)",
|
||||
category="model/loaders",
|
||||
description="Recipes:\nhidream: long clip-l, long clip-g, t5xxl, llama_8b_3.1_instruct",
|
||||
inputs=[
|
||||
io.Combo.Input("clip_name1", options=folder_paths.get_filename_list("text_encoders")),
|
||||
io.Combo.Input("clip_name2", options=folder_paths.get_filename_list("text_encoders")),
|
||||
@ -38,8 +39,9 @@ class CLIPTextEncodeHiDream(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="CLIPTextEncodeHiDream",
|
||||
display_name="CLIP Text Encode (HiDream)",
|
||||
search_aliases=["hidream prompt"],
|
||||
category="advanced/conditioning",
|
||||
category="model/conditioning/hidream",
|
||||
inputs=[
|
||||
io.Clip.Input("clip"),
|
||||
io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
|
||||
|
||||
@ -14,7 +14,7 @@ class EmptyHiDreamO1LatentImage(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="EmptyHiDreamO1LatentImage",
|
||||
display_name="Empty HiDream-O1 Latent Image",
|
||||
category="model/latent/image",
|
||||
category="model/latent/hidream",
|
||||
description=(
|
||||
"Empty pixel-space latent for HiDream-O1-Image. The model was "
|
||||
"trained at ~4 megapixels; lower resolutions go off-distribution "
|
||||
@ -47,7 +47,7 @@ class HiDreamO1ReferenceImages(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="HiDreamO1ReferenceImages",
|
||||
display_name="HiDream-O1 Reference Images",
|
||||
category="model/conditioning/image",
|
||||
category="model/conditioning/hidream",
|
||||
description=(
|
||||
"Attach 1-10 reference images to conditioning, one for edit instruction"
|
||||
"or multiple for subject-driven personalization."
|
||||
@ -117,7 +117,7 @@ class HiDreamO1PatchSeamSmoothing(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="HiDreamO1PatchSeamSmoothing",
|
||||
display_name="HiDream-O1 Patch Seam Smoothing",
|
||||
category="advanced/model",
|
||||
category="model/patch/hidream",
|
||||
is_experimental=True,
|
||||
description=(
|
||||
"Average the model output across multiple shifted patch-grid "
|
||||
|
||||
@ -14,7 +14,8 @@ class CLIPTextEncodeHunyuanDiT(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="CLIPTextEncodeHunyuanDiT",
|
||||
category="advanced/conditioning",
|
||||
display_name="CLIP Text Encode (Hunyuan Image)",
|
||||
category="model/conditioning/hunyuan image",
|
||||
inputs=[
|
||||
io.Clip.Input("clip"),
|
||||
io.String.Input("bert", multiline=True, dynamic_prompts=True),
|
||||
@ -41,7 +42,7 @@ class EmptyHunyuanLatentVideo(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="EmptyHunyuanLatentVideo",
|
||||
display_name="Empty HunyuanVideo 1.0 Latent",
|
||||
category="model/latent/video",
|
||||
category="model/latent/hunyuan video",
|
||||
inputs=[
|
||||
io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
@ -67,6 +68,7 @@ class EmptyHunyuanVideo15Latent(EmptyHunyuanLatentVideo):
|
||||
schema = super().define_schema()
|
||||
schema.node_id = "EmptyHunyuanVideo15Latent"
|
||||
schema.display_name = "Empty HunyuanVideo 1.5 Latent"
|
||||
schema.category = "model/latent/hunyuan video"
|
||||
return schema
|
||||
|
||||
@classmethod
|
||||
@ -81,7 +83,7 @@ class HunyuanVideo15ImageToVideo(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="HunyuanVideo15ImageToVideo",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/hunyuan video",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -132,7 +134,7 @@ class HunyuanVideo15SuperResolution(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="HunyuanVideo15SuperResolution",
|
||||
display_name="Hunyuan Video 1.5 Super Resolution",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/hunyuan video",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -227,7 +229,7 @@ class HunyuanVideo15LatentUpscaleWithModel(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="HunyuanVideo15LatentUpscaleWithModel",
|
||||
display_name="Hunyuan Video 15 Latent Upscale With Model",
|
||||
category="model/latent",
|
||||
category="model/latent/hunyhuan video",
|
||||
inputs=[
|
||||
io.LatentUpscaleModel.Input("model"),
|
||||
io.Latent.Input("samples"),
|
||||
@ -276,7 +278,7 @@ class TextEncodeHunyuanVideo_ImageToVideo(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="TextEncodeHunyuanVideo_ImageToVideo",
|
||||
category="advanced/conditioning",
|
||||
category="model/conditioning/hunyuan video",
|
||||
inputs=[
|
||||
io.Clip.Input("clip"),
|
||||
io.ClipVisionOutput.Input("clip_vision_output"),
|
||||
@ -308,7 +310,7 @@ class HunyuanImageToVideo(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="HunyuanImageToVideo",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/hunyuan video",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Vae.Input("vae"),
|
||||
@ -359,7 +361,7 @@ class EmptyHunyuanImageLatent(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="EmptyHunyuanImageLatent",
|
||||
category="model/latent",
|
||||
category="model/latent/hunyuan image",
|
||||
inputs=[
|
||||
io.Int.Input("width", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32),
|
||||
io.Int.Input("height", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32),
|
||||
@ -384,7 +386,7 @@ class HunyuanRefinerLatent(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="HunyuanRefinerLatent",
|
||||
display_name="Hunyuan Latent Refiner",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/hunyuan video",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
|
||||
@ -12,7 +12,7 @@ class EmptyLatentHunyuan3Dv2(IO.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="EmptyLatentHunyuan3Dv2",
|
||||
category="model/latent/3d",
|
||||
category="model/latent/hunyuan 3d",
|
||||
inputs=[
|
||||
IO.Int.Input("resolution", default=3072, min=1, max=8192),
|
||||
IO.Int.Input("batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."),
|
||||
@ -35,7 +35,7 @@ class Hunyuan3Dv2Conditioning(IO.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="Hunyuan3Dv2Conditioning",
|
||||
category="model/conditioning/3d_models",
|
||||
category="model/conditioning/hunyuan 3d",
|
||||
inputs=[
|
||||
IO.ClipVisionOutput.Input("clip_vision_output"),
|
||||
],
|
||||
@ -60,7 +60,7 @@ class Hunyuan3Dv2ConditioningMultiView(IO.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="Hunyuan3Dv2ConditioningMultiView",
|
||||
category="model/conditioning/3d_models",
|
||||
category="model/conditioning/hunyuan 3d",
|
||||
inputs=[
|
||||
IO.ClipVisionOutput.Input("front", optional=True),
|
||||
IO.ClipVisionOutput.Input("left", optional=True),
|
||||
@ -97,7 +97,7 @@ class VAEDecodeHunyuan3D(IO.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="VAEDecodeHunyuan3D",
|
||||
category="model/latent/3d",
|
||||
category="model/latent/hunyuan 3d",
|
||||
inputs=[
|
||||
IO.Latent.Input("samples"),
|
||||
IO.Vae.Input("vae"),
|
||||
|
||||
@ -38,7 +38,7 @@ class Ideogram4Scheduler(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="Ideogram4Scheduler",
|
||||
display_name="Ideogram 4 Scheduler",
|
||||
category="sampling/custom_sampling/schedulers",
|
||||
category="model/sampling/schedulers",
|
||||
inputs=[
|
||||
io.Int.Input("steps", default=20, min=1, max=200),
|
||||
io.Int.Input("width", default=1024, min=256, max=8192, step=16),
|
||||
|
||||
@ -13,7 +13,7 @@ class Kandinsky5ImageToVideo(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="Kandinsky5ImageToVideo",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/kandinsky",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -71,7 +71,7 @@ class NormalizeVideoLatentStart(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="NormalizeVideoLatentStart",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning",
|
||||
description="Normalizes the initial frames of a video latent to match the mean and standard deviation of subsequent reference frames. Helps reduce differences between the starting frames and the rest of the video.",
|
||||
inputs=[
|
||||
io.Latent.Input("latent"),
|
||||
@ -104,8 +104,9 @@ class CLIPTextEncodeKandinsky5(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="CLIPTextEncodeKandinsky5",
|
||||
display_name="CLIP Text Encode (Kandinsky 5)",
|
||||
search_aliases=["kandinsky prompt"],
|
||||
category="advanced/conditioning/kandinsky5",
|
||||
category="model/conditioning/kandinsky",
|
||||
inputs=[
|
||||
io.Clip.Input("clip"),
|
||||
io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
|
||||
|
||||
@ -262,6 +262,7 @@ class LatentBatch(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="LatentBatch",
|
||||
search_aliases=["combine latents", "merge latents", "join latents"],
|
||||
display_name="Batch Latents (DEPRECATED)",
|
||||
category="model/latent/batch",
|
||||
is_deprecated=True,
|
||||
inputs=[
|
||||
@ -447,6 +448,7 @@ class ReplaceVideoLatentFrames(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="ReplaceVideoLatentFrames",
|
||||
display_name="Replace Video Latent Frames",
|
||||
category="model/latent/batch",
|
||||
inputs=[
|
||||
io.Latent.Input("destination", tooltip="The destination latent where frames will be replaced."),
|
||||
|
||||
@ -25,7 +25,7 @@ class GetICLoRAParameters(io.ComfyNode):
|
||||
display_name="Get IC-LoRA Parameters",
|
||||
description="Extracts IC-LoRA parameters from the safetensors metadata of a LoRA-loaded "
|
||||
"model and outputs them for LTXVAddGuide (eg. reference_downscale_factor).",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/ltxv",
|
||||
search_aliases=["ic-lora", "ic lora", "iclora", "downscale factor", "reference downscale"],
|
||||
inputs=[
|
||||
io.Model.Input(
|
||||
@ -62,7 +62,7 @@ class EmptyLTXVLatentVideo(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="EmptyLTXVLatentVideo",
|
||||
category="model/latent/video/ltxv",
|
||||
category="model/latent/ltxv",
|
||||
inputs=[
|
||||
io.Int.Input("width", default=768, min=64, max=nodes.MAX_RESOLUTION, step=32),
|
||||
io.Int.Input("height", default=512, min=64, max=nodes.MAX_RESOLUTION, step=32),
|
||||
@ -86,7 +86,7 @@ class LTXVImgToVideo(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="LTXVImgToVideo",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/ltxv",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -131,7 +131,7 @@ class LTXVImgToVideoInplace(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="LTXVImgToVideoInplace",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/ltxv",
|
||||
inputs=[
|
||||
io.Vae.Input("vae"),
|
||||
io.Image.Input("image"),
|
||||
@ -251,7 +251,7 @@ class LTXVAddGuide(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="LTXVAddGuide",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/ltxv",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -498,7 +498,7 @@ class LTXVCropGuides(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="LTXVCropGuides",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/ltxv",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -542,7 +542,7 @@ class LTXVConditioning(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="LTXVConditioning",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/ltxv",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -566,7 +566,7 @@ class ModelSamplingLTXV(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="ModelSamplingLTXV",
|
||||
category="advanced/model",
|
||||
category="model/patch/ltxv",
|
||||
inputs=[
|
||||
io.Model.Input("model"),
|
||||
io.Float.Input("max_shift", default=2.05, min=0.0, max=100.0, step=0.01),
|
||||
@ -746,7 +746,7 @@ class LTXVConcatAVLatent(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="LTXVConcatAVLatent",
|
||||
category="model/latent/video/ltxv",
|
||||
category="model/latent/ltxv",
|
||||
inputs=[
|
||||
io.Latent.Input("video_latent"),
|
||||
io.Latent.Input("audio_latent"),
|
||||
@ -781,7 +781,7 @@ class LTXVSeparateAVLatent(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="LTXVSeparateAVLatent",
|
||||
category="model/latent/video/ltxv",
|
||||
category="model/latent/ltxv",
|
||||
description="LTXV Separate AV Latent",
|
||||
inputs=[
|
||||
io.Latent.Input("av_latent"),
|
||||
@ -814,7 +814,7 @@ class LTXVReferenceAudio(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="LTXVReferenceAudio",
|
||||
display_name="LTXV Reference Audio (ID-LoRA)",
|
||||
category="model/conditioning/audio",
|
||||
category="model/conditioning/ltxv",
|
||||
description="Set reference audio for ID-LoRA speaker identity transfer. Encodes a reference audio clip into the conditioning and optionally patches the model with identity guidance (extra forward pass without reference, amplifying the speaker identity effect).",
|
||||
inputs=[
|
||||
io.Model.Input("model"),
|
||||
|
||||
@ -40,7 +40,7 @@ class LTXVAudioVAEEncode(VAEEncodeAudio):
|
||||
return io.Schema(
|
||||
node_id="LTXVAudioVAEEncode",
|
||||
display_name="LTXV Audio VAE Encode",
|
||||
category="model/latent/audio",
|
||||
category="model/latent/ltxv",
|
||||
inputs=[
|
||||
io.Audio.Input("audio", tooltip="The audio to be encoded."),
|
||||
io.Vae.Input(
|
||||
@ -63,7 +63,7 @@ class LTXVAudioVAEDecode(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="LTXVAudioVAEDecode",
|
||||
display_name="LTXV Audio VAE Decode",
|
||||
category="model/latent/audio",
|
||||
category="model/latent/ltxv",
|
||||
inputs=[
|
||||
io.Latent.Input("samples", tooltip="The latent to be decoded."),
|
||||
io.Vae.Input(
|
||||
@ -96,7 +96,7 @@ class LTXVEmptyLatentAudio(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="LTXVEmptyLatentAudio",
|
||||
display_name="LTXV Empty Latent Audio",
|
||||
category="model/latent/audio",
|
||||
category="model/latent/ltxv",
|
||||
inputs=[
|
||||
io.Int.Input(
|
||||
"frames_number",
|
||||
@ -168,9 +168,9 @@ class LTXAVTextEncoderLoader(io.ComfyNode):
|
||||
def define_schema(cls) -> io.Schema:
|
||||
return io.Schema(
|
||||
node_id="LTXAVTextEncoderLoader",
|
||||
display_name="LTXV Audio Text Encoder Loader",
|
||||
category="advanced/loaders",
|
||||
description="[Recipes]\n\nltxav: gemma 3 12B",
|
||||
display_name="Load LTXV Audio Text Encoder",
|
||||
category="model/loaders",
|
||||
description="Recipes:\nltxav: gemma 3 12B",
|
||||
inputs=[
|
||||
io.Combo.Input(
|
||||
"text_encoder",
|
||||
|
||||
@ -13,7 +13,7 @@ class LTXVLatentUpsampler(IO.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="LTXVLatentUpsampler",
|
||||
category="model/latent/video",
|
||||
category="model/latent/ltxv",
|
||||
is_experimental=True,
|
||||
inputs=[
|
||||
IO.Latent.Input("samples"),
|
||||
|
||||
@ -9,7 +9,7 @@ class RenormCFG(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="RenormCFG",
|
||||
category="advanced/model",
|
||||
category="model/patch",
|
||||
inputs=[
|
||||
io.Model.Input("model"),
|
||||
io.Float.Input("cfg_trunc", default=100, min=0.0, max=100.0, step=0.01, advanced=True),
|
||||
@ -80,8 +80,8 @@ class CLIPTextEncodeLumina2(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="CLIPTextEncodeLumina2",
|
||||
search_aliases=["lumina prompt"],
|
||||
display_name="CLIP Text Encode for Lumina2",
|
||||
category="model/conditioning",
|
||||
display_name="CLIP Text Encode (Lumina 2)",
|
||||
category="model/conditioning/lumina",
|
||||
description="Encodes a system prompt and a user prompt using a CLIP model into an embedding "
|
||||
"that can be used to guide the diffusion model towards generating specific images.",
|
||||
inputs=[
|
||||
|
||||
@ -53,6 +53,7 @@ class LatentCompositeMasked(IO.ComfyNode):
|
||||
return IO.Schema(
|
||||
node_id="LatentCompositeMasked",
|
||||
search_aliases=["overlay latent", "layer latent", "paste latent", "inpaint latent"],
|
||||
display_name="Latent Composite Masked",
|
||||
category="model/latent",
|
||||
inputs=[
|
||||
IO.Latent.Input("destination"),
|
||||
|
||||
@ -10,7 +10,7 @@ class EmptyMochiLatentVideo(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="EmptyMochiLatentVideo",
|
||||
category="model/latent/video",
|
||||
category="model/latent/mochi",
|
||||
inputs=[
|
||||
io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
|
||||
@ -59,7 +59,7 @@ class ModelSamplingDiscrete:
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
FUNCTION = "patch"
|
||||
|
||||
CATEGORY = "advanced/model"
|
||||
CATEGORY = "model/patch"
|
||||
|
||||
def patch(self, model, sampling, zsnr):
|
||||
m = model.clone()
|
||||
@ -97,7 +97,7 @@ class ModelSamplingStableCascade:
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
FUNCTION = "patch"
|
||||
|
||||
CATEGORY = "advanced/model"
|
||||
CATEGORY = "model/patch/stable cascade"
|
||||
|
||||
def patch(self, model, shift):
|
||||
m = model.clone()
|
||||
@ -123,7 +123,7 @@ class ModelSamplingSD3:
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
FUNCTION = "patch"
|
||||
|
||||
CATEGORY = "advanced/model"
|
||||
CATEGORY = "model/patch/stable diffusion"
|
||||
|
||||
def patch(self, model, shift, multiplier=1000):
|
||||
m = model.clone()
|
||||
@ -150,6 +150,7 @@ class ModelSamplingAuraFlow(ModelSamplingSD3):
|
||||
}}
|
||||
|
||||
FUNCTION = "patch_aura"
|
||||
CATEGORY = "model/patch"
|
||||
|
||||
def patch_aura(self, model, shift):
|
||||
return self.patch(model, shift, multiplier=1.0)
|
||||
@ -167,7 +168,7 @@ class ModelSamplingFlux:
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
FUNCTION = "patch"
|
||||
|
||||
CATEGORY = "advanced/model"
|
||||
CATEGORY = "model/patch/flux"
|
||||
|
||||
def patch(self, model, max_shift, base_shift, width, height):
|
||||
m = model.clone()
|
||||
@ -202,7 +203,7 @@ class ModelSamplingContinuousEDM:
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
FUNCTION = "patch"
|
||||
|
||||
CATEGORY = "advanced/model"
|
||||
CATEGORY = "model/patch"
|
||||
|
||||
def patch(self, model, sampling, sigma_max, sigma_min):
|
||||
m = model.clone()
|
||||
@ -247,7 +248,7 @@ class ModelSamplingContinuousV:
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
FUNCTION = "patch"
|
||||
|
||||
CATEGORY = "advanced/model"
|
||||
CATEGORY = "model/patch"
|
||||
|
||||
def patch(self, model, sampling, sigma_max, sigma_min):
|
||||
m = model.clone()
|
||||
@ -273,7 +274,7 @@ class RescaleCFG:
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
FUNCTION = "patch"
|
||||
|
||||
CATEGORY = "advanced/model"
|
||||
CATEGORY = "model/patch"
|
||||
|
||||
def patch(self, model, multiplier):
|
||||
def rescale_cfg(args):
|
||||
@ -314,7 +315,7 @@ class ModelNoiseScale:
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
FUNCTION = "patch"
|
||||
|
||||
CATEGORY = "advanced/model"
|
||||
CATEGORY = "model/patch"
|
||||
|
||||
def patch(self, model, noise_scale):
|
||||
m = model.clone()
|
||||
@ -337,7 +338,7 @@ class ModelComputeDtype:
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
FUNCTION = "patch"
|
||||
|
||||
CATEGORY = "advanced/debug/model"
|
||||
CATEGORY = "advanced/debug"
|
||||
|
||||
def patch(self, model, dtype):
|
||||
m = model.clone()
|
||||
|
||||
@ -21,7 +21,7 @@ class ModelMergeSimple:
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
FUNCTION = "merge"
|
||||
|
||||
CATEGORY = "advanced/model_merging"
|
||||
CATEGORY = "model/merging"
|
||||
|
||||
def merge(self, model1, model2, ratio):
|
||||
m = model1.clone()
|
||||
@ -40,7 +40,7 @@ class ModelSubtract:
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
FUNCTION = "merge"
|
||||
|
||||
CATEGORY = "advanced/model_merging"
|
||||
CATEGORY = "model/merging"
|
||||
|
||||
def merge(self, model1, model2, multiplier):
|
||||
m = model1.clone()
|
||||
@ -58,7 +58,7 @@ class ModelAdd:
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
FUNCTION = "merge"
|
||||
|
||||
CATEGORY = "advanced/model_merging"
|
||||
CATEGORY = "model/merging"
|
||||
|
||||
def merge(self, model1, model2):
|
||||
m = model1.clone()
|
||||
@ -78,7 +78,7 @@ class CLIPMergeSimple:
|
||||
RETURN_TYPES = ("CLIP",)
|
||||
FUNCTION = "merge"
|
||||
|
||||
CATEGORY = "advanced/model_merging"
|
||||
CATEGORY = "model/merging"
|
||||
|
||||
def merge(self, clip1, clip2, ratio):
|
||||
m = clip1.clone()
|
||||
@ -101,7 +101,7 @@ class CLIPSubtract:
|
||||
RETURN_TYPES = ("CLIP",)
|
||||
FUNCTION = "merge"
|
||||
|
||||
CATEGORY = "advanced/model_merging"
|
||||
CATEGORY = "model/merging"
|
||||
|
||||
def merge(self, clip1, clip2, multiplier):
|
||||
m = clip1.clone()
|
||||
@ -123,7 +123,7 @@ class CLIPAdd:
|
||||
RETURN_TYPES = ("CLIP",)
|
||||
FUNCTION = "merge"
|
||||
|
||||
CATEGORY = "advanced/model_merging"
|
||||
CATEGORY = "model/merging"
|
||||
|
||||
def merge(self, clip1, clip2):
|
||||
m = clip1.clone()
|
||||
@ -147,7 +147,7 @@ class ModelMergeBlocks:
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
FUNCTION = "merge"
|
||||
|
||||
CATEGORY = "advanced/model_merging"
|
||||
CATEGORY = "model/merging"
|
||||
|
||||
def merge(self, model1, model2, **kwargs):
|
||||
m = model1.clone()
|
||||
@ -242,7 +242,7 @@ class CheckpointSave:
|
||||
FUNCTION = "save"
|
||||
OUTPUT_NODE = True
|
||||
|
||||
CATEGORY = "advanced/model_merging"
|
||||
CATEGORY = "model/merging"
|
||||
|
||||
def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None):
|
||||
save_checkpoint(model, clip=clip, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo)
|
||||
@ -261,7 +261,7 @@ class CLIPSave:
|
||||
FUNCTION = "save"
|
||||
OUTPUT_NODE = True
|
||||
|
||||
CATEGORY = "advanced/model_merging"
|
||||
CATEGORY = "model/merging"
|
||||
|
||||
def save(self, clip, filename_prefix, prompt=None, extra_pnginfo=None):
|
||||
prompt_info = ""
|
||||
@ -318,7 +318,7 @@ class VAESave:
|
||||
FUNCTION = "save"
|
||||
OUTPUT_NODE = True
|
||||
|
||||
CATEGORY = "advanced/model_merging"
|
||||
CATEGORY = "model/merging"
|
||||
|
||||
def save(self, vae, filename_prefix, prompt=None, extra_pnginfo=None):
|
||||
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
|
||||
@ -353,7 +353,7 @@ class ModelSave:
|
||||
FUNCTION = "save"
|
||||
OUTPUT_NODE = True
|
||||
|
||||
CATEGORY = "advanced/model_merging"
|
||||
CATEGORY = "model/merging"
|
||||
|
||||
def save(self, model, filename_prefix, prompt=None, extra_pnginfo=None):
|
||||
save_checkpoint(model, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo)
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
import comfy_extras.nodes_model_merging
|
||||
|
||||
class ModelMergeSD1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
CATEGORY = "advanced/model_merging/model_specific"
|
||||
CATEGORY = "model/merging/model specific"
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
arg_dict = { "model1": ("MODEL",),
|
||||
@ -27,7 +27,7 @@ class ModelMergeSD1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
|
||||
|
||||
class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
CATEGORY = "advanced/model_merging/model_specific"
|
||||
CATEGORY = "model/merging/model specific"
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
@ -53,7 +53,7 @@ class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
return {"required": arg_dict}
|
||||
|
||||
class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
CATEGORY = "advanced/model_merging/model_specific"
|
||||
CATEGORY = "model/merging/model specific"
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
@ -77,7 +77,7 @@ class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
|
||||
|
||||
class ModelMergeAuraflow(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
CATEGORY = "advanced/model_merging/model_specific"
|
||||
CATEGORY = "model/merging/model specific"
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
@ -104,7 +104,7 @@ class ModelMergeAuraflow(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
return {"required": arg_dict}
|
||||
|
||||
class ModelMergeFlux1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
CATEGORY = "advanced/model_merging/model_specific"
|
||||
CATEGORY = "model/merging/model specific"
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
@ -130,7 +130,7 @@ class ModelMergeFlux1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
return {"required": arg_dict}
|
||||
|
||||
class ModelMergeSD35_Large(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
CATEGORY = "advanced/model_merging/model_specific"
|
||||
CATEGORY = "model/merging/model specific"
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
@ -153,7 +153,7 @@ class ModelMergeSD35_Large(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
return {"required": arg_dict}
|
||||
|
||||
class ModelMergeMochiPreview(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
CATEGORY = "advanced/model_merging/model_specific"
|
||||
CATEGORY = "model/merging/model specific"
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
@ -175,7 +175,7 @@ class ModelMergeMochiPreview(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
return {"required": arg_dict}
|
||||
|
||||
class ModelMergeLTXV(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
CATEGORY = "advanced/model_merging/model_specific"
|
||||
CATEGORY = "model/merging/model specific"
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
@ -197,7 +197,7 @@ class ModelMergeLTXV(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
return {"required": arg_dict}
|
||||
|
||||
class ModelMergeCosmos7B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
CATEGORY = "advanced/model_merging/model_specific"
|
||||
CATEGORY = "model/merging/model specific"
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
@ -221,7 +221,7 @@ class ModelMergeCosmos7B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
return {"required": arg_dict}
|
||||
|
||||
class ModelMergeCosmos14B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
CATEGORY = "advanced/model_merging/model_specific"
|
||||
CATEGORY = "model/merging/model specific"
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
@ -245,7 +245,7 @@ class ModelMergeCosmos14B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
return {"required": arg_dict}
|
||||
|
||||
class ModelMergeWAN2_1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
CATEGORY = "advanced/model_merging/model_specific"
|
||||
CATEGORY = "model/merging/model specific"
|
||||
DESCRIPTION = "1.3B model has 30 blocks, 14B model has 40 blocks. Image to video model has the extra img_emb."
|
||||
|
||||
@classmethod
|
||||
@ -269,7 +269,7 @@ class ModelMergeWAN2_1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
return {"required": arg_dict}
|
||||
|
||||
class ModelMergeCosmosPredict2_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
CATEGORY = "advanced/model_merging/model_specific"
|
||||
CATEGORY = "model/merging/model specific"
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
@ -292,7 +292,7 @@ class ModelMergeCosmosPredict2_2B(comfy_extras.nodes_model_merging.ModelMergeBlo
|
||||
return {"required": arg_dict}
|
||||
|
||||
class ModelMergeCosmosPredict2_14B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
CATEGORY = "advanced/model_merging/model_specific"
|
||||
CATEGORY = "model/merging/model specific"
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
@ -315,7 +315,7 @@ class ModelMergeCosmosPredict2_14B(comfy_extras.nodes_model_merging.ModelMergeBl
|
||||
return {"required": arg_dict}
|
||||
|
||||
class ModelMergeQwenImage(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
||||
CATEGORY = "advanced/model_merging/model_specific"
|
||||
CATEGORY = "model/merging/model specific"
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
|
||||
@ -232,7 +232,7 @@ class ModelPatchLoader:
|
||||
FUNCTION = "load_model_patch"
|
||||
EXPERIMENTAL = True
|
||||
|
||||
CATEGORY = "advanced/loaders"
|
||||
CATEGORY = "model/loaders"
|
||||
|
||||
def load_model_patch(self, name):
|
||||
model_patch_path = folder_paths.get_full_path_or_raise("model_patches", name)
|
||||
@ -479,7 +479,7 @@ class QwenImageDiffsynthControlnet:
|
||||
FUNCTION = "diffsynth_controlnet"
|
||||
EXPERIMENTAL = True
|
||||
|
||||
CATEGORY = "advanced/loaders/qwen"
|
||||
CATEGORY = "model/patch/qwen"
|
||||
|
||||
def diffsynth_controlnet(self, model, model_patch, vae, image=None, strength=1.0, inpaint_image=None, mask=None):
|
||||
model_patched = model.clone()
|
||||
@ -512,7 +512,7 @@ class ZImageFunControlnet(QwenImageDiffsynthControlnet):
|
||||
},
|
||||
"optional": {"image": ("IMAGE",), "inpaint_image": ("IMAGE",), "mask": ("MASK",)}}
|
||||
|
||||
CATEGORY = "advanced/loaders/zimage"
|
||||
CATEGORY = "model/patch/z-image"
|
||||
|
||||
class UsoStyleProjectorPatch:
|
||||
def __init__(self, model_patch, encoded_image):
|
||||
@ -675,3 +675,11 @@ NODE_CLASS_MAPPINGS = {
|
||||
"USOStyleReference": USOStyleReference,
|
||||
"SUPIRApply": SUPIRApply,
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"ModelPatchLoader": "Load Model Patch",
|
||||
"QwenImageDiffsynthControlnet": "Apply Qwen Image DiffSynth ControlNet",
|
||||
"ZImageFunControlnet": "Apply Z-Image Fun ControlNet",
|
||||
"USOStyleReference": "Apply USO Style Reference",
|
||||
"SUPIRApply": "Apply SUPIR Patch",
|
||||
}
|
||||
|
||||
@ -14,10 +14,8 @@ class PiDConditioning(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="PiDConditioning",
|
||||
display_name="PiD Conditioning",
|
||||
category="advanced/conditioning",
|
||||
description=(
|
||||
"Attaches a latent and a degrade_sigma scalar to a CONDITIONING for PiD decoding/upscaling"
|
||||
),
|
||||
category="model/conditioning",
|
||||
description=("Attaches a latent and a degrade_sigma scalar to a CONDITIONING for PiD decoding/upscaling"),
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Latent.Input("latent", tooltip="latent (from VAEEncode or a KSampler)."),
|
||||
|
||||
@ -7,8 +7,9 @@ class CLIPTextEncodePixArtAlpha(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="CLIPTextEncodePixArtAlpha",
|
||||
display_name="CLIP Text Encode (PixArt Alpha)",
|
||||
search_aliases=["pixart prompt"],
|
||||
category="advanced/conditioning",
|
||||
category="model/conditioning/pixart",
|
||||
description="Encodes text and sets the resolution conditioning for PixArt Alpha. Does not apply to PixArt Sigma.",
|
||||
inputs=[
|
||||
io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
|
||||
|
||||
@ -616,7 +616,7 @@ class BatchLatentsNode(io.ComfyNode):
|
||||
node_id="BatchLatentsNode",
|
||||
search_aliases=["combine latents", "stack latents", "merge latents"],
|
||||
display_name="Batch Latents",
|
||||
category="model/latent",
|
||||
category="model/latent/batch",
|
||||
inputs=[
|
||||
io.Autogrow.Input("latents", template=autogrow_template)
|
||||
],
|
||||
|
||||
@ -12,7 +12,7 @@ class TextEncodeQwenImageEdit(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="TextEncodeQwenImageEdit",
|
||||
category="advanced/conditioning",
|
||||
category="model/conditioning/qwen image",
|
||||
inputs=[
|
||||
io.Clip.Input("clip"),
|
||||
io.String.Input("prompt", multiline=True, dynamic_prompts=True),
|
||||
@ -55,7 +55,7 @@ class TextEncodeQwenImageEditPlus(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="TextEncodeQwenImageEditPlus",
|
||||
category="advanced/conditioning",
|
||||
category="model/conditioning/qwen image",
|
||||
inputs=[
|
||||
io.Clip.Input("clip"),
|
||||
io.String.Input("prompt", multiline=True, dynamic_prompts=True),
|
||||
|
||||
@ -123,7 +123,7 @@ class WanSCAILToVideo(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanSCAILToVideo",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/wan/scail",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -257,18 +257,16 @@ class SCAIL2ColoredMask(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="SCAIL2ColoredMask",
|
||||
display_name="Create SCAIL-2 Colored Mask",
|
||||
category="conditioning/video_models/scail",
|
||||
category="model/conditioning/wan/scail",
|
||||
inputs=[
|
||||
SAM3TrackData.Input("driving_track_data", tooltip="SAM3 track of the driving pose video. Will be rendered into the pose_video_mask output."),
|
||||
SAM3TrackData.Input("ref_track_data", optional=True,
|
||||
tooltip="SAM3 track of the reference image."),
|
||||
io.String.Input("object_indices", default="",
|
||||
tooltip="Comma-separated list of person indices to include (e.g. '0,2,3'). Applied to both reference and pose video masks. Empty = all."),
|
||||
SAM3TrackData.Input("ref_track_data", optional=True, tooltip="SAM3 track of the reference image."),
|
||||
io.String.Input("object_indices", default="", tooltip="Comma-separated list of person indices to include (e.g. '0,2,3'). Applied to both reference and pose video masks. Empty = all."),
|
||||
io.Combo.Input("sort_by", options=["none", "left_to_right", "area"], default="left_to_right",
|
||||
tooltip="Order in which palette colors are assigned to the tracked objects (applied to both reference and pose video so each identity keeps the same color). left_to_right = leftmost object (by first-frame centroid) gets the first color; area = biggest object (by first-frame mask area) gets the first color; none = keep SAM3's order."),
|
||||
tooltip="Order in which palette colors are assigned to the tracked objects (applied to both reference and pose video so each identity keeps the same color). left_to_right = leftmost object (by first-frame centroid) gets the first color; area = biggest object (by first-frame mask area) gets the first color; none = keep SAM3's order."),
|
||||
io.Boolean.Input("replacement_mode", default=False,
|
||||
tooltip="False = Animation Mode (pose_video_mask has black background, reference_image_mask has white background). "
|
||||
"True = Replacement Mode (pose_video_mask has white background, reference_image_mask has black background)."),
|
||||
tooltip="False = Animation Mode (pose_video_mask has black background, reference_image_mask has white background). "
|
||||
"True = Replacement Mode (pose_video_mask has white background, reference_image_mask has black background)."),
|
||||
],
|
||||
outputs=[
|
||||
io.Image.Output("pose_video_mask"),
|
||||
|
||||
@ -13,8 +13,9 @@ class TripleCLIPLoader(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="TripleCLIPLoader",
|
||||
category="advanced/loaders",
|
||||
description="[Recipes]\n\nsd3: clip-l, clip-g, t5",
|
||||
display_name="Load CLIP (Triple)",
|
||||
category="model/loaders",
|
||||
description="Recipes:\nsd3: clip-l, clip-g, t5",
|
||||
inputs=[
|
||||
io.Combo.Input("clip_name1", options=folder_paths.get_filename_list("text_encoders")),
|
||||
io.Combo.Input("clip_name2", options=folder_paths.get_filename_list("text_encoders")),
|
||||
@ -41,7 +42,7 @@ class EmptySD3LatentImage(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="EmptySD3LatentImage",
|
||||
category="model/latent/sd3",
|
||||
category="model/latent/stable diffusion",
|
||||
inputs=[
|
||||
io.Int.Input("width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
io.Int.Input("height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
@ -66,7 +67,8 @@ class CLIPTextEncodeSD3(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="CLIPTextEncodeSD3",
|
||||
search_aliases=["sd3 prompt"],
|
||||
category="advanced/conditioning",
|
||||
display_name="CLIP Text Encode (SD3)",
|
||||
category="model/conditioning/stable diffusion",
|
||||
inputs=[
|
||||
io.Clip.Input("clip"),
|
||||
io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
|
||||
|
||||
@ -96,8 +96,12 @@ class KeypointDraw:
|
||||
# Body connections - matching DWPose limbSeq (1-indexed, converted to 0-indexed)
|
||||
self.body_limbSeq = [
|
||||
[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10],
|
||||
[10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17],
|
||||
[1, 16], [16, 18]
|
||||
[10, 11], [2, 12], [12, 13], [13, 14]
|
||||
]
|
||||
|
||||
# Head connections (1-indexed, converted to 0-indexed)
|
||||
self.head_edges = [
|
||||
[2, 1], [1, 15], [15, 17], [1, 16], [16, 18]
|
||||
]
|
||||
|
||||
# Colors matching DWPose
|
||||
@ -215,7 +219,7 @@ class KeypointDraw:
|
||||
return unique_pts if len(unique_pts) > 1 else [[center[0], center[1]], [center[0], center[1]]]
|
||||
|
||||
def draw_wholebody_keypoints(self, canvas, keypoints, scores=None, threshold=0.3,
|
||||
draw_body=True, draw_feet=True, draw_face=True, draw_hands=True, stick_width=4, face_point_size=3):
|
||||
draw_body=True, draw_head=True, draw_feet=True, draw_face=True, draw_hands=True, stick_width=4, face_point_size=3):
|
||||
"""
|
||||
Draw wholebody keypoints (134 keypoints after processing) in DWPose style.
|
||||
|
||||
@ -237,9 +241,17 @@ class KeypointDraw:
|
||||
"""
|
||||
H, W, C = canvas.shape
|
||||
|
||||
# Draw body limbs
|
||||
if draw_body and len(keypoints) >= 18:
|
||||
for i, limb in enumerate(self.body_limbSeq):
|
||||
# Draw body limbs & head connections
|
||||
if (draw_body or draw_head) and len(keypoints) >= 18:
|
||||
colorIndexOffset = 0
|
||||
edges = []
|
||||
if draw_body:
|
||||
edges += self.body_limbSeq
|
||||
else:
|
||||
colorIndexOffset += len(self.body_limbSeq)
|
||||
if draw_head:
|
||||
edges += self.head_edges
|
||||
for i, limb in enumerate(edges):
|
||||
# Convert from 1-indexed to 0-indexed
|
||||
idx1, idx2 = limb[0] - 1, limb[1] - 1
|
||||
|
||||
@ -262,11 +274,17 @@ class KeypointDraw:
|
||||
|
||||
polygon = self.draw.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stick_width), int(angle), 0, 360, 1)
|
||||
|
||||
self.draw.fillConvexPoly(canvas, polygon, self.colors[i % len(self.colors)])
|
||||
self.draw.fillConvexPoly(canvas, polygon, self.colors[(i + colorIndexOffset) % len(self.colors)])
|
||||
|
||||
# Draw body keypoints
|
||||
if draw_body and len(keypoints) >= 18:
|
||||
# Draw body & head keypoints
|
||||
if (draw_body or draw_head) and len(keypoints) >= 18:
|
||||
head_keypoints = {0, 14, 15, 16, 17} # nose, eyes, ears
|
||||
neck_point = 1
|
||||
for i in range(18):
|
||||
if not draw_head and i in head_keypoints:
|
||||
continue
|
||||
if not draw_body and i not in head_keypoints and i != neck_point:
|
||||
continue
|
||||
if scores is not None and scores[i] < threshold:
|
||||
continue
|
||||
x, y = int(keypoints[i][0]), int(keypoints[i][1])
|
||||
@ -365,6 +383,7 @@ class SDPoseDrawKeypoints(io.ComfyNode):
|
||||
io.Int.Input("stick_width", default=4, min=1, max=10, step=1),
|
||||
io.Int.Input("face_point_size", default=3, min=1, max=10, step=1),
|
||||
io.Float.Input("score_threshold", default=0.3, min=0.0, max=1.0, step=0.01),
|
||||
io.Boolean.Input("draw_head", default=True),
|
||||
],
|
||||
outputs=[
|
||||
io.Image.Output(),
|
||||
@ -372,7 +391,7 @@ class SDPoseDrawKeypoints(io.ComfyNode):
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, keypoints, draw_body, draw_hands, draw_face, draw_feet, stick_width, face_point_size, score_threshold) -> io.NodeOutput:
|
||||
def execute(cls, keypoints, draw_body, draw_hands, draw_face, draw_feet, stick_width, face_point_size, score_threshold, draw_head) -> io.NodeOutput:
|
||||
if not keypoints:
|
||||
return io.NodeOutput(torch.zeros((1, 64, 64, 3), dtype=torch.float32))
|
||||
height = keypoints[0]["canvas_height"]
|
||||
@ -405,7 +424,7 @@ class SDPoseDrawKeypoints(io.ComfyNode):
|
||||
canvas = drawer.draw_wholebody_keypoints(
|
||||
canvas, kp, sc,
|
||||
threshold=score_threshold,
|
||||
draw_body=draw_body, draw_feet=draw_feet,
|
||||
draw_body=draw_body, draw_head=draw_head, draw_feet=draw_feet,
|
||||
draw_face=draw_face, draw_hands=draw_hands,
|
||||
stick_width=stick_width, face_point_size=face_point_size,
|
||||
)
|
||||
|
||||
@ -9,7 +9,7 @@ class SD_4XUpscale_Conditioning(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="SD_4XUpscale_Conditioning",
|
||||
category="model/conditioning/upscale_diffusion",
|
||||
category="model/conditioning/stable diffusion upscaler",
|
||||
inputs=[
|
||||
io.Image.Input("images"),
|
||||
io.Conditioning.Input("positive"),
|
||||
|
||||
@ -27,7 +27,7 @@ class StableZero123_Conditioning(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="StableZero123_Conditioning",
|
||||
category="model/conditioning/3d_models",
|
||||
category="model/conditioning/stable zero123",
|
||||
inputs=[
|
||||
io.ClipVision.Input("clip_vision"),
|
||||
io.Image.Input("init_image"),
|
||||
@ -65,7 +65,7 @@ class StableZero123_Conditioning_Batched(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="StableZero123_Conditioning_Batched",
|
||||
category="model/conditioning/3d_models",
|
||||
category="model/conditioning/stable zero123",
|
||||
inputs=[
|
||||
io.ClipVision.Input("clip_vision"),
|
||||
io.Image.Input("init_image"),
|
||||
@ -112,7 +112,7 @@ class SV3D_Conditioning(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="SV3D_Conditioning",
|
||||
category="model/conditioning/3d_models",
|
||||
category="model/conditioning/stable video 3d",
|
||||
inputs=[
|
||||
io.ClipVision.Input("clip_vision"),
|
||||
io.Image.Input("init_image"),
|
||||
|
||||
@ -29,7 +29,7 @@ class StableCascade_EmptyLatentImage(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="StableCascade_EmptyLatentImage",
|
||||
category="model/latent/stable_cascade",
|
||||
category="model/latent/stable cascade",
|
||||
inputs=[
|
||||
io.Int.Input("width", default=1024, min=256, max=nodes.MAX_RESOLUTION, step=8),
|
||||
io.Int.Input("height", default=1024, min=256, max=nodes.MAX_RESOLUTION, step=8),
|
||||
@ -58,7 +58,7 @@ class StableCascade_StageC_VAEEncode(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="StableCascade_StageC_VAEEncode",
|
||||
category="model/latent/stable_cascade",
|
||||
category="model/latent/stable cascade",
|
||||
inputs=[
|
||||
io.Image.Input("image"),
|
||||
io.Vae.Input("vae"),
|
||||
@ -93,7 +93,7 @@ class StableCascade_StageB_Conditioning(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="StableCascade_StageB_Conditioning",
|
||||
category="model/conditioning/stable_cascade",
|
||||
category="model/conditioning/stable cascade",
|
||||
inputs=[
|
||||
io.Conditioning.Input("conditioning"),
|
||||
io.Latent.Input("stage_c"),
|
||||
|
||||
@ -35,7 +35,7 @@ class TextGenerate(io.ComfyNode):
|
||||
io.Image.Input("image", optional=True),
|
||||
io.Image.Input("video", optional=True, tooltip="Video frames as image batch. Assumed to be 24 FPS; subsampled to 1 FPS internally."),
|
||||
io.Audio.Input("audio", optional=True),
|
||||
io.Int.Input("max_length", default=256, min=1, max=2048),
|
||||
io.Int.Input("max_length", default=512, min=1, max=32768),
|
||||
io.DynamicCombo.Input("sampling_mode", options=sampling_options, display_name="Sampling Mode"),
|
||||
io.Boolean.Input("thinking", optional=True, default=False, tooltip="Operate in thinking mode if the model supports it."),
|
||||
io.Boolean.Input("use_default_template", optional=True, default=True, tooltip="Use the built in system prompt/template if the model has one.", advanced=True),
|
||||
|
||||
@ -1367,7 +1367,7 @@ class SaveLoRA(io.ComfyNode):
|
||||
node_id="SaveLoRA",
|
||||
search_aliases=["export lora"],
|
||||
display_name="Save LoRA Weights",
|
||||
category="advanced/model_merging",
|
||||
category="model/merging",
|
||||
is_experimental=True,
|
||||
is_output_node=True,
|
||||
inputs=[
|
||||
|
||||
@ -41,7 +41,7 @@ class SVD_img2vid_Conditioning:
|
||||
|
||||
FUNCTION = "encode"
|
||||
|
||||
CATEGORY = "model/conditioning/video_models"
|
||||
CATEGORY = "model/conditioning/stable video"
|
||||
|
||||
def encode(self, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level):
|
||||
output = clip_vision.encode_image(init_image)
|
||||
@ -108,7 +108,7 @@ class VideoTriangleCFGGuidance:
|
||||
return (m, )
|
||||
|
||||
class ImageOnlyCheckpointSave(comfy_extras.nodes_model_merging.CheckpointSave):
|
||||
CATEGORY = "advanced/model_merging"
|
||||
CATEGORY = "model/merging"
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
@ -138,7 +138,7 @@ class ConditioningSetAreaPercentageVideo:
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
FUNCTION = "append"
|
||||
|
||||
CATEGORY = "model/conditioning"
|
||||
CATEGORY = "model/conditioning/transform"
|
||||
|
||||
def append(self, conditioning, width, height, temporal, x, y, z, strength):
|
||||
c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", temporal, height, width, z, y, x),
|
||||
@ -160,4 +160,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"ImageOnlyCheckpointLoader": "Load Checkpoint Image Only (img2vid model)",
|
||||
"VideoLinearCFGGuidance": "Video Linear CFG Guidance",
|
||||
"VideoTriangleCFGGuidance": "Video Triangle CFG Guidance",
|
||||
"ConditioningSetAreaPercentageVideo": "Conditioning (Set Area with Percentage for Video)",
|
||||
}
|
||||
|
||||
@ -175,7 +175,7 @@ class VOIDInpaintConditioning(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="VOIDInpaintConditioning",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/void",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -288,7 +288,7 @@ class VOIDWarpedNoise(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="VOIDWarpedNoise",
|
||||
category="model/latent/video",
|
||||
category="model/latent/void",
|
||||
inputs=[
|
||||
OpticalFlow.Input(
|
||||
"optical_flow",
|
||||
@ -393,7 +393,7 @@ class VOIDWarpedNoiseSource(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="VOIDWarpedNoiseSource",
|
||||
category="model/sampling/noise",
|
||||
category="model/latent/void",
|
||||
inputs=[
|
||||
io.Latent.Input("warped_noise",
|
||||
tooltip="Warped noise latent from VOIDWarpedNoise"),
|
||||
|
||||
@ -18,7 +18,7 @@ class WanImageToVideo(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanImageToVideo",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/wan",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -66,7 +66,7 @@ class WanFunControlToVideo(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanFunControlToVideo",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/wan/fun control",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -119,7 +119,7 @@ class Wan22FunControlToVideo(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="Wan22FunControlToVideo",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/wan/fun control",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -184,7 +184,7 @@ class WanFirstLastFrameToVideo(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanFirstLastFrameToVideo",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/wan",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -256,7 +256,7 @@ class WanFunInpaintToVideo(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanFunInpaintToVideo",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/wan/fun inpaint",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -288,7 +288,7 @@ class WanVaceToVideo(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="WanVaceToVideo",
|
||||
search_aliases=["video conditioning", "video control"],
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/wan/vace",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -375,7 +375,8 @@ class TrimVideoLatent(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="TrimVideoLatent",
|
||||
category="model/latent/video",
|
||||
display_name="Trim Video Latent",
|
||||
category="model/latent",
|
||||
inputs=[
|
||||
io.Latent.Input("samples"),
|
||||
io.Int.Input("trim_amount", default=0, min=0, max=99999),
|
||||
@ -398,7 +399,7 @@ class WanCameraImageToVideo(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanCameraImageToVideo",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/wan/camera",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -452,7 +453,7 @@ class WanPhantomSubjectToVideo(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanPhantomSubjectToVideo",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/wan/phantom subject",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -707,7 +708,7 @@ class WanTrackToVideo(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="WanTrackToVideo",
|
||||
search_aliases=["motion tracking", "trajectory video", "point tracking", "keypoint animation"],
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/wan/move",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -951,7 +952,7 @@ class WanSoundImageToVideo(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanSoundImageToVideo",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/wan/sound",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -984,7 +985,7 @@ class WanSoundImageToVideoExtend(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanSoundImageToVideoExtend",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/wan/sound",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -1046,7 +1047,7 @@ class WanHuMoImageToVideo(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanHuMoImageToVideo",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/wan/humo",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -1112,7 +1113,7 @@ class WanAnimateToVideo(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanAnimateToVideo",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/wan/animate",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
@ -1252,7 +1253,7 @@ class Wan22ImageToVideoLatent(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="Wan22ImageToVideoLatent",
|
||||
category="model/conditioning/inpaint",
|
||||
category="model/conditioning/wan",
|
||||
inputs=[
|
||||
io.Vae.Input("vae"),
|
||||
io.Int.Input("width", default=1280, min=32, max=nodes.MAX_RESOLUTION, step=32),
|
||||
@ -1302,7 +1303,7 @@ class WanInfiniteTalkToVideo(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanInfiniteTalkToVideo",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/wan/infinite talk",
|
||||
inputs=[
|
||||
io.DynamicCombo.Input("mode", options=[
|
||||
io.DynamicCombo.Option("single_speaker", []),
|
||||
|
||||
@ -713,7 +713,7 @@ class WanDancerEncodeAudio(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanDancerEncodeAudio",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/wan/dancer",
|
||||
inputs=[
|
||||
io.Audio.Input("audio"),
|
||||
io.Int.Input("video_frames", default=149, min=1, max=nodes.MAX_RESOLUTION, step=4),
|
||||
@ -787,7 +787,7 @@ class WanDancerVideo(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanDancerVideo",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/wan/dancer",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
|
||||
@ -247,7 +247,7 @@ class WanMoveVisualizeTracks(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanMoveVisualizeTracks",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/wan/move",
|
||||
inputs=[
|
||||
io.Image.Input("images"),
|
||||
io.Tracks.Input("tracks", optional=True),
|
||||
@ -283,7 +283,7 @@ class WanMoveTracksFromCoords(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanMoveTracksFromCoords",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/wan/move",
|
||||
inputs=[
|
||||
io.String.Input("track_coords", force_input=True, default="[]", optional=True),
|
||||
io.Mask.Input("track_mask", optional=True),
|
||||
@ -325,7 +325,8 @@ class GenerateTracks(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="GenerateTracks",
|
||||
search_aliases=["motion paths", "camera movement", "trajectory"],
|
||||
category="model/conditioning/video_models",
|
||||
display_name="Generate Video Tracks",
|
||||
category="model/conditioning/wan/move",
|
||||
inputs=[
|
||||
io.Int.Input("width", default=832, min=16, max=4096, step=16),
|
||||
io.Int.Input("height", default=480, min=16, max=4096, step=16),
|
||||
@ -434,7 +435,7 @@ class WanMoveConcatTrack(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanMoveConcatTrack",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/wan/move",
|
||||
inputs=[
|
||||
io.Tracks.Input("tracks_1"),
|
||||
io.Tracks.Input("tracks_2", optional=True),
|
||||
@ -463,7 +464,7 @@ class WanMoveTrackToVideo(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanMoveTrackToVideo",
|
||||
category="model/conditioning/video_models",
|
||||
category="model/conditioning/wan/move",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
|
||||
@ -10,7 +10,7 @@ class TextEncodeZImageOmni(io.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="TextEncodeZImageOmni",
|
||||
category="advanced/conditioning",
|
||||
category="model/conditioning/z-image",
|
||||
is_experimental=True,
|
||||
inputs=[
|
||||
io.Clip.Input("clip"),
|
||||
|
||||
4
main.py
4
main.py
@ -127,6 +127,10 @@ def apply_custom_paths():
|
||||
for config_path in itertools.chain(*args.extra_model_paths_config):
|
||||
utils.extra_config.load_extra_path_config(config_path)
|
||||
|
||||
# --base-directory
|
||||
if args.base_directory:
|
||||
logging.info(f"Setting base directory to: {folder_paths.base_path}")
|
||||
|
||||
# --output-directory, --input-directory, --user-directory
|
||||
if args.output_directory:
|
||||
output_dir = os.path.abspath(args.output_directory)
|
||||
|
||||
58
nodes.py
58
nodes.py
@ -87,7 +87,7 @@ class ConditioningCombine:
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
FUNCTION = "combine"
|
||||
|
||||
CATEGORY = "model/conditioning"
|
||||
CATEGORY = "model/conditioning/transform"
|
||||
SEARCH_ALIASES = ["combine", "merge conditioning", "combine prompts", "merge prompts", "mix prompts", "add prompt"]
|
||||
|
||||
def combine(self, conditioning_1, conditioning_2):
|
||||
@ -104,7 +104,7 @@ class ConditioningAverage :
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
FUNCTION = "addWeighted"
|
||||
|
||||
CATEGORY = "model/conditioning"
|
||||
CATEGORY = "model/conditioning/transform"
|
||||
|
||||
def addWeighted(self, conditioning_to, conditioning_from, conditioning_to_strength):
|
||||
out = []
|
||||
@ -143,7 +143,7 @@ class ConditioningConcat:
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
FUNCTION = "concat"
|
||||
|
||||
CATEGORY = "model/conditioning"
|
||||
CATEGORY = "model/conditioning/transform"
|
||||
|
||||
def concat(self, conditioning_to, conditioning_from):
|
||||
out = []
|
||||
@ -176,7 +176,7 @@ class ConditioningSetArea:
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
FUNCTION = "append"
|
||||
|
||||
CATEGORY = "model/conditioning"
|
||||
CATEGORY = "model/conditioning/transform"
|
||||
|
||||
def append(self, conditioning, width, height, x, y, strength):
|
||||
c = node_helpers.conditioning_set_values(conditioning, {"area": (height // 8, width // 8, y // 8, x // 8),
|
||||
@ -197,7 +197,7 @@ class ConditioningSetAreaPercentage:
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
FUNCTION = "append"
|
||||
|
||||
CATEGORY = "model/conditioning"
|
||||
CATEGORY = "model/conditioning/transform"
|
||||
|
||||
def append(self, conditioning, width, height, x, y, strength):
|
||||
c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", height, width, y, x),
|
||||
@ -214,7 +214,7 @@ class ConditioningSetAreaStrength:
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
FUNCTION = "append"
|
||||
|
||||
CATEGORY = "model/conditioning"
|
||||
CATEGORY = "model/conditioning/transform"
|
||||
|
||||
def append(self, conditioning, strength):
|
||||
c = node_helpers.conditioning_set_values(conditioning, {"strength": strength})
|
||||
@ -234,7 +234,7 @@ class ConditioningSetMask:
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
FUNCTION = "append"
|
||||
|
||||
CATEGORY = "model/conditioning"
|
||||
CATEGORY = "model/conditioning/transform"
|
||||
|
||||
def append(self, conditioning, mask, set_cond_area, strength):
|
||||
set_area_to_bounds = False
|
||||
@ -257,7 +257,7 @@ class ConditioningZeroOut:
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
FUNCTION = "zero_out"
|
||||
|
||||
CATEGORY = "advanced/conditioning"
|
||||
CATEGORY = "model/conditioning/transform"
|
||||
|
||||
def zero_out(self, conditioning):
|
||||
c = []
|
||||
@ -283,11 +283,10 @@ class ConditioningSetTimestepRange:
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
FUNCTION = "set_range"
|
||||
|
||||
CATEGORY = "advanced/conditioning"
|
||||
CATEGORY = "model/conditioning/transform"
|
||||
|
||||
def set_range(self, conditioning, start, end):
|
||||
c = node_helpers.conditioning_set_values(conditioning, {"start_percent": start,
|
||||
"end_percent": end})
|
||||
c = node_helpers.conditioning_set_values(conditioning, {"start_percent": start, "end_percent": end})
|
||||
return (c, )
|
||||
|
||||
class VAEDecode:
|
||||
@ -389,7 +388,7 @@ class VAEEncodeForInpaint:
|
||||
RETURN_TYPES = ("LATENT",)
|
||||
FUNCTION = "encode"
|
||||
|
||||
CATEGORY = "model/latent/inpaint"
|
||||
CATEGORY = "model/latent"
|
||||
|
||||
def encode(self, vae, pixels, mask, grow_mask_by=6):
|
||||
downscale_ratio = vae.spacial_compression_encode()
|
||||
@ -438,7 +437,7 @@ class InpaintModelConditioning:
|
||||
RETURN_NAMES = ("positive", "negative", "latent")
|
||||
FUNCTION = "encode"
|
||||
|
||||
CATEGORY = "model/conditioning/inpaint"
|
||||
CATEGORY = "model/conditioning"
|
||||
|
||||
def encode(self, positive, negative, pixels, vae, mask, noise_mask=True):
|
||||
x = (pixels.shape[1] // 8) * 8
|
||||
@ -578,7 +577,7 @@ class CheckpointLoader:
|
||||
RETURN_TYPES = ("MODEL", "CLIP", "VAE")
|
||||
FUNCTION = "load_checkpoint"
|
||||
|
||||
CATEGORY = "advanced/loaders"
|
||||
CATEGORY = "model/loaders"
|
||||
DEPRECATED = True
|
||||
|
||||
def load_checkpoint(self, config_name, ckpt_name):
|
||||
@ -624,8 +623,9 @@ class DiffusersLoader:
|
||||
return {"required": {"model_path": (paths,), }}
|
||||
RETURN_TYPES = ("MODEL", "CLIP", "VAE")
|
||||
FUNCTION = "load_checkpoint"
|
||||
DEPRECATED = True
|
||||
|
||||
CATEGORY = "advanced/loaders/deprecated"
|
||||
CATEGORY = "model/loaders"
|
||||
|
||||
def load_checkpoint(self, model_path, output_vae=True, output_clip=True):
|
||||
for search_path in folder_paths.get_folder_paths("diffusers"):
|
||||
@ -951,7 +951,7 @@ class UNETLoader:
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
FUNCTION = "load_unet"
|
||||
|
||||
CATEGORY = "advanced/loaders"
|
||||
CATEGORY = "model/loaders"
|
||||
|
||||
def load_unet(self, unet_name, weight_dtype):
|
||||
model_options = {}
|
||||
@ -979,9 +979,9 @@ class CLIPLoader:
|
||||
RETURN_TYPES = ("CLIP",)
|
||||
FUNCTION = "load_clip"
|
||||
|
||||
CATEGORY = "advanced/loaders"
|
||||
CATEGORY = "model/loaders"
|
||||
|
||||
DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 xxl/ clip-g / clip-l\nstable_audio: t5 base\nmochi: t5 xxl\ncogvideox: t5 xxl (226-token padding)\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\n hidream: llama-3.1 (Recommend) or t5\nomnigen2: qwen vl 2.5 3B\nlens: gpt-oss-20b\n pixeldit: gemma 2 2B elm"
|
||||
DESCRIPTION = "Recipes:\nsd: clip-l\nstable cascade: clip-g\nsd3: t5 xxl / clip-g / clip-l\nstable audio: t5 base\nmochi: t5 xxl\ncogvideox: t5 xxl (226-token padding)\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\nhidream: llama-3.1 (Recommend) or t5\nomnigen2: qwen vl 2.5 3B\nlens: gpt-oss-20b\npixeldit: gemma 2 2B elm"
|
||||
|
||||
def load_clip(self, clip_name, type="stable_diffusion", device="default"):
|
||||
clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
|
||||
@ -1007,9 +1007,9 @@ class DualCLIPLoader:
|
||||
RETURN_TYPES = ("CLIP",)
|
||||
FUNCTION = "load_clip"
|
||||
|
||||
CATEGORY = "advanced/loaders"
|
||||
CATEGORY = "model/loaders"
|
||||
|
||||
DESCRIPTION = "[Recipes]\n\nsdxl: clip-l, clip-g\nsd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\nflux: clip-l, t5\nhidream: at least one of t5 or llama, recommended t5 and llama\nhunyuan_image: qwen2.5vl 7b and byt5 small\nnewbie: gemma-3-4b-it, jina clip v2"
|
||||
DESCRIPTION = "Recipes:\nsdxl: clip-l, clip-g\nsd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\nflux: clip-l, t5\nhidream: at least one of t5 or llama, recommended t5 and llama\nhunyuan_image: qwen2.5vl 7b and byt5 small\nnewbie: gemma-3-4b-it, jina clip v2"
|
||||
|
||||
def load_clip(self, clip_name1, clip_name2, type, device="default"):
|
||||
clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
|
||||
@ -1090,7 +1090,7 @@ class StyleModelApply:
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
FUNCTION = "apply_stylemodel"
|
||||
|
||||
CATEGORY = "model/conditioning/style_model"
|
||||
CATEGORY = "model/conditioning"
|
||||
|
||||
def apply_stylemodel(self, conditioning, style_model, clip_vision_output, strength, strength_type):
|
||||
cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0)
|
||||
@ -1520,13 +1520,11 @@ class LatentCrop:
|
||||
class SetLatentNoiseMask:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": { "samples": ("LATENT",),
|
||||
"mask": ("MASK",),
|
||||
}}
|
||||
return {"required": { "samples": ("LATENT",), "mask": ("MASK",), }}
|
||||
RETURN_TYPES = ("LATENT",)
|
||||
FUNCTION = "set_mask"
|
||||
|
||||
CATEGORY = "model/latent/inpaint"
|
||||
CATEGORY = "model/latent"
|
||||
|
||||
def set_mask(self, samples, mask):
|
||||
s = samples.copy()
|
||||
@ -2051,7 +2049,7 @@ NODE_CLASS_MAPPINGS = {
|
||||
"ImageBatch": ImageBatch,
|
||||
"ImagePadForOutpaint": ImagePadForOutpaint,
|
||||
"EmptyImage": EmptyImage,
|
||||
"ConditioningAverage": ConditioningAverage ,
|
||||
"ConditioningAverage": ConditioningAverage,
|
||||
"ConditioningCombine": ConditioningCombine,
|
||||
"ConditioningConcat": ConditioningConcat,
|
||||
"ConditioningSetArea": ConditioningSetArea,
|
||||
@ -2107,6 +2105,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"LoraLoader": "Load LoRA (Model and CLIP)",
|
||||
"LoraLoaderModelOnly": "Load LoRA",
|
||||
"CLIPLoader": "Load CLIP",
|
||||
"DualCLIPLoader": "Load CLIP (Dual)",
|
||||
"ControlNetLoader": "Load ControlNet Model",
|
||||
"DiffControlNetLoader": "Load ControlNet Model (diff)",
|
||||
"StyleModelLoader": "Load Style Model",
|
||||
@ -2114,6 +2113,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"UNETLoader": "Load Diffusion Model",
|
||||
"unCLIPCheckpointLoader": "Load unCLIP Checkpoint",
|
||||
"GLIGENLoader": "Load GLIGEN Model",
|
||||
"DiffusersLoader": "Load Diffusers Model (DEPRECATED)",
|
||||
# Conditioning
|
||||
"CLIPVisionEncode": "CLIP Vision Encode",
|
||||
"StyleModelApply": "Apply Style Model",
|
||||
@ -2121,12 +2121,16 @@ NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"CLIPSetLastLayer": "CLIP Set Last Layer",
|
||||
"ConditioningCombine": "Conditioning (Combine)",
|
||||
"ConditioningAverage ": "Conditioning (Average)",
|
||||
"ConditioningAverage": "Conditioning (Average)",
|
||||
"ConditioningConcat": "Conditioning (Concat)",
|
||||
"ConditioningSetArea": "Conditioning (Set Area)",
|
||||
"ConditioningSetAreaPercentage": "Conditioning (Set Area with Percentage)",
|
||||
"ConditioningSetAreaStrength": "Conditioning (Set Area Strength)",
|
||||
"ConditioningSetMask": "Conditioning (Set Mask)",
|
||||
"ControlNetApply": "Apply ControlNet (DEPRECATED)",
|
||||
"ControlNetApplyAdvanced": "Apply ControlNet",
|
||||
"GLIGENTextBoxApply": "Apply GLIGEN Text Box",
|
||||
"ConditioningZeroOut": "Conditioning Zero Out",
|
||||
# Latent
|
||||
"VAEEncodeForInpaint": "VAE Encode (for Inpainting)",
|
||||
"SetLatentNoiseMask": "Set Latent Noise Mask",
|
||||
@ -2140,7 +2144,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"LatentUpscaleBy": "Upscale Latent By",
|
||||
"LatentComposite": "Latent Composite",
|
||||
"LatentBlend": "Latent Blend",
|
||||
"LatentFromBatch" : "Latent From Batch",
|
||||
"LatentFromBatch" : "Get Latent From Batch",
|
||||
"RepeatLatentBatch": "Repeat Latent Batch",
|
||||
# Image
|
||||
"EmptyImage": "Empty Image",
|
||||
|
||||
Loading…
Reference in New Issue
Block a user