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Merge branch 'master' into cursor/mark-deprecated-cloud-endpoints-e81e
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This commit is contained in:
Matt Miller
GitHub
commit
3beca6e30e
@ -431,9 +431,10 @@
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|
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|
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@ -162,7 +162,7 @@
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@ -1553,7 +1553,8 @@
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"category": "Image generation and editing/Canny to image",
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@ -1574,4 +1575,4 @@
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1412
blueprints/ControlNet (Z-Image-Turbo).json
Normal file
1412
blueprints/ControlNet (Z-Image-Turbo).json
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File diff suppressed because it is too large
Load Diff
@ -1609,7 +1609,8 @@
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@ -2946,7 +2946,8 @@
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@ -1579,7 +1579,8 @@
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@ -4233,7 +4233,8 @@
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@ -450,9 +450,10 @@
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@ -3350,7 +3350,8 @@
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3361
blueprints/First-Last-Frame to Video.json
Normal file
3361
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Normal file
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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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|
||||
{
|
||||
"localized_name": "BOOLEAN",
|
||||
"name": "BOOLEAN",
|
||||
"type": "BOOLEAN",
|
||||
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|
||||
15
|
||||
]
|
||||
}
|
||||
],
|
||||
"title": "Boolean (Apply multiplier to FPS?)",
|
||||
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|
||||
"Node name for S&R": "PrimitiveBoolean",
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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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|
||||
}
|
||||
],
|
||||
"extra": {},
|
||||
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|
||||
"description": "Increases video frame rate by synthesizing intermediate frames with a frame interpolation model."
|
||||
}
|
||||
]
|
||||
},
|
||||
"extra": {}
|
||||
}
|
||||
485
blueprints/Get Any Video Frame.json
Normal file
485
blueprints/Get Any Video Frame.json
Normal file
@ -0,0 +1,485 @@
|
||||
{
|
||||
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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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|
||||
},
|
||||
{
|
||||
"label": "frame_index",
|
||||
"name": "value",
|
||||
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|
||||
"widget": {
|
||||
"name": "value"
|
||||
},
|
||||
"link": null
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "IMAGE",
|
||||
"type": "IMAGE",
|
||||
"links": []
|
||||
}
|
||||
],
|
||||
"title": "Get Any Video Frame",
|
||||
"properties": {
|
||||
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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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|
||||
"link": 1
|
||||
}
|
||||
],
|
||||
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|
||||
{
|
||||
"localized_name": "width",
|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"localized_name": "height",
|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"localized_name": "batch_size",
|
||||
"name": "batch_size",
|
||||
"type": "INT",
|
||||
"links": [
|
||||
285
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "GetImageSize"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 3,
|
||||
"type": "ImageFromBatch",
|
||||
"pos": [
|
||||
1130,
|
||||
-150
|
||||
],
|
||||
"size": [
|
||||
270,
|
||||
140
|
||||
],
|
||||
"flags": {},
|
||||
"order": 2,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "image",
|
||||
"name": "image",
|
||||
"type": "IMAGE",
|
||||
"link": 2
|
||||
},
|
||||
{
|
||||
"localized_name": "batch_index",
|
||||
"name": "batch_index",
|
||||
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|
||||
"widget": {
|
||||
"name": "batch_index"
|
||||
},
|
||||
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|
||||
},
|
||||
{
|
||||
"localized_name": "length",
|
||||
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|
||||
"type": "INT",
|
||||
"widget": {
|
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"name": "length"
|
||||
},
|
||||
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|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "IMAGE",
|
||||
"name": "IMAGE",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
5
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "ImageFromBatch"
|
||||
},
|
||||
"widgets_values": [
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1
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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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|
||||
200
|
||||
],
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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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|
||||
"shape": 7,
|
||||
"type": "FLOAT,INT",
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||||
"link": 285
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},
|
||||
{
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"label": "c",
|
||||
"localized_name": "values.c",
|
||||
"name": "values.c",
|
||||
"shape": 7,
|
||||
"type": "FLOAT,INT",
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"localized_name": "expression",
|
||||
"name": "expression",
|
||||
"type": "STRING",
|
||||
"widget": {
|
||||
"name": "expression"
|
||||
},
|
||||
"link": null
|
||||
}
|
||||
],
|
||||
"outputs": [
|
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{
|
||||
"localized_name": "FLOAT",
|
||||
"name": "FLOAT",
|
||||
"type": "FLOAT",
|
||||
"links": null
|
||||
},
|
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{
|
||||
"localized_name": "INT",
|
||||
"name": "INT",
|
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"type": "INT",
|
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"links": [
|
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286
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||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "ComfyMathExpression"
|
||||
},
|
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"widgets_values": [
|
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"min(max(int(a if a >= 0 else b + a), 0), b - 1)"
|
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]
|
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},
|
||||
{
|
||||
"id": 100,
|
||||
"type": "PrimitiveInt",
|
||||
"pos": [
|
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560,
|
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250
|
||||
],
|
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"size": [
|
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270,
|
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110
|
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],
|
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"flags": {},
|
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"order": 4,
|
||||
"mode": 0,
|
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"inputs": [
|
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{
|
||||
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|
||||
"name": "value",
|
||||
"type": "INT",
|
||||
"widget": {
|
||||
"name": "value"
|
||||
},
|
||||
"link": 283
|
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}
|
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],
|
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"outputs": [
|
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{
|
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"localized_name": "INT",
|
||||
"name": "INT",
|
||||
"type": "INT",
|
||||
"links": [
|
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284
|
||||
]
|
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}
|
||||
],
|
||||
"properties": {
|
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"Node name for S&R": "PrimitiveInt"
|
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},
|
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"widgets_values": [
|
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0,
|
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"fixed"
|
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]
|
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}
|
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],
|
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"groups": [],
|
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|
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{
|
||||
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|
||||
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|
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|
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"target_id": 2,
|
||||
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|
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"type": "IMAGE"
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"origin_id": 1,
|
||||
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|
||||
"target_id": 3,
|
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"target_slot": 0,
|
||||
"type": "IMAGE"
|
||||
},
|
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{
|
||||
"id": 4,
|
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"origin_id": -10,
|
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|
||||
"target_id": 1,
|
||||
"target_slot": 0,
|
||||
"type": "VIDEO"
|
||||
},
|
||||
{
|
||||
"id": 5,
|
||||
"origin_id": 3,
|
||||
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|
||||
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|
||||
"target_slot": 0,
|
||||
"type": "IMAGE"
|
||||
},
|
||||
{
|
||||
"id": 283,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 1,
|
||||
"target_id": 100,
|
||||
"target_slot": 0,
|
||||
"type": "INT"
|
||||
},
|
||||
{
|
||||
"id": 284,
|
||||
"origin_id": 100,
|
||||
"origin_slot": 0,
|
||||
"target_id": 99,
|
||||
"target_slot": 0,
|
||||
"type": "INT"
|
||||
},
|
||||
{
|
||||
"id": 285,
|
||||
"origin_id": 2,
|
||||
"origin_slot": 2,
|
||||
"target_id": 99,
|
||||
"target_slot": 1,
|
||||
"type": "INT"
|
||||
},
|
||||
{
|
||||
"id": 286,
|
||||
"origin_id": 99,
|
||||
"origin_slot": 1,
|
||||
"target_id": 3,
|
||||
"target_slot": 1,
|
||||
"type": "INT"
|
||||
}
|
||||
],
|
||||
"extra": {},
|
||||
"category": "Video Tools",
|
||||
"description": "Extracts one image frame from a video at a chosen index, with optional trim and FPS control."
|
||||
}
|
||||
]
|
||||
},
|
||||
"extra": {
|
||||
"ds": {
|
||||
"scale": 1.197015527856339,
|
||||
"offset": [
|
||||
-168.76833554248222,
|
||||
540.6638955283997
|
||||
]
|
||||
},
|
||||
"frontendVersion": "1.42.8"
|
||||
}
|
||||
}
|
||||
@ -575,8 +575,9 @@
|
||||
"extra": {
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
||||
"category": "Image Tools/Color adjust"
|
||||
"category": "Image Tools/Color adjust",
|
||||
"description": "Adds a glow/bloom effect around bright image areas via GPU fragment shader."
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -752,8 +752,9 @@
|
||||
"extra": {
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
||||
"category": "Image Tools/Color adjust"
|
||||
"category": "Image Tools/Color adjust",
|
||||
"description": "Adjusts hue, saturation, and lightness of an image using a real-time GPU fragment shader."
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -374,7 +374,8 @@
|
||||
"extra": {
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
||||
"category": "Image Tools/Blur"
|
||||
"category": "Image Tools/Blur",
|
||||
"description": "Applies Gaussian, Box, or Radial blur to soften images and create stylized depth or motion effects."
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
@ -310,7 +310,8 @@
|
||||
"extra": {
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
||||
"category": "Text generation/Image Captioning"
|
||||
"category": "Text generation/Image Captioning",
|
||||
"description": "Generates descriptive captions for images using Google's Gemini multimodal LLM."
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
@ -315,8 +315,9 @@
|
||||
"extra": {
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
||||
"category": "Image Tools/Color adjust"
|
||||
"category": "Image Tools/Color adjust",
|
||||
"description": "Manipulates individual RGBA channels for masking, compositing, and channel effects."
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
2050
blueprints/Image Edit (Flux.2 Dev).json
Normal file
2050
blueprints/Image Edit (Flux.2 Dev).json
Normal file
File diff suppressed because it is too large
Load Diff
@ -1472,7 +1472,8 @@
|
||||
"extra": {
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
||||
"category": "Image generation and editing/Edit image"
|
||||
"category": "Image generation and editing/Edit image",
|
||||
"description": "Edits an input image via text instructions using FLUX.2 [klein] 4B."
|
||||
},
|
||||
{
|
||||
"id": "6007e698-2ebd-4917-84d8-299b35d7b7ab",
|
||||
@ -1821,7 +1822,8 @@
|
||||
],
|
||||
"extra": {
|
||||
"workflowRendererVersion": "LG"
|
||||
}
|
||||
},
|
||||
"description": "Applies reference image conditioning for style/identity transfer (Flux.2 Klein 4B)."
|
||||
}
|
||||
]
|
||||
},
|
||||
@ -1837,4 +1839,4 @@
|
||||
}
|
||||
},
|
||||
"version": 0.4
|
||||
}
|
||||
}
|
||||
|
||||
@ -1417,7 +1417,8 @@
|
||||
}
|
||||
],
|
||||
"extra": {},
|
||||
"category": "Image generation and editing/Edit image"
|
||||
"category": "Image generation and editing/Edit image",
|
||||
"description": "Edits images via text instructions using LongCat Image Edit, an instruction-following image editing diffusion model."
|
||||
}
|
||||
]
|
||||
},
|
||||
|
||||
1947
blueprints/Image Edit (Qwen 2509).json
Normal file
1947
blueprints/Image Edit (Qwen 2509).json
Normal file
File diff suppressed because it is too large
Load Diff
@ -132,7 +132,7 @@
|
||||
},
|
||||
"revision": 0,
|
||||
"config": {},
|
||||
"name": "local-Image Edit (Qwen 2511)",
|
||||
"name": "Image Edit (Qwen 2511)",
|
||||
"inputNode": {
|
||||
"id": -10,
|
||||
"bounding": [
|
||||
@ -1468,7 +1468,8 @@
|
||||
"VHS_MetadataImage": true,
|
||||
"VHS_KeepIntermediate": true
|
||||
},
|
||||
"category": "Image generation and editing/Edit image"
|
||||
"category": "Image generation and editing/Edit image",
|
||||
"description": "Edits images via text instructions using Qwen-Image-Edit-2511 with improved character consistency and integrated LoRA."
|
||||
}
|
||||
]
|
||||
},
|
||||
@ -1489,4 +1490,4 @@
|
||||
}
|
||||
},
|
||||
"version": 0.4
|
||||
}
|
||||
}
|
||||
@ -1188,7 +1188,8 @@
|
||||
"extra": {
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
||||
"category": "Image generation and editing/Inpaint image"
|
||||
"category": "Image generation and editing/Inpaint image",
|
||||
"description": "Inpaints masked image regions using Flux.1 fill [dev], Black Forest Labs' inpainting/outpainting model."
|
||||
}
|
||||
]
|
||||
},
|
||||
@ -1202,4 +1203,4 @@
|
||||
},
|
||||
"ue_links": []
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -1548,7 +1548,8 @@
|
||||
"extra": {
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
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@ -1907,7 +1908,8 @@
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@ -742,9 +742,10 @@
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714
blueprints/Image Segmentation (SAM3).json
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714
blueprints/Image Segmentation (SAM3).json
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|
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"name": "CONDITIONING",
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]
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@ -141,7 +141,7 @@
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@ -99,7 +99,7 @@
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@ -948,7 +948,8 @@
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@ -964,4 +965,4 @@
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@ -72,7 +72,7 @@
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397
blueprints/Remove Background (BiRefNet).json
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397
blueprints/Remove Background (BiRefNet).json
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|
||||
"name": "mask",
|
||||
"type": "MASK",
|
||||
"links": [
|
||||
4,
|
||||
11
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "RemoveBackground"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 14,
|
||||
"type": "LoadBackgroundRemovalModel",
|
||||
"pos": [
|
||||
-6540.534070722246,
|
||||
1302.223464635445
|
||||
],
|
||||
"size": [
|
||||
311.484375,
|
||||
85.515625
|
||||
],
|
||||
"flags": {},
|
||||
"order": 1,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "bg_removal_name",
|
||||
"name": "bg_removal_name",
|
||||
"type": "COMBO",
|
||||
"widget": {
|
||||
"name": "bg_removal_name"
|
||||
},
|
||||
"link": 12
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "bg_model",
|
||||
"name": "bg_model",
|
||||
"type": "BACKGROUND_REMOVAL",
|
||||
"links": [
|
||||
3
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "LoadBackgroundRemovalModel",
|
||||
"models": [
|
||||
{
|
||||
"name": "birefnet.safetensors",
|
||||
"url": "https://huggingface.co/Comfy-Org/BiRefNet/resolve/main/background_removal/birefnet.safetensors",
|
||||
"directory": "background_removal"
|
||||
}
|
||||
]
|
||||
},
|
||||
"widgets_values": [
|
||||
"birefnet.safetensors"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 15,
|
||||
"type": "InvertMask",
|
||||
"pos": [
|
||||
-6532.446160529669,
|
||||
1571.1111286839914
|
||||
],
|
||||
"size": [
|
||||
285.984375,
|
||||
48
|
||||
],
|
||||
"flags": {},
|
||||
"order": 2,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "mask",
|
||||
"name": "mask",
|
||||
"type": "MASK",
|
||||
"link": 4
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "MASK",
|
||||
"name": "MASK",
|
||||
"type": "MASK",
|
||||
"links": [
|
||||
6
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "InvertMask"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 16,
|
||||
"type": "JoinImageWithAlpha",
|
||||
"pos": [
|
||||
-6527.4370171636665,
|
||||
1674.3004951902876
|
||||
],
|
||||
"size": [
|
||||
284.96875,
|
||||
72
|
||||
],
|
||||
"flags": {},
|
||||
"order": 3,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "image",
|
||||
"name": "image",
|
||||
"type": "IMAGE",
|
||||
"link": 7
|
||||
},
|
||||
{
|
||||
"localized_name": "alpha",
|
||||
"name": "alpha",
|
||||
"type": "MASK",
|
||||
"link": 6
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "IMAGE",
|
||||
"name": "IMAGE",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
8
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "JoinImageWithAlpha"
|
||||
}
|
||||
}
|
||||
],
|
||||
"groups": [],
|
||||
"links": [
|
||||
{
|
||||
"id": 3,
|
||||
"origin_id": 14,
|
||||
"origin_slot": 0,
|
||||
"target_id": 13,
|
||||
"target_slot": 1,
|
||||
"type": "BACKGROUND_REMOVAL"
|
||||
},
|
||||
{
|
||||
"id": 4,
|
||||
"origin_id": 13,
|
||||
"origin_slot": 0,
|
||||
"target_id": 15,
|
||||
"target_slot": 0,
|
||||
"type": "MASK"
|
||||
},
|
||||
{
|
||||
"id": 6,
|
||||
"origin_id": 15,
|
||||
"origin_slot": 0,
|
||||
"target_id": 16,
|
||||
"target_slot": 1,
|
||||
"type": "MASK"
|
||||
},
|
||||
{
|
||||
"id": 5,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 0,
|
||||
"target_id": 13,
|
||||
"target_slot": 0,
|
||||
"type": "IMAGE"
|
||||
},
|
||||
{
|
||||
"id": 7,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 0,
|
||||
"target_id": 16,
|
||||
"target_slot": 0,
|
||||
"type": "IMAGE"
|
||||
},
|
||||
{
|
||||
"id": 8,
|
||||
"origin_id": 16,
|
||||
"origin_slot": 0,
|
||||
"target_id": -20,
|
||||
"target_slot": 0,
|
||||
"type": "IMAGE"
|
||||
},
|
||||
{
|
||||
"id": 11,
|
||||
"origin_id": 13,
|
||||
"origin_slot": 0,
|
||||
"target_id": -20,
|
||||
"target_slot": 1,
|
||||
"type": "MASK"
|
||||
},
|
||||
{
|
||||
"id": 12,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 1,
|
||||
"target_id": 14,
|
||||
"target_slot": 0,
|
||||
"type": "COMBO"
|
||||
}
|
||||
],
|
||||
"extra": {},
|
||||
"category": "Image generation and editing/Background Removal"
|
||||
}
|
||||
]
|
||||
},
|
||||
"extra": {}
|
||||
}
|
||||
@ -302,8 +302,9 @@
|
||||
"extra": {
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
||||
"category": "Image Tools/Sharpen"
|
||||
"category": "Image Tools/Sharpen",
|
||||
"description": "Sharpens image details using a GPU fragment shader for enhanced clarity."
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -222,7 +222,7 @@
|
||||
},
|
||||
"revision": 0,
|
||||
"config": {},
|
||||
"name": "local-Text to Audio (ACE-Step 1.5)",
|
||||
"name": "Text to Audio (ACE-Step 1.5)",
|
||||
"inputNode": {
|
||||
"id": -10,
|
||||
"bounding": [
|
||||
@ -1502,7 +1502,8 @@
|
||||
"extra": {
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
||||
"category": "Audio/Music generation"
|
||||
"category": "Audio/Music generation",
|
||||
"description": "Generates audio/music from text prompts using ACE-Step 1.5, a diffusion-based audio generation model."
|
||||
}
|
||||
]
|
||||
},
|
||||
@ -1518,4 +1519,4 @@
|
||||
}
|
||||
},
|
||||
"version": 0.4
|
||||
}
|
||||
}
|
||||
2112
blueprints/Text to Image (Ernie Image Turbo).json
Normal file
2112
blueprints/Text to Image (Ernie Image Turbo).json
Normal file
File diff suppressed because it is too large
Load Diff
2190
blueprints/Text to Image (Ernie Image).json
Normal file
2190
blueprints/Text to Image (Ernie Image).json
Normal file
File diff suppressed because it is too large
Load Diff
@ -1029,7 +1029,8 @@
|
||||
"extra": {
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
||||
"category": "Image generation and editing/Text to image"
|
||||
"category": "Image generation and editing/Text to image",
|
||||
"description": "Generates images from prompts using FLUX.1 [dev]: a 12B rectified-flow MMDiT with dual CLIP plus T5-XXL text encoders and guidance-distilled sampling for sharp prompt following versus classic DDPM diffusion."
|
||||
}
|
||||
]
|
||||
},
|
||||
@ -1043,4 +1044,4 @@
|
||||
},
|
||||
"ue_links": []
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -1023,7 +1023,8 @@
|
||||
"extra": {
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
||||
"category": "Image generation and editing/Text to image"
|
||||
"category": "Image generation and editing/Text to image",
|
||||
"description": "FLUX.1 Krea [dev] (Black Forest Labs × Krea): open-weight 12B rectified-flow text-to-image drop-in alongside FLUX.1 [dev], tuned away from overcooked saturation toward more natural diversity in people, realism, and style while keeping ecosystem compatibility."
|
||||
}
|
||||
]
|
||||
},
|
||||
@ -1037,4 +1038,4 @@
|
||||
},
|
||||
"ue_links": []
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
1870
blueprints/Text to Image (Flux.2 Dev).json
Normal file
1870
blueprints/Text to Image (Flux.2 Dev).json
Normal file
File diff suppressed because it is too large
Load Diff
@ -1104,7 +1104,8 @@
|
||||
"extra": {
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
||||
"category": "Image generation and editing/Text to image"
|
||||
"category": "Image generation and editing/Text to image",
|
||||
"description": "Generates images from text prompts using NetaYume Lumina, fine-tuned from Neta Lumina for anime-style and illustration generation."
|
||||
},
|
||||
{
|
||||
"id": "a07fdf06-1bda-4dac-bdbd-63ee8ebca1c9",
|
||||
@ -1458,11 +1459,12 @@
|
||||
],
|
||||
"extra": {
|
||||
"workflowRendererVersion": "LG"
|
||||
}
|
||||
},
|
||||
"description": "Encodes a negative text prompt via CLIP for classifier-free guidance in anime-style generation (NetaYume Lumina)."
|
||||
}
|
||||
]
|
||||
},
|
||||
"extra": {
|
||||
"ue_links": []
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -1941,7 +1941,8 @@
|
||||
"extra": {
|
||||
"workflowRendererVersion": "Vue-corrected"
|
||||
},
|
||||
"category": "Image generation and editing/Text to image"
|
||||
"category": "Image generation and editing/Text to image",
|
||||
"description": "Generates images from text prompts using Qwen-Image-2512, with enhanced human realism and finer natural detail over the base version."
|
||||
}
|
||||
]
|
||||
},
|
||||
|
||||
@ -1873,7 +1873,8 @@
|
||||
"extra": {
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
||||
"category": "Image generation and editing/Text to image"
|
||||
"category": "Image generation and editing/Text to image",
|
||||
"description": "Generates images from text prompts using Qwen-Image, Alibaba's 20B MMDiT model with excellent multilingual text rendering."
|
||||
}
|
||||
]
|
||||
},
|
||||
|
||||
1184
blueprints/Text to Image (Z-Image-Base).json
Normal file
1184
blueprints/Text to Image (Z-Image-Base).json
Normal file
File diff suppressed because it is too large
Load Diff
@ -1,22 +1,21 @@
|
||||
{
|
||||
"id": "1c3eaa76-5cfa-4dc7-8571-97a570324e01",
|
||||
"revision": 0,
|
||||
"last_node_id": 34,
|
||||
"last_link_id": 40,
|
||||
"last_node_id": 57,
|
||||
"last_link_id": 0,
|
||||
"nodes": [
|
||||
{
|
||||
"id": 5,
|
||||
"type": "dfe9eb32-97c0-43a5-90d5-4fd37768d91b",
|
||||
"id": 57,
|
||||
"type": "f2fdebf6-dfaf-43b6-9eb2-7f70613cfdc1",
|
||||
"pos": [
|
||||
-2.5766491043910378e-05,
|
||||
1229.999928629805
|
||||
130,
|
||||
200
|
||||
],
|
||||
"size": [
|
||||
400,
|
||||
470
|
||||
],
|
||||
"flags": {},
|
||||
"order": 0,
|
||||
"order": 1,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
@ -44,6 +43,22 @@
|
||||
},
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"name": "seed",
|
||||
"type": "INT",
|
||||
"widget": {
|
||||
"name": "seed"
|
||||
},
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"name": "steps",
|
||||
"type": "INT",
|
||||
"widget": {
|
||||
"name": "steps"
|
||||
},
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"name": "unet_name",
|
||||
"type": "COMBO",
|
||||
@ -80,15 +95,15 @@
|
||||
"properties": {
|
||||
"proxyWidgets": [
|
||||
[
|
||||
"-1",
|
||||
"27",
|
||||
"text"
|
||||
],
|
||||
[
|
||||
"-1",
|
||||
"13",
|
||||
"width"
|
||||
],
|
||||
[
|
||||
"-1",
|
||||
"13",
|
||||
"height"
|
||||
],
|
||||
[
|
||||
@ -97,19 +112,23 @@
|
||||
],
|
||||
[
|
||||
"3",
|
||||
"control_after_generate"
|
||||
"steps"
|
||||
],
|
||||
[
|
||||
"-1",
|
||||
"28",
|
||||
"unet_name"
|
||||
],
|
||||
[
|
||||
"-1",
|
||||
"30",
|
||||
"clip_name"
|
||||
],
|
||||
[
|
||||
"-1",
|
||||
"29",
|
||||
"vae_name"
|
||||
],
|
||||
[
|
||||
"3",
|
||||
"control_after_generate"
|
||||
]
|
||||
],
|
||||
"cnr_id": "comfy-core",
|
||||
@ -122,48 +141,40 @@
|
||||
"secondTabOffset": 80,
|
||||
"secondTabWidth": 65
|
||||
},
|
||||
"widgets_values": [
|
||||
"",
|
||||
1024,
|
||||
1024,
|
||||
null,
|
||||
null,
|
||||
"z_image_turbo_bf16.safetensors",
|
||||
"qwen_3_4b.safetensors",
|
||||
"ae.safetensors"
|
||||
]
|
||||
"widgets_values": [],
|
||||
"title": "Text to Image (Z-Image-Turbo)"
|
||||
}
|
||||
],
|
||||
"links": [],
|
||||
"groups": [],
|
||||
"version": 0.4,
|
||||
"definitions": {
|
||||
"subgraphs": [
|
||||
{
|
||||
"id": "dfe9eb32-97c0-43a5-90d5-4fd37768d91b",
|
||||
"id": "f2fdebf6-dfaf-43b6-9eb2-7f70613cfdc1",
|
||||
"version": 1,
|
||||
"state": {
|
||||
"lastGroupId": 4,
|
||||
"lastNodeId": 34,
|
||||
"lastLinkId": 40,
|
||||
"lastNodeId": 61,
|
||||
"lastLinkId": 75,
|
||||
"lastRerouteId": 0
|
||||
},
|
||||
"revision": 0,
|
||||
"config": {},
|
||||
"name": "local-Text to Image (Z-Image-Turbo)",
|
||||
"name": "Text to Image (Z-Image-Turbo)",
|
||||
"inputNode": {
|
||||
"id": -10,
|
||||
"bounding": [
|
||||
-80,
|
||||
425,
|
||||
-560,
|
||||
480,
|
||||
120,
|
||||
160
|
||||
200
|
||||
]
|
||||
},
|
||||
"outputNode": {
|
||||
"id": -20,
|
||||
"bounding": [
|
||||
1490,
|
||||
415,
|
||||
1670,
|
||||
320,
|
||||
120,
|
||||
60
|
||||
]
|
||||
@ -178,8 +189,8 @@
|
||||
],
|
||||
"label": "prompt",
|
||||
"pos": [
|
||||
20,
|
||||
445
|
||||
-460,
|
||||
500
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -190,8 +201,8 @@
|
||||
35
|
||||
],
|
||||
"pos": [
|
||||
20,
|
||||
465
|
||||
-460,
|
||||
520
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -202,44 +213,68 @@
|
||||
36
|
||||
],
|
||||
"pos": [
|
||||
20,
|
||||
485
|
||||
-460,
|
||||
540
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": "23087d15-8412-4fbd-b71e-9b6d7ef76de1",
|
||||
"id": "f77677f7-6bf6-4c19-a71f-c4a553d5981e",
|
||||
"name": "seed",
|
||||
"type": "INT",
|
||||
"linkIds": [
|
||||
71
|
||||
],
|
||||
"pos": [
|
||||
-460,
|
||||
560
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": "ef9a9fb1-5983-4bc9-a60b-cf5aec48bff1",
|
||||
"name": "steps",
|
||||
"type": "INT",
|
||||
"linkIds": [
|
||||
72
|
||||
],
|
||||
"pos": [
|
||||
-460,
|
||||
580
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": "a20a1b30-785f-4a04-bb6d-3d61adab9764",
|
||||
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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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|
||||
@ -1054,25 +1103,10 @@
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||||
"extra": {
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||||
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||||
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||||
1132
blueprints/Text to Image.json
Normal file
1132
blueprints/Text to Image.json
Normal file
File diff suppressed because it is too large
Load Diff
@ -4286,7 +4286,8 @@
|
||||
"extra": {
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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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||||
|
||||
@ -1572,7 +1572,8 @@
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"extra": {
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},
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||||
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}
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]
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||||
},
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@ -1586,4 +1587,4 @@
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"VHS_KeepIntermediate": true
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||||
},
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}
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@ -434,8 +434,9 @@
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||||
}
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]
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||||
}
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}
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@ -307,7 +307,8 @@
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@ -165,7 +165,7 @@
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||||
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||||
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||||
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||||
827
blueprints/Video Segmentation (SAM3).json
Normal file
827
blueprints/Video Segmentation (SAM3).json
Normal file
@ -0,0 +1,827 @@
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|
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"Max tokens for this model is only 32, to separately prompt multiple subjects you can separate prompts with comma, and set the max amount of objects detected for each prompt with :N\n\nFor example above test prompt finds 2 cakes, one apron, 4 window panels"
|
||||
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|
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|
||||
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|
||||
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|
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|
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||||
@ -1,21 +1,21 @@
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{
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{
|
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@ -76,31 +76,26 @@
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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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|
||||
"widgets_values": [],
|
||||
"title": "Video Stitch"
|
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|
||||
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|
||||
@ -109,12 +104,12 @@
|
||||
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|
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|
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@ -149,8 +144,8 @@
|
||||
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|
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|
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|
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@ -163,8 +158,8 @@
|
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|
||||
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|
||||
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@ -238,11 +233,11 @@
|
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|
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|
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|
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|
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|
||||
@ -291,8 +286,8 @@
|
||||
2420
|
||||
],
|
||||
"size": [
|
||||
193.530859375,
|
||||
66
|
||||
230,
|
||||
120
|
||||
],
|
||||
"flags": {},
|
||||
"order": 0,
|
||||
@ -332,21 +327,254 @@
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "GetVideoComponents",
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.13.0",
|
||||
"Node name for S&R": "GetVideoComponents"
|
||||
"ver": "0.13.0"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 90,
|
||||
"type": "GetImageSize",
|
||||
"pos": [
|
||||
-6390,
|
||||
3030
|
||||
],
|
||||
"size": [
|
||||
230,
|
||||
120
|
||||
],
|
||||
"flags": {},
|
||||
"order": 4,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "image",
|
||||
"name": "image",
|
||||
"type": "IMAGE",
|
||||
"link": 266
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "width",
|
||||
"name": "width",
|
||||
"type": "INT",
|
||||
"links": [
|
||||
274
|
||||
]
|
||||
},
|
||||
{
|
||||
"localized_name": "height",
|
||||
"name": "height",
|
||||
"type": "INT",
|
||||
"links": [
|
||||
276
|
||||
]
|
||||
},
|
||||
{
|
||||
"localized_name": "batch_size",
|
||||
"name": "batch_size",
|
||||
"type": "INT",
|
||||
"links": null
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "GetImageSize"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 80,
|
||||
"type": "CreateVideo",
|
||||
"pos": [
|
||||
-5190,
|
||||
2420
|
||||
],
|
||||
"size": [
|
||||
270,
|
||||
130
|
||||
],
|
||||
"flags": {},
|
||||
"order": 3,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "images",
|
||||
"name": "images",
|
||||
"type": "IMAGE",
|
||||
"link": 282
|
||||
},
|
||||
{
|
||||
"localized_name": "audio",
|
||||
"name": "audio",
|
||||
"shape": 7,
|
||||
"type": "AUDIO",
|
||||
"link": 251
|
||||
},
|
||||
{
|
||||
"localized_name": "fps",
|
||||
"name": "fps",
|
||||
"type": "FLOAT",
|
||||
"widget": {
|
||||
"name": "fps"
|
||||
},
|
||||
"link": 252
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "VIDEO",
|
||||
"name": "VIDEO",
|
||||
"type": "VIDEO",
|
||||
"links": [
|
||||
255
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "CreateVideo",
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.13.0"
|
||||
},
|
||||
"widgets_values": [
|
||||
30
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 95,
|
||||
"type": "ComfyMathExpression",
|
||||
"pos": [
|
||||
-6040,
|
||||
3020
|
||||
],
|
||||
"size": [
|
||||
400,
|
||||
200
|
||||
],
|
||||
"flags": {},
|
||||
"order": 5,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"label": "a",
|
||||
"localized_name": "values.a",
|
||||
"name": "values.a",
|
||||
"type": "FLOAT,INT",
|
||||
"link": 274
|
||||
},
|
||||
{
|
||||
"label": "b",
|
||||
"localized_name": "values.b",
|
||||
"name": "values.b",
|
||||
"shape": 7,
|
||||
"type": "FLOAT,INT",
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"localized_name": "expression",
|
||||
"name": "expression",
|
||||
"type": "STRING",
|
||||
"widget": {
|
||||
"name": "expression"
|
||||
},
|
||||
"link": null
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "FLOAT",
|
||||
"name": "FLOAT",
|
||||
"type": "FLOAT",
|
||||
"links": null
|
||||
},
|
||||
{
|
||||
"localized_name": "INT",
|
||||
"name": "INT",
|
||||
"type": "INT",
|
||||
"links": [
|
||||
279
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "ComfyMathExpression"
|
||||
},
|
||||
"widgets_values": [
|
||||
"a & ~1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 96,
|
||||
"type": "ComfyMathExpression",
|
||||
"pos": [
|
||||
-6040,
|
||||
3290
|
||||
],
|
||||
"size": [
|
||||
400,
|
||||
200
|
||||
],
|
||||
"flags": {},
|
||||
"order": 6,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"label": "a",
|
||||
"localized_name": "values.a",
|
||||
"name": "values.a",
|
||||
"type": "FLOAT,INT",
|
||||
"link": 276
|
||||
},
|
||||
{
|
||||
"label": "b",
|
||||
"localized_name": "values.b",
|
||||
"name": "values.b",
|
||||
"shape": 7,
|
||||
"type": "FLOAT,INT",
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"localized_name": "expression",
|
||||
"name": "expression",
|
||||
"type": "STRING",
|
||||
"widget": {
|
||||
"name": "expression"
|
||||
},
|
||||
"link": null
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "FLOAT",
|
||||
"name": "FLOAT",
|
||||
"type": "FLOAT",
|
||||
"links": null
|
||||
},
|
||||
{
|
||||
"localized_name": "INT",
|
||||
"name": "INT",
|
||||
"type": "INT",
|
||||
"links": [
|
||||
280
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "ComfyMathExpression"
|
||||
},
|
||||
"widgets_values": [
|
||||
"a & ~1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 79,
|
||||
"type": "ImageStitch",
|
||||
"pos": [
|
||||
-6390,
|
||||
2700
|
||||
2780
|
||||
],
|
||||
"size": [
|
||||
270,
|
||||
150
|
||||
160
|
||||
],
|
||||
"flags": {},
|
||||
"order": 2,
|
||||
@ -408,14 +636,15 @@
|
||||
"name": "IMAGE",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
250
|
||||
266,
|
||||
281
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "ImageStitch",
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.13.0",
|
||||
"Node name for S&R": "ImageStitch"
|
||||
"ver": "0.13.0"
|
||||
},
|
||||
"widgets_values": [
|
||||
"right",
|
||||
@ -425,60 +654,91 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 80,
|
||||
"type": "CreateVideo",
|
||||
"id": 97,
|
||||
"type": "ResizeImageMaskNode",
|
||||
"pos": [
|
||||
-6040,
|
||||
2610
|
||||
-5560,
|
||||
2790
|
||||
],
|
||||
"size": [
|
||||
270,
|
||||
78
|
||||
160
|
||||
],
|
||||
"flags": {},
|
||||
"order": 3,
|
||||
"order": 7,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "images",
|
||||
"name": "images",
|
||||
"type": "IMAGE",
|
||||
"link": 250
|
||||
"localized_name": "input",
|
||||
"name": "input",
|
||||
"type": "IMAGE,MASK",
|
||||
"link": 281
|
||||
},
|
||||
{
|
||||
"localized_name": "audio",
|
||||
"name": "audio",
|
||||
"shape": 7,
|
||||
"type": "AUDIO",
|
||||
"link": 251
|
||||
},
|
||||
{
|
||||
"localized_name": "fps",
|
||||
"name": "fps",
|
||||
"type": "FLOAT",
|
||||
"localized_name": "resize_type",
|
||||
"name": "resize_type",
|
||||
"type": "COMFY_DYNAMICCOMBO_V3",
|
||||
"widget": {
|
||||
"name": "fps"
|
||||
"name": "resize_type"
|
||||
},
|
||||
"link": 252
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"localized_name": "width",
|
||||
"name": "resize_type.width",
|
||||
"type": "INT",
|
||||
"widget": {
|
||||
"name": "resize_type.width"
|
||||
},
|
||||
"link": 279
|
||||
},
|
||||
{
|
||||
"localized_name": "height",
|
||||
"name": "resize_type.height",
|
||||
"type": "INT",
|
||||
"widget": {
|
||||
"name": "resize_type.height"
|
||||
},
|
||||
"link": 280
|
||||
},
|
||||
{
|
||||
"localized_name": "crop",
|
||||
"name": "resize_type.crop",
|
||||
"type": "COMBO",
|
||||
"widget": {
|
||||
"name": "resize_type.crop"
|
||||
},
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"localized_name": "scale_method",
|
||||
"name": "scale_method",
|
||||
"type": "COMBO",
|
||||
"widget": {
|
||||
"name": "scale_method"
|
||||
},
|
||||
"link": null
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "VIDEO",
|
||||
"name": "VIDEO",
|
||||
"type": "VIDEO",
|
||||
"localized_name": "resized",
|
||||
"name": "resized",
|
||||
"type": "*",
|
||||
"links": [
|
||||
255
|
||||
282
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.13.0",
|
||||
"Node name for S&R": "CreateVideo"
|
||||
"Node name for S&R": "ResizeImageMaskNode"
|
||||
},
|
||||
"widgets_values": [
|
||||
30
|
||||
"scale dimensions",
|
||||
512,
|
||||
512,
|
||||
"center",
|
||||
"area"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -500,14 +760,6 @@
|
||||
"target_slot": 1,
|
||||
"type": "IMAGE"
|
||||
},
|
||||
{
|
||||
"id": 250,
|
||||
"origin_id": 79,
|
||||
"origin_slot": 0,
|
||||
"target_id": 80,
|
||||
"target_slot": 0,
|
||||
"type": "IMAGE"
|
||||
},
|
||||
{
|
||||
"id": 251,
|
||||
"origin_id": 77,
|
||||
@ -579,13 +831,71 @@
|
||||
"target_id": 79,
|
||||
"target_slot": 5,
|
||||
"type": "COMBO"
|
||||
},
|
||||
{
|
||||
"id": 266,
|
||||
"origin_id": 79,
|
||||
"origin_slot": 0,
|
||||
"target_id": 90,
|
||||
"target_slot": 0,
|
||||
"type": "IMAGE"
|
||||
},
|
||||
{
|
||||
"id": 274,
|
||||
"origin_id": 90,
|
||||
"origin_slot": 0,
|
||||
"target_id": 95,
|
||||
"target_slot": 0,
|
||||
"type": "INT"
|
||||
},
|
||||
{
|
||||
"id": 276,
|
||||
"origin_id": 90,
|
||||
"origin_slot": 1,
|
||||
"target_id": 96,
|
||||
"target_slot": 0,
|
||||
"type": "INT"
|
||||
},
|
||||
{
|
||||
"id": 279,
|
||||
"origin_id": 95,
|
||||
"origin_slot": 1,
|
||||
"target_id": 97,
|
||||
"target_slot": 2,
|
||||
"type": "INT"
|
||||
},
|
||||
{
|
||||
"id": 280,
|
||||
"origin_id": 96,
|
||||
"origin_slot": 1,
|
||||
"target_id": 97,
|
||||
"target_slot": 3,
|
||||
"type": "INT"
|
||||
},
|
||||
{
|
||||
"id": 281,
|
||||
"origin_id": 79,
|
||||
"origin_slot": 0,
|
||||
"target_id": 97,
|
||||
"target_slot": 0,
|
||||
"type": "IMAGE"
|
||||
},
|
||||
{
|
||||
"id": 282,
|
||||
"origin_id": 97,
|
||||
"origin_slot": 0,
|
||||
"target_id": 80,
|
||||
"target_slot": 0,
|
||||
"type": "IMAGE"
|
||||
}
|
||||
],
|
||||
"extra": {
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
||||
"category": "Video Tools/Stitch videos"
|
||||
"category": "Video Tools/Stitch videos",
|
||||
"description": "Stitches multiple video clips into a single sequential video file."
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
},
|
||||
"extra": {}
|
||||
}
|
||||
@ -412,9 +412,10 @@
|
||||
"extra": {
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
||||
"category": "Video generation and editing/Enhance video"
|
||||
"category": "Video generation and editing/Enhance video",
|
||||
"description": "Upscales video to 4× resolution using a GAN-based upscaling model."
|
||||
}
|
||||
]
|
||||
},
|
||||
"extra": {}
|
||||
}
|
||||
}
|
||||
@ -1135,7 +1135,7 @@ class AudioInjector_WAN(nn.Module):
|
||||
self.injector_adain_output_layers = nn.ModuleList(
|
||||
[operations.Linear(dim, dim, dtype=dtype, device=device) for _ in range(audio_injector_id)])
|
||||
|
||||
def forward(self, x, block_id, audio_emb, audio_emb_global, seq_len):
|
||||
def forward(self, x, block_id, audio_emb, audio_emb_global, seq_len, scale=1.0):
|
||||
audio_attn_id = self.injected_block_id.get(block_id, None)
|
||||
if audio_attn_id is None:
|
||||
return x
|
||||
@ -1148,12 +1148,15 @@ class AudioInjector_WAN(nn.Module):
|
||||
attn_hidden_states = adain_hidden_states
|
||||
else:
|
||||
attn_hidden_states = self.injector_pre_norm_feat[audio_attn_id](input_hidden_states)
|
||||
audio_emb = rearrange(audio_emb, "b t n c -> (b t) n c", t=num_frames)
|
||||
attn_audio_emb = audio_emb
|
||||
|
||||
if audio_emb.dim() == 3: # WanDancer case
|
||||
attn_audio_emb = rearrange(audio_emb, "b t c -> (b t) 1 c", t=num_frames)
|
||||
else: # S2V case
|
||||
attn_audio_emb = rearrange(audio_emb, "b t n c -> (b t) n c", t=num_frames)
|
||||
|
||||
residual_out = self.injector[audio_attn_id](x=attn_hidden_states, context=attn_audio_emb)
|
||||
residual_out = rearrange(
|
||||
residual_out, "(b t) n c -> b (t n) c", t=num_frames)
|
||||
x[:, :seq_len] = x[:, :seq_len] + residual_out
|
||||
residual_out = rearrange(residual_out, "(b t) n c -> b (t n) c", t=num_frames)
|
||||
x[:, :seq_len] = x[:, :seq_len] + residual_out * scale
|
||||
return x
|
||||
|
||||
|
||||
|
||||
251
comfy/ldm/wan/model_wandancer.py
Normal file
251
comfy/ldm/wan/model_wandancer.py
Normal file
@ -0,0 +1,251 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import comfy
|
||||
from comfy.ldm.modules.attention import optimized_attention
|
||||
from comfy.ldm.flux.math import apply_rope1
|
||||
from comfy.ldm.flux.layers import EmbedND
|
||||
|
||||
from .model import AudioInjector_WAN, WanModel, MLPProj, Head, sinusoidal_embedding_1d
|
||||
|
||||
|
||||
class MusicSelfAttention(nn.Module):
|
||||
def __init__(self, dim, num_heads, device=None, dtype=None, operations=None):
|
||||
assert dim % num_heads == 0
|
||||
super().__init__()
|
||||
self.embed_dim = dim
|
||||
self.num_heads = num_heads
|
||||
self.head_dim = dim // num_heads
|
||||
|
||||
self.q_proj = operations.Linear(dim, dim, device=device, dtype=dtype)
|
||||
self.k_proj = operations.Linear(dim, dim, device=device, dtype=dtype)
|
||||
self.v_proj = operations.Linear(dim, dim, device=device, dtype=dtype)
|
||||
self.out_proj = operations.Linear(dim, dim, device=device, dtype=dtype)
|
||||
|
||||
def forward(self, x, freqs):
|
||||
b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
|
||||
|
||||
q = self.q_proj(x).view(b, s, n, d)
|
||||
q = apply_rope1(q, freqs)
|
||||
|
||||
k = self.k_proj(x).view(b, s, n, d)
|
||||
k = apply_rope1(k, freqs)
|
||||
|
||||
x = optimized_attention(
|
||||
q.view(b, s, n * d),
|
||||
k.view(b, s, n * d),
|
||||
self.v_proj(x).view(b, s, n * d),
|
||||
heads=self.num_heads,
|
||||
)
|
||||
|
||||
return self.out_proj(x)
|
||||
|
||||
|
||||
class MusicEncoderLayer(nn.Module):
|
||||
def __init__(self, dim: int, num_heads: int, ffn_dim: int, device=None, dtype=None, operations=None):
|
||||
super().__init__()
|
||||
self.self_attn = MusicSelfAttention(dim, num_heads, device=device, dtype=dtype, operations=operations)
|
||||
|
||||
self.linear1 = operations.Linear(dim, ffn_dim, device=device, dtype=dtype)
|
||||
self.linear2 = operations.Linear(ffn_dim, dim, device=device, dtype=dtype)
|
||||
|
||||
self.norm1 = operations.LayerNorm(dim, device=device, dtype=dtype)
|
||||
self.norm2 = operations.LayerNorm(dim, device=device, dtype=dtype)
|
||||
|
||||
def forward(self, x: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
|
||||
x = x + self.self_attn(self.norm1(x), freqs=freqs)
|
||||
x = x + self.linear2(torch.nn.functional.gelu(self.linear1(self.norm2(x)))) # ffn
|
||||
return x
|
||||
|
||||
|
||||
class WanDancerModel(WanModel):
|
||||
def __init__(self,
|
||||
model_type='wandancer',
|
||||
patch_size=(1, 2, 2),
|
||||
text_len=512,
|
||||
in_dim=16,
|
||||
dim=5120,
|
||||
ffn_dim=8192,
|
||||
freq_dim=256,
|
||||
text_dim=4096,
|
||||
out_dim=16,
|
||||
num_heads=16,
|
||||
num_layers=40,
|
||||
window_size=(-1, -1),
|
||||
qk_norm=True,
|
||||
cross_attn_norm=True,
|
||||
eps=1e-6,
|
||||
in_dim_ref_conv=None,
|
||||
image_model=None,
|
||||
device=None, dtype=None, operations=None,
|
||||
audio_inject_layers=[0, 4, 8, 12, 16, 20, 24, 27],
|
||||
music_dim = 256,
|
||||
music_heads = 4,
|
||||
music_feature_dim = 35,
|
||||
music_latent_dim = 256
|
||||
):
|
||||
|
||||
super().__init__(model_type='i2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim,
|
||||
num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, image_model=image_model, in_dim_ref_conv=in_dim_ref_conv,
|
||||
device=device, dtype=dtype, operations=operations)
|
||||
|
||||
self.dtype = dtype
|
||||
operation_settings = {"operations": operations, "device": device, "dtype": dtype}
|
||||
|
||||
self.patch_embedding_global = operations.Conv3d(in_dim, dim, kernel_size=patch_size, stride=patch_size, device=operation_settings.get("device"), dtype=torch.float32)
|
||||
self.img_emb_refimage = MLPProj(1280, dim, operation_settings=operation_settings)
|
||||
self.head_global = Head(dim, out_dim, patch_size, eps, operation_settings=operation_settings)
|
||||
|
||||
self.music_injector = AudioInjector_WAN(
|
||||
dim=self.dim,
|
||||
num_heads=self.num_heads,
|
||||
inject_layer=audio_inject_layers,
|
||||
root_net=self,
|
||||
enable_adain=False,
|
||||
dtype=dtype, device=device, operations=operations
|
||||
)
|
||||
|
||||
self.music_projection = operations.Linear(music_feature_dim, music_latent_dim, device=device, dtype=dtype)
|
||||
self.music_encoder = nn.ModuleList([MusicEncoderLayer(dim=music_dim, num_heads=music_heads, ffn_dim=1024, device=device, dtype=dtype, operations=operations) for _ in range(2)])
|
||||
music_head_dim = music_dim // music_heads
|
||||
self.music_rope_embedder = EmbedND(dim=music_head_dim, theta=10000.0, axes_dim=[music_head_dim])
|
||||
|
||||
def forward_orig(self, x, t, context, clip_fea=None, clip_fea_ref=None, freqs=None, audio_embed=None, fps=30, audio_inject_scale=1.0, transformer_options={}, **kwargs):
|
||||
# embeddings
|
||||
if int(fps + 0.5) != 30:
|
||||
x = self.patch_embedding_global(x.float()).to(x.dtype)
|
||||
else:
|
||||
x = self.patch_embedding(x.float()).to(x.dtype)
|
||||
|
||||
grid_sizes = x.shape[2:]
|
||||
latent_frames = grid_sizes[0]
|
||||
transformer_options["grid_sizes"] = grid_sizes
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
seq_len = x.size(1)
|
||||
|
||||
# time embeddings
|
||||
e = self.time_embedding(sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype))
|
||||
e = e.reshape(t.shape[0], -1, e.shape[-1])
|
||||
e0 = self.time_projection(e).unflatten(2, (6, self.dim))
|
||||
|
||||
full_ref = None
|
||||
if self.ref_conv is not None: # model has the weight, but this wasn't used in the original pipeline
|
||||
full_ref = kwargs.get("reference_latent", None)
|
||||
if full_ref is not None:
|
||||
full_ref = self.ref_conv(full_ref).flatten(2).transpose(1, 2)
|
||||
x = torch.concat((full_ref, x), dim=1)
|
||||
|
||||
# context
|
||||
context = self.text_embedding(context)
|
||||
|
||||
audio_emb = None
|
||||
if audio_embed is not None: # encode music feature，[1, frame_num, 35] -> [1, F*8, dim]
|
||||
music_feature = self.music_projection(audio_embed)
|
||||
|
||||
music_seq_len = music_feature.shape[1]
|
||||
music_ids = torch.arange(music_seq_len, device=music_feature.device, dtype=music_feature.dtype).reshape(1, -1, 1) # create 1D position IDs
|
||||
music_freqs = self.music_rope_embedder(music_ids).movedim(1, 2)
|
||||
|
||||
# apply encoder layers
|
||||
for layer in self.music_encoder:
|
||||
music_feature = layer(music_feature, music_freqs)
|
||||
|
||||
# interpolate
|
||||
audio_emb = torch.nn.functional.interpolate(music_feature.unsqueeze(1), size=(latent_frames * 8, self.dim), mode='bilinear').squeeze(1)
|
||||
|
||||
context_img_len = 0
|
||||
if self.img_emb is not None and clip_fea is not None:
|
||||
context_clip = self.img_emb(clip_fea) # bs x 257 x dim
|
||||
context = torch.cat([context_clip, context], dim=1)
|
||||
context_img_len += clip_fea.shape[-2]
|
||||
if self.img_emb_refimage is not None and clip_fea_ref is not None:
|
||||
context_clip_ref = self.img_emb_refimage(clip_fea_ref)
|
||||
context = torch.cat([context_clip_ref, context], dim=1)
|
||||
context_img_len += clip_fea_ref.shape[-2]
|
||||
|
||||
patches_replace = transformer_options.get("patches_replace", {})
|
||||
blocks_replace = patches_replace.get("dit", {})
|
||||
transformer_options["total_blocks"] = len(self.blocks)
|
||||
transformer_options["block_type"] = "double"
|
||||
for i, block in enumerate(self.blocks):
|
||||
transformer_options["block_index"] = i
|
||||
if ("double_block", i) in blocks_replace:
|
||||
def block_wrap(args):
|
||||
out = {}
|
||||
out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"])
|
||||
return out
|
||||
out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})
|
||||
x = out["img"]
|
||||
else:
|
||||
x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
|
||||
if audio_emb is not None:
|
||||
x = self.music_injector(x, i, audio_emb, audio_emb_global=None, seq_len=seq_len, scale=audio_inject_scale)
|
||||
|
||||
# head
|
||||
if int(fps + 0.5) != 30:
|
||||
x = self.head_global(x, e)
|
||||
else:
|
||||
x = self.head(x, e)
|
||||
|
||||
if full_ref is not None:
|
||||
x = x[:, full_ref.shape[1]:]
|
||||
|
||||
# unpatchify
|
||||
x = self.unpatchify(x, grid_sizes)
|
||||
return x
|
||||
|
||||
def _forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, clip_fea_ref=None, fps=30, audio_inject_scale=1.0, **kwargs):
|
||||
bs, c, t, h, w = x.shape
|
||||
x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
|
||||
|
||||
t_len = t
|
||||
if time_dim_concat is not None:
|
||||
time_dim_concat = comfy.ldm.common_dit.pad_to_patch_size(time_dim_concat, self.patch_size)
|
||||
x = torch.cat([x, time_dim_concat], dim=2)
|
||||
t_len = x.shape[2]
|
||||
|
||||
freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype, fps=fps, transformer_options=transformer_options)
|
||||
return self.forward_orig(x, timestep, context, clip_fea=clip_fea, clip_fea_ref=clip_fea_ref, freqs=freqs, fps=fps, audio_inject_scale=audio_inject_scale, transformer_options=transformer_options, **kwargs)[:, :, :t, :h, :w]
|
||||
|
||||
def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, fps=30, device=None, dtype=None, transformer_options={}):
|
||||
patch_size = self.patch_size
|
||||
t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
|
||||
h_len = ((h + (patch_size[1] // 2)) // patch_size[1])
|
||||
w_len = ((w + (patch_size[2] // 2)) // patch_size[2])
|
||||
|
||||
if steps_t is None:
|
||||
steps_t = t_len
|
||||
if steps_h is None:
|
||||
steps_h = h_len
|
||||
if steps_w is None:
|
||||
steps_w = w_len
|
||||
|
||||
h_start = 0
|
||||
w_start = 0
|
||||
rope_options = transformer_options.get("rope_options", None)
|
||||
if rope_options is not None:
|
||||
t_len = (t_len - 1.0) * rope_options.get("scale_t", 1.0) + 1.0
|
||||
h_len = (h_len - 1.0) * rope_options.get("scale_y", 1.0) + 1.0
|
||||
w_len = (w_len - 1.0) * rope_options.get("scale_x", 1.0) + 1.0
|
||||
|
||||
t_start += rope_options.get("shift_t", 0.0)
|
||||
h_start += rope_options.get("shift_y", 0.0)
|
||||
w_start += rope_options.get("shift_x", 0.0)
|
||||
|
||||
img_ids = torch.zeros((steps_t, steps_h, steps_w, 3), device=device, dtype=dtype)
|
||||
|
||||
if int(fps + 0.5) != 30:
|
||||
time_scale = 30.0 / fps # how many time units each frame represents relative to 30fps
|
||||
positions_new = torch.arange(steps_t, device=device, dtype=dtype) * time_scale + t_start
|
||||
total_frames_at_30fps = int(time_scale * steps_t + 0.5)
|
||||
positions_new[-1] = t_start + (total_frames_at_30fps - 1)
|
||||
|
||||
img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + positions_new.reshape(-1, 1, 1)
|
||||
else:
|
||||
img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(t_start, t_start + (t_len - 1), steps=steps_t, device=device, dtype=dtype).reshape(-1, 1, 1)
|
||||
|
||||
img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(h_start, h_start + (h_len - 1), steps=steps_h, device=device, dtype=dtype).reshape(1, -1, 1)
|
||||
img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(w_start, w_start + (w_len - 1), steps=steps_w, device=device, dtype=dtype).reshape(1, 1, -1)
|
||||
img_ids = img_ids.reshape(1, -1, img_ids.shape[-1])
|
||||
|
||||
freqs = self.rope_embedder(img_ids).movedim(1, 2)
|
||||
return freqs
|
||||
@ -43,6 +43,7 @@ import comfy.ldm.lumina.model
|
||||
import comfy.ldm.wan.model
|
||||
import comfy.ldm.wan.model_animate
|
||||
import comfy.ldm.wan.ar_model
|
||||
import comfy.ldm.wan.model_wandancer
|
||||
import comfy.ldm.hunyuan3d.model
|
||||
import comfy.ldm.hidream.model
|
||||
import comfy.ldm.chroma.model
|
||||
@ -1599,6 +1600,30 @@ class WAN21_SCAIL(WAN21):
|
||||
|
||||
return out
|
||||
|
||||
class WAN22_WanDancer(WAN21):
|
||||
def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=True, device=None):
|
||||
super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model_wandancer.WanDancerModel)
|
||||
self.image_to_video = image_to_video
|
||||
|
||||
def extra_conds(self, **kwargs):
|
||||
out = super().extra_conds(**kwargs)
|
||||
audio_embed = kwargs.get("audio_embed", None)
|
||||
if audio_embed is not None:
|
||||
out['audio_embed'] = comfy.conds.CONDRegular(audio_embed)
|
||||
|
||||
clip_vision_output_ref = kwargs.get("clip_vision_output_ref", None)
|
||||
if clip_vision_output_ref is not None:
|
||||
out['clip_fea_ref'] = comfy.conds.CONDRegular(clip_vision_output_ref.penultimate_hidden_states)
|
||||
|
||||
fps = kwargs.get("fps", None)
|
||||
if fps is not None:
|
||||
out['fps'] = comfy.conds.CONDRegular(torch.FloatTensor([fps]))
|
||||
|
||||
audio_inject_scale = kwargs.get("audio_inject_scale", None)
|
||||
if audio_inject_scale is not None:
|
||||
out['audio_inject_scale'] = comfy.conds.CONDRegular(torch.FloatTensor([audio_inject_scale]))
|
||||
return out
|
||||
|
||||
class Hunyuan3Dv2(BaseModel):
|
||||
def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
|
||||
super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan3d.model.Hunyuan3Dv2)
|
||||
|
||||
@ -572,6 +572,8 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
|
||||
dit_config["model_type"] = "animate"
|
||||
elif '{}patch_embedding_pose.weight'.format(key_prefix) in state_dict_keys:
|
||||
dit_config["model_type"] = "scail"
|
||||
elif '{}patch_embedding_global.weight'.format(key_prefix) in state_dict_keys:
|
||||
dit_config["model_type"] = "wandancer"
|
||||
else:
|
||||
if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys:
|
||||
dit_config["model_type"] = "i2v"
|
||||
|
||||
@ -1313,6 +1313,37 @@ class WAN21_SCAIL(WAN21_T2V):
|
||||
out = model_base.WAN21_SCAIL(self, image_to_video=False, device=device)
|
||||
return out
|
||||
|
||||
class WAN22_WanDancer(WAN21_T2V):
|
||||
unet_config = {
|
||||
"image_model": "wan2.1",
|
||||
"model_type": "wandancer",
|
||||
"in_dim": 36,
|
||||
}
|
||||
|
||||
def __init__(self, unet_config):
|
||||
super().__init__(unet_config)
|
||||
self.memory_usage_factor = 1.8
|
||||
|
||||
def get_model(self, state_dict, prefix="", device=None):
|
||||
out = model_base.WAN22_WanDancer(self, image_to_video=True, device=device)
|
||||
return out
|
||||
|
||||
def process_unet_state_dict(self, state_dict):
|
||||
out_sd = {}
|
||||
for k in list(state_dict.keys()):
|
||||
# split music_encoder in_proj into q_proj, k_proj, v_proj
|
||||
if "music_encoder" in k and "self_attn.in_proj" in k:
|
||||
suffix = "weight" if k.endswith("weight") else "bias"
|
||||
tensor = state_dict[k]
|
||||
d = tensor.shape[0] // 3
|
||||
prefix = k.replace(f"in_proj_{suffix}", "")
|
||||
out_sd[f"{prefix}q_proj.{suffix}"] = tensor[:d]
|
||||
out_sd[f"{prefix}k_proj.{suffix}"] = tensor[d:2*d]
|
||||
out_sd[f"{prefix}v_proj.{suffix}"] = tensor[2*d:]
|
||||
else:
|
||||
out_sd[k] = state_dict[k]
|
||||
return out_sd
|
||||
|
||||
class Hunyuan3Dv2(supported_models_base.BASE):
|
||||
unet_config = {
|
||||
"image_model": "hunyuan3d2",
|
||||
@ -1982,6 +2013,7 @@ models = [
|
||||
WAN22_Animate,
|
||||
WAN21_FlowRVS,
|
||||
WAN21_SCAIL,
|
||||
WAN22_WanDancer,
|
||||
Hunyuan3Dv2mini,
|
||||
Hunyuan3Dv2,
|
||||
Hunyuan3Dv2_1,
|
||||
|
||||
@ -1196,7 +1196,7 @@ def model_trange(*args, **kwargs):
|
||||
pbar.i1_time = time.time()
|
||||
pbar.set_postfix_str(" Model Initialization complete! ")
|
||||
elif pbar._i == 2:
|
||||
#bring forward the effective start time based the the diff between first and second iteration
|
||||
#bring forward the effective start time based the diff between first and second iteration
|
||||
#to attempt to remove load overhead from the final step rate estimate.
|
||||
pbar.start_t = pbar.i1_time - (time.time() - pbar.i1_time)
|
||||
pbar.set_postfix_str("")
|
||||
|
||||
@ -23,7 +23,7 @@ class BriaEditImageRequest(BaseModel):
|
||||
None,
|
||||
description="Mask image (black and white). Black areas will be preserved, white areas will be edited. "
|
||||
"If omitted, the edit applies to the entire image. "
|
||||
"The input image and the the input mask must be of the same size.",
|
||||
"The input image and the input mask must be of the same size.",
|
||||
)
|
||||
negative_prompt: str | None = Field(None)
|
||||
guidance_scale: float = Field(...)
|
||||
|
||||
@ -198,6 +198,62 @@ RECOMMENDED_PRESETS_SEEDREAM_4 = [
|
||||
("Custom", None, None),
|
||||
]
|
||||
|
||||
_PRESETS_SEEDREAM_1K = [
|
||||
("(1K) 1024x1024 (1:1)", 1024, 1024),
|
||||
("(1K) 864x1152 (3:4)", 864, 1152),
|
||||
("(1K) 1152x864 (4:3)", 1152, 864),
|
||||
("(1K) 1312x736 (16:9)", 1312, 736),
|
||||
("(1K) 736x1312 (9:16)", 736, 1312),
|
||||
("(1K) 832x1248 (2:3)", 832, 1248),
|
||||
("(1K) 1248x832 (3:2)", 1248, 832),
|
||||
("(1K) 1568x672 (21:9)", 1568, 672),
|
||||
]
|
||||
|
||||
_PRESETS_SEEDREAM_2K = [
|
||||
("(2K) 2048x2048 (1:1)", 2048, 2048),
|
||||
("(2K) 1728x2304 (3:4)", 1728, 2304),
|
||||
("(2K) 2304x1728 (4:3)", 2304, 1728),
|
||||
("(2K) 2848x1600 (16:9)", 2848, 1600),
|
||||
("(2K) 1600x2848 (9:16)", 1600, 2848),
|
||||
("(2K) 1664x2496 (2:3)", 1664, 2496),
|
||||
("(2K) 2496x1664 (3:2)", 2496, 1664),
|
||||
("(2K) 3136x1344 (21:9)", 3136, 1344),
|
||||
]
|
||||
|
||||
_PRESETS_SEEDREAM_3K = [
|
||||
("(3K) 3072x3072 (1:1)", 3072, 3072),
|
||||
("(3K) 2592x3456 (3:4)", 2592, 3456),
|
||||
("(3K) 3456x2592 (4:3)", 3456, 2592),
|
||||
("(3K) 4096x2304 (16:9)", 4096, 2304),
|
||||
("(3K) 2304x4096 (9:16)", 2304, 4096),
|
||||
("(3K) 2496x3744 (2:3)", 2496, 3744),
|
||||
("(3K) 3744x2496 (3:2)", 3744, 2496),
|
||||
("(3K) 4704x2016 (21:9)", 4704, 2016),
|
||||
]
|
||||
|
||||
_PRESETS_SEEDREAM_4K = [
|
||||
("(4K) 4096x4096 (1:1)", 4096, 4096),
|
||||
("(4K) 3520x4704 (3:4)", 3520, 4704),
|
||||
("(4K) 4704x3520 (4:3)", 4704, 3520),
|
||||
("(4K) 5504x3040 (16:9)", 5504, 3040),
|
||||
("(4K) 3040x5504 (9:16)", 3040, 5504),
|
||||
("(4K) 3328x4992 (2:3)", 3328, 4992),
|
||||
("(4K) 4992x3328 (3:2)", 4992, 3328),
|
||||
("(4K) 6240x2656 (21:9)", 6240, 2656),
|
||||
]
|
||||
|
||||
_CUSTOM_PRESET = [("Custom", None, None)]
|
||||
|
||||
RECOMMENDED_PRESETS_SEEDREAM_5_LITE = (
|
||||
_PRESETS_SEEDREAM_2K + _PRESETS_SEEDREAM_3K + _PRESETS_SEEDREAM_4K + _CUSTOM_PRESET
|
||||
)
|
||||
RECOMMENDED_PRESETS_SEEDREAM_4_5 = (
|
||||
_PRESETS_SEEDREAM_2K + _PRESETS_SEEDREAM_4K + _CUSTOM_PRESET
|
||||
)
|
||||
RECOMMENDED_PRESETS_SEEDREAM_4_0 = (
|
||||
_PRESETS_SEEDREAM_1K + _PRESETS_SEEDREAM_2K + _PRESETS_SEEDREAM_4K + _CUSTOM_PRESET
|
||||
)
|
||||
|
||||
# Seedance 2.0 reference video pixel count limits per model and output resolution.
|
||||
SEEDANCE2_REF_VIDEO_PIXEL_LIMITS = {
|
||||
"dreamina-seedance-2-0-260128": {
|
||||
|
||||
@ -1,10 +1,11 @@
|
||||
from __future__ import annotations
|
||||
from enum import Enum
|
||||
from typing import Optional, List, Dict, Any, Union
|
||||
from typing import Optional, Any
|
||||
|
||||
from pydantic import BaseModel, Field, RootModel
|
||||
|
||||
|
||||
class TripoModelVersion(str, Enum):
|
||||
v3_1_20260211 = 'v3.1-20260211'
|
||||
v3_0_20250812 = 'v3.0-20250812'
|
||||
v2_5_20250123 = 'v2.5-20250123'
|
||||
v2_0_20240919 = 'v2.0-20240919'
|
||||
@ -142,7 +143,7 @@ class TripoFileEmptyReference(BaseModel):
|
||||
pass
|
||||
|
||||
class TripoFileReference(RootModel):
|
||||
root: Union[TripoFileTokenReference, TripoUrlReference, TripoObjectReference, TripoFileEmptyReference]
|
||||
root: TripoFileTokenReference | TripoUrlReference | TripoObjectReference | TripoFileEmptyReference
|
||||
|
||||
class TripoGetStsTokenRequest(BaseModel):
|
||||
format: str = Field(..., description='The format of the image')
|
||||
@ -183,7 +184,7 @@ class TripoImageToModelRequest(BaseModel):
|
||||
|
||||
class TripoMultiviewToModelRequest(BaseModel):
|
||||
type: TripoTaskType = TripoTaskType.MULTIVIEW_TO_MODEL
|
||||
files: List[TripoFileReference] = Field(..., description='The file references to convert to a model')
|
||||
files: list[TripoFileReference] = Field(..., description='The file references to convert to a model')
|
||||
model_version: Optional[TripoModelVersion] = Field(None, description='The model version to use for generation')
|
||||
orthographic_projection: Optional[bool] = Field(False, description='Whether to use orthographic projection')
|
||||
face_limit: Optional[int] = Field(None, description='The number of faces to limit the generation to')
|
||||
@ -251,27 +252,13 @@ class TripoConvertModelRequest(BaseModel):
|
||||
with_animation: Optional[bool] = Field(None, description='Whether to include animations')
|
||||
pack_uv: Optional[bool] = Field(None, description='Whether to pack the UVs')
|
||||
bake: Optional[bool] = Field(None, description='Whether to bake the model')
|
||||
part_names: Optional[List[str]] = Field(None, description='The names of the parts to include')
|
||||
part_names: Optional[list[str]] = Field(None, description='The names of the parts to include')
|
||||
fbx_preset: Optional[TripoFbxPreset] = Field(None, description='The preset for the FBX export')
|
||||
export_vertex_colors: Optional[bool] = Field(None, description='Whether to export the vertex colors')
|
||||
export_orientation: Optional[TripoOrientation] = Field(None, description='The orientation for the export')
|
||||
animate_in_place: Optional[bool] = Field(None, description='Whether to animate in place')
|
||||
|
||||
|
||||
class TripoTaskRequest(RootModel):
|
||||
root: Union[
|
||||
TripoTextToModelRequest,
|
||||
TripoImageToModelRequest,
|
||||
TripoMultiviewToModelRequest,
|
||||
TripoTextureModelRequest,
|
||||
TripoRefineModelRequest,
|
||||
TripoAnimatePrerigcheckRequest,
|
||||
TripoAnimateRigRequest,
|
||||
TripoAnimateRetargetRequest,
|
||||
TripoStylizeModelRequest,
|
||||
TripoConvertModelRequest
|
||||
]
|
||||
|
||||
class TripoTaskOutput(BaseModel):
|
||||
model: Optional[str] = Field(None, description='URL to the model')
|
||||
base_model: Optional[str] = Field(None, description='URL to the base model')
|
||||
@ -283,12 +270,13 @@ class TripoTask(BaseModel):
|
||||
task_id: str = Field(..., description='The task ID')
|
||||
type: Optional[str] = Field(None, description='The type of task')
|
||||
status: Optional[TripoTaskStatus] = Field(None, description='The status of the task')
|
||||
input: Optional[Dict[str, Any]] = Field(None, description='The input parameters for the task')
|
||||
input: Optional[dict[str, Any]] = Field(None, description='The input parameters for the task')
|
||||
output: Optional[TripoTaskOutput] = Field(None, description='The output of the task')
|
||||
progress: Optional[int] = Field(None, description='The progress of the task', ge=0, le=100)
|
||||
create_time: Optional[int] = Field(None, description='The creation time of the task')
|
||||
running_left_time: Optional[int] = Field(None, description='The estimated time left for the task')
|
||||
queue_position: Optional[int] = Field(None, description='The position in the queue')
|
||||
consumed_credit: int | None = Field(None)
|
||||
|
||||
class TripoTaskResponse(BaseModel):
|
||||
code: int = Field(0, description='The response code')
|
||||
@ -296,7 +284,7 @@ class TripoTaskResponse(BaseModel):
|
||||
|
||||
class TripoGeneralResponse(BaseModel):
|
||||
code: int = Field(0, description='The response code')
|
||||
data: Dict[str, str] = Field(..., description='The task ID data')
|
||||
data: dict[str, str] = Field(..., description='The task ID data')
|
||||
|
||||
class TripoBalanceData(BaseModel):
|
||||
balance: float = Field(..., description='The account balance')
|
||||
|
||||
@ -596,6 +596,7 @@ class Flux2ProImageNode(IO.ComfyNode):
|
||||
depends_on=IO.PriceBadgeDepends(widgets=["width", "height"], inputs=["images"]),
|
||||
expr=cls.PRICE_BADGE_EXPR,
|
||||
),
|
||||
is_deprecated=True,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
@ -674,6 +675,175 @@ class Flux2MaxImageNode(Flux2ProImageNode):
|
||||
"""
|
||||
|
||||
|
||||
_FLUX2_MODEL_ENDPOINTS = {
|
||||
"Flux.2 [pro]": "/proxy/bfl/flux-2-pro/generate",
|
||||
"Flux.2 [max]": "/proxy/bfl/flux-2-max/generate",
|
||||
}
|
||||
|
||||
|
||||
def _flux2_model_inputs():
|
||||
return [
|
||||
IO.Int.Input(
|
||||
"width",
|
||||
default=1024,
|
||||
min=256,
|
||||
max=2048,
|
||||
step=32,
|
||||
),
|
||||
IO.Int.Input(
|
||||
"height",
|
||||
default=768,
|
||||
min=256,
|
||||
max=2048,
|
||||
step=32,
|
||||
),
|
||||
IO.Autogrow.Input(
|
||||
"images",
|
||||
template=IO.Autogrow.TemplateNames(
|
||||
IO.Image.Input("image"),
|
||||
names=[f"image_{i}" for i in range(1, 9)],
|
||||
min=0,
|
||||
),
|
||||
tooltip="Optional reference image(s) for image-to-image generation. Up to 8 images.",
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
class Flux2ImageNode(IO.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls) -> IO.Schema:
|
||||
return IO.Schema(
|
||||
node_id="Flux2ImageNode",
|
||||
display_name="Flux.2 Image",
|
||||
category="api node/image/BFL",
|
||||
description="Generate images via Flux.2 [pro] or Flux.2 [max] from a prompt and optional reference images.",
|
||||
inputs=[
|
||||
IO.String.Input(
|
||||
"prompt",
|
||||
multiline=True,
|
||||
default="",
|
||||
tooltip="Prompt for the image generation or edit",
|
||||
),
|
||||
IO.DynamicCombo.Input(
|
||||
"model",
|
||||
options=[
|
||||
IO.DynamicCombo.Option("Flux.2 [pro]", _flux2_model_inputs()),
|
||||
IO.DynamicCombo.Option("Flux.2 [max]", _flux2_model_inputs()),
|
||||
],
|
||||
),
|
||||
IO.Int.Input(
|
||||
"seed",
|
||||
default=0,
|
||||
min=0,
|
||||
max=0xFFFFFFFFFFFFFFFF,
|
||||
control_after_generate=True,
|
||||
tooltip="The random seed used for creating the noise.",
|
||||
),
|
||||
],
|
||||
outputs=[IO.Image.Output()],
|
||||
hidden=[
|
||||
IO.Hidden.auth_token_comfy_org,
|
||||
IO.Hidden.api_key_comfy_org,
|
||||
IO.Hidden.unique_id,
|
||||
],
|
||||
is_api_node=True,
|
||||
price_badge=IO.PriceBadge(
|
||||
depends_on=IO.PriceBadgeDepends(
|
||||
widgets=["model", "model.width", "model.height"],
|
||||
input_groups=["model.images"],
|
||||
),
|
||||
expr="""
|
||||
(
|
||||
$isMax := widgets.model = "flux.2 [max]";
|
||||
$MP := 1024 * 1024;
|
||||
$w := $lookup(widgets, "model.width");
|
||||
$h := $lookup(widgets, "model.height");
|
||||
$outMP := $max([1, $floor((($w * $h) + $MP - 1) / $MP)]);
|
||||
$outputCost := $isMax
|
||||
? (0.07 + 0.03 * ($outMP - 1))
|
||||
: (0.03 + 0.015 * ($outMP - 1));
|
||||
$refMin := $isMax ? 0.03 : 0.015;
|
||||
$refMax := $isMax ? 0.24 : 0.12;
|
||||
$hasRefs := $lookup(inputGroups, "model.images") > 0;
|
||||
$hasRefs
|
||||
? {
|
||||
"type": "range_usd",
|
||||
"min_usd": $outputCost + $refMin,
|
||||
"max_usd": $outputCost + $refMax,
|
||||
"format": { "approximate": true }
|
||||
}
|
||||
: {"type": "usd", "usd": $outputCost}
|
||||
)
|
||||
""",
|
||||
),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
prompt: str,
|
||||
model: dict,
|
||||
seed: int,
|
||||
) -> IO.NodeOutput:
|
||||
model_choice = model["model"]
|
||||
endpoint = _FLUX2_MODEL_ENDPOINTS[model_choice]
|
||||
width = model["width"]
|
||||
height = model["height"]
|
||||
images_dict = model.get("images") or {}
|
||||
|
||||
image_tensors: list[Input.Image] = [t for t in images_dict.values() if t is not None]
|
||||
n_images = sum(get_number_of_images(t) for t in image_tensors)
|
||||
if n_images > 8:
|
||||
raise ValueError("The current maximum number of supported images is 8.")
|
||||
|
||||
flat_tensors: list[torch.Tensor] = []
|
||||
for tensor in image_tensors:
|
||||
if len(tensor.shape) == 4:
|
||||
flat_tensors.extend(tensor[i] for i in range(tensor.shape[0]))
|
||||
else:
|
||||
flat_tensors.append(tensor)
|
||||
|
||||
reference_images: dict[str, str] = {}
|
||||
for idx, tensor in enumerate(flat_tensors):
|
||||
key_name = f"input_image_{idx + 1}" if idx else "input_image"
|
||||
reference_images[key_name] = tensor_to_base64_string(tensor, total_pixels=2048 * 2048)
|
||||
|
||||
initial_response = await sync_op(
|
||||
cls,
|
||||
ApiEndpoint(path=endpoint, method="POST"),
|
||||
response_model=BFLFluxProGenerateResponse,
|
||||
data=Flux2ProGenerateRequest(
|
||||
prompt=prompt,
|
||||
width=width,
|
||||
height=height,
|
||||
seed=seed,
|
||||
**reference_images,
|
||||
),
|
||||
)
|
||||
|
||||
def price_extractor(_r: BaseModel) -> float | None:
|
||||
return None if initial_response.cost is None else initial_response.cost / 100
|
||||
|
||||
response = await poll_op(
|
||||
cls,
|
||||
ApiEndpoint(initial_response.polling_url),
|
||||
response_model=BFLFluxStatusResponse,
|
||||
status_extractor=lambda r: r.status,
|
||||
progress_extractor=lambda r: r.progress,
|
||||
price_extractor=price_extractor,
|
||||
completed_statuses=[BFLStatus.ready],
|
||||
failed_statuses=[
|
||||
BFLStatus.request_moderated,
|
||||
BFLStatus.content_moderated,
|
||||
BFLStatus.error,
|
||||
BFLStatus.task_not_found,
|
||||
],
|
||||
queued_statuses=[],
|
||||
)
|
||||
return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"]))
|
||||
|
||||
|
||||
class BFLExtension(ComfyExtension):
|
||||
@override
|
||||
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
|
||||
@ -685,6 +855,7 @@ class BFLExtension(ComfyExtension):
|
||||
FluxProFillNode,
|
||||
Flux2ProImageNode,
|
||||
Flux2MaxImageNode,
|
||||
Flux2ImageNode,
|
||||
]
|
||||
|
||||
|
||||
|
||||
@ -10,6 +10,9 @@ from comfy_api.latest import IO, ComfyExtension, Input
|
||||
from comfy_api_nodes.apis.bytedance import (
|
||||
RECOMMENDED_PRESETS,
|
||||
RECOMMENDED_PRESETS_SEEDREAM_4,
|
||||
RECOMMENDED_PRESETS_SEEDREAM_4_0,
|
||||
RECOMMENDED_PRESETS_SEEDREAM_4_5,
|
||||
RECOMMENDED_PRESETS_SEEDREAM_5_LITE,
|
||||
SEEDANCE2_PRICE_PER_1K_TOKENS,
|
||||
SEEDANCE2_REF_VIDEO_PIXEL_LIMITS,
|
||||
VIDEO_TASKS_EXECUTION_TIME,
|
||||
@ -68,6 +71,12 @@ SEEDREAM_MODELS = {
|
||||
"seedream-4-0-250828": "seedream-4-0-250828",
|
||||
}
|
||||
|
||||
SEEDREAM_PRESETS = {
|
||||
"seedream-5-0-260128": RECOMMENDED_PRESETS_SEEDREAM_5_LITE,
|
||||
"seedream-4-5-251128": RECOMMENDED_PRESETS_SEEDREAM_4_5,
|
||||
"seedream-4-0-250828": RECOMMENDED_PRESETS_SEEDREAM_4_0,
|
||||
}
|
||||
|
||||
# Long-running tasks endpoints(e.g., video)
|
||||
BYTEPLUS_TASK_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks"
|
||||
BYTEPLUS_TASK_STATUS_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks" # + /{task_id}
|
||||
@ -562,6 +571,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
|
||||
)
|
||||
""",
|
||||
),
|
||||
is_deprecated=True,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
@ -651,6 +661,226 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
|
||||
return IO.NodeOutput(torch.cat([await download_url_to_image_tensor(i) for i in urls]))
|
||||
|
||||
|
||||
def _seedream_model_inputs(*, max_ref_images: int, presets: list):
|
||||
return [
|
||||
IO.Combo.Input(
|
||||
"size_preset",
|
||||
options=[label for label, _, _ in presets],
|
||||
tooltip="Pick a recommended size. Select Custom to use the width and height below.",
|
||||
),
|
||||
IO.Int.Input(
|
||||
"width",
|
||||
default=2048,
|
||||
min=1024,
|
||||
max=6240,
|
||||
step=2,
|
||||
tooltip="Custom width for image. Value is working only if `size_preset` is set to `Custom`",
|
||||
),
|
||||
IO.Int.Input(
|
||||
"height",
|
||||
default=2048,
|
||||
min=1024,
|
||||
max=4992,
|
||||
step=2,
|
||||
tooltip="Custom height for image. Value is working only if `size_preset` is set to `Custom`",
|
||||
),
|
||||
IO.Int.Input(
|
||||
"max_images",
|
||||
default=1,
|
||||
min=1,
|
||||
max=max_ref_images,
|
||||
step=1,
|
||||
display_mode=IO.NumberDisplay.number,
|
||||
tooltip="Maximum number of images to generate. With 1, exactly one image is produced. "
|
||||
"With >1, the model generates between 1 and max_images related images "
|
||||
"(e.g., story scenes, character variations). "
|
||||
"Total images (input + generated) cannot exceed 15.",
|
||||
),
|
||||
IO.Autogrow.Input(
|
||||
"images",
|
||||
template=IO.Autogrow.TemplateNames(
|
||||
IO.Image.Input("image"),
|
||||
names=[f"image_{i}" for i in range(1, max_ref_images + 1)],
|
||||
min=0,
|
||||
),
|
||||
tooltip=f"Optional reference image(s) for image-to-image or multi-reference generation. "
|
||||
f"Up to {max_ref_images} images.",
|
||||
),
|
||||
IO.Boolean.Input(
|
||||
"fail_on_partial",
|
||||
default=False,
|
||||
tooltip="If enabled, abort execution if any requested images are missing or return an error.",
|
||||
advanced=True,
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
class ByteDanceSeedreamNodeV2(IO.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="ByteDanceSeedreamNodeV2",
|
||||
display_name="ByteDance Seedream 4.5 & 5.0",
|
||||
category="api node/image/ByteDance",
|
||||
description="Unified text-to-image generation and precise single-sentence editing at up to 4K resolution.",
|
||||
inputs=[
|
||||
IO.String.Input(
|
||||
"prompt",
|
||||
multiline=True,
|
||||
default="",
|
||||
tooltip="Text prompt for creating or editing an image.",
|
||||
),
|
||||
IO.DynamicCombo.Input(
|
||||
"model",
|
||||
options=[
|
||||
IO.DynamicCombo.Option(
|
||||
"seedream 5.0 lite",
|
||||
_seedream_model_inputs(max_ref_images=14, presets=RECOMMENDED_PRESETS_SEEDREAM_5_LITE),
|
||||
),
|
||||
IO.DynamicCombo.Option(
|
||||
"seedream-4-5-251128",
|
||||
_seedream_model_inputs(max_ref_images=10, presets=RECOMMENDED_PRESETS_SEEDREAM_4_5),
|
||||
),
|
||||
IO.DynamicCombo.Option(
|
||||
"seedream-4-0-250828",
|
||||
_seedream_model_inputs(max_ref_images=10, presets=RECOMMENDED_PRESETS_SEEDREAM_4_0),
|
||||
),
|
||||
],
|
||||
),
|
||||
IO.Int.Input(
|
||||
"seed",
|
||||
default=0,
|
||||
min=0,
|
||||
max=2147483647,
|
||||
step=1,
|
||||
display_mode=IO.NumberDisplay.number,
|
||||
control_after_generate=True,
|
||||
tooltip="Seed to use for generation.",
|
||||
),
|
||||
IO.Boolean.Input(
|
||||
"watermark",
|
||||
default=False,
|
||||
tooltip='Whether to add an "AI generated" watermark to the image.',
|
||||
advanced=True,
|
||||
),
|
||||
],
|
||||
outputs=[
|
||||
IO.Image.Output(),
|
||||
],
|
||||
hidden=[
|
||||
IO.Hidden.auth_token_comfy_org,
|
||||
IO.Hidden.api_key_comfy_org,
|
||||
IO.Hidden.unique_id,
|
||||
],
|
||||
is_api_node=True,
|
||||
price_badge=IO.PriceBadge(
|
||||
depends_on=IO.PriceBadgeDepends(widgets=["model"]),
|
||||
expr="""
|
||||
(
|
||||
$price := $contains(widgets.model, "5.0 lite") ? 0.035 :
|
||||
$contains(widgets.model, "4-5") ? 0.04 : 0.03;
|
||||
{
|
||||
"type":"usd",
|
||||
"usd": $price,
|
||||
"format": { "suffix":" x images/Run", "approximate": true }
|
||||
}
|
||||
)
|
||||
""",
|
||||
),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
prompt: str,
|
||||
model: dict,
|
||||
seed: int = 0,
|
||||
watermark: bool = False,
|
||||
) -> IO.NodeOutput:
|
||||
validate_string(prompt, strip_whitespace=True, min_length=1)
|
||||
model_id = SEEDREAM_MODELS[model["model"]]
|
||||
presets = SEEDREAM_PRESETS[model_id]
|
||||
|
||||
size_preset = model.get("size_preset", presets[0][0])
|
||||
width = model.get("width", 2048)
|
||||
height = model.get("height", 2048)
|
||||
max_images = model.get("max_images", 1)
|
||||
sequential_image_generation = "disabled" if max_images == 1 else "auto"
|
||||
images_dict = model.get("images") or {}
|
||||
fail_on_partial = model.get("fail_on_partial", False)
|
||||
|
||||
w = h = None
|
||||
for label, tw, th in presets:
|
||||
if label == size_preset:
|
||||
w, h = tw, th
|
||||
break
|
||||
if w is None or h is None:
|
||||
w, h = width, height
|
||||
|
||||
out_num_pixels = w * h
|
||||
mp_provided = out_num_pixels / 1_000_000.0
|
||||
if ("seedream-4-5" in model_id or "seedream-5-0" in model_id) and out_num_pixels < 3686400:
|
||||
raise ValueError(
|
||||
f"Minimum image resolution for the selected model is 3.68MP, but {mp_provided:.2f}MP provided."
|
||||
)
|
||||
if "seedream-4-0" in model_id and out_num_pixels < 921600:
|
||||
raise ValueError(
|
||||
f"Minimum image resolution that the selected model can generate is 0.92MP, "
|
||||
f"but {mp_provided:.2f}MP provided."
|
||||
)
|
||||
if out_num_pixels > 16_777_216:
|
||||
raise ValueError(
|
||||
f"Maximum image resolution for the selected model is 16.78MP, but {mp_provided:.2f}MP provided."
|
||||
)
|
||||
|
||||
image_tensors: list[Input.Image] = [t for t in images_dict.values() if t is not None]
|
||||
n_input_images = sum(get_number_of_images(t) for t in image_tensors)
|
||||
max_num_of_images = 14 if model_id == "seedream-5-0-260128" else 10
|
||||
if n_input_images > max_num_of_images:
|
||||
raise ValueError(
|
||||
f"Maximum of {max_num_of_images} reference images are supported, but {n_input_images} received."
|
||||
)
|
||||
if sequential_image_generation == "auto" and n_input_images + max_images > 15:
|
||||
raise ValueError(
|
||||
"The maximum number of generated images plus the number of reference images cannot exceed 15."
|
||||
)
|
||||
|
||||
reference_images_urls: list[str] = []
|
||||
if image_tensors:
|
||||
for tensor in image_tensors:
|
||||
validate_image_aspect_ratio(tensor, (1, 3), (3, 1))
|
||||
reference_images_urls = await upload_images_to_comfyapi(
|
||||
cls,
|
||||
image_tensors,
|
||||
max_images=n_input_images,
|
||||
mime_type="image/png",
|
||||
wait_label="Uploading reference images",
|
||||
)
|
||||
|
||||
response = await sync_op(
|
||||
cls,
|
||||
ApiEndpoint(path=BYTEPLUS_IMAGE_ENDPOINT, method="POST"),
|
||||
response_model=ImageTaskCreationResponse,
|
||||
data=Seedream4TaskCreationRequest(
|
||||
model=model_id,
|
||||
prompt=prompt,
|
||||
image=reference_images_urls,
|
||||
size=f"{w}x{h}",
|
||||
seed=seed,
|
||||
sequential_image_generation=sequential_image_generation,
|
||||
sequential_image_generation_options=Seedream4Options(max_images=max_images),
|
||||
watermark=watermark,
|
||||
),
|
||||
)
|
||||
if len(response.data) == 1:
|
||||
return IO.NodeOutput(await download_url_to_image_tensor(get_image_url_from_response(response)))
|
||||
urls = [str(d["url"]) for d in response.data if isinstance(d, dict) and "url" in d]
|
||||
if fail_on_partial and len(urls) < len(response.data):
|
||||
raise RuntimeError(f"Only {len(urls)} of {len(response.data)} images were generated before error.")
|
||||
return IO.NodeOutput(torch.cat([await download_url_to_image_tensor(i) for i in urls]))
|
||||
|
||||
|
||||
class ByteDanceTextToVideoNode(IO.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
@ -2105,6 +2335,7 @@ class ByteDanceExtension(ComfyExtension):
|
||||
return [
|
||||
ByteDanceImageNode,
|
||||
ByteDanceSeedreamNode,
|
||||
ByteDanceSeedreamNodeV2,
|
||||
ByteDanceTextToVideoNode,
|
||||
ByteDanceImageToVideoNode,
|
||||
ByteDanceFirstLastFrameNode,
|
||||
|
||||
@ -162,6 +162,61 @@ class GrokImageNode(IO.ComfyNode):
|
||||
)
|
||||
|
||||
|
||||
_GROK_IMAGE_EDIT_ASPECT_RATIO_OPTIONS = [
|
||||
"auto",
|
||||
"1:1",
|
||||
"2:3",
|
||||
"3:2",
|
||||
"3:4",
|
||||
"4:3",
|
||||
"9:16",
|
||||
"16:9",
|
||||
"9:19.5",
|
||||
"19.5:9",
|
||||
"9:20",
|
||||
"20:9",
|
||||
"1:2",
|
||||
"2:1",
|
||||
]
|
||||
|
||||
|
||||
def _grok_image_edit_model_inputs(*, max_ref_images: int, with_aspect_ratio: bool):
|
||||
inputs = [
|
||||
IO.Autogrow.Input(
|
||||
"images",
|
||||
template=IO.Autogrow.TemplateNames(
|
||||
IO.Image.Input("image"),
|
||||
names=[f"image_{i}" for i in range(1, max_ref_images + 1)],
|
||||
min=1,
|
||||
),
|
||||
tooltip=(
|
||||
"Reference image to edit."
|
||||
if max_ref_images == 1
|
||||
else f"Reference image(s) to edit. Up to {max_ref_images} images."
|
||||
),
|
||||
),
|
||||
IO.Combo.Input("resolution", options=["1K", "2K"]),
|
||||
IO.Int.Input(
|
||||
"number_of_images",
|
||||
default=1,
|
||||
min=1,
|
||||
max=10,
|
||||
step=1,
|
||||
tooltip="Number of edited images to generate",
|
||||
display_mode=IO.NumberDisplay.number,
|
||||
),
|
||||
]
|
||||
if with_aspect_ratio:
|
||||
inputs.append(
|
||||
IO.Combo.Input(
|
||||
"aspect_ratio",
|
||||
options=_GROK_IMAGE_EDIT_ASPECT_RATIO_OPTIONS,
|
||||
tooltip="Only allowed when multiple images are connected.",
|
||||
)
|
||||
)
|
||||
return inputs
|
||||
|
||||
|
||||
class GrokImageEditNode(IO.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
@ -256,6 +311,7 @@ class GrokImageEditNode(IO.ComfyNode):
|
||||
)
|
||||
""",
|
||||
),
|
||||
is_deprecated=True,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
@ -303,6 +359,143 @@ class GrokImageEditNode(IO.ComfyNode):
|
||||
)
|
||||
|
||||
|
||||
class GrokImageEditNodeV2(IO.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="GrokImageEditNodeV2",
|
||||
display_name="Grok Image Edit",
|
||||
category="api node/image/Grok",
|
||||
description="Modify an existing image based on a text prompt",
|
||||
inputs=[
|
||||
IO.String.Input(
|
||||
"prompt",
|
||||
multiline=True,
|
||||
default="",
|
||||
tooltip="The text prompt used to generate the image",
|
||||
),
|
||||
IO.DynamicCombo.Input(
|
||||
"model",
|
||||
options=[
|
||||
IO.DynamicCombo.Option(
|
||||
"grok-imagine-image-quality",
|
||||
_grok_image_edit_model_inputs(max_ref_images=3, with_aspect_ratio=True),
|
||||
),
|
||||
IO.DynamicCombo.Option(
|
||||
"grok-imagine-image-pro",
|
||||
_grok_image_edit_model_inputs(max_ref_images=1, with_aspect_ratio=False),
|
||||
),
|
||||
IO.DynamicCombo.Option(
|
||||
"grok-imagine-image",
|
||||
_grok_image_edit_model_inputs(max_ref_images=3, with_aspect_ratio=True),
|
||||
),
|
||||
],
|
||||
),
|
||||
IO.Int.Input(
|
||||
"seed",
|
||||
default=0,
|
||||
min=0,
|
||||
max=2147483647,
|
||||
step=1,
|
||||
display_mode=IO.NumberDisplay.number,
|
||||
control_after_generate=True,
|
||||
tooltip="Seed to determine if node should re-run; "
|
||||
"actual results are nondeterministic regardless of seed.",
|
||||
),
|
||||
],
|
||||
outputs=[
|
||||
IO.Image.Output(),
|
||||
],
|
||||
hidden=[
|
||||
IO.Hidden.auth_token_comfy_org,
|
||||
IO.Hidden.api_key_comfy_org,
|
||||
IO.Hidden.unique_id,
|
||||
],
|
||||
is_api_node=True,
|
||||
price_badge=IO.PriceBadge(
|
||||
depends_on=IO.PriceBadgeDepends(
|
||||
widgets=["model", "model.resolution", "model.number_of_images"],
|
||||
),
|
||||
expr="""
|
||||
(
|
||||
$isQualityModel := widgets.model = "grok-imagine-image-quality";
|
||||
$isPro := $contains(widgets.model, "pro");
|
||||
$res := $lookup(widgets, "model.resolution");
|
||||
$n := $lookup(widgets, "model.number_of_images");
|
||||
$rate := $isQualityModel
|
||||
? ($res = "1k" ? 0.05 : 0.07)
|
||||
: ($isPro ? 0.07 : 0.02);
|
||||
$base := $isQualityModel ? 0.01 : 0.002;
|
||||
$output := $rate * $n;
|
||||
$isPro
|
||||
? {"type":"usd","usd": $base + $output}
|
||||
: {"type":"range_usd","min_usd": $base + $output, "max_usd": 3 * $base + $output}
|
||||
)
|
||||
""",
|
||||
),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
prompt: str,
|
||||
model: dict,
|
||||
seed: int,
|
||||
) -> IO.NodeOutput:
|
||||
validate_string(prompt, strip_whitespace=True, min_length=1)
|
||||
model_id = model["model"]
|
||||
resolution = model["resolution"]
|
||||
number_of_images = model["number_of_images"]
|
||||
images_dict = model.get("images") or {}
|
||||
aspect_ratio = model.get("aspect_ratio", "auto")
|
||||
|
||||
image_tensors: list[Input.Image] = [t for t in images_dict.values() if t is not None]
|
||||
n_images = sum(get_number_of_images(t) for t in image_tensors)
|
||||
if n_images < 1:
|
||||
raise ValueError("At least one image is required for editing.")
|
||||
if model_id == "grok-imagine-image-pro" and n_images > 1:
|
||||
raise ValueError("The pro model supports only 1 input image.")
|
||||
if model_id != "grok-imagine-image-pro" and n_images > 3:
|
||||
raise ValueError("A maximum of 3 input images is supported.")
|
||||
if aspect_ratio != "auto" and n_images == 1:
|
||||
raise ValueError(
|
||||
"Custom aspect ratio is only allowed when multiple images are connected to the image input."
|
||||
)
|
||||
|
||||
flat_tensors: list[torch.Tensor] = []
|
||||
for tensor in image_tensors:
|
||||
if len(tensor.shape) == 4:
|
||||
flat_tensors.extend(tensor[i] for i in range(tensor.shape[0]))
|
||||
else:
|
||||
flat_tensors.append(tensor)
|
||||
|
||||
response = await sync_op(
|
||||
cls,
|
||||
ApiEndpoint(path="/proxy/xai/v1/images/edits", method="POST"),
|
||||
data=ImageEditRequest(
|
||||
model=model_id,
|
||||
images=[
|
||||
InputUrlObject(url=f"data:image/png;base64,{tensor_to_base64_string(i)}") for i in flat_tensors
|
||||
],
|
||||
prompt=prompt,
|
||||
resolution=resolution.lower(),
|
||||
n=number_of_images,
|
||||
seed=seed,
|
||||
aspect_ratio=None if aspect_ratio == "auto" else aspect_ratio,
|
||||
),
|
||||
response_model=ImageGenerationResponse,
|
||||
price_extractor=_extract_grok_price,
|
||||
)
|
||||
if len(response.data) == 1:
|
||||
return IO.NodeOutput(await download_url_to_image_tensor(response.data[0].url))
|
||||
return IO.NodeOutput(
|
||||
torch.cat(
|
||||
[await download_url_to_image_tensor(i) for i in [str(d.url) for d in response.data if d.url]],
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
class GrokVideoNode(IO.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
@ -737,6 +930,7 @@ class GrokExtension(ComfyExtension):
|
||||
return [
|
||||
GrokImageNode,
|
||||
GrokImageEditNode,
|
||||
GrokImageEditNodeV2,
|
||||
GrokVideoNode,
|
||||
GrokVideoReferenceNode,
|
||||
GrokVideoEditNode,
|
||||
|
||||
@ -27,6 +27,7 @@ from comfy_api_nodes.util import (
|
||||
ApiEndpoint,
|
||||
download_url_to_bytesio,
|
||||
downscale_image_tensor,
|
||||
get_number_of_images,
|
||||
poll_op,
|
||||
sync_op,
|
||||
tensor_to_base64_string,
|
||||
@ -372,6 +373,7 @@ class OpenAIGPTImage1(IO.ComfyNode):
|
||||
display_name="OpenAI GPT Image 2",
|
||||
category="api node/image/OpenAI",
|
||||
description="Generates images synchronously via OpenAI's GPT Image endpoint.",
|
||||
is_deprecated=True,
|
||||
inputs=[
|
||||
IO.String.Input(
|
||||
"prompt",
|
||||
@ -640,6 +642,316 @@ class OpenAIGPTImage1(IO.ComfyNode):
|
||||
return IO.NodeOutput(await validate_and_cast_response(response))
|
||||
|
||||
|
||||
def _gpt_image_shared_inputs():
|
||||
"""Inputs shared by all GPT Image models (quality + reference images + mask)."""
|
||||
return [
|
||||
IO.Combo.Input(
|
||||
"quality",
|
||||
default="low",
|
||||
options=["low", "medium", "high"],
|
||||
tooltip="Image quality, affects cost and generation time.",
|
||||
),
|
||||
IO.Autogrow.Input(
|
||||
"images",
|
||||
template=IO.Autogrow.TemplateNames(
|
||||
IO.Image.Input("image"),
|
||||
names=[f"image_{i}" for i in range(1, 17)],
|
||||
min=0,
|
||||
),
|
||||
tooltip="Optional reference image(s) for image editing. Up to 16 images.",
|
||||
),
|
||||
IO.Mask.Input(
|
||||
"mask",
|
||||
optional=True,
|
||||
tooltip="Optional mask for inpainting (white areas will be replaced). "
|
||||
"Requires exactly one reference image.",
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
def _gpt_image_legacy_model_inputs():
|
||||
"""Per-model widget set for legacy gpt-image-1 / gpt-image-1.5 (4 base sizes, transparent bg allowed)."""
|
||||
return [
|
||||
IO.Combo.Input(
|
||||
"size",
|
||||
default="auto",
|
||||
options=["auto", "1024x1024", "1024x1536", "1536x1024"],
|
||||
tooltip="Image size.",
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"background",
|
||||
default="auto",
|
||||
options=["auto", "opaque", "transparent"],
|
||||
tooltip="Return image with or without background.",
|
||||
),
|
||||
*_gpt_image_shared_inputs(),
|
||||
]
|
||||
|
||||
|
||||
class OpenAIGPTImageNodeV2(IO.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="OpenAIGPTImageNodeV2",
|
||||
display_name="OpenAI GPT Image 2",
|
||||
category="api node/image/OpenAI",
|
||||
description="Generates images via OpenAI's GPT Image endpoint.",
|
||||
inputs=[
|
||||
IO.String.Input(
|
||||
"prompt",
|
||||
default="",
|
||||
multiline=True,
|
||||
tooltip="Text prompt for GPT Image",
|
||||
),
|
||||
IO.DynamicCombo.Input(
|
||||
"model",
|
||||
options=[
|
||||
IO.DynamicCombo.Option(
|
||||
"gpt-image-2",
|
||||
[
|
||||
IO.Combo.Input(
|
||||
"size",
|
||||
default="auto",
|
||||
options=[
|
||||
"auto",
|
||||
"1024x1024",
|
||||
"1024x1536",
|
||||
"1536x1024",
|
||||
"2048x2048",
|
||||
"2048x1152",
|
||||
"1152x2048",
|
||||
"3840x2160",
|
||||
"2160x3840",
|
||||
"Custom",
|
||||
],
|
||||
tooltip="Image size. Select 'Custom' to use the custom width and height.",
|
||||
),
|
||||
IO.Int.Input(
|
||||
"custom_width",
|
||||
default=1024,
|
||||
min=1024,
|
||||
max=3840,
|
||||
step=16,
|
||||
tooltip="Used only when `size` is 'Custom'. Must be a multiple of 16.",
|
||||
),
|
||||
IO.Int.Input(
|
||||
"custom_height",
|
||||
default=1024,
|
||||
min=1024,
|
||||
max=3840,
|
||||
step=16,
|
||||
tooltip="Used only when `size` is 'Custom'. Must be a multiple of 16.",
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"background",
|
||||
default="auto",
|
||||
options=["auto", "opaque"],
|
||||
tooltip="Return image with or without background.",
|
||||
),
|
||||
*_gpt_image_shared_inputs(),
|
||||
],
|
||||
),
|
||||
IO.DynamicCombo.Option("gpt-image-1.5", _gpt_image_legacy_model_inputs()),
|
||||
IO.DynamicCombo.Option("gpt-image-1", _gpt_image_legacy_model_inputs()),
|
||||
],
|
||||
),
|
||||
IO.Int.Input(
|
||||
"n",
|
||||
default=1,
|
||||
min=1,
|
||||
max=8,
|
||||
step=1,
|
||||
tooltip="How many images to generate",
|
||||
display_mode=IO.NumberDisplay.number,
|
||||
),
|
||||
IO.Int.Input(
|
||||
"seed",
|
||||
default=0,
|
||||
min=0,
|
||||
max=2147483647,
|
||||
step=1,
|
||||
display_mode=IO.NumberDisplay.number,
|
||||
control_after_generate=True,
|
||||
tooltip="not implemented yet in backend",
|
||||
),
|
||||
],
|
||||
outputs=[IO.Image.Output()],
|
||||
hidden=[
|
||||
IO.Hidden.auth_token_comfy_org,
|
||||
IO.Hidden.api_key_comfy_org,
|
||||
IO.Hidden.unique_id,
|
||||
],
|
||||
is_api_node=True,
|
||||
price_badge=IO.PriceBadge(
|
||||
depends_on=IO.PriceBadgeDepends(widgets=["model", "model.quality", "n"]),
|
||||
expr="""
|
||||
(
|
||||
$ranges := {
|
||||
"gpt-image-1": {
|
||||
"low": [0.011, 0.02],
|
||||
"medium": [0.042, 0.07],
|
||||
"high": [0.167, 0.25]
|
||||
},
|
||||
"gpt-image-1.5": {
|
||||
"low": [0.009, 0.02],
|
||||
"medium": [0.034, 0.062],
|
||||
"high": [0.133, 0.22]
|
||||
},
|
||||
"gpt-image-2": {
|
||||
"low": [0.0048, 0.019],
|
||||
"medium": [0.041, 0.168],
|
||||
"high": [0.165, 0.67]
|
||||
}
|
||||
};
|
||||
$range := $lookup($lookup($ranges, widgets.model), $lookup(widgets, "model.quality"));
|
||||
$nRaw := widgets.n;
|
||||
$n := ($nRaw != null and $nRaw != 0) ? $nRaw : 1;
|
||||
($n = 1)
|
||||
? {"type":"range_usd","min_usd": $range[0], "max_usd": $range[1], "format": {"approximate": true}}
|
||||
: {
|
||||
"type":"range_usd",
|
||||
"min_usd": $range[0] * $n,
|
||||
"max_usd": $range[1] * $n,
|
||||
"format": { "suffix": "/Run", "approximate": true }
|
||||
}
|
||||
)
|
||||
""",
|
||||
),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
prompt: str,
|
||||
model: dict,
|
||||
n: int,
|
||||
seed: int,
|
||||
) -> IO.NodeOutput:
|
||||
validate_string(prompt, strip_whitespace=False)
|
||||
|
||||
model_id = model["model"]
|
||||
size = model["size"]
|
||||
background = model["background"]
|
||||
quality = model["quality"]
|
||||
custom_width = model.get("custom_width", 1024)
|
||||
custom_height = model.get("custom_height", 1024)
|
||||
|
||||
images_dict = model.get("images") or {}
|
||||
image_tensors: list[Input.Image] = [t for t in images_dict.values() if t is not None]
|
||||
n_images = sum(get_number_of_images(t) for t in image_tensors)
|
||||
mask = model.get("mask")
|
||||
|
||||
if mask is not None and n_images == 0:
|
||||
raise ValueError("Cannot use a mask without an input image")
|
||||
|
||||
if size == "Custom":
|
||||
if custom_width % 16 != 0 or custom_height % 16 != 0:
|
||||
raise ValueError(
|
||||
f"Custom width and height must be multiples of 16, got {custom_width}x{custom_height}"
|
||||
)
|
||||
if max(custom_width, custom_height) > 3840:
|
||||
raise ValueError(
|
||||
f"Custom resolution max edge must be <= 3840, got {custom_width}x{custom_height}"
|
||||
)
|
||||
ratio = max(custom_width, custom_height) / min(custom_width, custom_height)
|
||||
if ratio > 3:
|
||||
raise ValueError(
|
||||
f"Custom resolution aspect ratio must not exceed 3:1, got {custom_width}x{custom_height}"
|
||||
)
|
||||
total_pixels = custom_width * custom_height
|
||||
if not 655_360 <= total_pixels <= 8_294_400:
|
||||
raise ValueError(
|
||||
f"Custom resolution total pixels must be between 655,360 and 8,294,400, got {total_pixels}"
|
||||
)
|
||||
size = f"{custom_width}x{custom_height}"
|
||||
|
||||
if model_id == "gpt-image-1":
|
||||
price_extractor = calculate_tokens_price_image_1
|
||||
elif model_id == "gpt-image-1.5":
|
||||
price_extractor = calculate_tokens_price_image_1_5
|
||||
elif model_id == "gpt-image-2":
|
||||
price_extractor = calculate_tokens_price_image_2_0
|
||||
else:
|
||||
raise ValueError(f"Unknown model: {model_id}")
|
||||
|
||||
if image_tensors:
|
||||
flat: list[torch.Tensor] = []
|
||||
for tensor in image_tensors:
|
||||
if len(tensor.shape) == 4:
|
||||
flat.extend(tensor[i : i + 1] for i in range(tensor.shape[0]))
|
||||
else:
|
||||
flat.append(tensor.unsqueeze(0))
|
||||
|
||||
files = []
|
||||
for i, single_image in enumerate(flat):
|
||||
scaled_image = downscale_image_tensor(single_image, total_pixels=2048 * 2048).squeeze()
|
||||
image_np = (scaled_image.numpy() * 255).astype(np.uint8)
|
||||
img = Image.fromarray(image_np)
|
||||
img_byte_arr = BytesIO()
|
||||
img.save(img_byte_arr, format="PNG")
|
||||
img_byte_arr.seek(0)
|
||||
|
||||
if len(flat) == 1:
|
||||
files.append(("image", (f"image_{i}.png", img_byte_arr, "image/png")))
|
||||
else:
|
||||
files.append(("image[]", (f"image_{i}.png", img_byte_arr, "image/png")))
|
||||
|
||||
if mask is not None:
|
||||
if len(flat) != 1:
|
||||
raise Exception("Cannot use a mask with multiple image")
|
||||
ref_image = flat[0]
|
||||
if mask.shape[1:] != ref_image.shape[1:-1]:
|
||||
raise Exception("Mask and Image must be the same size")
|
||||
_, height, width = mask.shape
|
||||
rgba_mask = torch.zeros(height, width, 4, device="cpu")
|
||||
rgba_mask[:, :, 3] = 1 - mask.squeeze().cpu()
|
||||
scaled_mask = downscale_image_tensor(
|
||||
rgba_mask.unsqueeze(0), total_pixels=2048 * 2048
|
||||
).squeeze()
|
||||
mask_np = (scaled_mask.numpy() * 255).astype(np.uint8)
|
||||
mask_img = Image.fromarray(mask_np)
|
||||
mask_img_byte_arr = BytesIO()
|
||||
mask_img.save(mask_img_byte_arr, format="PNG")
|
||||
mask_img_byte_arr.seek(0)
|
||||
files.append(("mask", ("mask.png", mask_img_byte_arr, "image/png")))
|
||||
|
||||
response = await sync_op(
|
||||
cls,
|
||||
ApiEndpoint(path="/proxy/openai/images/edits", method="POST"),
|
||||
response_model=OpenAIImageGenerationResponse,
|
||||
data=OpenAIImageEditRequest(
|
||||
model=model_id,
|
||||
prompt=prompt,
|
||||
quality=quality,
|
||||
background=background,
|
||||
n=n,
|
||||
size=size,
|
||||
moderation="low",
|
||||
),
|
||||
content_type="multipart/form-data",
|
||||
files=files,
|
||||
price_extractor=price_extractor,
|
||||
)
|
||||
else:
|
||||
response = await sync_op(
|
||||
cls,
|
||||
ApiEndpoint(path="/proxy/openai/images/generations", method="POST"),
|
||||
response_model=OpenAIImageGenerationResponse,
|
||||
data=OpenAIImageGenerationRequest(
|
||||
model=model_id,
|
||||
prompt=prompt,
|
||||
quality=quality,
|
||||
background=background,
|
||||
n=n,
|
||||
size=size,
|
||||
moderation="low",
|
||||
),
|
||||
price_extractor=price_extractor,
|
||||
)
|
||||
return IO.NodeOutput(await validate_and_cast_response(response))
|
||||
|
||||
|
||||
class OpenAIChatNode(IO.ComfyNode):
|
||||
"""
|
||||
Node to generate text responses from an OpenAI model.
|
||||
@ -999,6 +1311,7 @@ class OpenAIExtension(ComfyExtension):
|
||||
OpenAIDalle2,
|
||||
OpenAIDalle3,
|
||||
OpenAIGPTImage1,
|
||||
OpenAIGPTImageNodeV2,
|
||||
OpenAIChatNode,
|
||||
OpenAIInputFiles,
|
||||
OpenAIChatConfig,
|
||||
|
||||
@ -60,6 +60,7 @@ async def poll_until_finished(
|
||||
],
|
||||
status_extractor=lambda x: x.data.status,
|
||||
progress_extractor=lambda x: x.data.progress,
|
||||
price_extractor=lambda x: x.data.consumed_credit * 0.01 if x.data.consumed_credit else None,
|
||||
estimated_duration=average_duration,
|
||||
)
|
||||
if response_poll.data.status == TripoTaskStatus.SUCCESS:
|
||||
@ -113,7 +114,6 @@ class TripoTextToModelNode(IO.ComfyNode):
|
||||
depends_on=IO.PriceBadgeDepends(
|
||||
widgets=[
|
||||
"model_version",
|
||||
"style",
|
||||
"texture",
|
||||
"pbr",
|
||||
"quad",
|
||||
@ -124,20 +124,17 @@ class TripoTextToModelNode(IO.ComfyNode):
|
||||
expr="""
|
||||
(
|
||||
$isV14 := $contains(widgets.model_version,"v1.4");
|
||||
$style := widgets.style;
|
||||
$hasStyle := ($style != "" and $style != "none");
|
||||
$isV3OrLater := $contains(widgets.model_version,"v3.");
|
||||
$withTexture := widgets.texture or widgets.pbr;
|
||||
$isHdTexture := (widgets.texture_quality = "detailed");
|
||||
$isDetailedGeometry := (widgets.geometry_quality = "detailed");
|
||||
$baseCredits :=
|
||||
$isV14 ? 20 : ($withTexture ? 20 : 10);
|
||||
$credits :=
|
||||
$baseCredits
|
||||
+ ($hasStyle ? 5 : 0)
|
||||
$credits := $isV14 ? 20 : (
|
||||
($withTexture ? 20 : 10)
|
||||
+ (widgets.quad ? 5 : 0)
|
||||
+ ($isHdTexture ? 10 : 0)
|
||||
+ ($isDetailedGeometry ? 20 : 0);
|
||||
{"type":"usd","usd": $round($credits * 0.01, 2)}
|
||||
+ (($isDetailedGeometry and $isV3OrLater) ? 20 : 0)
|
||||
);
|
||||
{"type":"usd","usd": $round($credits * 0.01, 2), "format": {"approximate": true}}
|
||||
)
|
||||
""",
|
||||
),
|
||||
@ -239,7 +236,6 @@ class TripoImageToModelNode(IO.ComfyNode):
|
||||
depends_on=IO.PriceBadgeDepends(
|
||||
widgets=[
|
||||
"model_version",
|
||||
"style",
|
||||
"texture",
|
||||
"pbr",
|
||||
"quad",
|
||||
@ -250,20 +246,17 @@ class TripoImageToModelNode(IO.ComfyNode):
|
||||
expr="""
|
||||
(
|
||||
$isV14 := $contains(widgets.model_version,"v1.4");
|
||||
$style := widgets.style;
|
||||
$hasStyle := ($style != "" and $style != "none");
|
||||
$isV3OrLater := $contains(widgets.model_version,"v3.");
|
||||
$withTexture := widgets.texture or widgets.pbr;
|
||||
$isHdTexture := (widgets.texture_quality = "detailed");
|
||||
$isDetailedGeometry := (widgets.geometry_quality = "detailed");
|
||||
$baseCredits :=
|
||||
$isV14 ? 30 : ($withTexture ? 30 : 20);
|
||||
$credits :=
|
||||
$baseCredits
|
||||
+ ($hasStyle ? 5 : 0)
|
||||
$credits := $isV14 ? 30 : (
|
||||
($withTexture ? 30 : 20)
|
||||
+ (widgets.quad ? 5 : 0)
|
||||
+ ($isHdTexture ? 10 : 0)
|
||||
+ ($isDetailedGeometry ? 20 : 0);
|
||||
{"type":"usd","usd": $round($credits * 0.01, 2)}
|
||||
+ (($isDetailedGeometry and $isV3OrLater) ? 20 : 0)
|
||||
);
|
||||
{"type":"usd","usd": $round($credits * 0.01, 2), "format": {"approximate": true}}
|
||||
)
|
||||
""",
|
||||
),
|
||||
@ -358,7 +351,7 @@ class TripoMultiviewToModelNode(IO.ComfyNode):
|
||||
"texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True, advanced=True
|
||||
),
|
||||
IO.Int.Input("face_limit", default=-1, min=-1, max=500000, optional=True, advanced=True),
|
||||
IO.Boolean.Input("quad", default=False, optional=True, advanced=True),
|
||||
IO.Boolean.Input("quad", default=False, optional=True, advanced=True, tooltip="This parameter is deprecated and does nothing."),
|
||||
IO.Combo.Input("geometry_quality", default="standard", options=["standard", "detailed"], optional=True, advanced=True),
|
||||
],
|
||||
outputs=[
|
||||
@ -379,7 +372,6 @@ class TripoMultiviewToModelNode(IO.ComfyNode):
|
||||
"model_version",
|
||||
"texture",
|
||||
"pbr",
|
||||
"quad",
|
||||
"texture_quality",
|
||||
"geometry_quality",
|
||||
],
|
||||
@ -387,17 +379,16 @@ class TripoMultiviewToModelNode(IO.ComfyNode):
|
||||
expr="""
|
||||
(
|
||||
$isV14 := $contains(widgets.model_version,"v1.4");
|
||||
$isV3OrLater := $contains(widgets.model_version,"v3.");
|
||||
$withTexture := widgets.texture or widgets.pbr;
|
||||
$isHdTexture := (widgets.texture_quality = "detailed");
|
||||
$isDetailedGeometry := (widgets.geometry_quality = "detailed");
|
||||
$baseCredits :=
|
||||
$isV14 ? 30 : ($withTexture ? 30 : 20);
|
||||
$credits :=
|
||||
$baseCredits
|
||||
+ (widgets.quad ? 5 : 0)
|
||||
$credits := $isV14 ? 30 : (
|
||||
($withTexture ? 30 : 20)
|
||||
+ ($isHdTexture ? 10 : 0)
|
||||
+ ($isDetailedGeometry ? 20 : 0);
|
||||
{"type":"usd","usd": $round($credits * 0.01, 2)}
|
||||
+ (($isDetailedGeometry and $isV3OrLater) ? 20 : 0)
|
||||
);
|
||||
{"type":"usd","usd": $round($credits * 0.01, 2), "format": {"approximate": true}}
|
||||
)
|
||||
""",
|
||||
),
|
||||
@ -457,7 +448,7 @@ class TripoMultiviewToModelNode(IO.ComfyNode):
|
||||
geometry_quality=geometry_quality,
|
||||
texture_alignment=texture_alignment,
|
||||
face_limit=face_limit if face_limit != -1 else None,
|
||||
quad=quad,
|
||||
quad=None,
|
||||
),
|
||||
)
|
||||
return await poll_until_finished(cls, response, average_duration=80)
|
||||
@ -498,7 +489,7 @@ class TripoTextureNode(IO.ComfyNode):
|
||||
expr="""
|
||||
(
|
||||
$tq := widgets.texture_quality;
|
||||
{"type":"usd","usd": ($contains($tq,"detailed") ? 0.2 : 0.1)}
|
||||
{"type":"usd","usd": ($contains($tq,"detailed") ? 0.2 : 0.1), "format": {"approximate": true}}
|
||||
)
|
||||
""",
|
||||
),
|
||||
@ -555,7 +546,7 @@ class TripoRefineNode(IO.ComfyNode):
|
||||
is_api_node=True,
|
||||
is_output_node=True,
|
||||
price_badge=IO.PriceBadge(
|
||||
expr="""{"type":"usd","usd":0.3}""",
|
||||
expr="""{"type":"usd","usd":0.3, "format": {"approximate": true}}""",
|
||||
),
|
||||
)
|
||||
|
||||
@ -592,7 +583,7 @@ class TripoRigNode(IO.ComfyNode):
|
||||
is_api_node=True,
|
||||
is_output_node=True,
|
||||
price_badge=IO.PriceBadge(
|
||||
expr="""{"type":"usd","usd":0.25}""",
|
||||
expr="""{"type":"usd","usd":0.25, "format": {"approximate": true}}""",
|
||||
),
|
||||
)
|
||||
|
||||
@ -652,7 +643,7 @@ class TripoRetargetNode(IO.ComfyNode):
|
||||
is_api_node=True,
|
||||
is_output_node=True,
|
||||
price_badge=IO.PriceBadge(
|
||||
expr="""{"type":"usd","usd":0.1}""",
|
||||
expr="""{"type":"usd","usd":0.1, "format": {"approximate": true}}""",
|
||||
),
|
||||
)
|
||||
|
||||
@ -761,19 +752,10 @@ class TripoConversionNode(IO.ComfyNode):
|
||||
"face_limit",
|
||||
"texture_size",
|
||||
"texture_format",
|
||||
"force_symmetry",
|
||||
"flatten_bottom",
|
||||
"flatten_bottom_threshold",
|
||||
"pivot_to_center_bottom",
|
||||
"scale_factor",
|
||||
"with_animation",
|
||||
"pack_uv",
|
||||
"bake",
|
||||
"part_names",
|
||||
"fbx_preset",
|
||||
"export_vertex_colors",
|
||||
"export_orientation",
|
||||
"animate_in_place",
|
||||
],
|
||||
),
|
||||
expr="""
|
||||
@ -783,28 +765,16 @@ class TripoConversionNode(IO.ComfyNode):
|
||||
$flatThresh := (widgets.flatten_bottom_threshold != null) ? widgets.flatten_bottom_threshold : 0;
|
||||
$scale := (widgets.scale_factor != null) ? widgets.scale_factor : 1;
|
||||
$texFmt := (widgets.texture_format != "" ? widgets.texture_format : "jpeg");
|
||||
$part := widgets.part_names;
|
||||
$fbx := (widgets.fbx_preset != "" ? widgets.fbx_preset : "blender");
|
||||
$orient := (widgets.export_orientation != "" ? widgets.export_orientation : "default");
|
||||
$advanced :=
|
||||
widgets.quad or
|
||||
widgets.force_symmetry or
|
||||
widgets.flatten_bottom or
|
||||
widgets.pivot_to_center_bottom or
|
||||
widgets.with_animation or
|
||||
widgets.pack_uv or
|
||||
widgets.bake or
|
||||
widgets.export_vertex_colors or
|
||||
widgets.animate_in_place or
|
||||
($face != -1) or
|
||||
($texSize != 4096) or
|
||||
($flatThresh != 0) or
|
||||
($scale != 1) or
|
||||
($texFmt != "jpeg") or
|
||||
($part != "") or
|
||||
($fbx != "blender") or
|
||||
($orient != "default");
|
||||
{"type":"usd","usd": ($advanced ? 0.1 : 0.05)}
|
||||
($texFmt != "jpeg");
|
||||
{"type":"usd","usd": ($advanced ? 0.1 : 0.05), "format": {"approximate": true}}
|
||||
)
|
||||
""",
|
||||
),
|
||||
|
||||
@ -63,7 +63,7 @@ class MathExpressionNode(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls) -> io.Schema:
|
||||
autogrow = io.Autogrow.TemplateNames(
|
||||
input=io.MultiType.Input("value", [io.Float, io.Int]),
|
||||
input=io.MultiType.Input("value", [io.Float, io.Int, io.Boolean]),
|
||||
names=list(string.ascii_lowercase),
|
||||
min=1,
|
||||
)
|
||||
@ -82,6 +82,7 @@ class MathExpressionNode(io.ComfyNode):
|
||||
outputs=[
|
||||
io.Float.Output(display_name="FLOAT"),
|
||||
io.Int.Output(display_name="INT"),
|
||||
io.Boolean.Output(display_name="BOOL"),
|
||||
],
|
||||
)
|
||||
|
||||
@ -97,7 +98,7 @@ class MathExpressionNode(io.ComfyNode):
|
||||
|
||||
result = simple_eval(expression, names=context, functions=MATH_FUNCTIONS)
|
||||
# bool check must come first because bool is a subclass of int in Python
|
||||
if isinstance(result, bool) or not isinstance(result, (int, float)):
|
||||
if not isinstance(result, (int, float)):
|
||||
raise ValueError(
|
||||
f"Math Expression '{expression}' must evaluate to a numeric result, "
|
||||
f"got {type(result).__name__}: {result!r}"
|
||||
@ -106,7 +107,7 @@ class MathExpressionNode(io.ComfyNode):
|
||||
raise ValueError(
|
||||
f"Math Expression '{expression}' produced a non-finite result: {result}"
|
||||
)
|
||||
return io.NodeOutput(float(result), int(result))
|
||||
return io.NodeOutput(float(result), int(result), bool(result))
|
||||
|
||||
|
||||
class MathExtension(ComfyExtension):
|
||||
|
||||
@ -116,7 +116,7 @@ class EmptyQwenImageLayeredLatentImage(io.ComfyNode):
|
||||
inputs=[
|
||||
io.Int.Input("width", default=640, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
io.Int.Input("height", default=640, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
io.Int.Input("layers", default=3, min=0, max=nodes.MAX_RESOLUTION, step=1, advanced=True),
|
||||
io.Int.Input("layers", default=3, min=0, max=nodes.MAX_RESOLUTION, step=1),
|
||||
io.Int.Input("batch_size", default=1, min=1, max=4096),
|
||||
],
|
||||
outputs=[
|
||||
|
||||
971
comfy_extras/nodes_wandancer.py
Normal file
971
comfy_extras/nodes_wandancer.py
Normal file
@ -0,0 +1,971 @@
|
||||
import math
|
||||
import nodes
|
||||
import node_helpers
|
||||
import torch
|
||||
import torchaudio
|
||||
import comfy.model_management
|
||||
import comfy.utils
|
||||
import numpy as np
|
||||
import logging
|
||||
from typing_extensions import override
|
||||
from comfy_api.latest import ComfyExtension, io
|
||||
|
||||
import scipy.signal
|
||||
import scipy.ndimage
|
||||
import scipy.fft
|
||||
import scipy.sparse
|
||||
|
||||
# Audio Processing Functions - Derived from librosa (https://github.com/librosa/librosa)
|
||||
# Copyright (c) 2013--2023, librosa development team.
|
||||
|
||||
def mel_to_hz(mels, htk=False):
|
||||
"""Convert mel to Hz (slaney)"""
|
||||
mels = np.asanyarray(mels)
|
||||
if htk:
|
||||
return 700.0 * (10.0 ** (mels / 2595.0) - 1.0)
|
||||
f_min = 0.0
|
||||
f_sp = 200.0 / 3
|
||||
freqs = f_min + f_sp * mels
|
||||
min_log_hz = 1000.0
|
||||
min_log_mel = (min_log_hz - f_min) / f_sp
|
||||
logstep = np.log(6.4) / 27.0
|
||||
if mels.ndim:
|
||||
log_t = mels >= min_log_mel
|
||||
freqs[log_t] = min_log_hz * np.exp(logstep * (mels[log_t] - min_log_mel))
|
||||
elif mels >= min_log_mel:
|
||||
freqs = min_log_hz * np.exp(logstep * (mels - min_log_mel))
|
||||
return freqs
|
||||
|
||||
def hz_to_mel(frequencies, htk=False):
|
||||
"""Convert Hz to mel (slaney)"""
|
||||
frequencies = np.asanyarray(frequencies)
|
||||
if htk:
|
||||
return 2595.0 * np.log10(1.0 + frequencies / 700.0)
|
||||
f_min = 0.0
|
||||
f_sp = 200.0 / 3
|
||||
mels = (frequencies - f_min) / f_sp
|
||||
min_log_hz = 1000.0
|
||||
min_log_mel = (min_log_hz - f_min) / f_sp
|
||||
logstep = np.log(6.4) / 27.0
|
||||
if frequencies.ndim:
|
||||
log_t = frequencies >= min_log_hz
|
||||
mels[log_t] = min_log_mel + np.log(frequencies[log_t] / min_log_hz) / logstep
|
||||
elif frequencies >= min_log_hz:
|
||||
mels = min_log_mel + np.log(frequencies / min_log_hz) / logstep
|
||||
return mels
|
||||
|
||||
def compute_cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84, bins_per_octave=12, tuning=0.0):
|
||||
"""Compute Constant-Q Transform (CQT) spectrogram."""
|
||||
|
||||
def _relative_bandwidth(freqs):
|
||||
bpo = np.empty_like(freqs)
|
||||
logf = np.log2(freqs)
|
||||
bpo[0] = 1.0 / (logf[1] - logf[0])
|
||||
bpo[-1] = 1.0 / (logf[-1] - logf[-2])
|
||||
bpo[1:-1] = 2.0 / (logf[2:] - logf[:-2])
|
||||
return (2.0 ** (2.0 / bpo) - 1.0) / (2.0 ** (2.0 / bpo) + 1.0)
|
||||
|
||||
def _wavelet_lengths(freqs, sr, filter_scale, alpha):
|
||||
Q = float(filter_scale) / alpha
|
||||
return Q * sr / freqs # shape (n_bins,) floats
|
||||
|
||||
def _build_wavelet(freqs_oct, sr, filter_scale, alpha_oct):
|
||||
lengths = _wavelet_lengths(freqs_oct, sr, filter_scale, alpha_oct)
|
||||
filters = []
|
||||
for ilen, freq in zip(lengths, freqs_oct):
|
||||
t = np.arange(int(-ilen // 2), int(ilen // 2), dtype=float)
|
||||
sig = (np.cos(t * 2 * np.pi * freq / sr)
|
||||
+ 1j * np.sin(t * 2 * np.pi * freq / sr)).astype(np.complex64)
|
||||
sig *= scipy.signal.get_window('hann', len(sig), fftbins=True)
|
||||
l1 = np.sum(np.abs(sig))
|
||||
tiny = np.finfo(np.float32).tiny
|
||||
sig /= max(l1, tiny)
|
||||
filters.append(sig)
|
||||
max_len = max(lengths)
|
||||
n_fft = int(2.0 ** np.ceil(np.log2(max_len)))
|
||||
out = np.zeros((len(filters), n_fft), dtype=np.complex64)
|
||||
for k, f in enumerate(filters):
|
||||
lpad = int((n_fft - len(f)) // 2)
|
||||
out[k, lpad: lpad + len(f)] = f
|
||||
return out, lengths
|
||||
|
||||
def _resample_half(y):
|
||||
ratio = 0.5
|
||||
n_samples = int(np.ceil(len(y) * ratio))
|
||||
# Kaiser-windowed FIR matches librosa/soxr more closely than scipy's default Hamming filter
|
||||
L = 2
|
||||
h = scipy.signal.firwin(160 * L + 1, 0.96 / L, window=('kaiser', 6.5))
|
||||
y_hat = scipy.signal.resample_poly(y.astype(np.float32), 1, 2, window=h)
|
||||
if len(y_hat) > n_samples:
|
||||
y_hat = y_hat[:n_samples]
|
||||
elif len(y_hat) < n_samples:
|
||||
y_hat = np.pad(y_hat, (0, n_samples - len(y_hat)))
|
||||
y_hat /= np.sqrt(ratio)
|
||||
return y_hat.astype(np.float32)
|
||||
|
||||
def _sparsify_rows(x, quantile=0.01):
|
||||
mags = np.abs(x)
|
||||
norms = np.sum(mags, axis=1, keepdims=True)
|
||||
norms = np.where(norms == 0, 1.0, norms)
|
||||
mag_sort = np.sort(mags, axis=1)
|
||||
cumulative_mag = np.cumsum(mag_sort / norms, axis=1)
|
||||
threshold_idx = np.argmin(cumulative_mag < quantile, axis=1)
|
||||
x_sparse = scipy.sparse.lil_matrix(x.shape, dtype=x.dtype)
|
||||
for i, j in enumerate(threshold_idx):
|
||||
idx = np.where(mags[i] >= mag_sort[i, j])
|
||||
x_sparse[i, idx] = x[i, idx]
|
||||
return x_sparse.tocsr()
|
||||
|
||||
if fmin is None:
|
||||
fmin = 32.70319566257483 # C1 note frequency
|
||||
|
||||
fmin = fmin * (2.0 ** (tuning / bins_per_octave))
|
||||
freqs = fmin * (2.0 ** (np.arange(n_bins) / bins_per_octave))
|
||||
|
||||
alpha = _relative_bandwidth(freqs)
|
||||
lengths = _wavelet_lengths(freqs, float(sr), 1, alpha)
|
||||
|
||||
n_octaves = int(np.ceil(float(n_bins) / bins_per_octave))
|
||||
n_filters = min(bins_per_octave, n_bins)
|
||||
|
||||
cqt_resp = []
|
||||
my_y = y.astype(np.float32)
|
||||
my_sr = float(sr)
|
||||
my_hop = int(hop_length)
|
||||
|
||||
for i in range(n_octaves):
|
||||
if i == 0:
|
||||
sl = slice(-n_filters, None)
|
||||
else:
|
||||
sl = slice(-n_filters * (i + 1), -n_filters * i)
|
||||
|
||||
freqs_oct = freqs[sl]
|
||||
alpha_oct = alpha[sl]
|
||||
|
||||
basis, basis_lengths = _build_wavelet(freqs_oct, my_sr, 1, alpha_oct)
|
||||
n_fft_oct = basis.shape[1]
|
||||
|
||||
# Frequency-domain normalisation
|
||||
basis = basis.astype(np.complex64)
|
||||
basis *= basis_lengths[:, np.newaxis] / float(n_fft_oct)
|
||||
fft_basis = scipy.fft.fft(basis, n=n_fft_oct, axis=1)[:, :(n_fft_oct // 2) + 1]
|
||||
fft_basis = _sparsify_rows(fft_basis, quantile=0.01)
|
||||
fft_basis = fft_basis * np.sqrt(sr / my_sr)
|
||||
|
||||
y_pad = np.pad(my_y, int(n_fft_oct // 2), mode='constant')
|
||||
n_frames = 1 + (len(y_pad) - n_fft_oct) // my_hop
|
||||
frames = np.lib.stride_tricks.as_strided(
|
||||
y_pad,
|
||||
shape=(n_fft_oct, n_frames),
|
||||
strides=(y_pad.strides[0], y_pad.strides[0] * my_hop),
|
||||
)
|
||||
stft_result = scipy.fft.rfft(frames, axis=0)
|
||||
cqt_resp.append(fft_basis.dot(stft_result))
|
||||
|
||||
if my_hop % 2 == 0:
|
||||
my_hop //= 2
|
||||
my_sr /= 2.0
|
||||
my_y = _resample_half(my_y)
|
||||
|
||||
max_col = min(c.shape[-1] for c in cqt_resp)
|
||||
cqt_out = np.empty((n_bins, max_col), dtype=np.complex64)
|
||||
end = n_bins
|
||||
for c_i in cqt_resp:
|
||||
n_oct = c_i.shape[0]
|
||||
if end < n_oct:
|
||||
cqt_out[:end, :] = c_i[-end:, :max_col]
|
||||
else:
|
||||
cqt_out[end - n_oct:end, :] = c_i[:, :max_col]
|
||||
end -= n_oct
|
||||
|
||||
cqt_out /= np.sqrt(lengths)[:, np.newaxis]
|
||||
return np.abs(cqt_out).astype(np.float32)
|
||||
|
||||
|
||||
def cq_to_chroma_mapping(n_input, bins_per_octave=12, n_chroma=12, fmin=None):
|
||||
"""Map CQT bins to chroma bins."""
|
||||
|
||||
if fmin is None:
|
||||
fmin = 32.70319566257483 # C1 note frequency
|
||||
|
||||
n_merge = bins_per_octave / n_chroma
|
||||
cq_to_ch = np.repeat(np.eye(n_chroma), int(n_merge), axis=1)
|
||||
cq_to_ch = np.roll(cq_to_ch, -int(n_merge // 2), axis=1)
|
||||
n_octaves = int(np.ceil(n_input / bins_per_octave))
|
||||
cq_to_ch = np.tile(cq_to_ch, n_octaves)[:, :n_input]
|
||||
|
||||
midi_0 = np.mod(12 * np.log2(fmin / 440.0) + 69, 12)
|
||||
roll = int(np.round(midi_0 * (n_chroma / 12.0)))
|
||||
cq_to_ch = np.roll(cq_to_ch, roll, axis=0)
|
||||
|
||||
return cq_to_ch.astype(np.float32)
|
||||
|
||||
|
||||
def _parabolic_interpolation(S, axis=-2):
|
||||
"""Compute parabolic interpolation shift for peak refinement."""
|
||||
S_next = np.roll(S, -1, axis=axis)
|
||||
S_prev = np.roll(S, 1, axis=axis)
|
||||
|
||||
a = S_next + S_prev - 2 * S
|
||||
b = (S_next - S_prev) / 2.0
|
||||
|
||||
shifts = np.zeros_like(S)
|
||||
valid = np.abs(b) < np.abs(a)
|
||||
shifts[valid] = -b[valid] / a[valid]
|
||||
|
||||
if axis == -2 or axis == S.ndim - 2:
|
||||
shifts[0, :] = 0
|
||||
shifts[-1, :] = 0
|
||||
elif axis == 0:
|
||||
shifts[0, ...] = 0
|
||||
shifts[-1, ...] = 0
|
||||
|
||||
return shifts
|
||||
|
||||
|
||||
def _localmax(S, axis=-2):
|
||||
"""Find local maxima along an axis."""
|
||||
|
||||
S_prev = np.roll(S, 1, axis=axis)
|
||||
S_next = np.roll(S, -1, axis=axis)
|
||||
|
||||
local_max = (S > S_prev) & (S >= S_next)
|
||||
|
||||
if axis == -2 or axis == S.ndim - 2:
|
||||
local_max[-1, :] = S[-1, :] > S[-2, :]
|
||||
# First element is never a local max (strict inequality with previous)
|
||||
local_max[0, :] = False
|
||||
elif axis == 0:
|
||||
local_max[-1, ...] = S[-1, ...] > S[-2, ...]
|
||||
local_max[0, ...] = False
|
||||
|
||||
return local_max
|
||||
|
||||
|
||||
def piptrack(y=None, sr=22050, S=None, n_fft=2048, hop_length=512,
|
||||
fmin=150.0, fmax=4000.0, threshold=0.1):
|
||||
"""Pitch tracking on thresholded parabolically-interpolated STFT."""
|
||||
|
||||
# Compute STFT if not provided
|
||||
if S is None:
|
||||
if y is None:
|
||||
raise ValueError("Either y or S must be provided")
|
||||
|
||||
fft_window = scipy.signal.get_window('hann', n_fft, fftbins=True)
|
||||
if len(fft_window) < n_fft:
|
||||
lpad = int((n_fft - len(fft_window)) // 2)
|
||||
fft_window = np.pad(fft_window, (lpad, int(n_fft - len(fft_window) - lpad)), mode='constant')
|
||||
fft_window = fft_window.reshape((-1, 1))
|
||||
|
||||
y_pad = np.pad(y, int(n_fft // 2), mode='constant')
|
||||
n_frames = 1 + (len(y_pad) - n_fft) // hop_length
|
||||
frames = np.lib.stride_tricks.as_strided(
|
||||
y_pad,
|
||||
shape=(n_fft, n_frames),
|
||||
strides=(y_pad.strides[0], y_pad.strides[0] * hop_length)
|
||||
)
|
||||
|
||||
S = scipy.fft.rfft((fft_window * frames).astype(np.float32), axis=0)
|
||||
|
||||
S = np.abs(S)
|
||||
|
||||
fmin = max(fmin, 0)
|
||||
fmax = min(fmax, float(sr) / 2)
|
||||
|
||||
fft_freqs = np.fft.rfftfreq(S.shape[0] * 2 - 2, 1.0 / sr)
|
||||
if len(fft_freqs) > S.shape[0]:
|
||||
fft_freqs = fft_freqs[:S.shape[0]]
|
||||
|
||||
shift = _parabolic_interpolation(S, axis=0)
|
||||
avg = np.gradient(S, axis=0)
|
||||
dskew = 0.5 * avg * shift
|
||||
|
||||
pitches = np.zeros_like(S)
|
||||
mags = np.zeros_like(S)
|
||||
|
||||
freq_mask = (fmin <= fft_freqs) & (fft_freqs < fmax)
|
||||
freq_mask = freq_mask.reshape(-1, 1)
|
||||
|
||||
ref_value = threshold * np.max(S, axis=0, keepdims=True)
|
||||
local_max = _localmax(S * (S > ref_value), axis=0)
|
||||
idx = np.nonzero(freq_mask & local_max)
|
||||
|
||||
pitches[idx] = (idx[0] + shift[idx]) * float(sr) / (S.shape[0] * 2 - 2)
|
||||
mags[idx] = S[idx] + dskew[idx]
|
||||
|
||||
return pitches, mags
|
||||
|
||||
|
||||
def hz_to_octs(frequencies, tuning=0.0, bins_per_octave=12):
|
||||
"""Convert frequencies (Hz) to octave numbers."""
|
||||
|
||||
A440 = 440.0 * 2.0 ** (tuning / bins_per_octave)
|
||||
octs = np.log2(np.asanyarray(frequencies) / (float(A440) / 16))
|
||||
return octs
|
||||
|
||||
|
||||
def pitch_tuning(frequencies, resolution=0.01, bins_per_octave=12):
|
||||
"""Estimate tuning offset from a collection of pitches."""
|
||||
|
||||
frequencies = np.atleast_1d(frequencies)
|
||||
frequencies = frequencies[frequencies > 0]
|
||||
|
||||
if not np.any(frequencies):
|
||||
return 0.0
|
||||
|
||||
residual = np.mod(bins_per_octave * hz_to_octs(frequencies, tuning=0.0,
|
||||
bins_per_octave=bins_per_octave), 1.0)
|
||||
residual[residual >= 0.5] -= 1.0
|
||||
|
||||
bins = np.linspace(-0.5, 0.5, int(np.ceil(1.0 / resolution)) + 1)
|
||||
counts, tuning = np.histogram(residual, bins)
|
||||
tuning_est = tuning[np.argmax(counts)]
|
||||
return tuning_est
|
||||
|
||||
|
||||
def estimate_tuning(y, sr=22050, bins_per_octave=12):
|
||||
"""Estimate global tuning deviation from 12-TET."""
|
||||
n_fft = 2048
|
||||
hop_length = 512
|
||||
|
||||
if len(y) < n_fft:
|
||||
return 0.0
|
||||
|
||||
pitch, mag = piptrack(y=y, sr=sr, n_fft=n_fft, hop_length=hop_length,
|
||||
fmin=150.0, fmax=4000.0, threshold=0.1)
|
||||
|
||||
pitch_mask = pitch > 0
|
||||
|
||||
if not pitch_mask.any():
|
||||
return 0.0
|
||||
|
||||
threshold = np.median(mag[pitch_mask])
|
||||
valid_pitches = pitch[(mag >= threshold) & pitch_mask]
|
||||
|
||||
if len(valid_pitches) == 0:
|
||||
return 0.0
|
||||
|
||||
tuning = pitch_tuning(valid_pitches, resolution=0.01, bins_per_octave=bins_per_octave)
|
||||
|
||||
return float(tuning)
|
||||
|
||||
|
||||
def compute_chroma_cens(y, sr=22050, hop_length=512, n_chroma=12,
|
||||
n_octaves=7, bins_per_octave=36,
|
||||
win_len_smooth=41, norm=2):
|
||||
"""Compute Chroma Energy Normalized Statistics (CENS) features."""
|
||||
|
||||
tuning = estimate_tuning(y, sr, bins_per_octave=bins_per_octave)
|
||||
|
||||
fmin = 32.70319566257483 # C1 note frequency
|
||||
n_bins = n_octaves * bins_per_octave
|
||||
cqt_mag = compute_cqt(y, sr=sr, hop_length=hop_length,
|
||||
fmin=fmin, n_bins=n_bins,
|
||||
bins_per_octave=bins_per_octave,
|
||||
tuning=tuning)
|
||||
|
||||
chroma_map = cq_to_chroma_mapping(n_bins, bins_per_octave=bins_per_octave,
|
||||
n_chroma=n_chroma, fmin=fmin)
|
||||
chroma = np.dot(chroma_map, cqt_mag)
|
||||
|
||||
threshold = np.finfo(chroma.dtype).tiny
|
||||
chroma_sum = np.sum(np.abs(chroma), axis=0, keepdims=True)
|
||||
chroma_sum = np.maximum(chroma_sum, threshold)
|
||||
chroma = chroma / chroma_sum
|
||||
|
||||
quant_steps = [0.4, 0.2, 0.1, 0.05]
|
||||
quant_weights = [0.25, 0.25, 0.25, 0.25]
|
||||
chroma_quant = np.zeros_like(chroma)
|
||||
for step, weight in zip(quant_steps, quant_weights):
|
||||
chroma_quant += (chroma > step) * weight
|
||||
|
||||
if win_len_smooth is not None and win_len_smooth > 0:
|
||||
win = scipy.signal.get_window('hann', win_len_smooth + 2, fftbins=False)
|
||||
win /= np.sum(win)
|
||||
win = win.reshape(1, -1)
|
||||
chroma_smooth = scipy.ndimage.convolve(chroma_quant, win, mode='constant')
|
||||
else:
|
||||
chroma_smooth = chroma_quant
|
||||
|
||||
if norm == 2:
|
||||
threshold = np.finfo(chroma_smooth.dtype).tiny
|
||||
chroma_norm = np.sqrt(np.sum(chroma_smooth ** 2, axis=0, keepdims=True))
|
||||
chroma_norm = np.maximum(chroma_norm, threshold)
|
||||
chroma_smooth = chroma_smooth / chroma_norm
|
||||
elif norm == np.inf:
|
||||
threshold = np.finfo(chroma_smooth.dtype).tiny
|
||||
chroma_norm = np.max(np.abs(chroma_smooth), axis=0, keepdims=True)
|
||||
chroma_norm = np.maximum(chroma_norm, threshold)
|
||||
chroma_smooth = chroma_smooth / chroma_norm
|
||||
|
||||
return chroma_smooth
|
||||
|
||||
|
||||
def _create_mel_filterbank(sr, n_fft, n_mels=128, fmin=0.0, fmax=None):
|
||||
"""Create mel-scale filterbank matrix."""
|
||||
if fmax is None:
|
||||
fmax = sr / 2.0
|
||||
mel_basis = np.zeros((n_mels, int(1 + n_fft // 2)), dtype=np.float32)
|
||||
fftfreqs = np.fft.rfftfreq(n=n_fft, d=1.0 / sr)
|
||||
min_mel = hz_to_mel(fmin)
|
||||
max_mel = hz_to_mel(fmax)
|
||||
mels = np.linspace(min_mel, max_mel, n_mels + 2)
|
||||
mel_f = mel_to_hz(mels)
|
||||
fdiff = np.diff(mel_f)
|
||||
ramps = np.subtract.outer(mel_f, fftfreqs)
|
||||
|
||||
for i in range(n_mels):
|
||||
lower = -ramps[i] / fdiff[i]
|
||||
upper = ramps[i + 2] / fdiff[i + 1]
|
||||
mel_basis[i] = np.maximum(0, np.minimum(lower, upper))
|
||||
|
||||
enorm = 2.0 / (mel_f[2:n_mels + 2] - mel_f[:n_mels])
|
||||
mel_basis *= enorm[:, np.newaxis]
|
||||
return mel_basis
|
||||
|
||||
|
||||
def _compute_mel_spectrogram(data, sr, n_fft=2048, hop_length=512, n_mels=128):
|
||||
"""Compute mel spectrogram from audio signal."""
|
||||
fft_window = scipy.signal.get_window('hann', n_fft, fftbins=True)
|
||||
if len(fft_window) < n_fft:
|
||||
lpad = int((n_fft - len(fft_window)) // 2)
|
||||
fft_window = np.pad(fft_window, (lpad, int(n_fft - len(fft_window) - lpad)), mode='constant')
|
||||
|
||||
fft_window = fft_window.reshape((-1, 1))
|
||||
data_padded = np.pad(data, int(n_fft // 2), mode='constant')
|
||||
n_frames = 1 + (len(data_padded) - n_fft) // hop_length
|
||||
shape = (n_fft, n_frames)
|
||||
strides = (data_padded.strides[0], data_padded.strides[0] * hop_length)
|
||||
frames = np.lib.stride_tricks.as_strided(data_padded, shape=shape, strides=strides)
|
||||
|
||||
stft_result = scipy.fft.rfft(fft_window * frames, axis=0).astype(np.complex64)
|
||||
power_spec = np.abs(stft_result) ** 2
|
||||
|
||||
mel_basis = _create_mel_filterbank(sr, n_fft, n_mels=n_mels, fmin=0.0, fmax=sr / 2.0)
|
||||
mel_spec = np.dot(mel_basis, power_spec)
|
||||
return mel_spec.astype(np.float32)
|
||||
|
||||
|
||||
def quick_tempo_estimate(audio_np, sr, start_bpm=120.0, std_bpm=1.0, hop_length=512):
|
||||
"""Estimate tempo using autocorrelation tempogram."""
|
||||
|
||||
if len(audio_np) < hop_length * 10:
|
||||
logging.warning("Audio too short for tempo estimation, returning default BPM of 120.0")
|
||||
return 120.0
|
||||
|
||||
n_fft = 2048
|
||||
mel_S = _compute_mel_spectrogram(audio_np, sr, n_fft=n_fft, hop_length=hop_length, n_mels=128)
|
||||
log_mel_S = 10.0 * np.log10(np.maximum(1e-10, mel_S))
|
||||
|
||||
lag = 1
|
||||
S_diff = log_mel_S[:, lag:] - log_mel_S[:, :-lag]
|
||||
S_onset = np.maximum(0.0, S_diff)
|
||||
onset_env_pre = np.mean(S_onset, axis=0)
|
||||
pad_width = lag + n_fft // (2 * hop_length)
|
||||
onset_env = np.pad(onset_env_pre, (pad_width, 0), mode='constant')
|
||||
onset_env = onset_env[:mel_S.shape[1]]
|
||||
|
||||
return estimate_tempo_from_onset(onset_env, sr, hop_length, start_bpm, std_bpm, max_tempo=320.0)
|
||||
|
||||
|
||||
def estimate_tempo_from_onset(onset_env, sr, hop_length, start_bpm=120.0, std_bpm=1.0, max_tempo=320.0):
|
||||
"""Estimate tempo from onset strength envelope using autocorrelation tempogram."""
|
||||
if len(onset_env) < 20:
|
||||
return 120.0
|
||||
|
||||
ac_size = 8.0
|
||||
win_length = int(np.round(ac_size * sr / hop_length))
|
||||
win_length = min(win_length, len(onset_env))
|
||||
|
||||
pad_width = win_length // 2
|
||||
onset_padded = np.pad(onset_env, (pad_width, pad_width), mode='linear_ramp', end_values=(0, 0))
|
||||
|
||||
n_frames = len(onset_env)
|
||||
shape = (win_length, n_frames)
|
||||
strides = (onset_padded.strides[0], onset_padded.strides[0])
|
||||
frames = np.lib.stride_tricks.as_strided(onset_padded, shape=shape, strides=strides)
|
||||
|
||||
hann_window = scipy.signal.get_window('hann', win_length, fftbins=True)
|
||||
windowed_frames = frames * hann_window[:, np.newaxis]
|
||||
|
||||
tempogram = np.zeros((win_length, n_frames))
|
||||
for i in range(n_frames):
|
||||
frame = windowed_frames[:, i]
|
||||
n_pad = scipy.fft.next_fast_len(2 * len(frame) - 1)
|
||||
fft_result = scipy.fft.rfft(frame, n=n_pad)
|
||||
powspec = np.abs(fft_result) ** 2
|
||||
ac = scipy.fft.irfft(powspec, n=n_pad)
|
||||
tempogram[:, i] = ac[:win_length]
|
||||
|
||||
ac_max = np.max(np.abs(tempogram), axis=0)
|
||||
mask = ac_max > 0
|
||||
tempogram[:, mask] /= ac_max[mask]
|
||||
|
||||
tempogram_mean = np.mean(tempogram, axis=1)
|
||||
tempogram_mean = np.maximum(tempogram_mean, 0)
|
||||
|
||||
bpms = np.zeros(win_length, dtype=np.float64)
|
||||
bpms[0] = np.inf
|
||||
bpms[1:] = 60.0 * sr / (hop_length * np.arange(1.0, win_length))
|
||||
|
||||
logprior = -0.5 * ((np.log2(bpms) - np.log2(start_bpm)) / std_bpm) ** 2
|
||||
|
||||
if max_tempo is not None:
|
||||
max_idx = int(np.argmax(bpms < max_tempo))
|
||||
if max_idx > 0:
|
||||
logprior[:max_idx] = -np.inf
|
||||
|
||||
weighted = np.log1p(1e6 * tempogram_mean) + logprior
|
||||
best_idx = int(np.argmax(weighted[1:])) + 1
|
||||
tempo = bpms[best_idx]
|
||||
|
||||
return tempo
|
||||
|
||||
|
||||
def detect_onset_peaks(onset_env, sr=22050, hop_length=512, pre_max=0.03, post_max=0.0,
|
||||
pre_avg=0.10, post_avg=0.10, wait=0.03, delta=0.07):
|
||||
"""Detect onset peaks using peak picking algorithm."""
|
||||
|
||||
onset_normalized = onset_env - np.min(onset_env)
|
||||
onset_max = np.max(onset_normalized)
|
||||
if onset_max > 0:
|
||||
onset_normalized = onset_normalized / onset_max
|
||||
|
||||
pre_max_frames = int(pre_max * sr / hop_length)
|
||||
post_max_frames = int(post_max * sr / hop_length) + 1
|
||||
pre_avg_frames = int(pre_avg * sr / hop_length)
|
||||
post_avg_frames = int(post_avg * sr / hop_length) + 1
|
||||
wait_frames = int(wait * sr / hop_length)
|
||||
|
||||
peaks = np.zeros(len(onset_normalized), dtype=bool)
|
||||
peaks[0] = (onset_normalized[0] >= np.max(onset_normalized[:min(post_max_frames, len(onset_normalized))]))
|
||||
peaks[0] &= (onset_normalized[0] >= np.mean(onset_normalized[:min(post_avg_frames, len(onset_normalized))]) + delta)
|
||||
|
||||
if peaks[0]:
|
||||
n = wait_frames + 1
|
||||
else:
|
||||
n = 1
|
||||
|
||||
while n < len(onset_normalized):
|
||||
maxn = np.max(onset_normalized[max(0, n - pre_max_frames):min(n + post_max_frames, len(onset_normalized))])
|
||||
peaks[n] = (onset_normalized[n] == maxn)
|
||||
|
||||
if not peaks[n]:
|
||||
n += 1
|
||||
continue
|
||||
|
||||
avgn = np.mean(onset_normalized[max(0, n - pre_avg_frames):min(n + post_avg_frames, len(onset_normalized))])
|
||||
peaks[n] &= (onset_normalized[n] >= avgn + delta)
|
||||
|
||||
if not peaks[n]:
|
||||
n += 1
|
||||
continue
|
||||
|
||||
n += wait_frames + 1
|
||||
|
||||
return np.flatnonzero(peaks).astype(np.int32)
|
||||
|
||||
|
||||
def track_beats(onset_env, tempo, sr, hop_length, tightness=100, trim=True):
|
||||
"""Track beats using dynamic programming."""
|
||||
|
||||
frame_rate = sr / hop_length
|
||||
frames_per_beat = np.round(frame_rate * 60.0 / tempo)
|
||||
|
||||
if frames_per_beat <= 0 or len(onset_env) < 2:
|
||||
return np.array([], dtype=np.int32)
|
||||
|
||||
onset_std = np.std(onset_env, ddof=1)
|
||||
if onset_std > 0:
|
||||
onset_normalized = onset_env / onset_std
|
||||
else:
|
||||
onset_normalized = onset_env
|
||||
|
||||
window_range = np.arange(-frames_per_beat, frames_per_beat + 1)
|
||||
window = np.exp(-0.5 * (window_range * 32.0 / frames_per_beat) ** 2)
|
||||
|
||||
localscore = scipy.signal.convolve(onset_normalized, window, mode='same')
|
||||
|
||||
backlink = np.full(len(localscore), -1, dtype=np.int32)
|
||||
cumscore = np.zeros(len(localscore), dtype=np.float64)
|
||||
|
||||
score_thresh = 0.01 * localscore.max()
|
||||
first_beat = True
|
||||
|
||||
backlink[0] = -1
|
||||
cumscore[0] = localscore[0]
|
||||
|
||||
fpb = int(frames_per_beat)
|
||||
|
||||
for i in range(1, len(localscore)):
|
||||
score_i = localscore[i]
|
||||
best_score = -np.inf
|
||||
beat_location = -1
|
||||
|
||||
search_start = int(i - np.round(fpb / 2.0))
|
||||
search_end = int(i - 2 * fpb - 1)
|
||||
|
||||
for loc in range(search_start, search_end, -1):
|
||||
if loc < 0:
|
||||
break
|
||||
|
||||
score = cumscore[loc] - tightness * (np.log(i - loc) - np.log(fpb)) ** 2
|
||||
|
||||
if score > best_score:
|
||||
best_score = score
|
||||
beat_location = loc
|
||||
|
||||
if beat_location >= 0:
|
||||
cumscore[i] = score_i + best_score
|
||||
else:
|
||||
cumscore[i] = score_i
|
||||
|
||||
if first_beat and score_i < score_thresh:
|
||||
backlink[i] = -1
|
||||
else:
|
||||
backlink[i] = beat_location
|
||||
first_beat = False
|
||||
|
||||
local_max_mask = np.zeros(len(cumscore), dtype=bool)
|
||||
|
||||
local_max_mask[0] = False
|
||||
|
||||
for i in range(1, len(cumscore) - 1):
|
||||
local_max_mask[i] = (cumscore[i] > cumscore[i-1]) and (cumscore[i] >= cumscore[i+1])
|
||||
|
||||
if len(cumscore) > 1:
|
||||
local_max_mask[-1] = cumscore[-1] > cumscore[-2]
|
||||
|
||||
if np.any(local_max_mask):
|
||||
median_max = np.median(cumscore[local_max_mask])
|
||||
threshold = 0.5 * median_max
|
||||
|
||||
tail = -1
|
||||
for i in range(len(cumscore) - 1, -1, -1):
|
||||
if local_max_mask[i] and cumscore[i] >= threshold:
|
||||
tail = i
|
||||
break
|
||||
else:
|
||||
tail = len(cumscore) - 1
|
||||
|
||||
beats = np.zeros(len(localscore), dtype=bool)
|
||||
n = tail
|
||||
visited = set()
|
||||
while n >= 0 and n not in visited:
|
||||
beats[n] = True
|
||||
visited.add(n)
|
||||
n = backlink[n]
|
||||
|
||||
if trim and np.any(beats):
|
||||
beat_positions = np.flatnonzero(beats)
|
||||
|
||||
beat_localscores = localscore[beat_positions]
|
||||
|
||||
w = np.hanning(5)
|
||||
smooth_boe_full = np.convolve(beat_localscores, w)
|
||||
smooth_boe = smooth_boe_full[len(w)//2 : len(localscore) + len(w)//2]
|
||||
|
||||
threshold = 0.5 * np.sqrt(np.mean(smooth_boe ** 2))
|
||||
|
||||
start_frame = 0
|
||||
while start_frame < len(localscore) and localscore[start_frame] <= threshold:
|
||||
beats[start_frame] = False
|
||||
start_frame += 1
|
||||
|
||||
end_frame = len(localscore) - 1
|
||||
while end_frame >= 0 and localscore[end_frame] <= threshold:
|
||||
beats[end_frame] = False
|
||||
end_frame -= 1
|
||||
|
||||
return np.flatnonzero(beats).astype(np.int32)
|
||||
|
||||
def compute_onset_envelope(mel_spec_db, n_fft=2048, hop_length=512):
|
||||
"""Compute onset strength envelope from a log-mel spectrogram (dB)."""
|
||||
lag = 1
|
||||
onset_diff = mel_spec_db[:, lag:] - mel_spec_db[:, :-lag]
|
||||
onset_diff = np.maximum(0.0, onset_diff)
|
||||
envelope_pre_pad = np.mean(onset_diff, axis=0)
|
||||
|
||||
pad_width = lag + n_fft // (2 * hop_length)
|
||||
envelope = np.pad(envelope_pre_pad, (pad_width, 0), mode='constant')
|
||||
envelope = envelope[:mel_spec_db.shape[1]]
|
||||
|
||||
return envelope
|
||||
|
||||
def compute_mfcc(mel_spec_db, n_mfcc=20):
|
||||
"""Compute MFCC features from a log-mel spectrogram (dB)."""
|
||||
mfcc = scipy.fft.dct(mel_spec_db, axis=0, type=2, norm='ortho')[:n_mfcc].T
|
||||
return mfcc.astype(np.float32)
|
||||
|
||||
|
||||
def power_to_db(S, amin=1e-10, top_db=80.0, ref=1.0):
|
||||
"""Convert a power spectrogram (amplitude squared) to decibel (dB) units"""
|
||||
S = np.asarray(S)
|
||||
log_spec = 10.0 * np.log10(np.maximum(amin, S))
|
||||
log_spec -= 10.0 * np.log10(np.maximum(amin, ref))
|
||||
if top_db is not None:
|
||||
log_spec = np.maximum(log_spec, log_spec.max() - top_db)
|
||||
return log_spec
|
||||
|
||||
|
||||
class WanDancerEncodeAudio(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanDancerEncodeAudio",
|
||||
category="conditioning/video_models",
|
||||
inputs=[
|
||||
io.Audio.Input("audio"),
|
||||
io.Int.Input("video_frames", default=149, min=1, max=nodes.MAX_RESOLUTION, step=4),
|
||||
io.Float.Input("audio_inject_scale", default=1.0, min=0.0, max=10.0, step=0.01, tooltip="The scale for the audio features when injected into the video model."),
|
||||
],
|
||||
outputs=[
|
||||
io.AudioEncoderOutput.Output(display_name="audio_encoder_output"),
|
||||
io.String.Output(display_name="fps_string", tooltip="The calculated fps based on the audio length and the number of video frames. Used in the prompt."),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, video_frames, audio_inject_scale, audio) -> io.NodeOutput:
|
||||
waveform = audio["waveform"][0]
|
||||
sample_rate = audio["sample_rate"]
|
||||
base_fps = 30
|
||||
hop_length = 512
|
||||
model_sr = 22050
|
||||
n_fft = 2048
|
||||
|
||||
# start tempo from original audio (not the resampled one) to match the reference pipeline
|
||||
if waveform.shape[0] > 1:
|
||||
waveform = waveform.mean(dim=0, keepdim=False)
|
||||
|
||||
start_bpm = quick_tempo_estimate(waveform.squeeze().cpu().numpy(), sample_rate, hop_length=hop_length)
|
||||
|
||||
# resample to the sample rate used for feature extraction
|
||||
resample_sr = base_fps * hop_length
|
||||
waveform = torchaudio.functional.resample(waveform, sample_rate, resample_sr)
|
||||
|
||||
waveform_np = waveform.cpu().numpy().squeeze()
|
||||
mel_spec = _compute_mel_spectrogram(waveform_np, model_sr, n_fft, hop_length, n_mels=128)
|
||||
mel_spec_db = power_to_db(mel_spec, amin=1e-10, top_db=80.0, ref=1.0)
|
||||
envelope = compute_onset_envelope(mel_spec_db, n_fft, hop_length)
|
||||
mfcc = compute_mfcc(mel_spec_db, n_mfcc=20)
|
||||
chroma = compute_chroma_cens(y=waveform_np, sr=model_sr, hop_length=hop_length).T
|
||||
# detect peaks
|
||||
peak_idxs = detect_onset_peaks(envelope, sr=model_sr, hop_length=hop_length)
|
||||
peak_onehot = np.zeros_like(envelope, dtype=np.float32)
|
||||
peak_onehot[peak_idxs] = 1.0
|
||||
# detect beats
|
||||
beat_tracking_tempo = estimate_tempo_from_onset(envelope, sr=model_sr, hop_length=hop_length, start_bpm=start_bpm)
|
||||
beat_idxs = track_beats(envelope, beat_tracking_tempo, model_sr, hop_length, tightness=100, trim=True)
|
||||
beat_onehot = np.zeros_like(envelope, dtype=np.float32)
|
||||
beat_onehot[beat_idxs] = 1.0
|
||||
|
||||
audio_feature = np.concatenate(
|
||||
[envelope[:, None], mfcc, chroma, peak_onehot[:, None], beat_onehot[:, None]],
|
||||
axis=-1,
|
||||
)
|
||||
audio_feature = torch.from_numpy(audio_feature).unsqueeze(0).to(comfy.model_management.intermediate_device())
|
||||
|
||||
fps = float(base_fps / int(audio_feature.shape[1] / video_frames + 0.5))
|
||||
|
||||
audio_encoder_output = {
|
||||
"audio_feature": audio_feature,
|
||||
"fps": fps,
|
||||
"audio_inject_scale": audio_inject_scale,
|
||||
}
|
||||
|
||||
if int(fps + 0.5) != 30:
|
||||
fps_string = " 帧率是{:.4f}".format(fps) # "frame rate is" in Chinese, as it was in the original pipeline
|
||||
else:
|
||||
fps_string = ", 帧率是30fps。" # to match the reference pipeline when the fps is 30
|
||||
|
||||
return io.NodeOutput(audio_encoder_output, fps_string)
|
||||
|
||||
|
||||
class WanDancerVideo(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanDancerVideo",
|
||||
category="conditioning/video_models",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
io.Vae.Input("vae"),
|
||||
io.Int.Input("width", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
io.Int.Input("height", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
||||
io.Int.Input("length", default=149, min=1, max=nodes.MAX_RESOLUTION, step=4, tooltip="The number of frames in the generated video. Should stay 149 for WanDancer."),
|
||||
io.ClipVisionOutput.Input("clip_vision_output", optional=True, tooltip="The CLIP vision embeds for the first frame."),
|
||||
io.ClipVisionOutput.Input("clip_vision_output_ref", optional=True, tooltip="The CLIP vision embeds for the reference image."),
|
||||
io.Image.Input("start_image", optional=True, tooltip="The initial image(s) to be encoded, can be any number of frames."),
|
||||
io.Mask.Input("mask", optional=True, tooltip="Image conditioning mask for the start image(s). White is kept, black is generated. Used for the local generations."),
|
||||
io.AudioEncoderOutput.Input("audio_encoder_output", optional=True),
|
||||
],
|
||||
outputs=[
|
||||
io.Conditioning.Output(display_name="positive"),
|
||||
io.Conditioning.Output(display_name="negative"),
|
||||
io.Latent.Output(display_name="latent", tooltip="Empty latent."),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, positive, negative, vae, width, height, length, start_image=None, mask=None, clip_vision_output=None, clip_vision_output_ref=None, audio_encoder_output=None) -> io.NodeOutput:
|
||||
latent = torch.zeros([1, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
|
||||
if start_image is not None:
|
||||
start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
|
||||
image = torch.zeros((length, height, width, start_image.shape[-1]), device=start_image.device, dtype=start_image.dtype)
|
||||
image[:start_image.shape[0]] = start_image
|
||||
|
||||
concat_latent_image = vae.encode(image[:, :, :, :3])
|
||||
if mask is None:
|
||||
concat_mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype)
|
||||
concat_mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0
|
||||
else:
|
||||
concat_mask = 1 - mask[:length].unsqueeze(0)
|
||||
concat_mask = comfy.utils.common_upscale(concat_mask, concat_latent_image.shape[-2], concat_latent_image.shape[-1], "nearest-exact", "disabled")
|
||||
concat_mask = torch.cat([torch.repeat_interleave(concat_mask[:, 0:1], repeats=4, dim=1), concat_mask[:, 1:]], dim=1)
|
||||
concat_mask = concat_mask.view(1, concat_mask.shape[1] // 4, 4, concat_latent_image.shape[-2], concat_latent_image.shape[-1]).transpose(1, 2)
|
||||
|
||||
positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": concat_mask})
|
||||
negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": concat_mask})
|
||||
|
||||
if clip_vision_output is not None:
|
||||
positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output, "clip_vision_output_ref": clip_vision_output_ref})
|
||||
negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output, "clip_vision_output_ref": clip_vision_output_ref})
|
||||
|
||||
if audio_encoder_output is not None:
|
||||
positive = node_helpers.conditioning_set_values(positive, {"audio_embed": audio_encoder_output["audio_feature"], "fps": audio_encoder_output["fps"], "audio_inject_scale": audio_encoder_output.get("audio_inject_scale", 1.0)})
|
||||
negative = node_helpers.conditioning_set_values(negative, {"audio_embed": audio_encoder_output["audio_feature"], "fps": audio_encoder_output["fps"], "audio_inject_scale": audio_encoder_output.get("audio_inject_scale", 1.0)})
|
||||
|
||||
out_latent = {}
|
||||
out_latent["samples"] = latent
|
||||
return io.NodeOutput(positive, negative, out_latent)
|
||||
|
||||
|
||||
class WanDancerPadKeyframes(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanDancerPadKeyframes",
|
||||
category="image/video",
|
||||
inputs=[
|
||||
io.Image.Input("images",),
|
||||
io.Int.Input("segment_length", default=149, min=1, max=10000, tooltip="Length of this segment (usually 149 frames)"),
|
||||
io.Int.Input("segment_index", default=0, min=0, max=100, tooltip="Which segment this is (0 for first, 1 for second, etc.)"),
|
||||
io.Audio.Input("audio", tooltip="Audio to calculate total output frames from and extract segment audio."),
|
||||
],
|
||||
outputs=[
|
||||
io.Image.Output(display_name="keyframes_sequence", tooltip="Padded keyframe sequence"),
|
||||
io.Mask.Output(display_name="keyframes_mask", tooltip="Mask indicating valid frames"),
|
||||
io.Audio.Output(display_name="audio_segment", tooltip="Audio segment for this video segment"),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def do_execute(cls, images, segment_length, segment_index, audio):
|
||||
B, H, W, C = images.shape
|
||||
fps = 30
|
||||
|
||||
# calculate total frames
|
||||
audio_duration = audio["waveform"].shape[-1] / audio["sample_rate"]
|
||||
segment_duration = segment_length / fps
|
||||
buffer = 0.2
|
||||
num_segments = int((audio_duration - buffer) / segment_duration) + 1 if audio_duration > buffer else 0
|
||||
total_frames = num_segments * segment_length
|
||||
|
||||
mask = torch.zeros((segment_length, H, W), device=images.device, dtype=images.dtype)
|
||||
keyframes = torch.zeros((segment_length, H, W, C), dtype=images.dtype, device=images.device)
|
||||
|
||||
# guard: with no audio or no images, nothing to place — leave keyframes/mask zeroed
|
||||
if total_frames > 0 and B > 0:
|
||||
frame_interval = float(total_frames) / B
|
||||
seg_num = int(math.ceil(total_frames / segment_length))
|
||||
is_last_segment = (segment_index == seg_num - 1)
|
||||
|
||||
positions = []
|
||||
images_before_this_segment = 0
|
||||
|
||||
# count images consumed by previous segments
|
||||
for seg_idx in range(segment_index):
|
||||
end_idx = (total_frames - segment_length * seg_idx - 1) if seg_idx == seg_num - 1 else (segment_length - 1)
|
||||
cnt = 0
|
||||
while cnt * frame_interval < end_idx - frame_interval:
|
||||
cnt += 1
|
||||
images_before_this_segment += cnt
|
||||
|
||||
# positions for current segment
|
||||
end_index = (total_frames - segment_length * segment_index - 1) if is_last_segment else (segment_length - 1)
|
||||
cnt = 0
|
||||
while cnt * frame_interval < end_index - frame_interval:
|
||||
pos = int(math.ceil(frame_interval * cnt))
|
||||
positions.append((pos, images_before_this_segment + cnt))
|
||||
cnt += 1
|
||||
positions.append((end_index, images_before_this_segment + cnt))
|
||||
|
||||
valid_positions = [(pos, idx) for pos, idx in positions if idx < B and pos < segment_length]
|
||||
|
||||
if valid_positions:
|
||||
seg_positions, img_indices = zip(*valid_positions)
|
||||
seg_positions = torch.tensor(seg_positions, dtype=torch.long, device=images.device)
|
||||
img_indices = torch.tensor(img_indices, dtype=torch.long, device=images.device)
|
||||
mask[seg_positions] = 1
|
||||
keyframes[seg_positions] = images[img_indices]
|
||||
|
||||
# extract audio segment
|
||||
segment_duration = segment_length / fps
|
||||
start_time = segment_index * segment_duration
|
||||
end_time = min(start_time + segment_duration, audio_duration)
|
||||
|
||||
sample_rate = audio["sample_rate"]
|
||||
start_sample = int(start_time * sample_rate)
|
||||
end_sample = int(end_time * sample_rate)
|
||||
|
||||
audio_segment_waveform = audio["waveform"][:, :, start_sample:end_sample]
|
||||
audio_segment = {
|
||||
"waveform": audio_segment_waveform,
|
||||
"sample_rate": sample_rate
|
||||
}
|
||||
|
||||
return keyframes, mask, audio_segment
|
||||
|
||||
@classmethod
|
||||
def execute(cls, images, segment_length, segment_index, audio=None) -> io.NodeOutput:
|
||||
return io.NodeOutput(*cls.do_execute(images, segment_length, segment_index, audio))
|
||||
|
||||
class WanDancerPadKeyframesList(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanDancerPadKeyframesList",
|
||||
category="image/video",
|
||||
inputs=[
|
||||
io.Image.Input("images"),
|
||||
io.Int.Input("segment_length", default=149, min=1, max=10000, tooltip="Length of each segment (usually 149 frames)"),
|
||||
io.Int.Input("num_segments", default=1, min=1, max=100, tooltip="How many padded segments to emit as lists."),
|
||||
io.Audio.Input("audio", tooltip="Audio to slice for each emitted segment."),
|
||||
],
|
||||
outputs=[
|
||||
io.Image.Output(display_name="keyframes_sequence", tooltip="Padded keyframe sequences", is_output_list=True),
|
||||
io.Mask.Output(display_name="keyframes_mask", tooltip="Masks indicating valid frames", is_output_list=True),
|
||||
io.Audio.Output(display_name="audio_segment", tooltip="Audio segment for each video segment", is_output_list=True),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, images, segment_length, num_segments, audio=None) -> io.NodeOutput:
|
||||
outputs = [WanDancerPadKeyframes.do_execute(images, segment_length, i, audio) for i in range(num_segments)]
|
||||
keyframes, masks, audio_segments = zip(*outputs)
|
||||
return io.NodeOutput(list(keyframes), list(masks), list(audio_segments))
|
||||
|
||||
class WanDancerExtension(ComfyExtension):
|
||||
@override
|
||||
async def get_node_list(self) -> list[type[io.ComfyNode]]:
|
||||
return [
|
||||
WanDancerVideo,
|
||||
WanDancerEncodeAudio,
|
||||
WanDancerPadKeyframes,
|
||||
WanDancerPadKeyframesList,
|
||||
]
|
||||
|
||||
async def comfy_entrypoint() -> WanDancerExtension:
|
||||
return WanDancerExtension()
|
||||
@ -1,3 +1,3 @@
|
||||
# This file is automatically generated by the build process when version is
|
||||
# updated in pyproject.toml.
|
||||
__version__ = "0.20.1"
|
||||
__version__ = "0.21.0"
|
||||
|
||||
1
nodes.py
1
nodes.py
@ -2434,6 +2434,7 @@ async def init_builtin_extra_nodes():
|
||||
"nodes_frame_interpolation.py",
|
||||
"nodes_sam3.py",
|
||||
"nodes_void.py",
|
||||
"nodes_wandancer.py",
|
||||
]
|
||||
|
||||
import_failed = []
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
[project]
|
||||
name = "ComfyUI"
|
||||
version = "0.20.1"
|
||||
version = "0.21.0"
|
||||
readme = "README.md"
|
||||
license = { file = "LICENSE" }
|
||||
requires-python = ">=3.10"
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
comfyui-frontend-package==1.43.17
|
||||
comfyui-workflow-templates==0.9.72
|
||||
comfyui-frontend-package==1.43.18
|
||||
comfyui-workflow-templates==0.9.73
|
||||
comfyui-embedded-docs==0.4.4
|
||||
torch
|
||||
torchsde
|
||||
|
||||
@ -124,9 +124,11 @@ class TestMathExpressionExecute:
|
||||
with pytest.raises(Exception, match="not defined"):
|
||||
self._exec("str(a)", a=42)
|
||||
|
||||
def test_boolean_result_raises(self):
|
||||
with pytest.raises(ValueError, match="got bool"):
|
||||
self._exec("a > b", a=5, b=3)
|
||||
def test_boolean_result(self):
|
||||
result = self._exec("a > b", a=5, b=3)
|
||||
assert result[2] is True
|
||||
result = self._exec("a > b", a=3, b=5)
|
||||
assert result[2] is False
|
||||
|
||||
def test_empty_expression_raises(self):
|
||||
with pytest.raises(ValueError, match="Expression cannot be empty"):
|
||||
|
||||
Loading…
Reference in New Issue
Block a user