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Merge branch 'master' of https://git.yasue.org/ken/RandomVideoMaker

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Ken Yasue
2025-10-13 17:10:11 +02:00
parent 9ad4e7972b 6eec0890a7
commit 243f107ef5
9 changed files with 2704 additions and 39 deletions
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150
src/comfyworkflows/edit_image_2_qwen.json
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@ -57,7 +57,7 @@
},
"7": {
"inputs": {
"seed": 920668017513581,
"seed": 936152772258115,
"steps": 8,
"cfg": 1,
"sampler_name": "euler",
@ -76,8 +76,8 @@
0
],
"latent_image": [
"11",
6
"28",
0
]
},
"class_type": "KSampler",
@ -163,11 +163,11 @@
0
],
"image1": [
"23",
"30",
0
],
"image2": [
"24",
"27",
0
]
},
@ -178,20 +178,41 @@
},
"14": {
"inputs": {
"image": "f81662775bd0e7950e4794933ef4b3d973fbb9c2db397c8b46809797954d0074.png"
"image": "model_outfit_location_1760043932148.png"
},
"class_type": "LoadImage",
"_meta": {
"title": "Load Image"
"title": "load base image"
}
},
"15": {
"19": {
"inputs": {
"image": "monster_554.png"
"rgthree_comparer": {
"images": [
{
"name": "A",
"selected": true,
"url": "/api/view?filename=rgthree.compare._temp_dxzmg_00211_.png&type=temp&subfolder=&rand=0.09499077981761894"
},
"class_type": "LoadImage",
{
"name": "B",
"selected": true,
"url": "/api/view?filename=rgthree.compare._temp_dxzmg_00212_.png&type=temp&subfolder=&rand=0.21125213225471684"
}
]
},
"image_a": [
"11",
1
],
"image_b": [
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]
},
"class_type": "Image Comparer (rgthree)",
"_meta": {
"title": "Load Image"
"title": "Image Comparer (rgthree)"
}
},
"20": {
@ -209,7 +230,7 @@
},
"21": {
"inputs": {
"value": "只提取图2中的怪物，怪物站在图1的女生身后，使用图1的背景，并调整怪物的光线以符合图1。\n\n\n\n\n\n\n"
"value": "请将图2中的模特处理成手持图1中包包的照片。"
},
"class_type": "PrimitiveStringMultiline",
"_meta": {
@ -231,17 +252,65 @@
},
"23": {
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"width": [
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"height": 1280,
"batch_size": 1
},
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"_meta": {
"title": "Empty Latent Image"
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},
"24": {
"inputs": {
"vae_name": "sdxl_vae.safetensors"
},
"class_type": "VAELoader",
"_meta": {
"title": "Load VAE"
}
},
"25": {
"inputs": {
"samples": [
"23",
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],
"vae": [
"24",
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]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"26": {
"inputs": {
"images": [
"25",
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]
},
"class_type": "PreviewImage",
"_meta": {
"title": "Preview Image"
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],
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],
"upscale_method": "nearest-exact",
"keep_proportion": "stretch",
"pad_color": "0, 0, 0",
"keep_proportion": "resize",
"pad_color": "192,192,192",
"crop_position": "center",
"divisible_by": 2,
"device": "cpu",
@ -255,24 +324,49 @@
"title": "Resize Image v2"
}
},
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"28": {
"inputs": {
"pixels": [
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"vae": [
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"_meta": {
"title": "VAE Encode"
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},
"29": {
"inputs": {
"image": "handbag_1760043932148.png"
},
"class_type": "LoadImage",
"_meta": {
"title": "load reference image"
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},
"30": {
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"width": [
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"upscale_method": "nearest-exact",
"keep_proportion": "stretch",
"pad_color": "0, 0, 0",
"keep_proportion": "resize",
"pad_color": "192,192,192",
"crop_position": "center",
"divisible_by": 2,
"device": "cpu",
"image": [
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},
@ -281,20 +375,20 @@
"title": "Resize Image v2"
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"inputs": {
"Number": "1280"
"Value": 720
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"class_type": "Int",
"class_type": "DF_Integer",
"_meta": {
"title": "width"
}
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"26": {
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"inputs": {
"Number": "720"
"Value": 1280
},
"class_type": "Int",
"class_type": "DF_Integer",
"_meta": {
"title": "height"
}

444
src/comfyworkflows/edit_image_2_qwen_empty.json Normal file
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@ -0,0 +1,444 @@
{
"1": {
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"unet_name": "qwen_image_edit_2509_fp8_e4m3fn.safetensors",
"weight_dtype": "default"
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"class_type": "UNETLoader",
"_meta": {
"title": "Load Diffusion Model"
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},
"2": {
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"type": "qwen_image",
"device": "default"
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"class_type": "CLIPLoader",
"_meta": {
"title": "Load CLIP"
}
},
"3": {
"inputs": {
"vae_name": "qwen_image_vae.safetensors"
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"class_type": "VAELoader",
"_meta": {
"title": "Load VAE"
}
},
"4": {
"inputs": {
"lora_name": "Qwen-Image-Lightning-8steps-V2.0.safetensors",
"strength_model": 1,
"model": [
"1",
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]
},
"class_type": "LoraLoaderModelOnly",
"_meta": {
"title": "LoraLoaderModelOnly"
}
},
"5": {
"inputs": {
"conditioning": [
"11",
0
]
},
"class_type": "ConditioningZeroOut",
"_meta": {
"title": "ConditioningZeroOut"
}
},
"7": {
"inputs": {
"seed": 38026585691397,
"steps": 8,
"cfg": 1,
"sampler_name": "euler",
"scheduler": "beta",
"denoise": 1,
"model": [
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},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"9": {
"inputs": {
"font_file": "Alibaba-PuHuiTi-Heavy.ttf",
"font_size": 40,
"border": 32,
"color_theme": "light",
"reel_1": [
"10",
0
]
},
"class_type": "LayerUtility: ImageReelComposit",
"_meta": {
"title": "LayerUtility: Image Reel Composit"
}
},
"10": {
"inputs": {
"image1_text": "Original image",
"image2_text": "Reference",
"image3_text": "Result",
"image4_text": "image4",
"reel_height": 512,
"border": 32,
"image1": [
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],
"image2": [
"11",
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],
"image3": [
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]
},
"class_type": "LayerUtility: ImageReel",
"_meta": {
"title": "LayerUtility: Image Reel"
}
},
"11": {
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"21",
0
],
"enable_resize": false,
"enable_vl_resize": false,
"upscale_method": "lanczos",
"crop": "disabled",
"instruction": "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n",
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},
"class_type": "TextEncodeQwenImageEditPlus_lrzjason",
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"title": "TextEncodeQwenImageEditPlus 小志Jason(xiaozhijason)"
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},
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"image": "model_1760082843769.png"
},
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"_meta": {
"title": "load base image"
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},
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"selected": true,
"url": "/api/view?filename=rgthree.compare._temp_uoazy_00279_.png&type=temp&subfolder=&rand=0.4405150352070387"
},
{
"name": "B",
"selected": true,
"url": "/api/view?filename=rgthree.compare._temp_uoazy_00280_.png&type=temp&subfolder=&rand=0.9388629603648289"
}
]
},
"image_a": [
"11",
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],
"image_b": [
"8",
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]
},
"class_type": "Image Comparer (rgthree)",
"_meta": {
"title": "Image Comparer (rgthree)"
}
},
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"filename_prefix": "combined",
"images": [
"8",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
},
"21": {
"inputs": {
"value": "以图像2为基础，生成一张女性肖像照片。她穿着一件黑色薄纱长袖上衣，一条光滑的皮革及膝裙，和勃艮第色的尖头靴子，手提一个深红色的手提包。场景改为极简主义风格的客厅，摆放着中性的沙发、镜面墙饰、盆栽植物和浅色地板，营造出明亮而宽敞的美感。"
},
"class_type": "PrimitiveStringMultiline",
"_meta": {
"title": "String (Multiline)"
}
},
"22": {
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"9",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
},
"23": {
"inputs": {
"width": 720,
"height": 1280,
"batch_size": 1
},
"class_type": "EmptyLatentImage",
"_meta": {
"title": "Empty Latent Image"
}
},
"24": {
"inputs": {
"vae_name": "sdxl_vae.safetensors"
},
"class_type": "VAELoader",
"_meta": {
"title": "Load VAE"
}
},
"25": {
"inputs": {
"samples": [
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],
"vae": [
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]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"26": {
"inputs": {
"images": [
"25",
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]
},
"class_type": "PreviewImage",
"_meta": {
"title": "Preview Image"
}
},
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"inputs": {
"width": [
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],
"upscale_method": "nearest-exact",
"keep_proportion": "resize",
"pad_color": "192,192,192",
"crop_position": "center",
"divisible_by": 2,
"device": "cpu",
"image": [
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},
"class_type": "ImageResizeKJv2",
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"title": "VAE Encode"
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},
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"inputs": {
"image": "pose_1760082843769.png"
},
"class_type": "LoadImage",
"_meta": {
"title": "load reference image"
}
},
"30": {
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],
"upscale_method": "nearest-exact",
"keep_proportion": "resize",
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"crop_position": "center",
"divisible_by": 2,
"device": "cpu",
"image": [
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},
"class_type": "ImageResizeKJv2",
"_meta": {
"title": "Resize Image v2"
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},
"31": {
"inputs": {
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"_meta": {
"title": "width"
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},
"32": {
"inputs": {
"Value": 1280
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"class_type": "DF_Integer",
"_meta": {
"title": "height"
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},
"33": {
"inputs": {
"detect_hand": "enable",
"detect_body": "enable",
"detect_face": "enable",
"resolution": 512,
"bbox_detector": "yolox_l.onnx",
"pose_estimator": "dw-ll_ucoco_384_bs5.torchscript.pt",
"scale_stick_for_xinsr_cn": "disable",
"image": [
"30",
0
]
},
"class_type": "DWPreprocessor",
"_meta": {
"title": "DWPose Estimator"
}
},
"35": {
"inputs": {
"images": [
"33",
0
]
},
"class_type": "PreviewImage",
"_meta": {
"title": "Preview Image"
}
},
"36": {
"inputs": {
"width": [
"31",
0
],
"height": [
"32",
0
],
"batch_size": 1
},
"class_type": "EmptyLatentImage",
"_meta": {
"title": "Empty Latent Image"
}
}
}

396
src/comfyworkflows/edit_image_2_qwen_handbag.json Normal file
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@ -0,0 +1,396 @@
{
"1": {
"inputs": {
"unet_name": "qwen_image_edit_2509_fp8_e4m3fn.safetensors",
"weight_dtype": "default"
},
"class_type": "UNETLoader",
"_meta": {
"title": "Load Diffusion Model"
}
},
"2": {
"inputs": {
"clip_name": "qwen_2.5_vl_7b_fp8_scaled.safetensors",
"type": "qwen_image",
"device": "default"
},
"class_type": "CLIPLoader",
"_meta": {
"title": "Load CLIP"
}
},
"3": {
"inputs": {
"vae_name": "qwen_image_vae.safetensors"
},
"class_type": "VAELoader",
"_meta": {
"title": "Load VAE"
}
},
"4": {
"inputs": {
"lora_name": "Qwen-Image-Lightning-8steps-V2.0.safetensors",
"strength_model": 1,
"model": [
"1",
0
]
},
"class_type": "LoraLoaderModelOnly",
"_meta": {
"title": "LoraLoaderModelOnly"
}
},
"5": {
"inputs": {
"conditioning": [
"11",
0
]
},
"class_type": "ConditioningZeroOut",
"_meta": {
"title": "ConditioningZeroOut"
}
},
"7": {
"inputs": {
"seed": 323591075024702,
"steps": 8,
"cfg": 1,
"sampler_name": "euler",
"scheduler": "beta",
"denoise": 1,
"model": [
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],
"positive": [
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],
"negative": [
"5",
0
],
"latent_image": [
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0
]
},
"class_type": "KSampler",
"_meta": {
"title": "KSampler"
}
},
"8": {
"inputs": {
"samples": [
"7",
0
],
"vae": [
"3",
0
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"9": {
"inputs": {
"font_file": "Alibaba-PuHuiTi-Heavy.ttf",
"font_size": 40,
"border": 32,
"color_theme": "light",
"reel_1": [
"10",
0
]
},
"class_type": "LayerUtility: ImageReelComposit",
"_meta": {
"title": "LayerUtility: Image Reel Composit"
}
},
"10": {
"inputs": {
"image1_text": "Original image",
"image2_text": "Reference",
"image3_text": "Result",
"image4_text": "image4",
"reel_height": 512,
"border": 32,
"image1": [
"11",
1
],
"image2": [
"11",
2
],
"image3": [
"8",
0
]
},
"class_type": "LayerUtility: ImageReel",
"_meta": {
"title": "LayerUtility: Image Reel"
}
},
"11": {
"inputs": {
"prompt": [
"21",
0
],
"enable_resize": true,
"enable_vl_resize": true,
"upscale_method": "lanczos",
"crop": "disabled",
"instruction": "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n",
"clip": [
"2",
0
],
"vae": [
"3",
0
],
"image1": [
"27",
0
],
"image2": [
"30",
0
]
},
"class_type": "TextEncodeQwenImageEditPlus_lrzjason",
"_meta": {
"title": "TextEncodeQwenImageEditPlus 小志Jason(xiaozhijason)"
}
},
"14": {
"inputs": {
"image": "model_outfit_location_handbag1_1760085003312.png"
},
"class_type": "LoadImage",
"_meta": {
"title": "Load Image"
}
},
"19": {
"inputs": {
"rgthree_comparer": {
"images": [
{
"name": "A",
"selected": true,
"url": "/api/view?filename=rgthree.compare._temp_uoazy_00305_.png&type=temp&subfolder=&rand=0.5408789951924671"
},
{
"name": "B",
"selected": true,
"url": "/api/view?filename=rgthree.compare._temp_uoazy_00306_.png&type=temp&subfolder=&rand=0.2425856190711294"
}
]
},
"image_a": [
"11",
1
],
"image_b": [
"8",
0
]
},
"class_type": "Image Comparer (rgthree)",
"_meta": {
"title": "Image Comparer (rgthree)"
}
},
"20": {
"inputs": {
"filename_prefix": "combined",
"images": [
"8",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
},
"21": {
"inputs": {
"value": "请将图2中的女性修改成把图1的包背在肩上。"
},
"class_type": "PrimitiveStringMultiline",
"_meta": {
"title": "String (Multiline)"
}
},
"22": {
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"9",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
},
"23": {
"inputs": {
"width": 720,
"height": 1280,
"batch_size": 1
},
"class_type": "EmptyLatentImage",
"_meta": {
"title": "Empty Latent Image"
}
},
"24": {
"inputs": {
"vae_name": "sdxl_vae.safetensors"
},
"class_type": "VAELoader",
"_meta": {
"title": "Load VAE"
}
},
"25": {
"inputs": {
"samples": [
"23",
0
],
"vae": [
"24",
0
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"26": {
"inputs": {
"images": [
"25",
0
]
},
"class_type": "PreviewImage",
"_meta": {
"title": "Preview Image"
}
},
"27": {
"inputs": {
"width": [
"31",
0
],
"height": [
"32",
0
],
"upscale_method": "nearest-exact",
"keep_proportion": "crop",
"pad_color": "192,192,192",
"crop_position": "center",
"divisible_by": 2,
"device": "cpu",
"image": [
"14",
0
]
},
"class_type": "ImageResizeKJv2",
"_meta": {
"title": "Resize Image v2"
}
},
"28": {
"inputs": {
"pixels": [
"27",
0
],
"vae": [
"3",
0
]
},
"class_type": "VAEEncode",
"_meta": {
"title": "VAE Encode"
}
},
"29": {
"inputs": {
"image": "handbag_1760085003312.png"
},
"class_type": "LoadImage",
"_meta": {
"title": "Load Image"
}
},
"30": {
"inputs": {
"width": [
"31",
0
],
"height": [
"32",
0
],
"upscale_method": "nearest-exact",
"keep_proportion": "crop",
"pad_color": "192,192,192",
"crop_position": "center",
"divisible_by": 2,
"device": "cpu",
"image": [
"29",
0
]
},
"class_type": "ImageResizeKJv2",
"_meta": {
"title": "Resize Image v2"
}
},
"31": {
"inputs": {
"Value": 720
},
"class_type": "DF_Integer",
"_meta": {
"title": "width"
}
},
"32": {
"inputs": {
"Value": 1280
},
"class_type": "DF_Integer",
"_meta": {
"title": "height"
}
}
}

444
src/comfyworkflows/edit_image_2_qwen_pose.json Normal file
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@ -0,0 +1,444 @@
{
"1": {
"inputs": {
"unet_name": "qwen_image_edit_2509_fp8_e4m3fn.safetensors",
"weight_dtype": "default"
},
"class_type": "UNETLoader",
"_meta": {
"title": "Load Diffusion Model"
}
},
"2": {
"inputs": {
"clip_name": "qwen_2.5_vl_7b_fp8_scaled.safetensors",
"type": "qwen_image",
"device": "default"
},
"class_type": "CLIPLoader",
"_meta": {
"title": "Load CLIP"
}
},
"3": {
"inputs": {
"vae_name": "qwen_image_vae.safetensors"
},
"class_type": "VAELoader",
"_meta": {
"title": "Load VAE"
}
},
"4": {
"inputs": {
"lora_name": "Qwen-Image-Lightning-8steps-V2.0.safetensors",
"strength_model": 1,
"model": [
"1",
0
]
},
"class_type": "LoraLoaderModelOnly",
"_meta": {
"title": "LoraLoaderModelOnly"
}
},
"5": {
"inputs": {
"conditioning": [
"11",
0
]
},
"class_type": "ConditioningZeroOut",
"_meta": {
"title": "ConditioningZeroOut"
}
},
"7": {
"inputs": {
"seed": 38026585691397,
"steps": 8,
"cfg": 1,
"sampler_name": "euler",
"scheduler": "beta",
"denoise": 1,
"model": [
"4",
0
],
"positive": [
"11",
0
],
"negative": [
"5",
0
],
"latent_image": [
"36",
0
]
},
"class_type": "KSampler",
"_meta": {
"title": "KSampler"
}
},
"8": {
"inputs": {
"samples": [
"7",
0
],
"vae": [
"3",
0
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"9": {
"inputs": {
"font_file": "Alibaba-PuHuiTi-Heavy.ttf",
"font_size": 40,
"border": 32,
"color_theme": "light",
"reel_1": [
"10",
0
]
},
"class_type": "LayerUtility: ImageReelComposit",
"_meta": {
"title": "LayerUtility: Image Reel Composit"
}
},
"10": {
"inputs": {
"image1_text": "Original image",
"image2_text": "Reference",
"image3_text": "Result",
"image4_text": "image4",
"reel_height": 512,
"border": 32,
"image1": [
"11",
1
],
"image2": [
"11",
2
],
"image3": [
"8",
0
]
},
"class_type": "LayerUtility: ImageReel",
"_meta": {
"title": "LayerUtility: Image Reel"
}
},
"11": {
"inputs": {
"prompt": [
"21",
0
],
"enable_resize": false,
"enable_vl_resize": false,
"upscale_method": "lanczos",
"crop": "disabled",
"instruction": "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n",
"clip": [
"2",
0
],
"vae": [
"3",
0
],
"image1": [
"27",
0
],
"image2": [
"33",
0
]
},
"class_type": "TextEncodeQwenImageEditPlus_lrzjason",
"_meta": {
"title": "TextEncodeQwenImageEditPlus 小志Jason(xiaozhijason)"
}
},
"14": {
"inputs": {
"image": "model_1760082843769.png"
},
"class_type": "LoadImage",
"_meta": {
"title": "load base image"
}
},
"19": {
"inputs": {
"rgthree_comparer": {
"images": [
{
"name": "A",
"selected": true,
"url": "/api/view?filename=rgthree.compare._temp_uoazy_00279_.png&type=temp&subfolder=&rand=0.4405150352070387"
},
{
"name": "B",
"selected": true,
"url": "/api/view?filename=rgthree.compare._temp_uoazy_00280_.png&type=temp&subfolder=&rand=0.9388629603648289"
}
]
},
"image_a": [
"11",
1
],
"image_b": [
"8",
0
]
},
"class_type": "Image Comparer (rgthree)",
"_meta": {
"title": "Image Comparer (rgthree)"
}
},
"20": {
"inputs": {
"filename_prefix": "combined",
"images": [
"8",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
},
"21": {
"inputs": {
"value": "以图像2为基础，生成一张女性肖像照片。她穿着一件黑色薄纱长袖上衣，一条光滑的皮革及膝裙，和勃艮第色的尖头靴子，手提一个深红色的手提包。场景改为极简主义风格的客厅，摆放着中性的沙发、镜面墙饰、盆栽植物和浅色地板，营造出明亮而宽敞的美感。"
},
"class_type": "PrimitiveStringMultiline",
"_meta": {
"title": "String (Multiline)"
}
},
"22": {
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"9",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
},
"23": {
"inputs": {
"width": 720,
"height": 1280,
"batch_size": 1
},
"class_type": "EmptyLatentImage",
"_meta": {
"title": "Empty Latent Image"
}
},
"24": {
"inputs": {
"vae_name": "sdxl_vae.safetensors"
},
"class_type": "VAELoader",
"_meta": {
"title": "Load VAE"
}
},
"25": {
"inputs": {
"samples": [
"23",
0
],
"vae": [
"24",
0
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"26": {
"inputs": {
"images": [
"25",
0
]
},
"class_type": "PreviewImage",
"_meta": {
"title": "Preview Image"
}
},
"27": {
"inputs": {
"width": [
"31",
0
],
"height": [
"32",
0
],
"upscale_method": "nearest-exact",
"keep_proportion": "resize",
"pad_color": "192,192,192",
"crop_position": "center",
"divisible_by": 2,
"device": "cpu",
"image": [
"14",
0
]
},
"class_type": "ImageResizeKJv2",
"_meta": {
"title": "Resize Image v2"
}
},
"28": {
"inputs": {
"pixels": [
"27",
0
],
"vae": [
"3",
0
]
},
"class_type": "VAEEncode",
"_meta": {
"title": "VAE Encode"
}
},
"29": {
"inputs": {
"image": "pose_1760082843769.png"
},
"class_type": "LoadImage",
"_meta": {
"title": "load reference image"
}
},
"30": {
"inputs": {
"width": [
"31",
0
],
"height": [
"32",
0
],
"upscale_method": "nearest-exact",
"keep_proportion": "resize",
"pad_color": "192,192,192",
"crop_position": "center",
"divisible_by": 2,
"device": "cpu",
"image": [
"29",
0
]
},
"class_type": "ImageResizeKJv2",
"_meta": {
"title": "Resize Image v2"
}
},
"31": {
"inputs": {
"Value": 720
},
"class_type": "DF_Integer",
"_meta": {
"title": "width"
}
},
"32": {
"inputs": {
"Value": 1280
},
"class_type": "DF_Integer",
"_meta": {
"title": "height"
}
},
"33": {
"inputs": {
"detect_hand": "enable",
"detect_body": "enable",
"detect_face": "enable",
"resolution": 512,
"bbox_detector": "yolox_l.onnx",
"pose_estimator": "dw-ll_ucoco_384_bs5.torchscript.pt",
"scale_stick_for_xinsr_cn": "disable",
"image": [
"30",
0
]
},
"class_type": "DWPreprocessor",
"_meta": {
"title": "DWPose Estimator"
}
},
"35": {
"inputs": {
"images": [
"33",
0
]
},
"class_type": "PreviewImage",
"_meta": {
"title": "Preview Image"
}
},
"36": {
"inputs": {
"width": [
"31",
0
],
"height": [
"32",
0
],
"batch_size": 1
},
"class_type": "EmptyLatentImage",
"_meta": {
"title": "Empty Latent Image"
}
}
}

111
src/comfyworkflows/facerestore_upscale.json Normal file
View File

@ -0,0 +1,111 @@
{
"1": {
"inputs": {
"image": "model_outfit_location_handbag3_1760086053609.png"
},
"class_type": "LoadImage",
"_meta": {
"title": "Load Image"
}
},
"2": {
"inputs": {
"enabled": true,
"swap_model": "inswapper_128.onnx",
"facedetection": "YOLOv5l",
"face_restore_model": "GPEN-BFR-1024.onnx",
"face_restore_visibility": 0.5200000000000001,
"codeformer_weight": 0.5,
"detect_gender_input": "no",
"detect_gender_source": "no",
"input_faces_index": "0",
"source_faces_index": "0",
"console_log_level": 1,
"input_image": [
"6",
0
],
"source_image": [
"3",
0
]
},
"class_type": "ReActorFaceSwap",
"_meta": {
"title": "ReActor 🌌 Fast Face Swap"
}
},
"3": {
"inputs": {
"image": "outfit_1760086053609.png"
},
"class_type": "LoadImage",
"_meta": {
"title": "Load Image"
}
},
"4": {
"inputs": {
"images": [
"2",
0
]
},
"class_type": "PreviewImage",
"_meta": {
"title": "Preview Image"
}
},
"6": {
"inputs": {
"resize_to": "4k",
"images": [
"1",
0
],
"upscaler_trt_model": [
"8",
0
]
},
"class_type": "UpscalerTensorrt",
"_meta": {
"title": "Upscaler Tensorrt ⚡"
}
},
"7": {
"inputs": {
"images": [
"6",
0
]
},
"class_type": "PreviewImage",
"_meta": {
"title": "Preview Image"
}
},
"8": {
"inputs": {
"model": "4x-UltraSharp",
"precision": "fp16"
},
"class_type": "LoadUpscalerTensorrtModel",
"_meta": {
"title": "Load Upscale Tensorrt Model"
}
},
"9": {
"inputs": {
"filename_prefix": "upscaled",
"images": [
"2",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
}
}

388
src/comfyworkflows/upscale.json Normal file
View File

@ -0,0 +1,388 @@
{
"4": {
"inputs": {
"ckpt_name": "dreamshaperXL_v21TurboDPMSDE.safetensors"
},
"class_type": "CheckpointLoaderSimple",
"_meta": {
"title": "Load Checkpoint"
}
},
"12": {
"inputs": {
"seed": 302411063911982,
"steps": 8,
"cfg": 2,
"sampler_name": "dpmpp_sde",
"scheduler": "karras",
"denoise": 1,
"model": [
"4",
0
],
"positive": [
"65",
0
],
"negative": [
"69",
0
],
"latent_image": [
"13",
0
]
},
"class_type": "KSampler",
"_meta": {
"title": "KSampler"
}
},
"13": {
"inputs": {
"width": 1216,
"height": 832,
"batch_size": 1
},
"class_type": "EmptyLatentImage",
"_meta": {
"title": "Empty Latent Image"
}
},
"16": {
"inputs": {
"samples": [
"12",
0
],
"vae": [
"4",
2
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"36": {
"inputs": {
"method": "Mixture of Diffusers",
"tile_width": 1024,
"tile_height": 1024,
"tile_overlap": 32,
"tile_batch_size": 8,
"model": [
"4",
0
]
},
"class_type": "TiledDiffusion",
"_meta": {
"title": "Tiled Diffusion"
}
},
"51": {
"inputs": {
"tile_size": 1024,
"fast": false,
"samples": [
"80",
0
],
"vae": [
"4",
2
]
},
"class_type": "VAEDecodeTiled_TiledDiffusion",
"_meta": {
"title": "Tiled VAE Decode"
}
},
"65": {
"inputs": {
"text": "photo of a high end sports car",
"clip": [
"4",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"69": {
"inputs": {
"text": "text, watermark, (film grain, noise:1.2)",
"clip": [
"4",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"80": {
"inputs": {
"seed": 105566927616764,
"steps": 4,
"cfg": 2,
"sampler_name": "dpmpp_sde",
"scheduler": "karras",
"denoise": 1,
"model": [
"36",
0
],
"positive": [
"141",
0
],
"negative": [
"141",
1
],
"latent_image": [
"84",
0
]
},
"class_type": "KSampler",
"_meta": {
"title": "KSampler"
}
},
"84": {
"inputs": {
"width": [
"106",
0
],
"height": [
"107",
0
],
"batch_size": 1
},
"class_type": "EmptyLatentImage",
"_meta": {
"title": "Empty Latent Image"
}
},
"105": {
"inputs": {
"image": [
"115",
0
]
},
"class_type": "GetImageSizeAndCount",
"_meta": {
"title": "Get Image Size & Count"
}
},
"106": {
"inputs": {
"value": "a*b",
"a": [
"105",
1
],
"b": [
"117",
0
]
},
"class_type": "SimpleMath+",
"_meta": {
"title": "🔧 Simple Math"
}
},
"107": {
"inputs": {
"value": "a*b",
"a": [
"105",
2
],
"b": [
"117",
0
]
},
"class_type": "SimpleMath+",
"_meta": {
"title": "🔧 Simple Math"
}
},
"111": {
"inputs": {
"image": "model_outfit_location_handbag1_1760092227085.png"
},
"class_type": "LoadImage",
"_meta": {
"title": "Load Image"
}
},
"115": {
"inputs": {
"any_01": [
"111",
0
]
},
"class_type": "Any Switch (rgthree)",
"_meta": {
"title": "Any Switch (rgthree)"
}
},
"117": {
"inputs": {
"value": 4.000000000000001
},
"class_type": "FloatConstant",
"_meta": {
"title": "Float Constant"
}
},
"133": {
"inputs": {
"rgthree_comparer": {
"images": [
{
"name": "A",
"selected": true,
"url": "/api/view?filename=rgthree.compare._temp_ybqmm_00009_.png&type=temp&subfolder=&rand=0.02707950499627365"
},
{
"name": "B",
"selected": true,
"url": "/api/view?filename=rgthree.compare._temp_ybqmm_00010_.png&type=temp&subfolder=&rand=0.18690183070180255"
}
]
},
"image_a": [
"115",
0
],
"image_b": [
"149",
0
]
},
"class_type": "Image Comparer (rgthree)",
"_meta": {
"title": "Image Comparer (rgthree)"
}
},
"141": {
"inputs": {
"strength": 0.65,
"start_percent": 0,
"end_percent": 0.9,
"positive": [
"65",
0
],
"negative": [
"69",
0
],
"control_net": [
"142",
0
],
"image": [
"115",
0
]
},
"class_type": "ACN_AdvancedControlNetApply",
"_meta": {
"title": "Apply Advanced ControlNet 🛂🅐🅒🅝"
}
},
"142": {
"inputs": {
"control_net_name": "xinsircontrolnet-tile-sdxl-1.0.safetensors"
},
"class_type": "ControlNetLoaderAdvanced",
"_meta": {
"title": "Load Advanced ControlNet Model 🛂🅐🅒🅝"
}
},
"148": {
"inputs": {
"color_space": "LAB",
"factor": 0.8,
"device": "auto",
"batch_size": 0,
"image": [
"51",
0
],
"reference": [
"115",
0
]
},
"class_type": "ImageColorMatch+",
"_meta": {
"title": "🔧 Image Color Match"
}
},
"149": {
"inputs": {
"sharpen_radius": 1,
"sigma": 1,
"alpha": 0.05,
"image": [
"148",
0
]
},
"class_type": "ImageSharpen",
"_meta": {
"title": "Image Sharpen"
}
},
"154": {
"inputs": {
"filename_prefix": "Upscaled",
"images": [
"149",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
},
"165": {
"inputs": {
"image": "model_outfit_location_handbag1_1760092227085.png"
},
"class_type": "LoadImage",
"_meta": {
"title": "Load Image"
}
},
"166": {
"inputs": {
"filename_prefix": "upscaled",
"images": [
"149",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
}
}

144
src/lib/image-converter.ts
View File

@ -15,17 +15,27 @@ async function convertImage(
baseFileName: string,
comfyBaseUrl: string,
comfyOutputDir: string,
size: ImageSize = { width: 720, height: 1280 }
size: ImageSize = { width: 720, height: 1280 },
useEmpltyLatent: boolean = false
): Promise<string> {
const COMFY_BASE_URL = comfyBaseUrl.replace(/\/$/, '');
const COMFY_OUTPUT_DIR = comfyOutputDir;
let workflow;
if (useEmpltyLatent) {
workflow = JSON.parse(await fs.readFile('src/comfyworkflows/edit_image_2_qwen_empty.json', 'utf-8'));
workflow['21']['inputs']['value'] = prompt;
workflow['30']['inputs']['width'] = size.width;
workflow['31']['inputs']['height'] = size.height;
workflow['14']['inputs']['image'] = baseFileName;
} else {
workflow = JSON.parse(await fs.readFile('src/comfyworkflows/edit_image_qwen.json', 'utf-8'));
workflow['21']['inputs']['value'] = prompt;
workflow['23']['inputs']['width'] = size.width;
workflow['23']['inputs']['height'] = size.height;
workflow['14']['inputs']['image'] = baseFileName;
}
const response = await axios.post(`${COMFY_BASE_URL}/prompt`, { prompt: workflow });
const promptId = response.data.prompt_id;
@ -66,7 +76,8 @@ async function convertImage(
return newFilePath;
}
// basefilename is connected to image2
// sencondfilename is connect to image1
async function convertImageWithFile(
prompt: string,
baseFileName: string,
@ -81,10 +92,131 @@ async function convertImageWithFile(
workflow = JSON.parse(await fs.readFile('src/comfyworkflows/edit_image_2_qwen.json', 'utf-8'));
workflow['21']['inputs']['value'] = prompt;
workflow['25']['inputs']['width'] = size.width;
workflow['26']['inputs']['height'] = size.height;
workflow['31']['inputs']['Value'] = size.width;
workflow['32']['inputs']['Value'] = size.height;
workflow['14']['inputs']['image'] = baseFileName;
workflow['15']['inputs']['image'] = secondFileName;
workflow['29']['inputs']['image'] = secondFileName;
const response = await axios.post(`${COMFY_BASE_URL}/prompt`, { prompt: workflow });
const promptId = response.data.prompt_id;
let history;
do {
await new Promise(resolve => setTimeout(resolve, 1000));
const historyResponse = await axios.get(`${COMFY_BASE_URL}/history/${promptId}`);
history = historyResponse.data[promptId];
} while (!history || Object.keys(history.outputs).length === 0);
const files = await fs.readdir(COMFY_OUTPUT_DIR!);
const generatedFiles = files.filter(file => file.startsWith('combined'));
const fileStats = await Promise.all(
generatedFiles.map(async (file) => {
const stat = await fs.stat(path.join(COMFY_OUTPUT_DIR!, file));
return { file, mtime: stat.mtime };
})
);
fileStats.sort((a, b) => b.mtime.getTime() - a.mtime.getTime());
const latestFile = fileStats[0].file;
const newFilePath = path.resolve('./generated', baseFileName);
await fs.mkdir('./generated', { recursive: true });
const sourcePath = path.join(COMFY_OUTPUT_DIR!, latestFile);
try {
await fs.unlink(newFilePath);
} catch (err) {
// ignore if not exists
}
await fs.copyFile(sourcePath, newFilePath);
return newFilePath;
}
// basefilename is connected to image1
// sencondfilename is connect to image2
async function convertImageWithFileHandbag(
prompt: string,
baseFileName: string,
secondFileName: string,
comfyBaseUrl: string,
comfyOutputDir: string,
size: ImageSize = { width: 720, height: 1280 }
): Promise<string> {
const COMFY_BASE_URL = comfyBaseUrl.replace(/\/$/, '');
const COMFY_OUTPUT_DIR = comfyOutputDir;
let workflow;
workflow = JSON.parse(await fs.readFile('src/comfyworkflows/edit_image_2_qwen_handbag.json', 'utf-8'));
workflow['21']['inputs']['value'] = prompt;
workflow['31']['inputs']['Value'] = size.width;
workflow['32']['inputs']['Value'] = size.height;
workflow['14']['inputs']['image'] = baseFileName;
workflow['29']['inputs']['image'] = secondFileName;
const response = await axios.post(`${COMFY_BASE_URL}/prompt`, { prompt: workflow });
const promptId = response.data.prompt_id;
let history;
do {
await new Promise(resolve => setTimeout(resolve, 1000));
const historyResponse = await axios.get(`${COMFY_BASE_URL}/history/${promptId}`);
history = historyResponse.data[promptId];
} while (!history || Object.keys(history.outputs).length === 0);
const files = await fs.readdir(COMFY_OUTPUT_DIR!);
const generatedFiles = files.filter(file => file.startsWith('combined'));
const fileStats = await Promise.all(
generatedFiles.map(async (file) => {
const stat = await fs.stat(path.join(COMFY_OUTPUT_DIR!, file));
return { file, mtime: stat.mtime };
})
);
fileStats.sort((a, b) => b.mtime.getTime() - a.mtime.getTime());
const latestFile = fileStats[0].file;
const newFilePath = path.resolve('./generated', baseFileName);
await fs.mkdir('./generated', { recursive: true });
const sourcePath = path.join(COMFY_OUTPUT_DIR!, latestFile);
try {
await fs.unlink(newFilePath);
} catch (err) {
// ignore if not exists
}
await fs.copyFile(sourcePath, newFilePath);
return newFilePath;
}
// basefilename is connected to image1
// sencondfilename is connect to image2
async function convertImageWithFileForPose(
prompt: string,
baseFileName: string,
secondFileName: string,
comfyBaseUrl: string,
comfyOutputDir: string,
size: ImageSize = { width: 720, height: 1280 }
): Promise<string> {
const COMFY_BASE_URL = comfyBaseUrl.replace(/\/$/, '');
const COMFY_OUTPUT_DIR = comfyOutputDir;
let workflow;
workflow = JSON.parse(await fs.readFile('src/comfyworkflows/edit_image_2_qwen_pose.json', 'utf-8'));
workflow['21']['inputs']['value'] = prompt;
workflow['31']['inputs']['Value'] = size.width;
workflow['32']['inputs']['Value'] = size.height;
workflow['14']['inputs']['image'] = baseFileName;
workflow['29']['inputs']['image'] = secondFileName;
const response = await axios.post(`${COMFY_BASE_URL}/prompt`, { prompt: workflow });
const promptId = response.data.prompt_id;
@ -369,4 +501,4 @@ export async function convertImageVtonPose(
return newFilePath;
}
export { convertImage, convertImageWithFile };
export { convertImage, convertImageWithFile, convertImageWithFileForPose, convertImageWithFileHandbag };

118
src/lib/image-upscaler.ts Normal file
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@ -0,0 +1,118 @@
import * as fs from 'fs/promises';
import * as path from 'path';
import axios from 'axios';
import dotenv from 'dotenv';
dotenv.config();
interface ImageSize {
width: number;
height: number;
}
async function facerestore_upscale(
baseFileName: string,
faceReferenceName: string,
comfyBaseUrl: string,
comfyOutputDir: string,
): Promise<string> {
const COMFY_BASE_URL = comfyBaseUrl.replace(/\/$/, '');
const COMFY_OUTPUT_DIR = comfyOutputDir;
let workflow;
workflow = JSON.parse(await fs.readFile('src/comfyworkflows/facerestore_upscale.json', 'utf-8'));
workflow['1']['inputs']['image'] = baseFileName;
workflow['3']['inputs']['image'] = faceReferenceName;
const response = await axios.post(`${COMFY_BASE_URL}/prompt`, { prompt: workflow });
const promptId = response.data.prompt_id;
let history;
do {
await new Promise(resolve => setTimeout(resolve, 1000));
const historyResponse = await axios.get(`${COMFY_BASE_URL}/history/${promptId}`);
history = historyResponse.data[promptId];
} while (!history || Object.keys(history.outputs).length === 0);
const files = await fs.readdir(COMFY_OUTPUT_DIR!);
const generatedFiles = files.filter(file => file.startsWith('upscaled'));
const fileStats = await Promise.all(
generatedFiles.map(async (file) => {
const stat = await fs.stat(path.join(COMFY_OUTPUT_DIR!, file));
return { file, mtime: stat.mtime };
})
);
fileStats.sort((a, b) => b.mtime.getTime() - a.mtime.getTime());
const latestFile = fileStats[0].file;
const newFilePath = path.resolve('./generated', baseFileName);
await fs.mkdir('./generated', { recursive: true });
const sourcePath = path.join(COMFY_OUTPUT_DIR!, latestFile);
try {
await fs.unlink(newFilePath);
} catch (err) {
// ignore if not exists
}
await fs.copyFile(sourcePath, newFilePath);
return newFilePath;
}
async function upscale(
baseFileName: string,
comfyBaseUrl: string,
comfyOutputDir: string,
): Promise<string> {
const COMFY_BASE_URL = comfyBaseUrl.replace(/\/$/, '');
const COMFY_OUTPUT_DIR = comfyOutputDir;
let workflow;
workflow = JSON.parse(await fs.readFile('src/comfyworkflows/upscale.json', 'utf-8'));
workflow['111']['inputs']['image'] = baseFileName;
const response = await axios.post(`${COMFY_BASE_URL}/prompt`, { prompt: workflow });
const promptId = response.data.prompt_id;
let history;
do {
await new Promise(resolve => setTimeout(resolve, 1000));
const historyResponse = await axios.get(`${COMFY_BASE_URL}/history/${promptId}`);
history = historyResponse.data[promptId];
} while (!history || Object.keys(history.outputs).length === 0);
const files = await fs.readdir(COMFY_OUTPUT_DIR!);
const generatedFiles = files.filter(file => file.startsWith('upscaled'));
const fileStats = await Promise.all(
generatedFiles.map(async (file) => {
const stat = await fs.stat(path.join(COMFY_OUTPUT_DIR!, file));
return { file, mtime: stat.mtime };
})
);
fileStats.sort((a, b) => b.mtime.getTime() - a.mtime.getTime());
const latestFile = fileStats[0].file;
const newFilePath = path.resolve('./generated', baseFileName);
await fs.mkdir('./generated', { recursive: true });
const sourcePath = path.join(COMFY_OUTPUT_DIR!, latestFile);
try {
await fs.unlink(newFilePath);
} catch (err) {
// ignore if not exists
}
await fs.copyFile(sourcePath, newFilePath);
return newFilePath;
}
export { facerestore_upscale, upscale };

538
src/vton/handback_portrait.ts Normal file
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@ -0,0 +1,538 @@
import * as fs from 'fs/promises';
import * as path from 'path';
import dotenv from 'dotenv';
import { downloadImagesFromPinterestSearch } from '../lib/pinterest';
import { convertImage, convertImageWithFile, convertImageWithFileForPose, convertImageWithFileHandbag } from '../lib/image-converter';
import { logger } from '../lib/logger';
import { callLmstudio, callLMStudioAPIWithFile } from '../lib/lmstudio';
import { upscale } from '../lib/image-upscaler';
dotenv.config();
const SERVER1_COMFY_BASE_URL = process.env.SERVER1_COMFY_BASE_URL!;
const SERVER1_COMFY_OUTPUT_DIR = process.env.SERVER1_COMFY_OUTPUT_DIR!;
const imageSize: { width: number; height: number } = { width: 1280, height: 720 };
async function upscaleAndFix(
baseImage: string,
faceImage: string,
outputFilename: string,
outputDir: string,
): Promise<void> {
try {
// Copy both images to ComfyUI input directory
const inputFolderFullPath = SERVER1_COMFY_OUTPUT_DIR.replace('output', 'input');
await fs.mkdir(inputFolderFullPath, { recursive: true });
const baseFilePath = path.join(outputDir, baseImage);
const referenceFilePath = path.join(outputDir, faceImage);
const baseFileName = path.basename(baseImage);
const referenceFileName = path.basename(faceImage);
const inputBasePath = path.join(inputFolderFullPath, baseFileName);
const inputReferencePath = path.join(inputFolderFullPath, referenceFileName);
logger.info(`Copying base image to ComfyUI input: ${inputBasePath}`);
await fs.copyFile(baseFilePath, inputBasePath);
logger.info(`Copying reference image to ComfyUI input: ${inputReferencePath}`);
await fs.copyFile(referenceFilePath, inputReferencePath);
// Convert images with prompt
logger.info(`Processing images with convertImageWithFile...`);
const convertedImagePath = await upscale(
baseFileName,
SERVER1_COMFY_BASE_URL,
SERVER1_COMFY_OUTPUT_DIR,
)
logger.info(`Converted image: ${convertedImagePath}`);
// Copy the converted image to final destination
const finalOutputPath = path.join(outputDir, outputFilename);
logger.info(`Copying to final destination: ${finalOutputPath}`);
await fs.copyFile(convertedImagePath, finalOutputPath);
logger.info(`✓ Successfully generated: ${finalOutputPath}`);
} catch (error) {
logger.error(`Error processing two images:`, error);
throw error;
}
}
/**
* Process a single image: download from Pinterest, convert with prompt, and save
* @param keyword - Pinterest search keyword
* @param prompt - Image conversion prompt
* @param filename - Output filename
* @param outputDir - Directory to save the generated file
* @param shouldConvert - Whether to convert the image with prompt or just copy it
*/
async function processImage(
keyword: string,
prompt: string,
filename: string,
outputDir: string,
shouldConvert: boolean = true
): Promise<void> {
try {
logger.info(`\n=== Processing: ${filename} ===`);
logger.info(`Keyword: ${keyword}`);
logger.info(`Should convert: ${shouldConvert}`);
// Step 1: Download image from Pinterest
logger.info(`Step 1: Downloading image from Pinterest with keyword: "${keyword}"...`);
const downloadedImages = await downloadImagesFromPinterestSearch(keyword, 1);
if (downloadedImages.length === 0) {
logger.error(`Failed to download image for keyword: "${keyword}"`);
return;
}
const downloadedImagePath = downloadedImages[0];
logger.info(`Downloaded image: ${downloadedImagePath}`);
const finalOutputPath = path.join(outputDir, filename);
if (shouldConvert) {
logger.info(`Prompt: ${prompt}`);
// Step 2: Copy image to ComfyUI input directory
const inputFolderFullPath = SERVER1_COMFY_OUTPUT_DIR.replace('output', 'input');
await fs.mkdir(inputFolderFullPath, { recursive: true });
const imageFileName = path.basename(downloadedImagePath);
const inputImagePath = path.join(inputFolderFullPath, imageFileName);
logger.info(`Step 2: Copying image to ComfyUI input folder: ${inputImagePath}`);
await fs.copyFile(downloadedImagePath, inputImagePath);
// Step 3: Convert image with prompt
logger.info(`Step 3: Converting image with prompt...`);
const convertedImagePath = await convertImage(
prompt,
imageFileName,
SERVER1_COMFY_BASE_URL,
SERVER1_COMFY_OUTPUT_DIR,
imageSize
);
logger.info(`Converted image: ${convertedImagePath}`);
// Step 4: Copy the converted image to final destination
logger.info(`Step 4: Copying to final destination: ${finalOutputPath}`);
await fs.copyFile(convertedImagePath, finalOutputPath);
} else {
// Just copy the downloaded image directly to the output directory with the specified filename
logger.info(`Step 2: Copying directly to final destination: ${finalOutputPath}`);
await fs.copyFile(downloadedImagePath, finalOutputPath);
}
logger.info(`✓ Successfully generated: ${finalOutputPath}`);
} catch (error) {
logger.error(`Error processing image for keyword "${keyword}":`, error);
throw error;
}
}
/**
* Convert an existing image with a prompt
* @param prompt - Image conversion prompt
* @param imagePath - Path to the existing image
* @param outputFilename - Output filename
* @param outputDir - Directory to save the converted file
*/
async function convertImageWithPrompt(
prompt: string,
imagePath: string,
outputFilename: string,
outputDir: string
): Promise<void> {
try {
logger.info(`\n=== Converting Image: ${outputFilename} ===`);
logger.info(`Source: ${imagePath}`);
logger.info(`Prompt: ${prompt}`);
// Step 1: Copy image to ComfyUI input directory
const inputFolderFullPath = SERVER1_COMFY_OUTPUT_DIR.replace('output', 'input');
await fs.mkdir(inputFolderFullPath, { recursive: true });
const imageFileName = path.basename(imagePath);
const inputImagePath = path.join(inputFolderFullPath, imageFileName);
logger.info(`Step 1: Copying image to ComfyUI input folder: ${inputImagePath}`);
await fs.copyFile(imagePath, inputImagePath);
// Step 2: Convert image with prompt
logger.info(`Step 2: Converting image with prompt...`);
const convertedImagePath = await convertImage(
prompt,
imageFileName,
SERVER1_COMFY_BASE_URL,
SERVER1_COMFY_OUTPUT_DIR,
imageSize
);
logger.info(`Converted image: ${convertedImagePath}`);
// Step 3: Copy the converted image to final destination
const finalOutputPath = path.join(outputDir, outputFilename);
logger.info(`Step 3: Copying to final destination: ${finalOutputPath}`);
await fs.copyFile(convertedImagePath, finalOutputPath);
logger.info(`✓ Successfully converted: ${finalOutputPath}`);
} catch (error) {
logger.error(`Error converting image:`, error);
throw error;
}
}
/**
* Process two images together: combine base image with reference image using prompt
* @param prompt - Processing prompt
* @param baseFile - Base image filename (in generated folder)
* @param referenceFile - Reference image filename (in generated folder)
* @param outputFilename - Output filename
* @param outputDir - Directory to save the generated file
*/
async function processTwoImages(
prompt: string,
baseFile: string,
referenceFile: string,
outputFilename: string,
outputDir: string,
isPose: boolean = false
): Promise<void> {
try {
logger.info(`\n=== Processing: ${outputFilename} ===`);
logger.info(`Base: ${baseFile}, Reference: ${referenceFile}`);
logger.info(`Prompt: ${prompt}`);
// Copy both images to ComfyUI input directory
const inputFolderFullPath = SERVER1_COMFY_OUTPUT_DIR.replace('output', 'input');
await fs.mkdir(inputFolderFullPath, { recursive: true });
const baseFilePath = path.join(outputDir, baseFile);
const referenceFilePath = path.join(outputDir, referenceFile);
const baseFileName = path.basename(baseFile);
const referenceFileName = path.basename(referenceFile);
const inputBasePath = path.join(inputFolderFullPath, baseFileName);
const inputReferencePath = path.join(inputFolderFullPath, referenceFileName);
logger.info(`Copying base image to ComfyUI input: ${inputBasePath}`);
await fs.copyFile(baseFilePath, inputBasePath);
logger.info(`Copying reference image to ComfyUI input: ${inputReferencePath}`);
await fs.copyFile(referenceFilePath, inputReferencePath);
// Convert images with prompt
logger.info(`Processing images with convertImageWithFile...`);
const convertedImagePath = isPose ? await convertImageWithFileForPose(
prompt,
baseFileName,
referenceFileName,
SERVER1_COMFY_BASE_URL,
SERVER1_COMFY_OUTPUT_DIR,
imageSize
) : await convertImageWithFile(
prompt,
baseFileName,
referenceFileName,
SERVER1_COMFY_BASE_URL,
SERVER1_COMFY_OUTPUT_DIR,
imageSize
)
logger.info(`Converted image: ${convertedImagePath}`);
// Copy the converted image to final destination
const finalOutputPath = path.join(outputDir, outputFilename);
logger.info(`Copying to final destination: ${finalOutputPath}`);
await fs.copyFile(convertedImagePath, finalOutputPath);
logger.info(`✓ Successfully generated: ${finalOutputPath}`);
} catch (error) {
logger.error(`Error processing two images:`, error);
throw error;
}
}
/**
* Process two images together: combine base image with reference image using prompt
* @param prompt - Processing prompt
* @param baseFile - Base image filename (in generated folder)
* @param referenceFile - Reference image filename (in generated folder)
* @param outputFilename - Output filename
* @param outputDir - Directory to save the generated file
*/
async function processTwoImagesHandbag(
prompt: string,
baseFile: string,
referenceFile: string,
outputFilename: string,
outputDir: string,
): Promise<void> {
try {
logger.info(`\n=== Processing: ${outputFilename} ===`);
logger.info(`Base: ${baseFile}, Reference: ${referenceFile}`);
logger.info(`Prompt: ${prompt}`);
// Copy both images to ComfyUI input directory
const inputFolderFullPath = SERVER1_COMFY_OUTPUT_DIR.replace('output', 'input');
await fs.mkdir(inputFolderFullPath, { recursive: true });
const baseFilePath = path.join(outputDir, baseFile);
const referenceFilePath = path.join(outputDir, referenceFile);
const baseFileName = path.basename(baseFile);
const referenceFileName = path.basename(referenceFile);
const inputBasePath = path.join(inputFolderFullPath, baseFileName);
const inputReferencePath = path.join(inputFolderFullPath, referenceFileName);
logger.info(`Copying base image to ComfyUI input: ${inputBasePath}`);
await fs.copyFile(baseFilePath, inputBasePath);
logger.info(`Copying reference image to ComfyUI input: ${inputReferencePath}`);
await fs.copyFile(referenceFilePath, inputReferencePath);
// Convert images with prompt
logger.info(`Processing images with convertImageWithFile...`);
const convertedImagePath = await convertImageWithFileHandbag(
prompt,
baseFileName,
referenceFileName,
SERVER1_COMFY_BASE_URL,
SERVER1_COMFY_OUTPUT_DIR,
imageSize
)
logger.info(`Converted image: ${convertedImagePath}`);
// Copy the converted image to final destination
const finalOutputPath = path.join(outputDir, outputFilename);
logger.info(`Copying to final destination: ${finalOutputPath}`);
await fs.copyFile(convertedImagePath, finalOutputPath);
logger.info(`✓ Successfully generated: ${finalOutputPath}`);
} catch (error) {
logger.error(`Error processing two images:`, error);
throw error;
}
}
/**
* Process a complete iteration: download base images and apply sequential transformations
*/
async function processIteration(iteration: number): Promise<void> {
try {
const timestamp = Date.now();
logger.info(`\n${'='.repeat(80)}`);
logger.info(`ITERATION ${iteration} - Starting with timestamp: ${timestamp}`);
logger.info(`${'='.repeat(80)}`);
// Create output directory for this iteration
const outputDir = path.join(process.cwd(), 'generated', `vton_${timestamp}`);
await fs.mkdir(outputDir, { recursive: true });
logger.info(`Output directory created: ${outputDir}`);
// === PHASE 1: Download base images ===
logger.info(`\n--- PHASE 1: Downloading base images ---`);
await processImage(
'cute girl face high resolution',
'',
`model_${timestamp}.png`,
outputDir,
false
);
await processImage(
'woman elegant outfit fullbody single',
'',
`outfit_${timestamp}.png`,
outputDir,
false
);
await processImage(
'photo elegant indoor room',
'',
`room_${timestamp}.png`,
outputDir,
false
);
await processImage(
'handbag single product photography',
'请提取照片中的包，并将其正面朝向地放置在亮灰色背景上。',
`handbag_${timestamp}.png`,
outputDir,
true
);
await processImage(
'woman portrait standing',
'',
`pose_${timestamp}.png`,
outputDir,
false
);
// === PHASE 2: Sequential transformations ===
logger.info(`\n--- PHASE 2: Sequential transformations ---`);
// Step 1: Generate outfit prompt using LMStudio API
logger.info('Step 1: Generating outfit prompt with LMStudio API...');
const outfitImagePath = path.join(outputDir, `outfit_${timestamp}.png`);
const outfitPromptResponse = await callLMStudioAPIWithFile(
outfitImagePath,
'Describe this outfit in detail about 30 words. Focus on color and cloth type. Return the result in this format: {"result":""}'
);
const outfitPrompt = outfitPromptResponse.result || outfitPromptResponse;
logger.info(`Generated outfit prompt: ${outfitPrompt}`);
// Step 2: Generate location prompt using LMStudio API
logger.info('Step 2: Generating location prompt with LMStudio API...');
const roomImagePath = path.join(outputDir, `room_${timestamp}.png`);
const locationPromptResponse = await callLMStudioAPIWithFile(
roomImagePath,
'Describe this location/room in detail about 30 words. Return the result in this format: {"result":""}'
);
const locationPrompt = locationPromptResponse.result || locationPromptResponse;
logger.info(`Generated location prompt: ${locationPrompt}`);
// Step 3: Generate Chinese prompt using LMStudio API
logger.info('Step 3: Generating Chinese prompt for model transformation...');
const chinesePromptRequest = `Generate a Chinese prompt for image transformation that describes:
- Prefix: genereate a portarit photo of a woman in image1
- Use outfit to: ${outfitPrompt}
- Use location to: ${locationPrompt}
Return the result in this format: {"result":""}`;
const chinesePromptResponse = await callLmstudio(chinesePromptRequest);
const chinesePrompt = chinesePromptResponse.result || chinesePromptResponse;
logger.info(`Generated Chinese prompt: ${chinesePrompt}`);
// Process model with outfit and location using the Chinese prompt
logger.info('Step 4: Processing model with outfit and location...');
const modelImagePath = path.join(outputDir, `model_${timestamp}.png`);
// Copy model image to ComfyUI input directory
const inputFolderFullPath = SERVER1_COMFY_OUTPUT_DIR.replace('output', 'input');
await fs.mkdir(inputFolderFullPath, { recursive: true });
const modelFileName = path.basename(modelImagePath);
const inputModelPath = path.join(inputFolderFullPath, modelFileName);
await fs.copyFile(modelImagePath, inputModelPath);
// Convert image with Chinese prompt and pose
await processTwoImages(
`请将图1中模特的姿势更改为图2的姿势。, ${chinesePrompt}`,
modelFileName,
`pose_${timestamp}.png`,
`model_outfit_location_pose_${timestamp}.png`,
outputDir,
true
);
// Step 5: Add handbag to model
await processTwoImagesHandbag(
'请将图1中的女性修改成手持图2的包。',
`model_outfit_location_pose_${timestamp}.png`,
`handbag_${timestamp}.png`,
`model_outfit_location_handbag1_${timestamp}.png`,
outputDir
);
await processTwoImagesHandbag(
'请让图1的女性看起来像是在手里拿着图2的包。',
`model_outfit_location_pose_${timestamp}.png`,
`handbag_${timestamp}.png`,
`model_outfit_location_handbag2_${timestamp}.png`,
outputDir
);
await processTwoImagesHandbag(
'请将图1中的女性修改成双手拿着图2的包。',
`model_outfit_location_pose_${timestamp}.png`,
`handbag_${timestamp}.png`,
`model_outfit_location_handbag3_${timestamp}.png`,
outputDir
);
await upscaleAndFix(
`model_outfit_location_handbag1_${timestamp}.png`,
`model_${timestamp}.png`,
`model_outfit_location_handbag1_upscaled_${timestamp}.png`,
outputDir
);
await upscaleAndFix(
`model_outfit_location_handbag2_${timestamp}.png`,
`model_${timestamp}.png`,
`model_outfit_location_handbag2_upscaled_${timestamp}.png`,
outputDir
);
await upscaleAndFix(
`model_outfit_location_handbag3_${timestamp}.png`,
`model_${timestamp}.png`,
`model_outfit_location_handbag3_upscaled_${timestamp}.png`,
outputDir
);
logger.info(`\n${'='.repeat(80)}`);
logger.info(`ITERATION ${iteration} COMPLETED!`);
logger.info(`Generated files are saved in: ${outputDir}`);
logger.info(`${'='.repeat(80)}\n`);
} catch (error) {
logger.error(`Error in iteration ${iteration}:`, error);
throw error;
}
}
/**
* Main execution function with infinite iteration
*/
async function main() {
let iteration = 1;
try {
logger.info('Starting infinite processing loop...');
logger.info('Press Ctrl+C to stop the process\n');
while (true) {
await processIteration(iteration);
iteration++;
// Small delay between iterations
logger.info('Waiting 5 seconds before next iteration...\n');
await new Promise(resolve => setTimeout(resolve, 5000));
}
} catch (error) {
logger.error('Error in main execution:', error);
process.exit(1);
}
}
// Execute main function if this file is run directly
if (require.main === module) {
main();
}
export { processImage, convertImageWithPrompt, processTwoImages, processIteration, main };