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26 Commits
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18
.clinerules/generators.md
Normal file
18
.clinerules/generators.md
Normal file
@ -0,0 +1,18 @@
|
||||
#Image and Video generation
|
||||
|
||||
## Simple image generation with prompt
|
||||
Use this file src\lib\image-generator.ts
|
||||
|
||||
## Image generation with existing image
|
||||
Use this file src\lib\image-generator-face.ts
|
||||
|
||||
## Image converting
|
||||
Use this file src\lib\image-converter.ts
|
||||
|
||||
## Video generation with static image
|
||||
Use this ilfe src\lib\video-generator.ts
|
||||
|
||||
Everything when generator need to use existing image you have to copy to input folder to the server and use only filename.
|
||||
For example if
|
||||
inputfolderFullpath = SERVER1_COMFY_OUTPUT_DIR.replece("output","input");
|
||||
Then copy image to the path, and pass only filename to generator function
|
||||
10
.clinerules/lib.md
Normal file
10
.clinerules/lib.md
Normal file
@ -0,0 +1,10 @@
|
||||
# Library Functions
|
||||
|
||||
## PNG Metadata
|
||||
Use this file `src/lib/util.ts` for embedding and reading JSON data from PNG files.
|
||||
|
||||
### Embed JSON to PNG
|
||||
Use this method `embedJsonToPng(path, obj)`
|
||||
|
||||
### Read JSON from PNG
|
||||
Use this method `readJsonToPng(path)`
|
||||
26
.clinerules/llm.md
Normal file
26
.clinerules/llm.md
Normal file
@ -0,0 +1,26 @@
|
||||
# LLM API
|
||||
|
||||
## LMstudio
|
||||
Use this file src\lib\lmstudio.ts
|
||||
|
||||
- async function callLmstudio(prompt: string): Promise<any> {
|
||||
for just run prompt
|
||||
- async function callLMStudioAPIWithFile(imagePath: string, prompt: string): Promise<any> {
|
||||
for send file to llm
|
||||
|
||||
## OpenAI
|
||||
Use this file src\lib\openai.ts
|
||||
|
||||
- async function callOpenAI(prompt: string): Promise<any>
|
||||
for just run prompt
|
||||
- async function callOpenAIWithFile(imagePath: string, prompt: string): Promise<any>
|
||||
for send file to llm
|
||||
|
||||
|
||||
Please construct prompt to return json alswasy for calling llm api to generate text.
|
||||
If nothing specified add following instructin in the given prompt
|
||||
|
||||
Return the result in this forket
|
||||
{"result":""}
|
||||
|
||||
Then extract the result param in program you generate, don't change the original function
|
||||
9
.clinerules/pinterest.md
Normal file
9
.clinerules/pinterest.md
Normal file
@ -0,0 +1,9 @@
|
||||
# Pinterest operations
|
||||
Use this file src\lib\pinterest.ts for pinterest operation.
|
||||
You can modify if needed.
|
||||
|
||||
# Get PinId from Keyword
|
||||
Use this method getPinUrlFromPinterest
|
||||
|
||||
# Get image from pinId
|
||||
Use this method downloadImageFromPin
|
||||
3
.gitignore
vendored
3
.gitignore
vendored
@ -21,4 +21,5 @@ yarn-error.log*
|
||||
# Downloaded images
|
||||
/download/
|
||||
/generated/
|
||||
.env
|
||||
.env
|
||||
input/
|
||||
618
package-lock.json
generated
618
package-lock.json
generated
@ -10,15 +10,27 @@
|
||||
"license": "ISC",
|
||||
"dependencies": {
|
||||
"@types/axios": "^0.14.4",
|
||||
"@types/fs-extra": "^11.0.4",
|
||||
"@types/pngjs": "^6.0.5",
|
||||
"@types/sharp": "^0.32.0",
|
||||
"axios": "^1.11.0",
|
||||
"dotenv": "^17.2.1",
|
||||
"fs-extra": "^11.3.2",
|
||||
"mysql2": "^3.14.3",
|
||||
"open": "^10.2.0",
|
||||
"png-chunk-text": "^1.0.0",
|
||||
"png-chunks-encode": "^1.0.0",
|
||||
"png-chunks-extract": "^1.0.0",
|
||||
"pngjs": "^7.0.0",
|
||||
"puppeteer": "^24.16.2",
|
||||
"sharp": "^0.34.4",
|
||||
"uuid": "^11.1.0"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@types/node": "^20.0.0",
|
||||
"@types/node": "^20.19.19",
|
||||
"@types/png-chunk-text": "^1.0.3",
|
||||
"@types/png-chunks-encode": "^1.0.2",
|
||||
"@types/png-chunks-extract": "^1.0.2",
|
||||
"ts-node": "^10.9.2",
|
||||
"typescript": "^5.0.0"
|
||||
}
|
||||
@ -56,6 +68,419 @@
|
||||
"node": ">=12"
|
||||
}
|
||||
},
|
||||
"node_modules/@emnapi/runtime": {
|
||||
"version": "1.5.0",
|
||||
"resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.5.0.tgz",
|
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"integrity": "sha512-97/BJ3iXHww3djw6hYIfErCZFee7qCtrneuLa20UXFCOTCfBM2cvQHjWJ2EG0s0MtdNwInarqCTz35i4wWXHsQ==",
|
||||
"optional": true,
|
||||
"dependencies": {
|
||||
"tslib": "^2.4.0"
|
||||
}
|
||||
},
|
||||
"node_modules/@img/colour": {
|
||||
"version": "1.0.0",
|
||||
"resolved": "https://registry.npmjs.org/@img/colour/-/colour-1.0.0.tgz",
|
||||
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|
||||
"engines": {
|
||||
"node": ">=18"
|
||||
}
|
||||
},
|
||||
"node_modules/@img/sharp-darwin-arm64": {
|
||||
"version": "0.34.4",
|
||||
"resolved": "https://registry.npmjs.org/@img/sharp-darwin-arm64/-/sharp-darwin-arm64-0.34.4.tgz",
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|
||||
"cpu": [
|
||||
"arm64"
|
||||
],
|
||||
"optional": true,
|
||||
"os": [
|
||||
"darwin"
|
||||
],
|
||||
"engines": {
|
||||
"node": "^18.17.0 || ^20.3.0 || >=21.0.0"
|
||||
},
|
||||
"funding": {
|
||||
"url": "https://opencollective.com/libvips"
|
||||
},
|
||||
"optionalDependencies": {
|
||||
"@img/sharp-libvips-darwin-arm64": "1.2.3"
|
||||
}
|
||||
},
|
||||
"node_modules/@img/sharp-darwin-x64": {
|
||||
"version": "0.34.4",
|
||||
"resolved": "https://registry.npmjs.org/@img/sharp-darwin-x64/-/sharp-darwin-x64-0.34.4.tgz",
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"cpu": [
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||||
"x64"
|
||||
],
|
||||
"optional": true,
|
||||
"os": [
|
||||
"darwin"
|
||||
],
|
||||
"engines": {
|
||||
"node": "^18.17.0 || ^20.3.0 || >=21.0.0"
|
||||
},
|
||||
"funding": {
|
||||
"url": "https://opencollective.com/libvips"
|
||||
},
|
||||
"optionalDependencies": {
|
||||
"@img/sharp-libvips-darwin-x64": "1.2.3"
|
||||
}
|
||||
},
|
||||
"node_modules/@img/sharp-libvips-darwin-arm64": {
|
||||
"version": "1.2.3",
|
||||
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-darwin-arm64/-/sharp-libvips-darwin-arm64-1.2.3.tgz",
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"optional": true,
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"darwin"
|
||||
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||||
"funding": {
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||||
"url": "https://opencollective.com/libvips"
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||||
}
|
||||
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|
||||
"node_modules/@img/sharp-libvips-darwin-x64": {
|
||||
"version": "1.2.3",
|
||||
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-darwin-x64/-/sharp-libvips-darwin-x64-1.2.3.tgz",
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"optional": true,
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||||
"darwin"
|
||||
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|
||||
"funding": {
|
||||
"url": "https://opencollective.com/libvips"
|
||||
}
|
||||
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|
||||
"node_modules/@img/sharp-libvips-linux-arm": {
|
||||
"version": "1.2.3",
|
||||
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-linux-arm/-/sharp-libvips-linux-arm-1.2.3.tgz",
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"cpu": [
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||||
"arm"
|
||||
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||||
"optional": true,
|
||||
"os": [
|
||||
"linux"
|
||||
],
|
||||
"funding": {
|
||||
"url": "https://opencollective.com/libvips"
|
||||
}
|
||||
},
|
||||
"node_modules/@img/sharp-libvips-linux-arm64": {
|
||||
"version": "1.2.3",
|
||||
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-linux-arm64/-/sharp-libvips-linux-arm64-1.2.3.tgz",
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"cpu": [
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"arm64"
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"optional": true,
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||||
"os": [
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||||
"linux"
|
||||
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|
||||
"funding": {
|
||||
"url": "https://opencollective.com/libvips"
|
||||
}
|
||||
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|
||||
"node_modules/@img/sharp-libvips-linux-ppc64": {
|
||||
"version": "1.2.3",
|
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"resolved": "https://registry.npmjs.org/@img/sharp-libvips-linux-ppc64/-/sharp-libvips-linux-ppc64-1.2.3.tgz",
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"linux"
|
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||||
"funding": {
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||||
"url": "https://opencollective.com/libvips"
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||||
}
|
||||
},
|
||||
"node_modules/@img/sharp-libvips-linux-s390x": {
|
||||
"version": "1.2.3",
|
||||
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-linux-s390x/-/sharp-libvips-linux-s390x-1.2.3.tgz",
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"optional": true,
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||||
"os": [
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||||
"linux"
|
||||
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|
||||
"funding": {
|
||||
"url": "https://opencollective.com/libvips"
|
||||
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|
||||
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|
||||
"node_modules/@img/sharp-libvips-linux-x64": {
|
||||
"version": "1.2.3",
|
||||
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-linux-x64/-/sharp-libvips-linux-x64-1.2.3.tgz",
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"cpu": [
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"x64"
|
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"optional": true,
|
||||
"os": [
|
||||
"linux"
|
||||
],
|
||||
"funding": {
|
||||
"url": "https://opencollective.com/libvips"
|
||||
}
|
||||
},
|
||||
"node_modules/@img/sharp-libvips-linuxmusl-arm64": {
|
||||
"version": "1.2.3",
|
||||
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-linuxmusl-arm64/-/sharp-libvips-linuxmusl-arm64-1.2.3.tgz",
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||||
"cpu": [
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||||
"arm64"
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||||
],
|
||||
"optional": true,
|
||||
"os": [
|
||||
"linux"
|
||||
],
|
||||
"funding": {
|
||||
"url": "https://opencollective.com/libvips"
|
||||
}
|
||||
},
|
||||
"node_modules/@img/sharp-libvips-linuxmusl-x64": {
|
||||
"version": "1.2.3",
|
||||
"resolved": "https://registry.npmjs.org/@img/sharp-libvips-linuxmusl-x64/-/sharp-libvips-linuxmusl-x64-1.2.3.tgz",
|
||||
"integrity": "sha512-U5PUY5jbc45ANM6tSJpsgqmBF/VsL6LnxJmIf11kB7J5DctHgqm0SkuXzVWtIY90GnJxKnC/JT251TDnk1fu/g==",
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"cpu": [
|
||||
"x64"
|
||||
],
|
||||
"optional": true,
|
||||
"os": [
|
||||
"linux"
|
||||
],
|
||||
"funding": {
|
||||
"url": "https://opencollective.com/libvips"
|
||||
}
|
||||
},
|
||||
"node_modules/@img/sharp-linux-arm": {
|
||||
"version": "0.34.4",
|
||||
"resolved": "https://registry.npmjs.org/@img/sharp-linux-arm/-/sharp-linux-arm-0.34.4.tgz",
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"integrity": "sha512-Xyam4mlqM0KkTHYVSuc6wXRmM7LGN0P12li03jAnZ3EJWZqj83+hi8Y9UxZUbxsgsK1qOEwg7O0Bc0LjqQVtxA==",
|
||||
"cpu": [
|
||||
"arm"
|
||||
],
|
||||
"optional": true,
|
||||
"os": [
|
||||
"linux"
|
||||
],
|
||||
"engines": {
|
||||
"node": "^18.17.0 || ^20.3.0 || >=21.0.0"
|
||||
},
|
||||
"funding": {
|
||||
"url": "https://opencollective.com/libvips"
|
||||
},
|
||||
"optionalDependencies": {
|
||||
"@img/sharp-libvips-linux-arm": "1.2.3"
|
||||
}
|
||||
},
|
||||
"node_modules/@img/sharp-linux-arm64": {
|
||||
"version": "0.34.4",
|
||||
"resolved": "https://registry.npmjs.org/@img/sharp-linux-arm64/-/sharp-linux-arm64-0.34.4.tgz",
|
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"integrity": "sha512-YXU1F/mN/Wu786tl72CyJjP/Ngl8mGHN1hST4BGl+hiW5jhCnV2uRVTNOcaYPs73NeT/H8Upm3y9582JVuZHrQ==",
|
||||
"cpu": [
|
||||
"arm64"
|
||||
],
|
||||
"optional": true,
|
||||
"os": [
|
||||
"linux"
|
||||
],
|
||||
"engines": {
|
||||
"node": "^18.17.0 || ^20.3.0 || >=21.0.0"
|
||||
},
|
||||
"funding": {
|
||||
"url": "https://opencollective.com/libvips"
|
||||
},
|
||||
"optionalDependencies": {
|
||||
"@img/sharp-libvips-linux-arm64": "1.2.3"
|
||||
}
|
||||
},
|
||||
"node_modules/@img/sharp-linux-ppc64": {
|
||||
"version": "0.34.4",
|
||||
"resolved": "https://registry.npmjs.org/@img/sharp-linux-ppc64/-/sharp-linux-ppc64-0.34.4.tgz",
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"integrity": "sha512-F4PDtF4Cy8L8hXA2p3TO6s4aDt93v+LKmpcYFLAVdkkD3hSxZzee0rh6/+94FpAynsuMpLX5h+LRsSG3rIciUQ==",
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||||
"cpu": [
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||||
"ppc64"
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||||
],
|
||||
"optional": true,
|
||||
"os": [
|
||||
"linux"
|
||||
],
|
||||
"engines": {
|
||||
"node": "^18.17.0 || ^20.3.0 || >=21.0.0"
|
||||
},
|
||||
"funding": {
|
||||
"url": "https://opencollective.com/libvips"
|
||||
},
|
||||
"optionalDependencies": {
|
||||
"@img/sharp-libvips-linux-ppc64": "1.2.3"
|
||||
}
|
||||
},
|
||||
"node_modules/@img/sharp-linux-s390x": {
|
||||
"version": "0.34.4",
|
||||
"resolved": "https://registry.npmjs.org/@img/sharp-linux-s390x/-/sharp-linux-s390x-0.34.4.tgz",
|
||||
"integrity": "sha512-qVrZKE9Bsnzy+myf7lFKvng6bQzhNUAYcVORq2P7bDlvmF6u2sCmK2KyEQEBdYk+u3T01pVsPrkj943T1aJAsw==",
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||||
"cpu": [
|
||||
"s390x"
|
||||
],
|
||||
"optional": true,
|
||||
"os": [
|
||||
"linux"
|
||||
],
|
||||
"engines": {
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@ -455,6 +931,14 @@
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@ -551,6 +1035,14 @@
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@ -781,6 +1273,19 @@
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@ -878,6 +1383,11 @@
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@ -1056,6 +1566,17 @@
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@ -1262,6 +1783,36 @@
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"node_modules/sharp": {
|
||||
"version": "0.34.4",
|
||||
"resolved": "https://registry.npmjs.org/sharp/-/sharp-0.34.4.tgz",
|
||||
"integrity": "sha512-FUH39xp3SBPnxWvd5iib1X8XY7J0K0X7d93sie9CJg2PO8/7gmg89Nve6OjItK53/MlAushNNxteBYfM6DEuoA==",
|
||||
"hasInstallScript": true,
|
||||
"dependencies": {
|
||||
"@img/colour": "^1.0.0",
|
||||
"detect-libc": "^2.1.0",
|
||||
"semver": "^7.7.2"
|
||||
},
|
||||
"engines": {
|
||||
"node": "^18.17.0 || ^20.3.0 || >=21.0.0"
|
||||
},
|
||||
"funding": {
|
||||
"url": "https://opencollective.com/libvips"
|
||||
},
|
||||
"optionalDependencies": {
|
||||
"@img/sharp-darwin-arm64": "0.34.4",
|
||||
"@img/sharp-darwin-x64": "0.34.4",
|
||||
"@img/sharp-libvips-darwin-arm64": "1.2.3",
|
||||
"@img/sharp-libvips-darwin-x64": "1.2.3",
|
||||
"@img/sharp-libvips-linux-arm": "1.2.3",
|
||||
"@img/sharp-libvips-linux-arm64": "1.2.3",
|
||||
"@img/sharp-libvips-linux-ppc64": "1.2.3",
|
||||
"@img/sharp-libvips-linux-s390x": "1.2.3",
|
||||
"@img/sharp-libvips-linux-x64": "1.2.3",
|
||||
"@img/sharp-libvips-linuxmusl-arm64": "1.2.3",
|
||||
"@img/sharp-libvips-linuxmusl-x64": "1.2.3",
|
||||
"@img/sharp-linux-arm": "0.34.4",
|
||||
"@img/sharp-linux-arm64": "0.34.4",
|
||||
"@img/sharp-linux-ppc64": "0.34.4",
|
||||
"@img/sharp-linux-s390x": "0.34.4",
|
||||
"@img/sharp-linux-x64": "0.34.4",
|
||||
"@img/sharp-linuxmusl-arm64": "0.34.4",
|
||||
"@img/sharp-linuxmusl-x64": "0.34.4",
|
||||
"@img/sharp-wasm32": "0.34.4",
|
||||
"@img/sharp-win32-arm64": "0.34.4",
|
||||
"@img/sharp-win32-ia32": "0.34.4",
|
||||
"@img/sharp-win32-x64": "0.34.4"
|
||||
}
|
||||
},
|
||||
"node_modules/sliced": {
|
||||
"version": "1.0.1",
|
||||
"resolved": "https://registry.npmjs.org/sliced/-/sliced-1.0.1.tgz",
|
||||
"integrity": "sha512-VZBmZP8WU3sMOZm1bdgTadsQbcscK0UM8oKxKVBs4XAhUo2Xxzm/OFMGBkPusxw9xL3Uy8LrzEqGqJhclsr0yA=="
|
||||
},
|
||||
"node_modules/smart-buffer": {
|
||||
"version": "4.2.0",
|
||||
"resolved": "https://registry.npmjs.org/smart-buffer/-/smart-buffer-4.2.0.tgz",
|
||||
@ -1574,8 +2171,15 @@
|
||||
"node_modules/undici-types": {
|
||||
"version": "6.21.0",
|
||||
"resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.21.0.tgz",
|
||||
"integrity": "sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==",
|
||||
"devOptional": true
|
||||
"integrity": "sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ=="
|
||||
},
|
||||
"node_modules/universalify": {
|
||||
"version": "2.0.1",
|
||||
"resolved": "https://registry.npmjs.org/universalify/-/universalify-2.0.1.tgz",
|
||||
"integrity": "sha512-gptHNQghINnc/vTGIk0SOFGFNXw7JVrlRUtConJRlvaw6DuX0wO5Jeko9sWrMBhh+PsYAZ7oXAiOnf/UKogyiw==",
|
||||
"engines": {
|
||||
"node": ">= 10.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/uuid": {
|
||||
"version": "11.1.0",
|
||||
|
||||
20
package.json
20
package.json
@ -9,23 +9,37 @@
|
||||
"test": "echo \"Error: no test specified\" && exit 1",
|
||||
"db:schema": "ts-node src/schema.ts",
|
||||
"db:test": "ts-node src/testmysql.ts",
|
||||
"infinity:start": "ts-node src/infinityvideo_generator/start.ts"
|
||||
"infinity:start": "ts-node src/infinityvideo_generator/start.ts",
|
||||
"convert:pinterest-face": "ts-node src/imageconverter/pinterest_face_portrait.ts",
|
||||
"tool:generate-video-from-input": "ts-node src/tools/generateVideoFromInput.ts"
|
||||
},
|
||||
"keywords": [],
|
||||
"author": "",
|
||||
"license": "ISC",
|
||||
"devDependencies": {
|
||||
"@types/node": "^20.0.0",
|
||||
"@types/node": "^20.19.19",
|
||||
"@types/png-chunk-text": "^1.0.3",
|
||||
"@types/png-chunks-encode": "^1.0.2",
|
||||
"@types/png-chunks-extract": "^1.0.2",
|
||||
"ts-node": "^10.9.2",
|
||||
"typescript": "^5.0.0"
|
||||
},
|
||||
"dependencies": {
|
||||
"@types/axios": "^0.14.4",
|
||||
"@types/fs-extra": "^11.0.4",
|
||||
"@types/pngjs": "^6.0.5",
|
||||
"@types/sharp": "^0.32.0",
|
||||
"axios": "^1.11.0",
|
||||
"dotenv": "^17.2.1",
|
||||
"fs-extra": "^11.3.2",
|
||||
"mysql2": "^3.14.3",
|
||||
"open": "^10.2.0",
|
||||
"png-chunk-text": "^1.0.0",
|
||||
"png-chunks-encode": "^1.0.0",
|
||||
"png-chunks-extract": "^1.0.0",
|
||||
"pngjs": "^7.0.0",
|
||||
"puppeteer": "^24.16.2",
|
||||
"sharp": "^0.34.4",
|
||||
"uuid": "^11.1.0"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -407,7 +407,7 @@ async function generateImageForItem(item: PipelineItem, server: { baseUrl: strin
|
||||
server.baseUrl,
|
||||
server.outputDir,
|
||||
{ width: 1280, height: 720 },
|
||||
true,
|
||||
false,
|
||||
false
|
||||
);
|
||||
|
||||
|
||||
207
src/comfyworkflows/cloth_extractor.json
Normal file
207
src/comfyworkflows/cloth_extractor.json
Normal file
@ -0,0 +1,207 @@
|
||||
{
|
||||
"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": 1088883674457465,
|
||||
"steps": 8,
|
||||
"cfg": 1,
|
||||
"sampler_name": "euler",
|
||||
"scheduler": "beta",
|
||||
"denoise": 1,
|
||||
"model": [
|
||||
"66",
|
||||
0
|
||||
],
|
||||
"positive": [
|
||||
"11",
|
||||
0
|
||||
],
|
||||
"negative": [
|
||||
"5",
|
||||
0
|
||||
],
|
||||
"latent_image": [
|
||||
"11",
|
||||
6
|
||||
]
|
||||
},
|
||||
"class_type": "KSampler",
|
||||
"_meta": {
|
||||
"title": "KSampler"
|
||||
}
|
||||
},
|
||||
"8": {
|
||||
"inputs": {
|
||||
"samples": [
|
||||
"7",
|
||||
0
|
||||
],
|
||||
"vae": [
|
||||
"3",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "VAEDecode",
|
||||
"_meta": {
|
||||
"title": "VAE Decode"
|
||||
}
|
||||
},
|
||||
"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": [
|
||||
"24",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "TextEncodeQwenImageEditPlus_lrzjason",
|
||||
"_meta": {
|
||||
"title": "TextEncodeQwenImageEditPlus 小志Jason(xiaozhijason)"
|
||||
}
|
||||
},
|
||||
"20": {
|
||||
"inputs": {
|
||||
"filename_prefix": "qwenedit",
|
||||
"images": [
|
||||
"8",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "SaveImage",
|
||||
"_meta": {
|
||||
"title": "Save Image"
|
||||
}
|
||||
},
|
||||
"21": {
|
||||
"inputs": {
|
||||
"value": "extract the outfit onto a white background"
|
||||
},
|
||||
"class_type": "PrimitiveStringMultiline",
|
||||
"_meta": {
|
||||
"title": "String (Multiline)"
|
||||
}
|
||||
},
|
||||
"24": {
|
||||
"inputs": {
|
||||
"measurement": "pixels",
|
||||
"width": 720,
|
||||
"height": 1280,
|
||||
"fit": "contain",
|
||||
"method": "nearest-exact",
|
||||
"image": [
|
||||
"64",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "Image Resize (rgthree)",
|
||||
"_meta": {
|
||||
"title": "Image Resize (rgthree)"
|
||||
}
|
||||
},
|
||||
"64": {
|
||||
"inputs": {
|
||||
"image": "3096293489212792_1758825204441_2.png"
|
||||
},
|
||||
"class_type": "LoadImage",
|
||||
"_meta": {
|
||||
"title": "Load Image"
|
||||
}
|
||||
},
|
||||
"65": {
|
||||
"inputs": {
|
||||
"images": [
|
||||
"24",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "PreviewImage",
|
||||
"_meta": {
|
||||
"title": "Preview Image"
|
||||
}
|
||||
},
|
||||
"66": {
|
||||
"inputs": {
|
||||
"lora_name": "extract-outfit_v3.safetensors",
|
||||
"strength_model": 1,
|
||||
"model": [
|
||||
"4",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "LoraLoaderModelOnly",
|
||||
"_meta": {
|
||||
"title": "LoraLoaderModelOnly"
|
||||
}
|
||||
}
|
||||
}
|
||||
396
src/comfyworkflows/edit_image_2_qwen.json
Normal file
396
src/comfyworkflows/edit_image_2_qwen.json
Normal file
@ -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": 936152772258115,
|
||||
"steps": 8,
|
||||
"cfg": 1,
|
||||
"sampler_name": "euler",
|
||||
"scheduler": "beta",
|
||||
"denoise": 1,
|
||||
"model": [
|
||||
"4",
|
||||
0
|
||||
],
|
||||
"positive": [
|
||||
"11",
|
||||
0
|
||||
],
|
||||
"negative": [
|
||||
"5",
|
||||
0
|
||||
],
|
||||
"latent_image": [
|
||||
"28",
|
||||
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": [
|
||||
"30",
|
||||
0
|
||||
],
|
||||
"image2": [
|
||||
"27",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "TextEncodeQwenImageEditPlus_lrzjason",
|
||||
"_meta": {
|
||||
"title": "TextEncodeQwenImageEditPlus 小志Jason(xiaozhijason)"
|
||||
}
|
||||
},
|
||||
"14": {
|
||||
"inputs": {
|
||||
"image": "model_outfit_location_1760043932148.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_dxzmg_00211_.png&type=temp&subfolder=&rand=0.09499077981761894"
|
||||
},
|
||||
{
|
||||
"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": [
|
||||
"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中的模特处理成手持图1中包包的照片。"
|
||||
},
|
||||
"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": "handbag_1760043932148.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"
|
||||
}
|
||||
}
|
||||
}
|
||||
444
src/comfyworkflows/edit_image_2_qwen_empty.json
Normal file
444
src/comfyworkflows/edit_image_2_qwen_empty.json
Normal file
@ -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
|
||||
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|
||||
"height": [
|
||||
"32",
|
||||
0
|
||||
],
|
||||
"upscale_method": "nearest-exact",
|
||||
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|
||||
"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"
|
||||
}
|
||||
}
|
||||
}
|
||||
396
src/comfyworkflows/edit_image_2_qwen_handbag.json
Normal file
396
src/comfyworkflows/edit_image_2_qwen_handbag.json
Normal file
@ -0,0 +1,396 @@
|
||||
{
|
||||
"1": {
|
||||
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|
||||
"unet_name": "qwen_image_edit_2509_fp8_e4m3fn.safetensors",
|
||||
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|
||||
},
|
||||
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|
||||
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|
||||
"title": "Load Diffusion Model"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"type": "qwen_image",
|
||||
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|
||||
},
|
||||
"class_type": "CLIPLoader",
|
||||
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|
||||
"title": "Load CLIP"
|
||||
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|
||||
},
|
||||
"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,
|
||||
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|
||||
"1",
|
||||
0
|
||||
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|
||||
},
|
||||
"class_type": "LoraLoaderModelOnly",
|
||||
"_meta": {
|
||||
"title": "LoraLoaderModelOnly"
|
||||
}
|
||||
},
|
||||
"5": {
|
||||
"inputs": {
|
||||
"conditioning": [
|
||||
"11",
|
||||
0
|
||||
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|
||||
},
|
||||
"class_type": "ConditioningZeroOut",
|
||||
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|
||||
"title": "ConditioningZeroOut"
|
||||
}
|
||||
},
|
||||
"7": {
|
||||
"inputs": {
|
||||
"seed": 323591075024702,
|
||||
"steps": 8,
|
||||
"cfg": 1,
|
||||
"sampler_name": "euler",
|
||||
"scheduler": "beta",
|
||||
"denoise": 1,
|
||||
"model": [
|
||||
"4",
|
||||
0
|
||||
],
|
||||
"positive": [
|
||||
"11",
|
||||
0
|
||||
],
|
||||
"negative": [
|
||||
"5",
|
||||
0
|
||||
],
|
||||
"latent_image": [
|
||||
"28",
|
||||
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
444
src/comfyworkflows/edit_image_2_qwen_pose.json
Normal file
@ -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": {
|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
0
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
"9": {
|
||||
"inputs": {
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
},
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||||
"class_type": "LayerUtility: ImageReelComposit",
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||||
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||||
"title": "LayerUtility: Image Reel Composit"
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||||
}
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||||
},
|
||||
"10": {
|
||||
"inputs": {
|
||||
"image1_text": "Original image",
|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
"11",
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||||
1
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||||
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|
||||
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|
||||
"11",
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||||
2
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||||
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|
||||
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||||
"8",
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||||
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||||
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||||
},
|
||||
"class_type": "LayerUtility: ImageReel",
|
||||
"_meta": {
|
||||
"title": "LayerUtility: Image Reel"
|
||||
}
|
||||
},
|
||||
"11": {
|
||||
"inputs": {
|
||||
"prompt": [
|
||||
"21",
|
||||
0
|
||||
],
|
||||
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|
||||
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|
||||
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|
||||
"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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||||
"clip": [
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||||
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||||
0
|
||||
],
|
||||
"vae": [
|
||||
"3",
|
||||
0
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||||
],
|
||||
"image1": [
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||||
"27",
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||||
0
|
||||
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||||
"image2": [
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||||
"33",
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||||
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||||
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||||
},
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||||
"class_type": "TextEncodeQwenImageEditPlus_lrzjason",
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||||
"_meta": {
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||||
"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"
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||||
}
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||||
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||||
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||||
"image_a": [
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
},
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||||
"class_type": "Image Comparer (rgthree)",
|
||||
"_meta": {
|
||||
"title": "Image Comparer (rgthree)"
|
||||
}
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||||
},
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||||
"20": {
|
||||
"inputs": {
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||||
"filename_prefix": "combined",
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
"title": "Save Image"
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||||
}
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||||
},
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||||
"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。"
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||||
},
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"class_type": "PrimitiveStringMultiline",
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"title": "String (Multiline)"
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}
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||||
},
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||||
"22": {
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||||
"inputs": {
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"filename_prefix": "ComfyUI",
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"images": [
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||||
0
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||||
]
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||||
},
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"class_type": "SaveImage",
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"title": "Save Image"
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||||
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},
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||||
"23": {
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||||
"inputs": {
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||||
"width": 720,
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||||
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},
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||||
"class_type": "EmptyLatentImage",
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"_meta": {
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"title": "Empty Latent Image"
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},
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"24": {
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"inputs": {
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"vae_name": "sdxl_vae.safetensors"
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}
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},
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"31": {
|
||||
"inputs": {
|
||||
"Value": 720
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||||
},
|
||||
"class_type": "DF_Integer",
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"_meta": {
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"title": "width"
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"33": {
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||||
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||||
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"bbox_detector": "yolox_l.onnx",
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"image": [
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},
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"35": {
|
||||
"inputs": {
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||||
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},
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"36": {
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||||
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||||
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"_meta": {
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"title": "Empty Latent Image"
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||||
}
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||||
}
|
||||
}
|
||||
558
src/comfyworkflows/edit_image_multiple_qwen.json
Normal file
558
src/comfyworkflows/edit_image_multiple_qwen.json
Normal file
@ -0,0 +1,558 @@
|
||||
{
|
||||
"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",
|
||||
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|
||||
},
|
||||
"class_type": "CLIPLoader",
|
||||
"_meta": {
|
||||
"title": "Load CLIP"
|
||||
}
|
||||
},
|
||||
"3": {
|
||||
"inputs": {
|
||||
"vae_name": "qwen_image_vae.safetensors"
|
||||
},
|
||||
"class_type": "VAELoader",
|
||||
"_meta": {
|
||||
"title": "Load VAE"
|
||||
}
|
||||
},
|
||||
"4": {
|
||||
"inputs": {
|
||||
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|
||||
"strength_model": 1,
|
||||
"model": [
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||||
"1",
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||||
0
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||||
]
|
||||
},
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||||
"class_type": "LoraLoaderModelOnly",
|
||||
"_meta": {
|
||||
"title": "LoraLoaderModelOnly"
|
||||
}
|
||||
},
|
||||
"5": {
|
||||
"inputs": {
|
||||
"conditioning": [
|
||||
"11",
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||||
0
|
||||
]
|
||||
},
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||||
"class_type": "ConditioningZeroOut",
|
||||
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|
||||
"title": "ConditioningZeroOut"
|
||||
}
|
||||
},
|
||||
"7": {
|
||||
"inputs": {
|
||||
"seed": 1058883705232539,
|
||||
"steps": 8,
|
||||
"cfg": 1,
|
||||
"sampler_name": "euler",
|
||||
"scheduler": "beta",
|
||||
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|
||||
"model": [
|
||||
"66",
|
||||
0
|
||||
],
|
||||
"positive": [
|
||||
"11",
|
||||
0
|
||||
],
|
||||
"negative": [
|
||||
"5",
|
||||
0
|
||||
],
|
||||
"latent_image": [
|
||||
"11",
|
||||
6
|
||||
]
|
||||
},
|
||||
"class_type": "KSampler",
|
||||
"_meta": {
|
||||
"title": "KSampler"
|
||||
}
|
||||
},
|
||||
"8": {
|
||||
"inputs": {
|
||||
"samples": [
|
||||
"7",
|
||||
0
|
||||
],
|
||||
"vae": [
|
||||
"3",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "VAEDecode",
|
||||
"_meta": {
|
||||
"title": "VAE Decode"
|
||||
}
|
||||
},
|
||||
"11": {
|
||||
"inputs": {
|
||||
"prompt": [
|
||||
"21",
|
||||
0
|
||||
],
|
||||
"enable_resize": false,
|
||||
"enable_vl_resize": false,
|
||||
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|
||||
"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": [
|
||||
"84",
|
||||
0
|
||||
],
|
||||
"image2": [
|
||||
"82",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "TextEncodeQwenImageEditPlus_lrzjason",
|
||||
"_meta": {
|
||||
"title": "TextEncodeQwenImageEditPlus 小志Jason(xiaozhijason)"
|
||||
}
|
||||
},
|
||||
"15": {
|
||||
"inputs": {
|
||||
"image": "cloth_0001.png"
|
||||
},
|
||||
"class_type": "LoadImage",
|
||||
"_meta": {
|
||||
"title": "Load Image"
|
||||
}
|
||||
},
|
||||
"21": {
|
||||
"inputs": {
|
||||
"value": "change clothes of image1 with image2"
|
||||
},
|
||||
"class_type": "PrimitiveStringMultiline",
|
||||
"_meta": {
|
||||
"title": "String (Multiline)"
|
||||
}
|
||||
},
|
||||
"64": {
|
||||
"inputs": {
|
||||
"image": "Lauren_body.png"
|
||||
},
|
||||
"class_type": "LoadImage",
|
||||
"_meta": {
|
||||
"title": "Load Image"
|
||||
}
|
||||
},
|
||||
"66": {
|
||||
"inputs": {
|
||||
"lora_name": "extract-outfit_v3.safetensors",
|
||||
"strength_model": 1,
|
||||
"model": [
|
||||
"4",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "LoraLoaderModelOnly",
|
||||
"_meta": {
|
||||
"title": "LoraLoaderModelOnly"
|
||||
}
|
||||
},
|
||||
"67": {
|
||||
"inputs": {
|
||||
"detect_hand": "enable",
|
||||
"detect_body": "enable",
|
||||
"detect_face": "enable",
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||||
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333
src/comfyworkflows/edit_image_qwen.json
Normal file
333
src/comfyworkflows/edit_image_qwen.json
Normal file
@ -0,0 +1,333 @@
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||||
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||||
"url": "/api/view?filename=rgthree.compare._temp_niitk_00004_.png&type=temp&subfolder=&rand=0.06689875639286158"
|
||||
}
|
||||
]
|
||||
},
|
||||
"image_a": [
|
||||
"11",
|
||||
1
|
||||
],
|
||||
"image_b": [
|
||||
"8",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "Image Comparer (rgthree)",
|
||||
"_meta": {
|
||||
"title": "Image Comparer (rgthree)"
|
||||
}
|
||||
},
|
||||
"20": {
|
||||
"inputs": {
|
||||
"filename_prefix": "qwenedit",
|
||||
"images": [
|
||||
"8",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "SaveImage",
|
||||
"_meta": {
|
||||
"title": "Save Image"
|
||||
}
|
||||
},
|
||||
"21": {
|
||||
"inputs": {
|
||||
"value": "请从图1中提取主要主体，把背景设置为浅灰色，并让主体正面朝向，制作成产品照片。"
|
||||
},
|
||||
"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": 720,
|
||||
"height": 1280,
|
||||
"upscale_method": "nearest-exact",
|
||||
"keep_proportion": "pad",
|
||||
"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"
|
||||
}
|
||||
}
|
||||
}
|
||||
354
src/comfyworkflows/edit_image_vton.json
Normal file
354
src/comfyworkflows/edit_image_vton.json
Normal file
@ -0,0 +1,354 @@
|
||||
{
|
||||
"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": 506786026379830,
|
||||
"steps": 8,
|
||||
"cfg": 1,
|
||||
"sampler_name": "euler",
|
||||
"scheduler": "beta",
|
||||
"denoise": 1,
|
||||
"model": [
|
||||
"140",
|
||||
0
|
||||
],
|
||||
"positive": [
|
||||
"11",
|
||||
0
|
||||
],
|
||||
"negative": [
|
||||
"5",
|
||||
0
|
||||
],
|
||||
"latent_image": [
|
||||
"11",
|
||||
6
|
||||
]
|
||||
},
|
||||
"class_type": "KSampler",
|
||||
"_meta": {
|
||||
"title": "KSampler"
|
||||
}
|
||||
},
|
||||
"8": {
|
||||
"inputs": {
|
||||
"samples": [
|
||||
"7",
|
||||
0
|
||||
],
|
||||
"vae": [
|
||||
"3",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "VAEDecode",
|
||||
"_meta": {
|
||||
"title": "VAE Decode"
|
||||
}
|
||||
},
|
||||
"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": [
|
||||
"84",
|
||||
0
|
||||
],
|
||||
"image2": [
|
||||
"82",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "TextEncodeQwenImageEditPlus_lrzjason",
|
||||
"_meta": {
|
||||
"title": "TextEncodeQwenImageEditPlus 小志Jason(xiaozhijason)"
|
||||
}
|
||||
},
|
||||
"15": {
|
||||
"inputs": {
|
||||
"image": "cloth_0026.png"
|
||||
},
|
||||
"class_type": "LoadImage",
|
||||
"_meta": {
|
||||
"title": "Load cloth"
|
||||
}
|
||||
},
|
||||
"21": {
|
||||
"inputs": {
|
||||
"value": "change clothes of image1 to image2, remove the cap from head"
|
||||
},
|
||||
"class_type": "PrimitiveStringMultiline",
|
||||
"_meta": {
|
||||
"title": "String (Multiline)"
|
||||
}
|
||||
},
|
||||
"64": {
|
||||
"inputs": {
|
||||
"image": "Courtney_body.png"
|
||||
},
|
||||
"class_type": "LoadImage",
|
||||
"_meta": {
|
||||
"title": "Load model"
|
||||
}
|
||||
},
|
||||
"76": {
|
||||
"inputs": {
|
||||
"number": 832
|
||||
},
|
||||
"class_type": "StaticNumberInt",
|
||||
"_meta": {
|
||||
"title": "Static Number Int"
|
||||
}
|
||||
},
|
||||
"77": {
|
||||
"inputs": {
|
||||
"number": 1248
|
||||
},
|
||||
"class_type": "StaticNumberInt",
|
||||
"_meta": {
|
||||
"title": "Static Number Int"
|
||||
}
|
||||
},
|
||||
"78": {
|
||||
"inputs": {
|
||||
"width": [
|
||||
"76",
|
||||
0
|
||||
],
|
||||
"height": [
|
||||
"77",
|
||||
0
|
||||
],
|
||||
"batch_size": 1
|
||||
},
|
||||
"class_type": "EmptyLatentImage",
|
||||
"_meta": {
|
||||
"title": "Empty Latent Image"
|
||||
}
|
||||
},
|
||||
"82": {
|
||||
"inputs": {
|
||||
"width": [
|
||||
"76",
|
||||
0
|
||||
],
|
||||
"height": [
|
||||
"77",
|
||||
0
|
||||
],
|
||||
"upscale_method": "nearest-exact",
|
||||
"keep_proportion": "crop",
|
||||
"pad_color": "255,255,255",
|
||||
"crop_position": "center",
|
||||
"divisible_by": 2,
|
||||
"device": "cpu",
|
||||
"image": [
|
||||
"15",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "ImageResizeKJv2",
|
||||
"_meta": {
|
||||
"title": "Resize Image v2"
|
||||
}
|
||||
},
|
||||
"83": {
|
||||
"inputs": {
|
||||
"images": [
|
||||
"82",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "PreviewImage",
|
||||
"_meta": {
|
||||
"title": "Preview Image"
|
||||
}
|
||||
},
|
||||
"84": {
|
||||
"inputs": {
|
||||
"width": [
|
||||
"76",
|
||||
0
|
||||
],
|
||||
"height": [
|
||||
"77",
|
||||
0
|
||||
],
|
||||
"upscale_method": "nearest-exact",
|
||||
"keep_proportion": "crop",
|
||||
"pad_color": "0, 0, 0",
|
||||
"crop_position": "center",
|
||||
"divisible_by": 2,
|
||||
"device": "cpu",
|
||||
"image": [
|
||||
"64",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "ImageResizeKJv2",
|
||||
"_meta": {
|
||||
"title": "Resize Image v2"
|
||||
}
|
||||
},
|
||||
"85": {
|
||||
"inputs": {
|
||||
"images": [
|
||||
"84",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "PreviewImage",
|
||||
"_meta": {
|
||||
"title": "Preview Image"
|
||||
}
|
||||
},
|
||||
"93": {
|
||||
"inputs": {
|
||||
"filename_prefix": "qwenedit",
|
||||
"images": [
|
||||
"8",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "SaveImage",
|
||||
"_meta": {
|
||||
"title": "Save Image"
|
||||
}
|
||||
},
|
||||
"132": {
|
||||
"inputs": {
|
||||
"images": [
|
||||
"8",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "PreviewImage",
|
||||
"_meta": {
|
||||
"title": "Preview Image"
|
||||
}
|
||||
},
|
||||
"136": {
|
||||
"inputs": {
|
||||
"image": "281543721672978_1758880135639_0.png"
|
||||
},
|
||||
"class_type": "LoadImage",
|
||||
"_meta": {
|
||||
"title": "Load Image"
|
||||
}
|
||||
},
|
||||
"137": {
|
||||
"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": [
|
||||
"136",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "DWPreprocessor",
|
||||
"_meta": {
|
||||
"title": "DWPose Estimator"
|
||||
}
|
||||
},
|
||||
"139": {
|
||||
"inputs": {
|
||||
"images": [
|
||||
"137",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "PreviewImage",
|
||||
"_meta": {
|
||||
"title": "Preview Image"
|
||||
}
|
||||
},
|
||||
"140": {
|
||||
"inputs": {
|
||||
"lora_name": "Try_On_Qwen_Edit_Lora.safetensors",
|
||||
"strength_model": 1,
|
||||
"model": [
|
||||
"4",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "LoraLoaderModelOnly",
|
||||
"_meta": {
|
||||
"title": "LoraLoaderModelOnly"
|
||||
}
|
||||
}
|
||||
}
|
||||
111
src/comfyworkflows/facerestore_upscale.json
Normal file
111
src/comfyworkflows/facerestore_upscale.json
Normal 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"
|
||||
}
|
||||
}
|
||||
}
|
||||
229
src/comfyworkflows/generate_image_style_faceswap.json
Normal file
229
src/comfyworkflows/generate_image_style_faceswap.json
Normal file
@ -0,0 +1,229 @@
|
||||
{
|
||||
"1": {
|
||||
"inputs": {
|
||||
"clip_name1": "clip_l.safetensors",
|
||||
"clip_name2": "t5xxl_fp16.safetensors",
|
||||
"type": "flux",
|
||||
"device": "default"
|
||||
},
|
||||
"class_type": "DualCLIPLoader",
|
||||
"_meta": {
|
||||
"title": "DualCLIPLoader"
|
||||
}
|
||||
},
|
||||
"2": {
|
||||
"inputs": {
|
||||
"unet_name": "flux1-krea-dev_fp8_scaled.safetensors",
|
||||
"weight_dtype": "default"
|
||||
},
|
||||
"class_type": "UNETLoader",
|
||||
"_meta": {
|
||||
"title": "Load Diffusion Model"
|
||||
}
|
||||
},
|
||||
"3": {
|
||||
"inputs": {
|
||||
"width": 720,
|
||||
"height": 1280,
|
||||
"batch_size": 1
|
||||
},
|
||||
"class_type": "EmptySD3LatentImage",
|
||||
"_meta": {
|
||||
"title": "EmptySD3LatentImage"
|
||||
}
|
||||
},
|
||||
"4": {
|
||||
"inputs": {
|
||||
"seed": 445107772143446,
|
||||
"steps": 20,
|
||||
"cfg": 1,
|
||||
"sampler_name": "euler",
|
||||
"scheduler": "simple",
|
||||
"denoise": 1,
|
||||
"model": [
|
||||
"2",
|
||||
0
|
||||
],
|
||||
"positive": [
|
||||
"11",
|
||||
0
|
||||
],
|
||||
"negative": [
|
||||
"5",
|
||||
0
|
||||
],
|
||||
"latent_image": [
|
||||
"3",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "KSampler",
|
||||
"_meta": {
|
||||
"title": "KSampler"
|
||||
}
|
||||
},
|
||||
"5": {
|
||||
"inputs": {
|
||||
"conditioning": [
|
||||
"14",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "ConditioningZeroOut",
|
||||
"_meta": {
|
||||
"title": "ConditioningZeroOut"
|
||||
}
|
||||
},
|
||||
"6": {
|
||||
"inputs": {
|
||||
"samples": [
|
||||
"4",
|
||||
0
|
||||
],
|
||||
"vae": [
|
||||
"12",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "VAEDecode",
|
||||
"_meta": {
|
||||
"title": "VAE Decode"
|
||||
}
|
||||
},
|
||||
"8": {
|
||||
"inputs": {
|
||||
"style_model_name": "flux1-redux-dev.safetensors"
|
||||
},
|
||||
"class_type": "StyleModelLoader",
|
||||
"_meta": {
|
||||
"title": "Load Style Model"
|
||||
}
|
||||
},
|
||||
"9": {
|
||||
"inputs": {
|
||||
"crop": "center",
|
||||
"clip_vision": [
|
||||
"10",
|
||||
0
|
||||
],
|
||||
"image": [
|
||||
"13",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "CLIPVisionEncode",
|
||||
"_meta": {
|
||||
"title": "CLIP Vision Encode"
|
||||
}
|
||||
},
|
||||
"10": {
|
||||
"inputs": {
|
||||
"clip_name": "sigclip_vision_patch14_384.safetensors"
|
||||
},
|
||||
"class_type": "CLIPVisionLoader",
|
||||
"_meta": {
|
||||
"title": "Load CLIP Vision"
|
||||
}
|
||||
},
|
||||
"11": {
|
||||
"inputs": {
|
||||
"strength": 0.30000000000000004,
|
||||
"conditioning": [
|
||||
"14",
|
||||
0
|
||||
],
|
||||
"style_model": [
|
||||
"8",
|
||||
0
|
||||
],
|
||||
"clip_vision_output": [
|
||||
"9",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "ApplyStyleModelAdjust",
|
||||
"_meta": {
|
||||
"title": "Apply Style Model (Adjusted)"
|
||||
}
|
||||
},
|
||||
"12": {
|
||||
"inputs": {
|
||||
"vae_name": "ae.safetensors"
|
||||
},
|
||||
"class_type": "VAELoader",
|
||||
"_meta": {
|
||||
"title": "Load VAE"
|
||||
}
|
||||
},
|
||||
"13": {
|
||||
"inputs": {
|
||||
"image": "281543725739981_1759177922955_0.png"
|
||||
},
|
||||
"class_type": "LoadImage",
|
||||
"_meta": {
|
||||
"title": "Load Image 1"
|
||||
}
|
||||
},
|
||||
"14": {
|
||||
"inputs": {
|
||||
"text": "realistic photo of woman, wavy long blong hair, fullbody shot,, A dynamic dance scene begins with a distorted glitch effect mirroring the image’s grayscale aesthetic, quickly transitioning into a vibrant, fast-paced choreography featuring dancers in similar pale makeup and unsettling expressions. The music is electronic with heavy bass and industrial elements. The camera work should be kinetic and disorienting, utilizing quick cuts and unconventional angles, emphasizing the feeling of being trapped or haunted. The dance evolves from frantic movements to controlled yet eerie poses that echo the image’s gesture of covering the face. The setting changes between a stark white room similar to the image's background and abstract digital landscapes.",
|
||||
"clip": [
|
||||
"1",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "CLIPTextEncode",
|
||||
"_meta": {
|
||||
"title": "CLIP Text Encode (Prompt)"
|
||||
}
|
||||
},
|
||||
"15": {
|
||||
"inputs": {
|
||||
"filename_prefix": "STYLEDVIDEOMAKER",
|
||||
"images": [
|
||||
"20",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "SaveImage",
|
||||
"_meta": {
|
||||
"title": "Save Image"
|
||||
}
|
||||
},
|
||||
"19": {
|
||||
"inputs": {
|
||||
"image": "face.png"
|
||||
},
|
||||
"class_type": "LoadImage",
|
||||
"_meta": {
|
||||
"title": "Load Image"
|
||||
}
|
||||
},
|
||||
"20": {
|
||||
"inputs": {
|
||||
"enabled": true,
|
||||
"swap_model": "inswapper_128.onnx",
|
||||
"facedetection": "retinaface_resnet50",
|
||||
"face_restore_model": "GPEN-BFR-2048.onnx",
|
||||
"face_restore_visibility": 1,
|
||||
"codeformer_weight": 1,
|
||||
"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": [
|
||||
"19",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "ReActorFaceSwap",
|
||||
"_meta": {
|
||||
"title": "ReActor 🌌 Fast Face Swap"
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -216,7 +216,7 @@
|
||||
},
|
||||
"63": {
|
||||
"inputs": {
|
||||
"frame_rate": 32,
|
||||
"frame_rate": 24,
|
||||
"loop_count": 0,
|
||||
"filename_prefix": "wan22_",
|
||||
"format": "video/h264-mp4",
|
||||
|
||||
388
src/comfyworkflows/upscale.json
Normal file
388
src/comfyworkflows/upscale.json
Normal 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": [
|
||||
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|
||||
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": [
|
||||
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|
||||
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 🛂🅐🅒🅝"
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}
|
||||
},
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||||
"148": {
|
||||
"inputs": {
|
||||
"color_space": "LAB",
|
||||
"factor": 0.8,
|
||||
"device": "auto",
|
||||
"batch_size": 0,
|
||||
"image": [
|
||||
"51",
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||||
0
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||||
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"reference": [
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||||
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||||
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||||
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|
||||
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|
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"_meta": {
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|
||||
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|
||||
},
|
||||
"149": {
|
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"inputs": {
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||||
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"154": {
|
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"filename_prefix": "Upscaled",
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||||
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||||
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||||
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"_meta": {
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|
||||
}
|
||||
},
|
||||
"165": {
|
||||
"inputs": {
|
||||
"image": "model_outfit_location_handbag1_1760092227085.png"
|
||||
},
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"_meta": {
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||||
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||||
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|
||||
},
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||||
"166": {
|
||||
"inputs": {
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
"title": "Save Image"
|
||||
}
|
||||
}
|
||||
}
|
||||
425
src/comfyworkflows/vton.json
Normal file
425
src/comfyworkflows/vton.json
Normal file
@ -0,0 +1,425 @@
|
||||
{
|
||||
"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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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
"3": {
|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
}
|
||||
},
|
||||
"5": {
|
||||
"inputs": {
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
"7": {
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
"positive": [
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||||
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0
|
||||
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||||
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||||
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||||
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||||
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||||
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6
|
||||
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||||
},
|
||||
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|
||||
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||||
"title": "KSampler"
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||||
}
|
||||
},
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||||
"8": {
|
||||
"inputs": {
|
||||
"samples": [
|
||||
"7",
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||||
0
|
||||
],
|
||||
"vae": [
|
||||
"3",
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||||
0
|
||||
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||||
},
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||||
"class_type": "VAEDecode",
|
||||
"_meta": {
|
||||
"title": "VAE Decode"
|
||||
}
|
||||
},
|
||||
"11": {
|
||||
"inputs": {
|
||||
"prompt": [
|
||||
"21",
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||||
0
|
||||
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|
||||
"enable_resize": false,
|
||||
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|
||||
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|
||||
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|
||||
"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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||||
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||||
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||||
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||||
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||||
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||||
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||||
0
|
||||
],
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||||
"image1": [
|
||||
"84",
|
||||
0
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||||
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||||
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||||
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0
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||||
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||||
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||||
0
|
||||
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||||
},
|
||||
"class_type": "TextEncodeQwenImageEditPlus_lrzjason",
|
||||
"_meta": {
|
||||
"title": "TextEncodeQwenImageEditPlus 小志Jason(xiaozhijason)"
|
||||
}
|
||||
},
|
||||
"15": {
|
||||
"inputs": {
|
||||
"image": "Allison_body (1).png"
|
||||
},
|
||||
"class_type": "LoadImage",
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||||
"_meta": {
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||||
"title": "Load Image"
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||||
}
|
||||
},
|
||||
"20": {
|
||||
"inputs": {
|
||||
"filename_prefix": "qwenedit",
|
||||
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||||
"8",
|
||||
0
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||||
]
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||||
},
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||||
"class_type": "SaveImage",
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"_meta": {
|
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"title": "Save Image"
|
||||
}
|
||||
},
|
||||
"21": {
|
||||
"inputs": {
|
||||
"value": "图2中的女孩穿着图1的衣服，并以图3的姿势站立。背景保持浅灰色。"
|
||||
},
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||||
"class_type": "PrimitiveStringMultiline",
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||||
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||||
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||||
}
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||||
},
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||||
"64": {
|
||||
"inputs": {
|
||||
"image": "cloth_0111.png"
|
||||
},
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||||
"class_type": "LoadImage",
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||||
"_meta": {
|
||||
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||||
}
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},
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||||
"66": {
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||||
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||||
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||||
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||||
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||||
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||||
"67": {
|
||||
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"detect_hand": "enable",
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||||
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||||
"detect_face": "enable",
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||||
"resolution": 512,
|
||||
"bbox_detector": "yolox_l.onnx",
|
||||
"pose_estimator": "dw-ll_ucoco_384_bs5.torchscript.pt",
|
||||
"scale_stick_for_xinsr_cn": "disable",
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||||
"image": [
|
||||
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0
|
||||
]
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||||
},
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||||
"class_type": "DWPreprocessor",
|
||||
"_meta": {
|
||||
"title": "DWPose Estimator"
|
||||
}
|
||||
},
|
||||
"68": {
|
||||
"inputs": {
|
||||
"image": "633387441703331_1758877367350_1.png"
|
||||
},
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||||
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||||
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||||
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||||
},
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||||
"69": {
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||||
"inputs": {
|
||||
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||||
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0
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
"76": {
|
||||
"inputs": {
|
||||
"number": 720
|
||||
},
|
||||
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||||
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||||
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||||
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||||
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||||
"77": {
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||||
"inputs": {
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||||
"number": 1280
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||||
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||||
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||||
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||||
"title": "Static Number Int"
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||||
}
|
||||
},
|
||||
"78": {
|
||||
"inputs": {
|
||||
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|
||||
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|
||||
0
|
||||
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||||
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||||
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||||
0
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||||
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||||
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||||
},
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||||
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|
||||
"_meta": {
|
||||
"title": "Empty Latent Image"
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||||
}
|
||||
},
|
||||
"81": {
|
||||
"inputs": {
|
||||
"width": 480,
|
||||
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||||
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||||
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||||
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||||
"image": [
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||||
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0
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||||
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||||
},
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||||
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||||
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||||
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||||
}
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||||
},
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||||
"82": {
|
||||
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|
||||
"width": [
|
||||
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0
|
||||
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"height": [
|
||||
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||||
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||||
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||||
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||||
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||||
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"image": [
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||||
"15",
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||||
0
|
||||
]
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||||
},
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"class_type": "ImageResizeKJv2",
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||||
"_meta": {
|
||||
"title": "Resize Image v2"
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||||
}
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||||
},
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"83": {
|
||||
"inputs": {
|
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"images": [
|
||||
"82",
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||||
0
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||||
]
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||||
},
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"class_type": "PreviewImage",
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"_meta": {
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}
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},
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||||
"84": {
|
||||
"inputs": {
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||||
"width": [
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||||
"76",
|
||||
0
|
||||
],
|
||||
"height": [
|
||||
"77",
|
||||
0
|
||||
],
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||||
"upscale_method": "nearest-exact",
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||||
"keep_proportion": "pad",
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||||
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||||
"crop_position": "center",
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"divisible_by": 2,
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||||
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|
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"image": [
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||||
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|
||||
0
|
||||
]
|
||||
},
|
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"class_type": "ImageResizeKJv2",
|
||||
"_meta": {
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||||
}
|
||||
},
|
||||
"85": {
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||||
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|
||||
"images": [
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||||
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|
||||
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|
||||
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|
||||
},
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}
|
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},
|
||||
"86": {
|
||||
"inputs": {
|
||||
"image1_text": "image1",
|
||||
"image2_text": "image2",
|
||||
"image3_text": "image3",
|
||||
"image4_text": "image4",
|
||||
"reel_height": 512,
|
||||
"border": 32,
|
||||
"image1": [
|
||||
"15",
|
||||
0
|
||||
],
|
||||
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||||
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|
||||
0
|
||||
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|
||||
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||||
0
|
||||
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||||
"image4": [
|
||||
"8",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "LayerUtility: ImageReel",
|
||||
"_meta": {
|
||||
"title": "LayerUtility: Image Reel"
|
||||
}
|
||||
},
|
||||
"87": {
|
||||
"inputs": {
|
||||
"font_file": "Alibaba-PuHuiTi-Heavy.ttf",
|
||||
"font_size": 40,
|
||||
"border": 32,
|
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||||
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||||
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|
||||
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|
||||
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|
||||
},
|
||||
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|
||||
"_meta": {
|
||||
"title": "LayerUtility: Image Reel Composit"
|
||||
}
|
||||
},
|
||||
"88": {
|
||||
"inputs": {
|
||||
"filename_prefix": "vtonresult/vton",
|
||||
"images": [
|
||||
"87",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "SaveImage",
|
||||
"_meta": {
|
||||
"title": "Save Image"
|
||||
}
|
||||
}
|
||||
}
|
||||
357
src/comfyworkflows/vton_cloth.json
Normal file
357
src/comfyworkflows/vton_cloth.json
Normal file
@ -0,0 +1,357 @@
|
||||
{
|
||||
"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": [
|
||||
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|
||||
0
|
||||
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|
||||
},
|
||||
"class_type": "LoraLoaderModelOnly",
|
||||
"_meta": {
|
||||
"title": "LoraLoaderModelOnly"
|
||||
}
|
||||
},
|
||||
"5": {
|
||||
"inputs": {
|
||||
"conditioning": [
|
||||
"11",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "ConditioningZeroOut",
|
||||
"_meta": {
|
||||
"title": "ConditioningZeroOut"
|
||||
}
|
||||
},
|
||||
"7": {
|
||||
"inputs": {
|
||||
"seed": 559577834683401,
|
||||
"steps": 8,
|
||||
"cfg": 1,
|
||||
"sampler_name": "euler",
|
||||
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|
||||
"denoise": 1,
|
||||
"model": [
|
||||
"66",
|
||||
0
|
||||
],
|
||||
"positive": [
|
||||
"11",
|
||||
0
|
||||
],
|
||||
"negative": [
|
||||
"5",
|
||||
0
|
||||
],
|
||||
"latent_image": [
|
||||
"11",
|
||||
6
|
||||
]
|
||||
},
|
||||
"class_type": "KSampler",
|
||||
"_meta": {
|
||||
"title": "KSampler"
|
||||
}
|
||||
},
|
||||
"8": {
|
||||
"inputs": {
|
||||
"samples": [
|
||||
"7",
|
||||
0
|
||||
],
|
||||
"vae": [
|
||||
"3",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "VAEDecode",
|
||||
"_meta": {
|
||||
"title": "VAE Decode"
|
||||
}
|
||||
},
|
||||
"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": [
|
||||
"84",
|
||||
0
|
||||
],
|
||||
"image2": [
|
||||
"82",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "TextEncodeQwenImageEditPlus_lrzjason",
|
||||
"_meta": {
|
||||
"title": "TextEncodeQwenImageEditPlus 小志Jason(xiaozhijason)"
|
||||
}
|
||||
},
|
||||
"15": {
|
||||
"inputs": {
|
||||
"image": "Allison_body (1).png"
|
||||
},
|
||||
"class_type": "LoadImage",
|
||||
"_meta": {
|
||||
"title": "Load Image"
|
||||
}
|
||||
},
|
||||
"20": {
|
||||
"inputs": {
|
||||
"filename_prefix": "qwenedit",
|
||||
"images": [
|
||||
"8",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "SaveImage",
|
||||
"_meta": {
|
||||
"title": "Save Image"
|
||||
}
|
||||
},
|
||||
"21": {
|
||||
"inputs": {
|
||||
"value": "图2中的人物穿着图1的上衣、下装和配饰。"
|
||||
},
|
||||
"class_type": "PrimitiveStringMultiline",
|
||||
"_meta": {
|
||||
"title": "String (Multiline)"
|
||||
}
|
||||
},
|
||||
"64": {
|
||||
"inputs": {
|
||||
"image": "cloth_0111.png"
|
||||
},
|
||||
"class_type": "LoadImage",
|
||||
"_meta": {
|
||||
"title": "Load Image"
|
||||
}
|
||||
},
|
||||
"66": {
|
||||
"inputs": {
|
||||
"lora_name": "extract-outfit_v3.safetensors",
|
||||
"strength_model": 1,
|
||||
"model": [
|
||||
"4",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "LoraLoaderModelOnly",
|
||||
"_meta": {
|
||||
"title": "LoraLoaderModelOnly"
|
||||
}
|
||||
},
|
||||
"76": {
|
||||
"inputs": {
|
||||
"number": 720
|
||||
},
|
||||
"class_type": "StaticNumberInt",
|
||||
"_meta": {
|
||||
"title": "Static Number Int"
|
||||
}
|
||||
},
|
||||
"77": {
|
||||
"inputs": {
|
||||
"number": 1280
|
||||
},
|
||||
"class_type": "StaticNumberInt",
|
||||
"_meta": {
|
||||
"title": "Static Number Int"
|
||||
}
|
||||
},
|
||||
"78": {
|
||||
"inputs": {
|
||||
"width": [
|
||||
"76",
|
||||
0
|
||||
],
|
||||
"height": [
|
||||
"77",
|
||||
0
|
||||
],
|
||||
"batch_size": 1
|
||||
},
|
||||
"class_type": "EmptyLatentImage",
|
||||
"_meta": {
|
||||
"title": "Empty Latent Image"
|
||||
}
|
||||
},
|
||||
"82": {
|
||||
"inputs": {
|
||||
"width": [
|
||||
"76",
|
||||
0
|
||||
],
|
||||
"height": [
|
||||
"77",
|
||||
0
|
||||
],
|
||||
"upscale_method": "nearest-exact",
|
||||
"keep_proportion": "crop",
|
||||
"pad_color": "255,255,255",
|
||||
"crop_position": "center",
|
||||
"divisible_by": 2,
|
||||
"device": "cpu",
|
||||
"image": [
|
||||
"15",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "ImageResizeKJv2",
|
||||
"_meta": {
|
||||
"title": "Resize Image v2"
|
||||
}
|
||||
},
|
||||
"83": {
|
||||
"inputs": {
|
||||
"images": [
|
||||
"82",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "PreviewImage",
|
||||
"_meta": {
|
||||
"title": "Preview Image"
|
||||
}
|
||||
},
|
||||
"84": {
|
||||
"inputs": {
|
||||
"width": [
|
||||
"76",
|
||||
0
|
||||
],
|
||||
"height": [
|
||||
"77",
|
||||
0
|
||||
],
|
||||
"upscale_method": "nearest-exact",
|
||||
"keep_proportion": "pad",
|
||||
"pad_color": "0, 0, 0",
|
||||
"crop_position": "center",
|
||||
"divisible_by": 2,
|
||||
"device": "cpu",
|
||||
"image": [
|
||||
"64",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "ImageResizeKJv2",
|
||||
"_meta": {
|
||||
"title": "Resize Image v2"
|
||||
}
|
||||
},
|
||||
"85": {
|
||||
"inputs": {
|
||||
"images": [
|
||||
"84",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "PreviewImage",
|
||||
"_meta": {
|
||||
"title": "Preview Image"
|
||||
}
|
||||
},
|
||||
"86": {
|
||||
"inputs": {
|
||||
"image1_text": "image1",
|
||||
"image2_text": "image2",
|
||||
"image3_text": "image3",
|
||||
"image4_text": "image4",
|
||||
"reel_height": 512,
|
||||
"border": 32,
|
||||
"image1": [
|
||||
"15",
|
||||
0
|
||||
],
|
||||
"image2": [
|
||||
"64",
|
||||
0
|
||||
],
|
||||
"image3": [
|
||||
"8",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "LayerUtility: ImageReel",
|
||||
"_meta": {
|
||||
"title": "LayerUtility: Image Reel"
|
||||
}
|
||||
},
|
||||
"87": {
|
||||
"inputs": {
|
||||
"font_file": "Alibaba-PuHuiTi-Heavy.ttf",
|
||||
"font_size": 40,
|
||||
"border": 32,
|
||||
"color_theme": "light",
|
||||
"reel_1": [
|
||||
"86",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "LayerUtility: ImageReelComposit",
|
||||
"_meta": {
|
||||
"title": "LayerUtility: Image Reel Composit"
|
||||
}
|
||||
},
|
||||
"88": {
|
||||
"inputs": {
|
||||
"filename_prefix": "vtonresult/vton",
|
||||
"images": [
|
||||
"87",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "SaveImage",
|
||||
"_meta": {
|
||||
"title": "Save Image"
|
||||
}
|
||||
}
|
||||
}
|
||||
504
src/lib/image-converter.ts
Normal file
504
src/lib/image-converter.ts
Normal file
@ -0,0 +1,504 @@
|
||||
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 convertImage(
|
||||
prompt: string,
|
||||
baseFileName: string,
|
||||
comfyBaseUrl: string,
|
||||
comfyOutputDir: string,
|
||||
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;
|
||||
|
||||
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('qwenedit'));
|
||||
|
||||
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 image2
|
||||
// sencondfilename is connect to image1
|
||||
async function convertImageWithFile(
|
||||
prompt: string,
|
||||
baseFileName: string,
|
||||
secondFileName: string,
|
||||
comfyBaseUrl: string,
|
||||
comfyOutputDir: string,
|
||||
size: ImageSize = { width: 720, height: 1280 }
|
||||
): Promise<string> {
|
||||
const COMFY_BASE_URL = comfyBaseUrl.replace(/\/$/, '');
|
||||
const COMFY_OUTPUT_DIR = comfyOutputDir;
|
||||
let workflow;
|
||||
|
||||
workflow = JSON.parse(await fs.readFile('src/comfyworkflows/edit_image_2_qwen.json', 'utf-8'));
|
||||
workflow['21']['inputs']['value'] = prompt;
|
||||
workflow['31']['inputs']['Value'] = size.width;
|
||||
workflow['32']['inputs']['Value'] = size.height;
|
||||
workflow['14']['inputs']['image'] = baseFileName;
|
||||
workflow['29']['inputs']['image'] = secondFileName;
|
||||
|
||||
const response = await axios.post(`${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 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;
|
||||
|
||||
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;
|
||||
}
|
||||
|
||||
|
||||
export async function extractCloth(
|
||||
prompt: string,
|
||||
baseFileName: string,
|
||||
comfyBaseUrl: string,
|
||||
comfyOutputDir: string,
|
||||
size: ImageSize = { width: 720, height: 1280 }
|
||||
): Promise<string> {
|
||||
const COMFY_BASE_URL = comfyBaseUrl.replace(/\/$/, '');
|
||||
const COMFY_OUTPUT_DIR = comfyOutputDir;
|
||||
let workflow;
|
||||
|
||||
workflow = JSON.parse(await fs.readFile('src/comfyworkflows/cloth_extractor.json', 'utf-8'));
|
||||
workflow['21']['inputs']['value'] = prompt;
|
||||
workflow['24']['inputs']['width'] = size.width;
|
||||
workflow['24']['inputs']['height'] = size.height;
|
||||
workflow['64']['inputs']['image'] = baseFileName;
|
||||
|
||||
const response = await axios.post(`${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('qwenedit'));
|
||||
|
||||
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 async function convertImageWithMultipleFile(
|
||||
prompt: string,
|
||||
srcFiles: string[],
|
||||
outputFile: string,
|
||||
comfyBaseUrl: string,
|
||||
comfyOutputDir: string,
|
||||
size: ImageSize = { width: 720, height: 1280 }
|
||||
): Promise<string> {
|
||||
const COMFY_BASE_URL = comfyBaseUrl.replace(/\/$/, '');
|
||||
const COMFY_OUTPUT_DIR = comfyOutputDir;
|
||||
let workflow;
|
||||
|
||||
workflow = JSON.parse(await fs.readFile('src/comfyworkflows/edit_image_multiple_qwen.json', 'utf-8'));
|
||||
workflow['21']['inputs']['value'] = prompt;
|
||||
workflow['76']['inputs']['number'] = size.width;
|
||||
workflow['77']['inputs']['number'] = size.height;
|
||||
|
||||
if (srcFiles[0])
|
||||
workflow['64']['inputs']['image'] = srcFiles[0];
|
||||
|
||||
if (srcFiles[1])
|
||||
workflow['15']['inputs']['image'] = srcFiles[1];
|
||||
|
||||
if (srcFiles[2])
|
||||
workflow['68']['inputs']['image'] = srcFiles[2];
|
||||
|
||||
const response = await axios.post(`${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('qwenedit'));
|
||||
|
||||
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', outputFile);
|
||||
|
||||
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 async function convertImageVton(
|
||||
personFile: string,
|
||||
clothFile: string,
|
||||
outputFile: string,
|
||||
comfyBaseUrl: string,
|
||||
comfyOutputDir: string,
|
||||
size: ImageSize = { width: 720, height: 1280 }
|
||||
): Promise<string> {
|
||||
const COMFY_BASE_URL = comfyBaseUrl.replace(/\/$/, '');
|
||||
const COMFY_OUTPUT_DIR = comfyOutputDir;
|
||||
let workflow;
|
||||
|
||||
workflow = JSON.parse(await fs.readFile('src/comfyworkflows/vton_cloth.json', 'utf-8'));
|
||||
workflow['76']['inputs']['number'] = size.width;
|
||||
workflow['77']['inputs']['number'] = size.height;
|
||||
|
||||
workflow['15']['inputs']['image'] = personFile;
|
||||
workflow['64']['inputs']['image'] = clothFile;
|
||||
|
||||
const response = await axios.post(`${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('qwenedit'));
|
||||
|
||||
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', outputFile);
|
||||
|
||||
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 async function convertImageVtonPose(
|
||||
personFile: string,
|
||||
clothFile: string,
|
||||
poseFile: string,
|
||||
outputFile: string,
|
||||
comfyBaseUrl: string,
|
||||
comfyOutputDir: string,
|
||||
size: ImageSize = { width: 720, height: 1280 }
|
||||
): Promise<string> {
|
||||
const COMFY_BASE_URL = comfyBaseUrl.replace(/\/$/, '');
|
||||
const COMFY_OUTPUT_DIR = comfyOutputDir;
|
||||
let workflow;
|
||||
|
||||
workflow = JSON.parse(await fs.readFile('src/comfyworkflows/vton.json', 'utf-8'));
|
||||
workflow['76']['inputs']['number'] = size.width;
|
||||
workflow['77']['inputs']['number'] = size.height;
|
||||
|
||||
workflow['15']['inputs']['image'] = personFile;
|
||||
workflow['64']['inputs']['image'] = clothFile;
|
||||
workflow['68']['inputs']['image'] = poseFile;
|
||||
|
||||
const response = await axios.post(`${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('qwenedit'));
|
||||
|
||||
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', outputFile);
|
||||
|
||||
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 { convertImage, convertImageWithFile, convertImageWithFileForPose, convertImageWithFileHandbag };
|
||||
@ -30,7 +30,7 @@ async function generateImage(
|
||||
workflow['13']['inputs']['image'] = imageName1;
|
||||
|
||||
// Set image name
|
||||
//workflow['16']['inputs']['image'] = imageName2;
|
||||
workflow['16']['inputs']['image'] = imageName2;
|
||||
|
||||
workflow['3']['inputs']['width'] = size.width;
|
||||
workflow['3']['inputs']['height'] = size.height;
|
||||
|
||||
78
src/lib/image-generator-style-faceswap.ts
Normal file
78
src/lib/image-generator-style-faceswap.ts
Normal file
@ -0,0 +1,78 @@
|
||||
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 generateImage(
|
||||
prompt: string,
|
||||
faceImage: string,
|
||||
styleImage: string,
|
||||
newFileName: string,
|
||||
comfyBaseUrl: string,
|
||||
comfyOutputDir: string,
|
||||
size: ImageSize = { width: 720, height: 1280 }
|
||||
): Promise<string> {
|
||||
const COMFY_BASE_URL = comfyBaseUrl.replace(/\/$/, '');
|
||||
const COMFY_OUTPUT_DIR = comfyOutputDir;
|
||||
let workflow;
|
||||
|
||||
workflow = JSON.parse(await fs.readFile('src/comfyworkflows/generate_image_style_faceswap.json', 'utf-8'));
|
||||
workflow['14']['inputs']['text'] = prompt;
|
||||
|
||||
// Set image name
|
||||
workflow['13']['inputs']['image'] = styleImage;
|
||||
|
||||
// Set image name
|
||||
workflow['19']['inputs']['image'] = faceImage;
|
||||
|
||||
workflow['3']['inputs']['width'] = size.width;
|
||||
workflow['3']['inputs']['height'] = size.height;
|
||||
|
||||
|
||||
const response = await axios.post(`${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('STYLEDVIDEOMAKER'));
|
||||
|
||||
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', newFileName);
|
||||
|
||||
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 { generateImage };
|
||||
118
src/lib/image-upscaler.ts
Normal file
118
src/lib/image-upscaler.ts
Normal file
@ -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 };
|
||||
@ -4,25 +4,27 @@ import { logger } from './logger';
|
||||
|
||||
dotenv.config();
|
||||
|
||||
const LLM_BASE_URL = process.env.LLM_BASE_URL;
|
||||
const LMSTUDIO_BASE_URL = process.env.LMSTUDIO_BASE_URL;
|
||||
const LMSTUDIO_API_KEY = process.env.LMSTUDIO_API_KEY;
|
||||
const LMSTUDIO_MODEL = process.env.LMSTUDIO_MODEL;
|
||||
|
||||
async function callLMStudio(prompt: string): Promise<any> {
|
||||
if (!LLM_BASE_URL) {
|
||||
throw new Error('LLM_BASE_URL is not defined in the .env file');
|
||||
async function callLmstudio(prompt: string): Promise<any> {
|
||||
if (!LMSTUDIO_BASE_URL) {
|
||||
throw new Error('LMSTUDIO_BASE_URL is not defined in the .env file');
|
||||
}
|
||||
|
||||
for (let i = 0; i < 10; i++) {
|
||||
let llmResponse = "";
|
||||
|
||||
try {
|
||||
const requestUrl = new URL('v1/chat/completions', LLM_BASE_URL);
|
||||
const response = await fetch(requestUrl, {
|
||||
const response = await fetch(`${LMSTUDIO_BASE_URL}/chat/completions`, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
'Authorization': `Bearer ${LMSTUDIO_API_KEY}`,
|
||||
},
|
||||
body: JSON.stringify({
|
||||
model: 'local-model',
|
||||
model: LMSTUDIO_MODEL,
|
||||
messages: [
|
||||
{
|
||||
role: 'user',
|
||||
@ -40,15 +42,19 @@ async function callLMStudio(prompt: string): Promise<any> {
|
||||
const jsonMatch = content.match(/\{[\s\S]*\}/);
|
||||
if (jsonMatch) {
|
||||
return JSON.parse(jsonMatch[0]);
|
||||
} else {
|
||||
const arrayMatch = content.match(/\[[\s\S]*\]/);
|
||||
if (arrayMatch) {
|
||||
return JSON.parse(arrayMatch[0]);
|
||||
}
|
||||
}
|
||||
// If no JSON/array found, return the raw content
|
||||
return content;
|
||||
} else {
|
||||
logger.error('Unexpected API response:', data);
|
||||
}
|
||||
} catch (error) {
|
||||
logger.error(`Attempt ${i + 1} failed:`, error);
|
||||
if (error instanceof TypeError && error.message.includes('fetch failed')) {
|
||||
logger.error('Could not connect to the LM Studio server. Please ensure the server is running and accessible at the specified LLM_BASE_URL.');
|
||||
}
|
||||
logger.debug(`LLM response: ${llmResponse}`)
|
||||
}
|
||||
}
|
||||
@ -56,9 +62,9 @@ async function callLMStudio(prompt: string): Promise<any> {
|
||||
throw new Error('Failed to get response from LLM after 10 attempts');
|
||||
}
|
||||
|
||||
async function callLMStudioWithFile(imagePath: string, prompt: string): Promise<any> {
|
||||
if (!LLM_BASE_URL) {
|
||||
throw new Error('LLM_BASE_URL is not defined in the .env file');
|
||||
async function callLMStudioAPIWithFile(imagePath: string, prompt: string): Promise<any> {
|
||||
if (!LMSTUDIO_BASE_URL) {
|
||||
throw new Error('LMSTUDIO_BASE_URL is not defined in the .env file');
|
||||
}
|
||||
|
||||
const imageBuffer = fs.readFileSync(imagePath);
|
||||
@ -68,14 +74,14 @@ async function callLMStudioWithFile(imagePath: string, prompt: string): Promise<
|
||||
let llmResponse = "";
|
||||
|
||||
try {
|
||||
const requestUrl = new URL('v1/chat/completions', LLM_BASE_URL);
|
||||
const response = await fetch(requestUrl, {
|
||||
const response = await fetch(`${LMSTUDIO_BASE_URL}/chat/completions`, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
'Authorization': `Bearer ${LMSTUDIO_API_KEY}`,
|
||||
},
|
||||
body: JSON.stringify({
|
||||
model: 'local-model',
|
||||
model: LMSTUDIO_MODEL,
|
||||
messages: [
|
||||
{
|
||||
role: 'user',
|
||||
@ -96,15 +102,18 @@ async function callLMStudioWithFile(imagePath: string, prompt: string): Promise<
|
||||
const jsonMatch = content.match(/\{[\s\S]*\}/);
|
||||
if (jsonMatch) {
|
||||
return JSON.parse(jsonMatch[0]);
|
||||
} else {
|
||||
const arrayMatch = content.match(/\[[\s\S]*\]/);
|
||||
if (arrayMatch) {
|
||||
return JSON.parse(arrayMatch[0]);
|
||||
}
|
||||
}
|
||||
return content;
|
||||
} else {
|
||||
logger.error('Unexpected API response:', data);
|
||||
}
|
||||
} catch (error) {
|
||||
logger.error(`Attempt ${i + 1} failed:`, error);
|
||||
if (error instanceof TypeError && error.message.includes('fetch failed')) {
|
||||
logger.error('Could not connect to the LM Studio server. Please ensure the server is running and accessible at the specified LLM_BASE_URL.');
|
||||
}
|
||||
logger.debug(`LLM response: ${llmResponse}`)
|
||||
}
|
||||
}
|
||||
@ -112,4 +121,4 @@ async function callLMStudioWithFile(imagePath: string, prompt: string): Promise<
|
||||
throw new Error('Failed to describe image after 10 attempts');
|
||||
}
|
||||
|
||||
export { callLMStudio, callLMStudioWithFile };
|
||||
export { callLmstudio, callLMStudioAPIWithFile };
|
||||
|
||||
191
src/lib/pinterest.ts
Normal file
191
src/lib/pinterest.ts
Normal file
@ -0,0 +1,191 @@
|
||||
import { logger } from './logger';
|
||||
import * as fs from 'fs/promises';
|
||||
import dotenv from 'dotenv';
|
||||
import path from 'path';
|
||||
import puppeteer from 'puppeteer';
|
||||
|
||||
export async function getPinUrlFromPinterest(keyword: string): Promise<string | null> {
|
||||
const browser = await puppeteer.launch({ headless: false });
|
||||
const page = await browser.newPage();
|
||||
await page.setUserAgent('Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36');
|
||||
await page.setViewport({ width: 1920, height: 1080 });
|
||||
try {
|
||||
const searchUrl = `https://www.pinterest.com/search/pins/?q=${encodeURIComponent(keyword)}`;
|
||||
await page.goto(searchUrl, { waitUntil: 'networkidle2' });
|
||||
|
||||
const scrollCount = Math.floor(Math.random() * 5) + 1;
|
||||
logger.info(`Scrolling ${scrollCount} times...`);
|
||||
for (let i = 0; i < scrollCount; i++) {
|
||||
await page.evaluate('window.scrollTo(0, document.body.scrollHeight)');
|
||||
await new Promise(resolve => setTimeout(resolve, Math.random() * 1000 + 1000));
|
||||
}
|
||||
|
||||
const pinLinks = await page.$$eval('a', (anchors) =>
|
||||
anchors.map((a) => a.href).filter((href) => href.includes('/pin/'))
|
||||
);
|
||||
|
||||
if (pinLinks.length > 0) {
|
||||
return pinLinks[Math.floor(Math.random() * pinLinks.length)];
|
||||
}
|
||||
return null;
|
||||
} catch (error) {
|
||||
logger.error('Error while getting pin URL from Pinterest:', error);
|
||||
return null;
|
||||
} finally {
|
||||
await browser.close();
|
||||
}
|
||||
}
|
||||
|
||||
export async function downloadImagesFromPinterestSearch(keyword: string, count: number): Promise<string[]> {
|
||||
const browser = await puppeteer.launch({ headless: false });
|
||||
const page = await browser.newPage();
|
||||
await page.setUserAgent('Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36');
|
||||
await page.setViewport({ width: 1920, height: 1080 });
|
||||
try {
|
||||
const searchUrl = `https://www.pinterest.com/search/pins/?q=${encodeURIComponent(keyword)}`;
|
||||
await page.goto(searchUrl, { waitUntil: 'networkidle2' });
|
||||
|
||||
logger.info(`Scrolling 3 times...`);
|
||||
for (let i = 0; i < 3; i++) {
|
||||
await page.evaluate('window.scrollTo(0, document.body.scrollHeight)');
|
||||
await new Promise(resolve => setTimeout(resolve, Math.random() * 1000 + 1000));
|
||||
}
|
||||
|
||||
const imageUrls = await page.$$eval('img', (imgs) => {
|
||||
const urls: string[] = imgs.map(img => {
|
||||
const srcset = img.getAttribute('srcset') || '';
|
||||
if (!srcset) return '';
|
||||
const parts = srcset.split(',').map(p => p.trim());
|
||||
for (const part of parts) {
|
||||
const m = part.match(/^(\S+)\s+4x$/);
|
||||
if (m && m[1]) return m[1];
|
||||
}
|
||||
const src = img.src || '';
|
||||
if (src.includes('/originals/')) return src;
|
||||
return '';
|
||||
}).filter(s => !!s && s.includes('pinimg'));
|
||||
// Remove duplicates
|
||||
return [...new Set(urls)];
|
||||
});
|
||||
|
||||
if (imageUrls.length === 0) {
|
||||
logger.warn(`No 4x image URLs found for keyword "${keyword}"`);
|
||||
return [];
|
||||
}
|
||||
|
||||
// shuffle and pick up to `count` unique images
|
||||
const shuffled = imageUrls.slice().sort(() => 0.5 - Math.random());
|
||||
const chosen = shuffled.slice(0, Math.min(count, shuffled.length));
|
||||
|
||||
const outDir = path.join(process.cwd(), 'download');
|
||||
await fs.mkdir(outDir, { recursive: true });
|
||||
|
||||
const results: string[] = [];
|
||||
for (let i = 0; i < chosen.length; i++) {
|
||||
const src = chosen[i];
|
||||
try {
|
||||
const imgPage = await browser.newPage();
|
||||
const resp = await imgPage.goto(src, { timeout: 30000, waitUntil: 'networkidle2' });
|
||||
if (!resp) {
|
||||
logger.warn(`Failed to fetch image ${src}`);
|
||||
await imgPage.close();
|
||||
continue;
|
||||
}
|
||||
const buffer = await resp.buffer();
|
||||
const timestamp = Date.now();
|
||||
const outPath = path.join(outDir, `${keyword.replace(/\s+/g, '_')}_${timestamp}_${i}.png`);
|
||||
await fs.writeFile(outPath, buffer);
|
||||
results.push(outPath);
|
||||
await imgPage.close();
|
||||
} catch (err) {
|
||||
logger.error(`Failed to download image ${src}:`, err);
|
||||
}
|
||||
}
|
||||
return results;
|
||||
|
||||
} catch (error) {
|
||||
logger.error(`Error while downloading images for keyword "${keyword}":`, error);
|
||||
return [];
|
||||
} finally {
|
||||
await browser.close();
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// Download up to `count` images from a pin URL by opening the pin page and scro lling up to 5 times to trigger lazy loading
|
||||
// Returns an array of saved image paths (may be empty)
|
||||
export async function downloadImageFromPin(pinUrl: string, count: number = 1): Promise<string[]> {
|
||||
const browser = await puppeteer.launch({ headless: false });
|
||||
const page = await browser.newPage();
|
||||
await page.setUserAgent('Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36');
|
||||
await page.setViewport({ width: 1920, height: 1080 });
|
||||
try {
|
||||
await page.goto(pinUrl, { waitUntil: 'networkidle2', timeout: 30000 });
|
||||
for (let i = 0; i < 3; i++) {
|
||||
await page.evaluate('window.scrollTo(0, document.body.scrollHeight)');
|
||||
await new Promise((r) => setTimeout(r, 700 + Math.random() * 800));
|
||||
}
|
||||
|
||||
const imgs: string[] = await page.$$eval('img', imgs => {
|
||||
// For each <img> try to extract the 4x (original) URL from srcset.
|
||||
// srcset example:
|
||||
// "https://i.pinimg.com/236x/...jpg 1x, https://i.pinimg.com/474x/...jpg 2x, https://i.pinimg.com/736x/...jpg 3x, https://i.pinimg.com/originals/...jpg 4x"
|
||||
const urls: string[] = imgs.map(img => {
|
||||
const srcset = (img as HTMLImageElement).getAttribute('srcset') || '';
|
||||
if (!srcset) return '';
|
||||
const parts = srcset.split(',').map(p => p.trim());
|
||||
for (const part of parts) {
|
||||
const m = part.match(/^(\S+)\s+4x$/);
|
||||
if (m && m[1]) return m[1];
|
||||
}
|
||||
// fallback: if src contains "originals" return src
|
||||
const src = (img as HTMLImageElement).src || '';
|
||||
if (src.includes('/originals/')) return src;
|
||||
return '';
|
||||
}).filter(s => !!s && s.includes('pinimg'));
|
||||
return urls;
|
||||
});
|
||||
|
||||
if (!imgs || imgs.length === 0) {
|
||||
logger.warn(`No image src (4x) found on pin page ${pinUrl}`);
|
||||
return [];
|
||||
}
|
||||
|
||||
// shuffle and pick up to `count` unique images
|
||||
const shuffled = imgs.slice().sort(() => 0.5 - Math.random());
|
||||
const chosen = shuffled.slice(0, Math.min(count, shuffled.length));
|
||||
|
||||
const outDir = path.join(process.cwd(), 'download');
|
||||
await fs.mkdir(outDir, { recursive: true });
|
||||
|
||||
const results: string[] = [];
|
||||
for (let i = 0; i < chosen.length; i++) {
|
||||
const src = chosen[i];
|
||||
try {
|
||||
const imgPage = await browser.newPage();
|
||||
const resp = await imgPage.goto(src, { timeout: 30000, waitUntil: 'networkidle2' });
|
||||
if (!resp) {
|
||||
logger.warn(`Failed to fetch image ${src} from ${pinUrl}`);
|
||||
await imgPage.close();
|
||||
continue;
|
||||
}
|
||||
const buffer = await resp.buffer();
|
||||
const pinId = pinUrl.split('/').filter(Boolean).pop() || `pin_${Date.now()}`;
|
||||
const timestamp = Date.now();
|
||||
const outPath = path.join(outDir, `${pinId}_${timestamp}_${i}.png`);
|
||||
await fs.writeFile(outPath, buffer);
|
||||
results.push(outPath);
|
||||
await imgPage.close();
|
||||
} catch (err) {
|
||||
logger.error(`Failed to download image ${src} from ${pinUrl}:`, err);
|
||||
}
|
||||
}
|
||||
|
||||
return results;
|
||||
} catch (err) {
|
||||
logger.error(`Failed to download images from ${pinUrl}:`, err);
|
||||
return [];
|
||||
} finally {
|
||||
await browser.close();
|
||||
}
|
||||
}
|
||||
67
src/lib/util.ts
Normal file
67
src/lib/util.ts
Normal file
@ -0,0 +1,67 @@
|
||||
// png-json-metadata.ts
|
||||
import * as fs from "fs";
|
||||
import extract from "png-chunks-extract";
|
||||
import encodeChunks from "png-chunks-encode";
|
||||
import * as textChunk from "png-chunk-text";
|
||||
|
||||
type PngChunk = { name: string; data: Uint8Array };
|
||||
|
||||
/**
|
||||
* PNG へ JSON を Base64 で埋め込む（tEXt / keyword: "json-b64"）
|
||||
* - JSON は UTF-8 → Base64 にして ASCII 化（tEXt の Latin-1 制限を回避）
|
||||
* - 既存の "json-b64" tEXt があれば置き換え（重複回避）
|
||||
*/
|
||||
export async function embedJsonToPng(path: string, obj: unknown): Promise<void> {
|
||||
const input = fs.readFileSync(path);
|
||||
const chunks = extract(input) as PngChunk[];
|
||||
|
||||
// 既存の "json-b64" tEXt を除外
|
||||
const filtered: PngChunk[] = chunks.filter((c) => {
|
||||
if (c.name !== "tEXt") return true;
|
||||
try {
|
||||
const decoded = textChunk.decode(c.data); // { keyword, text }
|
||||
return decoded.keyword !== "json-b64";
|
||||
} catch {
|
||||
// decode 失敗（別の形式など）は残す
|
||||
return true;
|
||||
}
|
||||
});
|
||||
|
||||
const json = JSON.stringify(obj);
|
||||
const b64 = Buffer.from(json, "utf8").toString("base64"); // ASCII のみ
|
||||
|
||||
// encode() は { name:'tEXt', data: Uint8Array } を返す
|
||||
const newChunk = textChunk.encode("json-b64", b64) as PngChunk;
|
||||
|
||||
// IEND の直前に挿入（PNG の正しい順序を維持）
|
||||
const iendIndex = filtered.findIndex((c) => c.name === "IEND");
|
||||
if (iendIndex < 0) {
|
||||
throw new Error("Invalid PNG: missing IEND chunk.");
|
||||
}
|
||||
filtered.splice(iendIndex, 0, newChunk);
|
||||
|
||||
const out = Buffer.from(encodeChunks(filtered));
|
||||
fs.writeFileSync(path, out);
|
||||
}
|
||||
|
||||
/**
|
||||
* PNG から Base64 JSON（tEXt / keyword: "json-b64"）を読み出す
|
||||
*/
|
||||
export async function readJsonToPng(path: string): Promise<any> {
|
||||
const input = fs.readFileSync(path);
|
||||
const chunks = extract(input) as PngChunk[];
|
||||
|
||||
for (const c of chunks) {
|
||||
if (c.name !== "tEXt") continue;
|
||||
try {
|
||||
const { keyword, text } = textChunk.decode(c.data);
|
||||
if (keyword === "json-b64") {
|
||||
const json = Buffer.from(text, "base64").toString("utf8");
|
||||
return JSON.parse(json);
|
||||
}
|
||||
} catch {
|
||||
// 他の tEXt / 壊れたエントリは無視
|
||||
}
|
||||
}
|
||||
throw new Error("No base64 JSON found in PNG (tEXt keyword 'json-b64').");
|
||||
}
|
||||
|
Before Width: | Height: | Size: 1.7 MiB After Width: | Height: | Size: 1.7 MiB |
|
Before Width: | Height: | Size: 1.9 MiB After Width: | Height: | Size: 1.9 MiB |
|
Before Width: | Height: | Size: 1.8 MiB After Width: | Height: | Size: 1.8 MiB |
|
Before Width: | Height: | Size: 1.8 MiB After Width: | Height: | Size: 1.8 MiB |
@ -2,11 +2,11 @@ import dotenv from 'dotenv';
|
||||
import path from 'path';
|
||||
import fs from 'fs/promises';
|
||||
|
||||
import { logger } from '../lib/logger';
|
||||
import { callOpenAI } from '../lib/openai';
|
||||
import { callLMStudio } from '../lib/lmstudio';
|
||||
import { logger } from '../../lib/logger';
|
||||
import { callOpenAI } from '../../lib/openai';
|
||||
import { callLMStudio } from '../../lib/lmstudio';
|
||||
// import { generateImage as generateFaceImage } from '../lib/image-generator-face'; // Removed
|
||||
import { generateImage } from '../lib/image-generator'; // Added
|
||||
import { generateImage } from '../../lib/image-generator'; // Added
|
||||
|
||||
dotenv.config();
|
||||
|
||||
@ -2,10 +2,10 @@ import dotenv from 'dotenv';
|
||||
import path from 'path';
|
||||
import fs from 'fs/promises';
|
||||
|
||||
import { logger } from '../lib/logger';
|
||||
import { callOpenAI } from '../lib/openai';
|
||||
import { generateImage as generateFaceImage } from '../lib/image-generator-face';
|
||||
import { generateVideo } from '../lib/video-generator';
|
||||
import { logger } from '../../lib/logger';
|
||||
import { callOpenAI } from '../../lib/openai';
|
||||
import { generateImage as generateFaceImage } from '../../lib/image-generator-face';
|
||||
import { generateVideo } from '../../lib/video-generator';
|
||||
|
||||
dotenv.config();
|
||||
|
||||
|
Before Width: | Height: | Size: 1.8 MiB After Width: | Height: | Size: 1.8 MiB |
@ -2,9 +2,9 @@ import dotenv from 'dotenv';
|
||||
import path from 'path';
|
||||
import fs from 'fs/promises';
|
||||
|
||||
import { logger } from '../lib/logger';
|
||||
import { callOpenAI } from '../lib/openai';
|
||||
import { generateVideo } from '../lib/video-generator';
|
||||
import { logger } from '../../lib/logger';
|
||||
import { callOpenAI } from '../../lib/openai';
|
||||
import { generateVideo } from '../../lib/video-generator';
|
||||
|
||||
dotenv.config();
|
||||
|
||||
@ -234,7 +234,7 @@ async function main() {
|
||||
serverForVideo.baseUrl!,
|
||||
serverForVideo.outputDir!,
|
||||
DEFAULT_SIZE,
|
||||
true,
|
||||
false,
|
||||
false
|
||||
);
|
||||
|
||||
|
Before Width: | Height: | Size: 1.9 MiB After Width: | Height: | Size: 1.9 MiB |
78
src/musicspot_generator/v2/combine_image.ts
Normal file
78
src/musicspot_generator/v2/combine_image.ts
Normal file
@ -0,0 +1,78 @@
|
||||
import * as fs from 'fs';
|
||||
import * as path from 'path';
|
||||
import { convertImageWithFile } from '../../lib/image-converter';
|
||||
import dotenv from 'dotenv';
|
||||
|
||||
dotenv.config();
|
||||
|
||||
const girlDir = path.join(__dirname, '../../../input/girl');
|
||||
const monsterDir = path.join(__dirname, '../../../input/monster');
|
||||
const outputDir = path.join(__dirname, '../../../generated');
|
||||
const prompt = "只提取图1中的女生，把她放在图2的怪物之间。";
|
||||
|
||||
if (!fs.existsSync(outputDir)) {
|
||||
fs.mkdirSync(outputDir, { recursive: true });
|
||||
}
|
||||
|
||||
const comfyBaseUrl = process.env.SERVER2_COMFY_BASE_URL;
|
||||
const comfyOutputDir = process.env.SERVER2_COMFY_OUTPUT_DIR;
|
||||
|
||||
if (!comfyBaseUrl || !comfyOutputDir) {
|
||||
console.error("Please define SERVER1_COMFY_BASE_URL and SERVER1_COMFY_OUTPUT_DIR in your .env file");
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
const comfyInputDir = comfyOutputDir.replace("output", "input");
|
||||
if (!fs.existsSync(comfyInputDir)) {
|
||||
fs.mkdirSync(comfyInputDir, { recursive: true });
|
||||
}
|
||||
|
||||
async function combineImages() {
|
||||
while (true) {
|
||||
try {
|
||||
const girlImages = fs.readdirSync(girlDir).filter(file => /\.(jpg|jpeg|png)$/i.test(file));
|
||||
const monsterImages = fs.readdirSync(monsterDir).filter(file => /\.(jpg|jpeg|png)$/i.test(file));
|
||||
|
||||
if (girlImages.length === 0 || monsterImages.length === 0) {
|
||||
console.log('Input directories are empty. Waiting...');
|
||||
await new Promise(resolve => setTimeout(resolve, 5000));
|
||||
continue;
|
||||
}
|
||||
|
||||
const randomGirlImage = girlImages[Math.floor(Math.random() * girlImages.length)];
|
||||
const randomMonsterImage = monsterImages[Math.floor(Math.random() * monsterImages.length)];
|
||||
|
||||
const image1Path = path.join(girlDir, randomGirlImage);
|
||||
const image2Path = path.join(monsterDir, randomMonsterImage);
|
||||
|
||||
// Copy files to comfy input directory
|
||||
const destImage1Path = path.join(comfyInputDir, randomGirlImage);
|
||||
const destImage2Path = path.join(comfyInputDir, randomMonsterImage);
|
||||
fs.copyFileSync(image1Path, destImage1Path);
|
||||
fs.copyFileSync(image2Path, destImage2Path);
|
||||
|
||||
console.log(`Combining ${randomGirlImage} and ${randomMonsterImage}`);
|
||||
|
||||
const generatedFilePath = await convertImageWithFile(prompt, randomGirlImage, randomMonsterImage, comfyBaseUrl!, comfyOutputDir!);
|
||||
|
||||
if (generatedFilePath && fs.existsSync(generatedFilePath)) {
|
||||
const timestamp = new Date().getTime();
|
||||
const newFileName = `combined_${timestamp}.png`;
|
||||
const newFilePath = path.join(outputDir, newFileName);
|
||||
|
||||
fs.renameSync(generatedFilePath, newFilePath);
|
||||
console.log(`Renamed generated file to ${newFilePath}`);
|
||||
} else {
|
||||
console.log("Failed to generate or find the image file.");
|
||||
}
|
||||
|
||||
} catch (error) {
|
||||
console.error('An error occurred:', error);
|
||||
}
|
||||
|
||||
// Wait for a bit before the next iteration
|
||||
await new Promise(resolve => setTimeout(resolve, 5000));
|
||||
}
|
||||
}
|
||||
|
||||
combineImages();
|
||||
BIN
src/musicspot_generator/v2/face.png
Normal file
BIN
src/musicspot_generator/v2/face.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 1.5 MiB |
72
src/musicspot_generator/v2/generate_photo.ts
Normal file
72
src/musicspot_generator/v2/generate_photo.ts
Normal file
@ -0,0 +1,72 @@
|
||||
import fs from 'fs';
|
||||
import path from 'path';
|
||||
import crypto from 'crypto';
|
||||
import { generateImage } from '../../lib/image-generator-style-faceswap';
|
||||
import { logger } from '../../lib/logger';
|
||||
import dotenv from 'dotenv';
|
||||
|
||||
dotenv.config();
|
||||
|
||||
const scenesFilePath = path.resolve(process.cwd(), 'src/musicspot_generator/v2/scenes.json');
|
||||
const faceFilePath = path.resolve(process.cwd(), 'src/musicspot_generator/v2/face.png');
|
||||
const GENERATED_DIR = path.resolve('generated');
|
||||
const DEFAULT_SIZE = { width: 1280, height: 720 };
|
||||
|
||||
interface Scene {
|
||||
scene: string;
|
||||
imagePrompt: {
|
||||
description: string,
|
||||
style: string,
|
||||
lighting: string,
|
||||
outfit: string,
|
||||
location: string,
|
||||
poses: string,
|
||||
angle: string,
|
||||
};
|
||||
videoPromp: string;
|
||||
baseImagePath: string;
|
||||
}
|
||||
|
||||
const COMFY_BASE_URL = process.env.COMFY_BASE_URL;
|
||||
const COMFY_OUTPUT_DIR = process.env.COMFY_OUTPUT_DIR;
|
||||
|
||||
async function generatePhotos() {
|
||||
if (!COMFY_BASE_URL || !COMFY_OUTPUT_DIR) {
|
||||
throw new Error('COMFY_BASE_URL or COMFY_OUTPUT_DIR is not defined in the .env file');
|
||||
}
|
||||
|
||||
const scenesFileContent = fs.readFileSync(scenesFilePath, 'utf-8');
|
||||
const scenesData: { scenes: Scene[] } = JSON.parse(scenesFileContent);
|
||||
|
||||
for (const scene of scenesData.scenes) {
|
||||
const hash = crypto.createHash('sha256').update(scene.baseImagePath).digest('hex');
|
||||
const imgFileName = `${hash}.png`;
|
||||
const outputFilePath = path.join(GENERATED_DIR, imgFileName);
|
||||
|
||||
if (fs.existsSync(outputFilePath)) {
|
||||
logger.info(`Skipping already generated photo for: ${scene.baseImagePath}`);
|
||||
continue;
|
||||
}
|
||||
|
||||
logger.info(`Generating photo for: ${scene.baseImagePath}`);
|
||||
|
||||
try {
|
||||
await generateImage(
|
||||
`Scary realistic photo, ${scene.imagePrompt.location},${scene.imagePrompt.angle},${scene.imagePrompt.lighting},${scene.imagePrompt.outfit}`,
|
||||
faceFilePath,
|
||||
scene.baseImagePath,
|
||||
imgFileName,
|
||||
COMFY_BASE_URL,
|
||||
COMFY_OUTPUT_DIR,
|
||||
DEFAULT_SIZE
|
||||
);
|
||||
logger.info(`Successfully generated photo: ${imgFileName}`);
|
||||
} catch (error) {
|
||||
logger.error(`Error generating photo for scene ${scene.scene}:`, error);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
generatePhotos().catch(error => {
|
||||
logger.error('An unexpected error occurred:', error);
|
||||
});
|
||||
87
src/musicspot_generator/v2/generate_scenes.ts
Normal file
87
src/musicspot_generator/v2/generate_scenes.ts
Normal file
@ -0,0 +1,87 @@
|
||||
import fs from 'fs';
|
||||
import path from 'path';
|
||||
import { callLMStudioAPIWithFile } from '../../lib/lmstudio';
|
||||
import { logger } from '../../lib/logger';
|
||||
|
||||
const promptInstructions = `
|
||||
Video prompt: No slowmotion, Be creative and generate gengle action scene.
|
||||
`;
|
||||
6
|
||||
const inputDir = path.resolve(process.cwd(), 'input/static');
|
||||
const outputFilePath = path.resolve(process.cwd(), 'src/musicspot_generator/v2/scenes.json');
|
||||
|
||||
interface Scene {
|
||||
scene: string;
|
||||
imagePrompt: string;
|
||||
videoPromp: string;
|
||||
baseImagePath: string;
|
||||
}
|
||||
|
||||
async function processImages() {
|
||||
const imageFiles = fs.readdirSync(inputDir).filter(file => /\.(png|jpg|jpeg)$/i.test(file));
|
||||
let scenes: { scenes: Scene[] } = { scenes: [] };
|
||||
|
||||
if (fs.existsSync(outputFilePath)) {
|
||||
const fileContent = fs.readFileSync(outputFilePath, 'utf-8');
|
||||
if (fileContent) {
|
||||
scenes = JSON.parse(fileContent);
|
||||
}
|
||||
}
|
||||
|
||||
for (const imageFile of imageFiles) {
|
||||
const imagePath = path.resolve(inputDir, imageFile);
|
||||
const absoluteImagePath = path.resolve(imagePath);
|
||||
|
||||
const existingScene = scenes.scenes.find(s => s.baseImagePath === absoluteImagePath);
|
||||
if (existingScene) {
|
||||
logger.info(`Skipping already processed image: ${imageFile}`);
|
||||
continue;
|
||||
}
|
||||
|
||||
logger.info(`Processing image: ${imageFile} `);
|
||||
|
||||
const prompt = `
|
||||
Analyze the provided image and generate a JSON object with the following structure:
|
||||
{
|
||||
"scenes": [
|
||||
{
|
||||
"scene": "A descriptive title for the scene in the image.",
|
||||
"imagePrompt": {
|
||||
"description": "A detailed description of the image content.",
|
||||
"style": "Art style or photography style of the image.",
|
||||
"lighting": "Description of the lighting in the image.",
|
||||
"outfit": "Description of the outfit or clothing style in the image.",
|
||||
"location": "Description of the location or setting of the image.",
|
||||
"poses": "Description of the poses or actions of any subjects in the image.",
|
||||
"angle": "Description of the camera angle or perspective of the image.",
|
||||
}
|
||||
"videoPromp": "Based on the image, create a prompt for a video that shows what might happen next or brings the scene to life.",
|
||||
"baseImagePath": "The absolute path of the base image."
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
Instructions: ${promptInstructions}
|
||||
`;
|
||||
|
||||
try {
|
||||
const result = await callLMStudioAPIWithFile(imagePath, prompt);
|
||||
if (result && result.scenes) {
|
||||
const newScene = result.scenes[0];
|
||||
newScene.baseImagePath = absoluteImagePath; // Ensure the path is correct
|
||||
scenes.scenes.push(newScene);
|
||||
|
||||
fs.writeFileSync(outputFilePath, JSON.stringify(scenes, null, 2));
|
||||
logger.info(`Successfully processed and saved scene for: ${imageFile} `);
|
||||
} else {
|
||||
logger.error('Failed to get valid scene data from API for image:', imageFile);
|
||||
}
|
||||
} catch (error) {
|
||||
logger.error(`Error processing image ${imageFile}: `, error);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
processImages().catch(error => {
|
||||
logger.error('An unexpected error occurred:', error);
|
||||
});
|
||||
63
src/musicspot_generator/v2/generate_video.ts
Normal file
63
src/musicspot_generator/v2/generate_video.ts
Normal file
@ -0,0 +1,63 @@
|
||||
import * as fs from 'fs';
|
||||
import * as path from 'path';
|
||||
import * as crypto from 'crypto';
|
||||
import { generateVideo } from '../../lib/video-generator';
|
||||
import dotenv from 'dotenv';
|
||||
|
||||
dotenv.config();
|
||||
|
||||
interface Scene {
|
||||
scene: string;
|
||||
videoPrompt: string;
|
||||
baseImagePath: string;
|
||||
}
|
||||
|
||||
const scenesFilePath = path.join(__dirname, 'scenes.json');
|
||||
const generatedFolderPath = path.join(__dirname, '..', '..', '..', 'generated');
|
||||
|
||||
async function processScenes() {
|
||||
try {
|
||||
const scenesData = fs.readFileSync(scenesFilePath, 'utf-8');
|
||||
const scenes: Scene[] = JSON.parse(scenesData).scenes;
|
||||
|
||||
for (const scene of scenes) {
|
||||
|
||||
try {
|
||||
const hash = crypto.createHash('sha256').update(scene.baseImagePath).digest('hex');
|
||||
const imageFileName = `${hash}.png`;
|
||||
const imagePath = path.join(generatedFolderPath, imageFileName);
|
||||
|
||||
if (fs.existsSync(imagePath)) {
|
||||
const outputVideoFileName = `${hash}.mp4`;
|
||||
const outputVideoPath = path.join(generatedFolderPath, outputVideoFileName);
|
||||
|
||||
if (fs.existsSync(outputVideoPath)) {
|
||||
console.log(`Video already exists for scene ${scene.scene}, skipping.`);
|
||||
continue;
|
||||
}
|
||||
|
||||
console.log(`Generating video for scene ${scene.scene}...`);
|
||||
|
||||
await generateVideo(
|
||||
scene.videoPrompt,
|
||||
imagePath,
|
||||
outputVideoPath,
|
||||
process.env.COMFY_BASE_URL!,
|
||||
process.env.COMFY_OUTPUT_DIR!,
|
||||
{ width: 1280, height: 720 }
|
||||
);
|
||||
console.log(`Video for scene ${scene.scene} saved to ${outputVideoPath}`);
|
||||
} else {
|
||||
console.warn(`Image not found for scene ${scene.scene}: ${imagePath}`);
|
||||
}
|
||||
} catch (e) {
|
||||
continue;
|
||||
}
|
||||
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('Error processing scenes:', error);
|
||||
}
|
||||
}
|
||||
|
||||
processScenes();
|
||||
@ -0,0 +1,68 @@
|
||||
import * as fs from 'fs';
|
||||
import * as path from 'path';
|
||||
import * as crypto from 'crypto';
|
||||
import { generateVideo } from '../../lib/video-generator';
|
||||
import { callLMStudioAPIWithFile } from '../../lib/lmstudio';
|
||||
import dotenv from 'dotenv';
|
||||
|
||||
dotenv.config();
|
||||
|
||||
const inputFolderPath = path.join(__dirname, '..', '..', '..', 'input/static');
|
||||
const generatedFolderPath = path.join(__dirname, '..', '..', '..', 'generated');
|
||||
|
||||
async function processImages() {
|
||||
try {
|
||||
const imageFiles = fs.readdirSync(inputFolderPath);
|
||||
|
||||
for (const imageFile of imageFiles) {
|
||||
const imagePath = path.join(inputFolderPath, imageFile);
|
||||
|
||||
try {
|
||||
const hash = crypto.createHash('sha256').update(imageFile).digest('hex');
|
||||
const outputVideoFileName = `${hash}.mp4`;
|
||||
const outputVideoPath = path.join(generatedFolderPath, outputVideoFileName);
|
||||
|
||||
if (fs.existsSync(outputVideoPath)) {
|
||||
console.log(`Video already exists for image ${imageFile}, skipping.`);
|
||||
continue;
|
||||
}
|
||||
|
||||
console.log(`Generating video prompt for image ${imageFile}...`);
|
||||
const promptResult = await callLMStudioAPIWithFile(
|
||||
imagePath,
|
||||
`
|
||||
Generate a short, dancing video prompt for an image located at ${imagePath}.
|
||||
Return the result in this format: {"result":""}
|
||||
Instruction:
|
||||
- Find best dancing expression based on the photo
|
||||
`);
|
||||
const videoPrompt = promptResult.result;
|
||||
console.log(`Video prompt ${videoPrompt}`);
|
||||
|
||||
if (!videoPrompt) {
|
||||
console.error(`Could not generate video prompt for image ${imageFile}`);
|
||||
continue;
|
||||
}
|
||||
|
||||
console.log(`Generating video for image ${imageFile}...`);
|
||||
|
||||
await generateVideo(
|
||||
videoPrompt,
|
||||
imagePath,
|
||||
outputVideoPath,
|
||||
process.env.COMFY_BASE_URL!,
|
||||
process.env.COMFY_OUTPUT_DIR!,
|
||||
{ width: 1280, height: 720 }
|
||||
);
|
||||
console.log(`Video for image ${imageFile} saved to ${outputVideoPath}`);
|
||||
} catch (e) {
|
||||
console.error(`Error processing image ${imageFile}:`, e);
|
||||
continue;
|
||||
}
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('Error processing images:', error);
|
||||
}
|
||||
}
|
||||
|
||||
processImages();
|
||||
227
src/musicspot_generator/v2/photo_download.ts
Normal file
227
src/musicspot_generator/v2/photo_download.ts
Normal file
@ -0,0 +1,227 @@
|
||||
import { callLmstudio } from '../../lib/lmstudio';
|
||||
import { logger } from '../../lib/logger';
|
||||
import * as fs from 'fs/promises';
|
||||
import dotenv from 'dotenv';
|
||||
import path from 'path';
|
||||
import puppeteer from 'puppeteer';
|
||||
|
||||
dotenv.config();
|
||||
|
||||
const SCROLL_SEARCH = 3; // scroll times on search results
|
||||
const SCROLL_PIN = 3; // scroll times on pin page
|
||||
const PINS_TO_COLLECT = 5;
|
||||
|
||||
// Hard-coded user prompt
|
||||
const HARDCODED_USER_PROMPT = process.env.HARDCODED_USER_PROMPT || `
|
||||
Generate 20 keywords for perfume brand photo. List of 20 most famous perfume brands, and its popular perfume names:
|
||||
Example output : ["chanel N5", "dior j'adore", "gucci bloom"....]
|
||||
`;
|
||||
|
||||
async function getPinUrlsFromPinterest(keyword: string, scrollCount = SCROLL_SEARCH, limit = PINS_TO_COLLECT): Promise<string[]> {
|
||||
const browser = await puppeteer.launch({ headless: true });
|
||||
const page = await browser.newPage();
|
||||
await page.setUserAgent('Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36');
|
||||
await page.setViewport({ width: 1920, height: 1080 });
|
||||
try {
|
||||
const searchUrl = `https://www.pinterest.com/search/pins/?q=${encodeURIComponent(keyword)}`;
|
||||
await page.goto(searchUrl, { waitUntil: 'networkidle2' });
|
||||
|
||||
let pinLinks = new Set<string>();
|
||||
for (let i = 0; i < scrollCount; i++) {
|
||||
const linksBefore = pinLinks.size;
|
||||
const newLinks = await page.$$eval('a', (anchors) =>
|
||||
anchors.map((a) => a.href).filter((href) => href.includes('/pin/'))
|
||||
);
|
||||
newLinks.forEach(link => pinLinks.add(link));
|
||||
|
||||
if (pinLinks.size >= limit) {
|
||||
break;
|
||||
}
|
||||
|
||||
await page.evaluate('window.scrollTo(0, document.body.scrollHeight)');
|
||||
await new Promise(r => setTimeout(r, 500 + Math.random() * 1000));
|
||||
|
||||
if (pinLinks.size === linksBefore) {
|
||||
// If no new pins are loaded, stop scrolling
|
||||
logger.info(`No new pins loaded for "${keyword}", stopping scroll.`);
|
||||
break;
|
||||
}
|
||||
}
|
||||
return Array.from(pinLinks).slice(0, limit);
|
||||
} catch (error) {
|
||||
logger.error(`Error while getting pin URLs from Pinterest for keyword "${keyword}":`, error);
|
||||
return [];
|
||||
} finally {
|
||||
await browser.close();
|
||||
}
|
||||
}
|
||||
|
||||
async function downloadImagesFromPin(pinUrl: string, scrollTimes = SCROLL_PIN): Promise<string[]> {
|
||||
const browser = await puppeteer.launch({ headless: true });
|
||||
const page = await browser.newPage();
|
||||
await page.setUserAgent('Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36');
|
||||
await page.setViewport({ width: 1920, height: 1080 });
|
||||
try {
|
||||
await page.goto(pinUrl, { waitUntil: 'networkidle2', timeout: 30000 });
|
||||
for (let i = 0; i < scrollTimes; i++) {
|
||||
await page.evaluate('window.scrollTo(0, document.body.scrollHeight)');
|
||||
await new Promise((r) => setTimeout(r, 700 + Math.random() * 800));
|
||||
}
|
||||
const imgs: string[] = await page.$$eval('img', imgs => {
|
||||
const urls: string[] = imgs.map(img => {
|
||||
const srcset = (img as HTMLImageElement).getAttribute('srcset') || '';
|
||||
if (!srcset) {
|
||||
return ''; // Ignore images without srcset
|
||||
}
|
||||
|
||||
const parts = srcset.split(',').map(p => p.trim());
|
||||
for (const part of parts) {
|
||||
const match = part.match(/^(\S+)\s+4x$/);
|
||||
if (match && match[1]) {
|
||||
return match[1]; // Found the 4x version, return it
|
||||
}
|
||||
}
|
||||
|
||||
return ''; // No 4x version found for this image
|
||||
}).filter(s => !!s && s.includes('pinimg')); // Filter out empty strings and non-pinterest images
|
||||
return [...new Set(urls)]; // Return unique URLs
|
||||
});
|
||||
|
||||
if (!imgs || imgs.length === 0) {
|
||||
logger.warn(`No high-res images found on pin ${pinUrl}`);
|
||||
return [];
|
||||
}
|
||||
|
||||
const outDir = path.join(process.cwd(), 'download');
|
||||
await fs.mkdir(outDir, { recursive: true });
|
||||
const results: string[] = [];
|
||||
for (let i = 0; i < imgs.length; i++) {
|
||||
const src = imgs[i];
|
||||
try {
|
||||
const imgPage = await browser.newPage();
|
||||
const resp = await imgPage.goto(src, { timeout: 30000, waitUntil: 'load' });
|
||||
if (!resp) { await imgPage.close(); continue; }
|
||||
const buffer = await resp.buffer();
|
||||
const pinId = pinUrl.split('/').filter(Boolean).pop() || `pin_${Date.now()}`;
|
||||
const timestamp = Date.now();
|
||||
const outPath = path.join(outDir, `${pinId}_${timestamp}_${i}.png`);
|
||||
await fs.writeFile(outPath, buffer);
|
||||
results.push(outPath);
|
||||
await imgPage.close();
|
||||
} catch (err) {
|
||||
logger.error(`Failed to download image ${src} from ${pinUrl}:`, err);
|
||||
}
|
||||
}
|
||||
return results;
|
||||
} catch (err) {
|
||||
logger.error(`Failed to download images from ${pinUrl}:`, err);
|
||||
return [];
|
||||
} finally {
|
||||
await browser.close();
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// Re-usable helper to extract JSON embedded in text
|
||||
function extractJsonFromText(text: string): any | null {
|
||||
if (!text || typeof text !== 'string') return null;
|
||||
const fenced = text.match(/```(?:json)?\s*([\s\S]*?)\s*```/i);
|
||||
if (fenced && fenced[1]) {
|
||||
try { return JSON.parse(fenced[1].trim()); } catch (e) { /* fall through */ }
|
||||
}
|
||||
const brace = text.match(/\{[\s\S]*\}|\[[\s\S]*\]/);
|
||||
if (brace && brace[0]) {
|
||||
try { return JSON.parse(brace[0]); } catch (e) { return null; }
|
||||
}
|
||||
// Attempt line-separated keywords fallback
|
||||
const lines = text.split(/\r?\n/).map((l: string) => l.trim()).filter(Boolean);
|
||||
if (lines.length > 1) return lines;
|
||||
return null;
|
||||
}
|
||||
|
||||
async function extractKeywordsFromPromptWithLmstudio(prompt: string, count = 5): Promise<string[]> {
|
||||
const instruction = `You are given a short instruction describing the type of content to search for.
|
||||
Return exactly a JSON array of ${count} short keyword phrases suitable for searching Pinterest. `;
|
||||
|
||||
try {
|
||||
const res = await callLmstudio(`${instruction}\n\nInstruction: ${prompt}`);
|
||||
if (!res) {
|
||||
logger.warn('callLmstudio returned empty response for keyword extraction.');
|
||||
return [];
|
||||
}
|
||||
|
||||
let parsed: any;
|
||||
if (typeof res === 'object' && res.text) {
|
||||
parsed = extractJsonFromText(res.text);
|
||||
} else if (typeof res === 'string') {
|
||||
parsed = extractJsonFromText(res);
|
||||
} else if (typeof res === 'object') {
|
||||
parsed = res;
|
||||
}
|
||||
|
||||
if (Array.isArray(parsed)) {
|
||||
return parsed.map(String).slice(0, count);
|
||||
}
|
||||
|
||||
if (typeof parsed === 'object' && parsed !== null) {
|
||||
const maybe = parsed.keywords || parsed.list || parsed.items || parsed.keywords_list;
|
||||
if (Array.isArray(maybe)) return maybe.map(String).slice(0, count);
|
||||
}
|
||||
|
||||
const text = typeof res === 'string' ? res : (res && res.text) || JSON.stringify(res);
|
||||
const lines = text.split(/\r?\n/).map((l: string) => l.replace(/^\d+[\).\s-]*/, '').trim()).filter(Boolean);
|
||||
if (lines.length >= 1) {
|
||||
return lines.slice(0, count);
|
||||
}
|
||||
|
||||
logger.warn(`Could not parse keywords from LM Studio response: ${JSON.stringify(res)}`);
|
||||
return [];
|
||||
|
||||
} catch (error) {
|
||||
logger.error('Error during keyword extraction with callLmstudio:', error);
|
||||
return [];
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
(async () => {
|
||||
logger.info(`Starting photo download process with prompt: "${HARDCODED_USER_PROMPT}"`);
|
||||
|
||||
// 1. Extract keywords from the hardcoded prompt
|
||||
const keywords = await extractKeywordsFromPromptWithLmstudio(HARDCODED_USER_PROMPT, 20); // Using 5 keywords to get a good variety
|
||||
if (!keywords || keywords.length === 0) {
|
||||
logger.error("Could not extract keywords from prompt. Exiting.");
|
||||
return;
|
||||
}
|
||||
logger.info(`Extracted keywords: ${keywords.join(', ')}`);
|
||||
|
||||
// 2. Search Pinterest for each keyword and collect pin URLs
|
||||
let allPinUrls = new Set<string>();
|
||||
for (const keyword of keywords) {
|
||||
logger.info(`Searching Pinterest for keyword: "${keyword}"`);
|
||||
const pinUrls = await getPinUrlsFromPinterest(keyword, SCROLL_SEARCH, PINS_TO_COLLECT);
|
||||
pinUrls.forEach(url => allPinUrls.add(url));
|
||||
}
|
||||
|
||||
const finalPinUrls = Array.from(allPinUrls);
|
||||
logger.info(`Collected ${finalPinUrls.length} unique pin URLs to process.`);
|
||||
|
||||
// 3. Go through each pin URL, scroll, and download all photos
|
||||
let totalDownloads = 0;
|
||||
for (const pinUrl of finalPinUrls) {
|
||||
try {
|
||||
logger.info(`Processing pin: ${pinUrl}`);
|
||||
const downloadedPaths = await downloadImagesFromPin(pinUrl, SCROLL_PIN);
|
||||
if (downloadedPaths.length > 0) {
|
||||
logger.info(`Successfully downloaded ${downloadedPaths.length} images from ${pinUrl}`);
|
||||
totalDownloads += downloadedPaths.length;
|
||||
} else {
|
||||
logger.warn(`No images were downloaded from ${pinUrl}`);
|
||||
}
|
||||
} catch (error) {
|
||||
logger.error(`An error occurred while processing pin ${pinUrl}:`, error);
|
||||
}
|
||||
}
|
||||
|
||||
logger.info(`Photo download process finished. Total images downloaded: ${totalDownloads}`);
|
||||
})();
|
||||
75
src/product/clean_background.ts
Normal file
75
src/product/clean_background.ts
Normal file
@ -0,0 +1,75 @@
|
||||
import { convertImage } from '../lib/image-converter';
|
||||
import * as fs from 'fs-extra';
|
||||
import * as path from 'path';
|
||||
import dotenv from 'dotenv';
|
||||
|
||||
dotenv.config();
|
||||
|
||||
const inputDir = path.join(__dirname, '../../input');
|
||||
const outputDir = path.join(__dirname, '../../generated/clearned');
|
||||
|
||||
const comfyUrl = process.env.SERVER1_COMFY_BASE_URL;
|
||||
const comfyOutputDir = process.env.SERVER1_COMFY_OUTPUT_DIR;
|
||||
|
||||
if (!comfyUrl || !comfyOutputDir) {
|
||||
console.error("ComfyUI URL or Output Directory is not set in environment variables.");
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
const comfyInputDir = comfyOutputDir.replace("output", "input");
|
||||
|
||||
async function processImages() {
|
||||
await fs.ensureDir(outputDir);
|
||||
|
||||
const files = await fs.readdir(inputDir);
|
||||
let index = 1;
|
||||
|
||||
for (const file of files) {
|
||||
const sourceFilePath = path.join(inputDir, file);
|
||||
const stats = await fs.stat(sourceFilePath);
|
||||
|
||||
if (stats.isFile()) {
|
||||
console.log(`Processing ${file}...`);
|
||||
|
||||
const comfyInputPath = path.join(comfyInputDir, file);
|
||||
|
||||
try {
|
||||
// 1. Copy file to ComfyUI input directory
|
||||
await fs.copy(sourceFilePath, comfyInputPath);
|
||||
console.log(`Copied ${file} to ComfyUI input.`);
|
||||
|
||||
const prompt = "请从图1中提取主要主体，把背景设置为浅灰色，并让主体正面朝向，制作成产品照片。";
|
||||
|
||||
// 2. Call convertImage with correct parameters
|
||||
const generatedFilePath = await convertImage(prompt, file, comfyUrl!, comfyOutputDir!);
|
||||
|
||||
if (generatedFilePath && await fs.pathExists(generatedFilePath)) {
|
||||
const outputFilename = `clearned_${index}.png`;
|
||||
const finalOutputPath = path.join(outputDir, outputFilename);
|
||||
|
||||
// 3. Move the generated file to the final destination
|
||||
await fs.move(generatedFilePath, finalOutputPath, { overwrite: true });
|
||||
console.log(`Saved cleaned image to ${finalOutputPath}`);
|
||||
index++;
|
||||
|
||||
// 4. Delete the original file from the script's input directory
|
||||
await fs.unlink(sourceFilePath);
|
||||
console.log(`Deleted original file: ${file}`);
|
||||
}
|
||||
|
||||
// 5. Clean up the file from ComfyUI input directory
|
||||
await fs.unlink(comfyInputPath);
|
||||
console.log(`Cleaned up ${file} from ComfyUI input.`);
|
||||
|
||||
} catch (error) {
|
||||
console.error(`Failed to process ${file}:`, error);
|
||||
// If something fails, make sure to clean up the copied file if it exists
|
||||
if (await fs.pathExists(comfyInputPath)) {
|
||||
await fs.unlink(comfyInputPath);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
processImages().catch(console.error);
|
||||
88
src/product/generate_image.ts
Normal file
88
src/product/generate_image.ts
Normal file
@ -0,0 +1,88 @@
|
||||
import * as fs from 'fs/promises';
|
||||
import * as path from 'path';
|
||||
import dotenv from 'dotenv';
|
||||
import { readJsonToPng, embedJsonToPng } from '../lib/util';
|
||||
import { convertImage } from '../lib/image-converter';
|
||||
|
||||
dotenv.config();
|
||||
|
||||
const inputDir = './generated/prompts';
|
||||
const outputDir = './generated/image';
|
||||
|
||||
const COMFY_BASE_URL = process.env.SERVER1_COMFY_BASE_URL!;
|
||||
const COMFY_OUTPUT_DIR = process.env.SERVER1_COMFY_OUTPUT_DIR!;
|
||||
|
||||
interface PngMetadata {
|
||||
prompts: {
|
||||
imagePrompt: string;
|
||||
videoPrompt: string;
|
||||
}[];
|
||||
}
|
||||
|
||||
async function main() {
|
||||
await fs.mkdir(outputDir, { recursive: true });
|
||||
|
||||
const files = await fs.readdir(inputDir);
|
||||
let generatedImageIndex = 0;
|
||||
|
||||
for (const file of files) {
|
||||
if (path.extname(file).toLowerCase() !== '.png') {
|
||||
continue;
|
||||
}
|
||||
|
||||
const inputFile = path.join(inputDir, file);
|
||||
const metadata = await readJsonToPng(inputFile) as PngMetadata;
|
||||
|
||||
if (metadata && metadata.prompts && Array.isArray(metadata.prompts)) {
|
||||
console.log(`Processing ${file} with ${metadata.prompts.length} prompt pairs.`);
|
||||
|
||||
const inputfolderFullpath = COMFY_OUTPUT_DIR.replace("output", "input");
|
||||
await fs.copyFile(inputFile, path.join(inputfolderFullpath, file));
|
||||
|
||||
for (const promptPair of metadata.prompts) {
|
||||
const { imagePrompt, videoPrompt } = promptPair;
|
||||
const newFileName = `cleaned_prompt_generated_${generatedImageIndex}.png`;
|
||||
generatedImageIndex++;
|
||||
const outputPath = path.join(outputDir, newFileName);
|
||||
|
||||
try {
|
||||
await fs.access(outputPath);
|
||||
console.log(`File ${newFileName} already exists, skipping.`);
|
||||
continue;
|
||||
} catch (error) {
|
||||
// File does not exist, proceed with generation
|
||||
}
|
||||
|
||||
console.log(`Generating image for prompt: "${imagePrompt}"`);
|
||||
|
||||
try {
|
||||
const generatedFilePath = await convertImage(
|
||||
imagePrompt,
|
||||
file, // Using the same image for both inputs as per interpretation
|
||||
COMFY_BASE_URL,
|
||||
COMFY_OUTPUT_DIR
|
||||
);
|
||||
|
||||
// The convertImage function saves the file in a generic location.
|
||||
// We need to move it to the correct location with the correct name.
|
||||
await fs.rename(generatedFilePath, outputPath);
|
||||
|
||||
const newMetadata = {
|
||||
imagePrompt: imagePrompt,
|
||||
videoPrompt: videoPrompt
|
||||
};
|
||||
|
||||
await embedJsonToPng(outputPath, newMetadata);
|
||||
|
||||
console.log(`Successfully generated and saved ${newFileName} with metadata.`);
|
||||
} catch (error) {
|
||||
console.error(`Error generating image for prompt "${imagePrompt}":`, error);
|
||||
}
|
||||
}
|
||||
} else {
|
||||
console.log(`Skipping ${file}, no valid prompts metadata found.`);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
main().catch(console.error);
|
||||
169
src/product/generate_prompt.ts
Normal file
169
src/product/generate_prompt.ts
Normal file
@ -0,0 +1,169 @@
|
||||
import * as fs from 'fs';
|
||||
import * as path from 'path';
|
||||
import { callLMStudioAPIWithFile, callLmstudio } from '../lib/lmstudio';
|
||||
import { embedJsonToPng } from '../lib/util';
|
||||
import { downloadImagesFromPinterestSearch } from '../lib/pinterest';
|
||||
import { logger } from '../lib/logger';
|
||||
import sharp from 'sharp';
|
||||
|
||||
const INPUT_DIR = path.join(process.cwd(), 'input');
|
||||
const OUTPUT_DIR = path.join(process.cwd(), 'generated', 'prompts');
|
||||
|
||||
if (!fs.existsSync(OUTPUT_DIR)) {
|
||||
fs.mkdirSync(OUTPUT_DIR, { recursive: true });
|
||||
}
|
||||
|
||||
async function generatePromptsForImage(imagePath: string, index: number) {
|
||||
const outputFilePath = path.join(OUTPUT_DIR, `cleaned_prompt_${index}.png`);
|
||||
logger.info(`Processing image: ${path.basename(imagePath)} -> ${path.basename(outputFilePath)}`);
|
||||
|
||||
try {
|
||||
// Step 1: Detect main object and generate colors from the input image
|
||||
const colorGenerationPrompt = `
|
||||
You are a creative assistant. Analyze the provided image.
|
||||
Identify the main subject product ( not a product name).
|
||||
Then, list exactly five colors related to this subject:
|
||||
- Two colors that are common for this object.
|
||||
- Two colors that are uncommon but plausible.
|
||||
- One color that is completely crazy or surreal for this object.
|
||||
|
||||
Output strictly in this JSON format:
|
||||
{
|
||||
"result": {
|
||||
"main_object": "the identified noun",
|
||||
"colors": [
|
||||
"color1",
|
||||
"color2",
|
||||
"color3",
|
||||
"color4",
|
||||
"color5"
|
||||
]
|
||||
}
|
||||
}
|
||||
`;
|
||||
const colorResponse = await callLMStudioAPIWithFile(imagePath, colorGenerationPrompt);
|
||||
const { main_object, colors } = colorResponse.result;
|
||||
|
||||
if (!main_object || !Array.isArray(colors) || colors.length !== 5) {
|
||||
logger.error(`Failed to get a valid main object and color list for ${imagePath}.`);
|
||||
return;
|
||||
}
|
||||
|
||||
logger.info(`Main object: "${main_object}", Colors: ${colors.join(', ')}`);
|
||||
|
||||
const prompts: { imagePrompt: string, videoPrompt: string }[] = [];
|
||||
const themes = ["special", "unique", "beautiful", "crazy", "funny"];
|
||||
|
||||
// Step 2: Iterate through each color
|
||||
for (const color of colors) {
|
||||
const randomTheme = themes[Math.floor(Math.random() * themes.length)];
|
||||
const pinterestQuery = `${main_object} product photo ${color} background ${randomTheme}`;
|
||||
logger.info(`Searching Pinterest for: "${pinterestQuery}"`);
|
||||
|
||||
// Step 3: Get an image from Pinterest
|
||||
const downloadedImages = await downloadImagesFromPinterestSearch(pinterestQuery, 1);
|
||||
if (downloadedImages.length === 0) {
|
||||
logger.warn(`Could not find an image on Pinterest for query: "${pinterestQuery}"`);
|
||||
continue;
|
||||
}
|
||||
const pinterestImagePath = downloadedImages[0];
|
||||
logger.info(`Downloaded Pinterest image: ${pinterestImagePath}`);
|
||||
|
||||
// Step 4: Generate a detailed prompt from the Pinterest image
|
||||
const imagePromptRequest = `
|
||||
You are an expert in generating descriptive prompts for image generation models.
|
||||
Analyze the provided image and describe it in a single, detailed paragraph.
|
||||
Focus on style, mood, lighting, color palette, sub-objects, and composition.
|
||||
Do not mention the main object itself. The prompt should be about the scene.
|
||||
|
||||
Output strictly in this JSON format:
|
||||
{
|
||||
"result": "your generated prompt here"
|
||||
}
|
||||
`;
|
||||
const imagePromptResponse = await callLMStudioAPIWithFile(pinterestImagePath, imagePromptRequest);
|
||||
const imagePrompt = imagePromptResponse.result;
|
||||
|
||||
if (imagePrompt) {
|
||||
logger.info(`Generated image prompt for color ${color}: "${imagePrompt}"`);
|
||||
|
||||
// Step 5: Generate a matching video prompt
|
||||
const videoPromptRequest = `
|
||||
You are a creative director for a short, stylish video ad.
|
||||
Based on the provided image and the following scene description, generate an attractive video prompt.
|
||||
|
||||
Main Subject: ${main_object}
|
||||
Scene Description: ${imagePrompt}
|
||||
|
||||
The video prompt should:
|
||||
- Be in English and approximately 50 words.
|
||||
- Describe one clear action involving the main subject.
|
||||
- Include one specific camera movement (e.g., slow zoom in, orbiting shot, push-in, pull-out).
|
||||
- Be dynamic and visually appealing.
|
||||
|
||||
Output strictly in this JSON format:
|
||||
{
|
||||
"result": "your generated video prompt here"
|
||||
}
|
||||
`;
|
||||
const videoPromptResponse = await callLMStudioAPIWithFile(pinterestImagePath, videoPromptRequest);
|
||||
const videoPrompt = videoPromptResponse.result;
|
||||
|
||||
if (videoPrompt) {
|
||||
logger.info(`Generated video prompt for color ${color}: "${videoPrompt}"`);
|
||||
prompts.push({ imagePrompt, videoPrompt });
|
||||
} else {
|
||||
logger.warn(`Failed to generate a video prompt for ${pinterestImagePath}`);
|
||||
}
|
||||
} else {
|
||||
logger.warn(`Failed to generate an image prompt for ${pinterestImagePath}`);
|
||||
}
|
||||
}
|
||||
|
||||
if (prompts.length === 0) {
|
||||
logger.error(`No prompt pairs were generated for ${imagePath}. Aborting.`);
|
||||
return;
|
||||
}
|
||||
|
||||
// Step 6: Embed all prompts into the original image and save to the new location
|
||||
const metadata = {
|
||||
prompts: prompts
|
||||
};
|
||||
|
||||
// Convert original image to a valid PNG at the output path before embedding
|
||||
await sharp(imagePath)
|
||||
.toFormat('png')
|
||||
.toFile(outputFilePath);
|
||||
|
||||
await embedJsonToPng(outputFilePath, metadata);
|
||||
|
||||
logger.info(`Successfully generated prompts and saved metadata to ${outputFilePath}`);
|
||||
|
||||
} catch (error) {
|
||||
logger.error(`An error occurred while processing ${imagePath}:`, error);
|
||||
}
|
||||
}
|
||||
|
||||
async function main() {
|
||||
try {
|
||||
const files = fs.readdirSync(INPUT_DIR);
|
||||
const imageFiles = files.filter(file => /\.(png|jpg|jpeg)$/i.test(file));
|
||||
|
||||
if (imageFiles.length === 0) {
|
||||
console.log('No images found in the input directory.');
|
||||
return;
|
||||
}
|
||||
|
||||
for (let i = 0; i < imageFiles.length; i++) {
|
||||
const imageFile = imageFiles[i];
|
||||
const imagePath = path.join(INPUT_DIR, imageFile);
|
||||
await generatePromptsForImage(imagePath, i);
|
||||
}
|
||||
|
||||
console.log('All images processed.');
|
||||
} catch (error) {
|
||||
console.error('An error occurred in the main process:', error);
|
||||
}
|
||||
}
|
||||
|
||||
main();
|
||||
74
src/product/generate_video.ts
Normal file
74
src/product/generate_video.ts
Normal file
@ -0,0 +1,74 @@
|
||||
import * as fs from 'fs/promises';
|
||||
import * as path from 'path';
|
||||
import dotenv from 'dotenv';
|
||||
import { readJsonToPng } from '../lib/util';
|
||||
import { generateVideo } from '../lib/video-generator';
|
||||
|
||||
dotenv.config();
|
||||
|
||||
const inputDir = './input';
|
||||
const outputDir = './generated/video';
|
||||
|
||||
const COMFY_BASE_URL = process.env.SERVER1_COMFY_BASE_URL!;
|
||||
const COMFY_OUTPUT_DIR = process.env.SERVER1_COMFY_OUTPUT_DIR!;
|
||||
|
||||
|
||||
interface PngMetadata {
|
||||
imagePrompt: string;
|
||||
videoPrompt: string;
|
||||
}
|
||||
|
||||
async function main() {
|
||||
await fs.mkdir(outputDir, { recursive: true });
|
||||
|
||||
const files = await fs.readdir(inputDir);
|
||||
const pngFiles = files.filter(file => path.extname(file).toLowerCase() === '.png');
|
||||
|
||||
for (let i = 0; i < pngFiles.length; i++) {
|
||||
const file = pngFiles[i];
|
||||
|
||||
const inputFile = path.join(inputDir, file);
|
||||
const metadata = await readJsonToPng(inputFile) as PngMetadata;
|
||||
|
||||
if (metadata && metadata.videoPrompt) {
|
||||
console.log(`Processing ${file} for video generation.`);
|
||||
|
||||
const originalFileName = path.parse(file).name;
|
||||
const nameParts = originalFileName.split('_');
|
||||
const promptIndex = nameParts[nameParts.length - 1];
|
||||
const newFileName = `product_${i}_${promptIndex}.mp4`;
|
||||
const outputPath = path.join(outputDir, newFileName);
|
||||
|
||||
try {
|
||||
await fs.access(outputPath);
|
||||
console.log(`File ${newFileName} already exists, skipping.`);
|
||||
continue;
|
||||
} catch (error) {
|
||||
// File does not exist, proceed with generation
|
||||
}
|
||||
|
||||
console.log(`Generating video for prompt: "${metadata.videoPrompt}"`);
|
||||
|
||||
const inputfolderFullpath = COMFY_OUTPUT_DIR.replace("output", "input");
|
||||
await fs.copyFile(inputFile, path.join(inputfolderFullpath, file));
|
||||
|
||||
try {
|
||||
await generateVideo(
|
||||
metadata.videoPrompt,
|
||||
file,
|
||||
newFileName,
|
||||
COMFY_BASE_URL,
|
||||
COMFY_OUTPUT_DIR
|
||||
);
|
||||
|
||||
console.log(`Successfully generated and saved ${newFileName}`);
|
||||
} catch (error) {
|
||||
console.error(`Error generating video for ${file}:`, error);
|
||||
}
|
||||
} else {
|
||||
console.log(`Skipping ${file}, no valid videoPrompt metadata found.`);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
main().catch(console.error);
|
||||
64
src/product/photo_download.ts
Normal file
64
src/product/photo_download.ts
Normal file
@ -0,0 +1,64 @@
|
||||
import { callLmstudio } from '../lib/lmstudio';
|
||||
import { logger } from '../lib/logger';
|
||||
import dotenv from 'dotenv';
|
||||
import { downloadImagesFromPinterestSearch } from '../lib/pinterest';
|
||||
|
||||
dotenv.config();
|
||||
|
||||
const PHOTOS_PER_KEYWORD = 10;
|
||||
|
||||
// Hard-coded user prompt
|
||||
const HARDCODED_USER_PROMPT = process.env.HARDCODED_USER_PROMPT || `
|
||||
Generate 20 keywords for various photogeneric product. List of 20 most common photo generic product :
|
||||
Example output : ["food", "perfume", "accesory", "jewelry", "shoes"...]
|
||||
`;
|
||||
|
||||
// Re-usable helper to extract JSON embedded in text
|
||||
function extractJsonFromText(text: string): any | null {
|
||||
if (!text || typeof text !== 'string') return null;
|
||||
const fenced = text.match(/```(?:json)?\s*([\s\S]*?)\s*```/i);
|
||||
if (fenced && fenced[1]) {
|
||||
try { return JSON.parse(fenced[1].trim()); } catch (e) { /* fall through */ }
|
||||
}
|
||||
const brace = text.match(/\{[\s\S]*\}|\[[\s\S]*\]/);
|
||||
if (brace && brace[0]) {
|
||||
try { return JSON.parse(brace[0]); } catch (e) { return null; }
|
||||
}
|
||||
// Attempt line-separated keywords fallback
|
||||
const lines = text.split(/\r?\n/).map((l: string) => l.trim()).filter(Boolean);
|
||||
if (lines.length > 1) return lines;
|
||||
return null;
|
||||
}
|
||||
|
||||
|
||||
(async () => {
|
||||
logger.info(`Starting photo download process with prompt: "${HARDCODED_USER_PROMPT}"`);
|
||||
|
||||
// 1. Extract keywords from the hardcoded prompt
|
||||
const keywords = ["fullbody portrait girl", "fullbody portrait 18y girl", "fullbody portrait cute girl", "fullbody portrait blond girl", "fullbody portrait 20y girl"];
|
||||
|
||||
if (!keywords || keywords.length === 0) {
|
||||
logger.error("Could not extract keywords from prompt. Exiting.");
|
||||
return;
|
||||
}
|
||||
logger.info(`Extracted keywords: ${keywords.join(', ')}`);
|
||||
|
||||
// 2. Search Pinterest for each keyword and download photos directly
|
||||
let totalDownloads = 0;
|
||||
for (const keyword of keywords) {
|
||||
try {
|
||||
logger.info(`Downloading photos for keyword: "${keyword}"`);
|
||||
const downloadedPaths = await downloadImagesFromPinterestSearch(`${keyword}`, PHOTOS_PER_KEYWORD);
|
||||
if (downloadedPaths.length > 0) {
|
||||
logger.info(`Successfully downloaded ${downloadedPaths.length} images for "${keyword}"`);
|
||||
totalDownloads += downloadedPaths.length;
|
||||
} else {
|
||||
logger.warn(`No images were downloaded for "${keyword}"`);
|
||||
}
|
||||
} catch (error) {
|
||||
logger.error(`An error occurred while processing keyword ${keyword}:`, error);
|
||||
}
|
||||
}
|
||||
|
||||
logger.info(`Photo download process finished. Total images downloaded: ${totalDownloads}`);
|
||||
})();
|
||||
82
src/tools/batchConverter.ts
Normal file
82
src/tools/batchConverter.ts
Normal file
@ -0,0 +1,82 @@
|
||||
import * as fs from 'fs/promises';
|
||||
import * as path from 'path';
|
||||
import dotenv from 'dotenv';
|
||||
import { callLMStudioAPIWithFile, callLmstudio } from '../lib/lmstudio';
|
||||
import { convertImage } from '../lib/image-converter';
|
||||
import { logger } from '../lib/logger';
|
||||
|
||||
dotenv.config();
|
||||
|
||||
const COMFY_BASE_URL = process.env.SERVER2_COMFY_BASE_URL;
|
||||
const COMFY_OUTPUT_DIR = process.env.SERVER2_COMFY_OUTPUT_DIR;
|
||||
const INPUT_DIR = './input';
|
||||
const GENERATED_DIR = './generated';
|
||||
|
||||
async function batchConvert() {
|
||||
if (!COMFY_BASE_URL || !COMFY_OUTPUT_DIR) {
|
||||
throw new Error('ComfyUI server details are not defined in the .env file');
|
||||
}
|
||||
|
||||
try {
|
||||
await fs.mkdir(GENERATED_DIR, { recursive: true });
|
||||
const files = await fs.readdir(INPUT_DIR);
|
||||
|
||||
for (const file of files) {
|
||||
|
||||
try {
|
||||
const inputFile = path.join(INPUT_DIR, file);
|
||||
logger.info(`Processing ${inputFile}`);
|
||||
|
||||
const firstPrompt = `
|
||||
Read the file in the photo and detect the main subject. Extract the following information:
|
||||
- what is the main subject in one word
|
||||
- describe character in 5 words
|
||||
- describe background in 5 words
|
||||
- 3 ideas to make this character's look, 2 or 3 words per idea
|
||||
|
||||
Return the result in this format:
|
||||
{ "character":"", "characterDescription": "", "background":"", "idea":"idea1, idea2, idea3"}
|
||||
`;
|
||||
|
||||
const imageInfo = await callLMStudioAPIWithFile(inputFile, firstPrompt);
|
||||
|
||||
const { character, idea, background } = imageInfo;
|
||||
const ideas = idea.split(',').map((i: string) => i.trim());
|
||||
|
||||
const secondPrompt = `
|
||||
Generate a prompt to convert the photo to a group dancing photo.
|
||||
I need only the prompt in this format, main subject is ${character}
|
||||
{"result":"
|
||||
Change the camera angle to a full-body shot, place many ${character} in the scene messily,
|
||||
Change lighting to silhouette lighting,
|
||||
Change the style for each body using these ideas: ${ideas.join(', ')},
|
||||
Change color of each body,
|
||||
Change the background in horror movie style of ${background},
|
||||
Overall photo should be realistic and spooky,
|
||||
"} `;
|
||||
|
||||
const promptResult = await callLmstudio(secondPrompt);
|
||||
const finalPrompt = promptResult.result;
|
||||
|
||||
const inputFolderFullPath = COMFY_OUTPUT_DIR.replace("output", "input");
|
||||
const serverInputFile = path.join(inputFolderFullPath, file);
|
||||
await fs.copyFile(inputFile, serverInputFile);
|
||||
|
||||
logger.info(`Generating image for ${file} with prompt: ${finalPrompt}`);
|
||||
const generatedFile = await convertImage(finalPrompt, file, COMFY_BASE_URL, COMFY_OUTPUT_DIR);
|
||||
|
||||
const finalOutputPath = path.join(GENERATED_DIR, path.basename(generatedFile));
|
||||
await fs.copyFile(generatedFile, finalOutputPath);
|
||||
logger.info(`Saved converted image to ${finalOutputPath}`);
|
||||
} catch (e) {
|
||||
logger.error('An error occurred during batch conversion:', e);
|
||||
continue;
|
||||
}
|
||||
|
||||
}
|
||||
} catch (error) {
|
||||
logger.error('An error occurred during batch conversion:', error);
|
||||
}
|
||||
}
|
||||
|
||||
batchConvert();
|
||||
54
src/tools/embed_prompt_to_png.ts
Normal file
54
src/tools/embed_prompt_to_png.ts
Normal file
@ -0,0 +1,54 @@
|
||||
import * as fs from 'fs';
|
||||
import * as path from 'path';
|
||||
import { callLMStudioAPIWithFile } from '../lib/lmstudio';
|
||||
import { embedJsonToPng } from '../lib/util';
|
||||
|
||||
const imageDir = 'C:\\Users\\fm201\\Desktop\\vton\\bags';
|
||||
|
||||
async function processImages() {
|
||||
try {
|
||||
const files = fs.readdirSync(imageDir);
|
||||
const imageFiles = files.filter(file => /\.(png)$/i.test(file));
|
||||
|
||||
for (const imageFile of imageFiles) {
|
||||
const imagePath = path.join(imageDir, imageFile);
|
||||
console.log(`Processing ${imagePath}...`);
|
||||
|
||||
const prompt = `
|
||||
Based on the handbag in the image, generate 10 outfit prompts that would complement it.
|
||||
Each prompt should be a short, descriptive sentence of around 20 words.
|
||||
Return the result in the following JSON format:
|
||||
{"result": ["outfit prompt 1", "outfit prompt 2", ...]}
|
||||
`;
|
||||
|
||||
try {
|
||||
const response = await callLMStudioAPIWithFile(imagePath, prompt);
|
||||
let outfitPrompts;
|
||||
|
||||
if (typeof response === 'string') {
|
||||
try {
|
||||
outfitPrompts = JSON.parse(response);
|
||||
} catch (e) {
|
||||
console.error(`Failed to parse JSON string for ${imageFile}:`, response);
|
||||
continue;
|
||||
}
|
||||
} else {
|
||||
outfitPrompts = response;
|
||||
}
|
||||
|
||||
if (outfitPrompts && outfitPrompts.result) {
|
||||
await embedJsonToPng(imagePath, outfitPrompts);
|
||||
console.log(`Successfully embedded prompts into ${imageFile}`);
|
||||
} else {
|
||||
console.error(`Invalid JSON response for ${imageFile}:`, response);
|
||||
}
|
||||
} catch (error) {
|
||||
console.error(`Failed to process ${imageFile}:`, error);
|
||||
}
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('Error reading image directory:', error);
|
||||
}
|
||||
}
|
||||
|
||||
processImages();
|
||||
122
src/tools/face_normalizer.ts
Normal file
122
src/tools/face_normalizer.ts
Normal file
@ -0,0 +1,122 @@
|
||||
import * as fs from 'fs';
|
||||
import { promises as fsPromises } from 'fs';
|
||||
import * as path from 'path';
|
||||
import * as crypto from 'crypto';
|
||||
import dotenv from 'dotenv';
|
||||
import { callLMStudioAPIWithFile } from '../lib/lmstudio';
|
||||
import { generateImage } from '../lib/image-generator';
|
||||
import { convertImageWithFile } from '../lib/image-converter';
|
||||
|
||||
dotenv.config();
|
||||
|
||||
const inputDir = path.resolve('./input');
|
||||
const outputDir = path.resolve('./generated');
|
||||
|
||||
async function main() {
|
||||
const comfyBaseUrl = process.env.SERVER1_COMFY_BASE_URL;
|
||||
const comfyOutputDir = process.env.SERVER1_COMFY_OUTPUT_DIR;
|
||||
|
||||
if (!comfyBaseUrl || !comfyOutputDir) {
|
||||
throw new Error('SERVER1_COMFY_BASE_URL and SERVER1_COMFY_OUTPUT_DIR must be set in .env file');
|
||||
}
|
||||
const comfyInputDir = comfyOutputDir.replace("output", "input");
|
||||
|
||||
// Ensure output directory exists
|
||||
if (!fs.existsSync(outputDir)) {
|
||||
fs.mkdirSync(outputDir, { recursive: true });
|
||||
}
|
||||
|
||||
const files = fs.readdirSync(inputDir);
|
||||
|
||||
for (const file of files) {
|
||||
const imagePath = path.join(inputDir, file);
|
||||
|
||||
// Calculate file hash
|
||||
const fileBuffer = fs.readFileSync(imagePath);
|
||||
const hashSum = crypto.createHash('sha256');
|
||||
hashSum.update(fileBuffer);
|
||||
const hexHash = hashSum.digest('hex');
|
||||
|
||||
const genFileName = `${hexHash}_gen.png`;
|
||||
const convertedFileName = `${hexHash}_converted.png`;
|
||||
|
||||
const genFilePath = path.join(outputDir, genFileName);
|
||||
const convertedFilePath = path.join(outputDir, convertedFileName);
|
||||
|
||||
if (fs.existsSync(genFilePath) && fs.existsSync(convertedFilePath)) {
|
||||
console.log(`Skipping ${file} as it has already been processed.`);
|
||||
continue;
|
||||
}
|
||||
|
||||
console.log(`Processing ${imagePath}...`);
|
||||
|
||||
// 1. Ask LLM to make a prompt for this photo
|
||||
const llmPrompt = `
|
||||
Extract the following information from the attached image: face detail, hairstyle, eye color, hair color.
|
||||
Output instruction:
|
||||
{ result: "face detail, hairstyle, eye color, hair color" }
|
||||
`;
|
||||
const extractedInfoJSON = await callLMStudioAPIWithFile(imagePath, llmPrompt);
|
||||
|
||||
if (!extractedInfoJSON) {
|
||||
console.error(`Could not get prompt from LLM for ${imagePath}`);
|
||||
continue;
|
||||
}
|
||||
|
||||
const extractedInfo = extractedInfoJSON.result;
|
||||
|
||||
console.log(`LLM response: ${extractedInfo}`);
|
||||
|
||||
// 2. Generate photo using the prompt
|
||||
const prefix = "Highly detailed face portrait of 20 years old girl, light gray background with faint gradient, look at camera in front";
|
||||
const finalPrompt = prefix + extractedInfo;
|
||||
|
||||
console.log(`Generating image with prompt: ${finalPrompt}`);
|
||||
|
||||
const generatedImagePath = await generateImage(
|
||||
finalPrompt,
|
||||
genFileName,
|
||||
comfyBaseUrl,
|
||||
comfyOutputDir,
|
||||
'flux'
|
||||
);
|
||||
|
||||
if (generatedImagePath) {
|
||||
console.log(`Generated image saved to: ${generatedImagePath}`);
|
||||
|
||||
// 3. Convert the image
|
||||
const generatedImageFileName = path.basename(generatedImagePath);
|
||||
const originalImageFileName = file;
|
||||
|
||||
const destGeneratedImagePath = path.join(comfyInputDir, generatedImageFileName);
|
||||
const destOriginalImagePath = path.join(comfyInputDir, originalImageFileName);
|
||||
|
||||
// Copy files to comfy input directory
|
||||
await fsPromises.copyFile(generatedImagePath, destGeneratedImagePath);
|
||||
await fsPromises.copyFile(imagePath, destOriginalImagePath);
|
||||
console.log(`Copied ${generatedImageFileName} and ${originalImageFileName} to ${comfyInputDir}`);
|
||||
|
||||
const conversionPrompt = "Replace the hairs of image1 with hairs of image2, change face of image1 with face of image2.";
|
||||
const convertedResultPath = await convertImageWithFile(
|
||||
conversionPrompt,
|
||||
generatedImageFileName,
|
||||
originalImageFileName,
|
||||
comfyBaseUrl,
|
||||
comfyOutputDir
|
||||
);
|
||||
|
||||
if (convertedResultPath) {
|
||||
// Rename the output file to the final converted name
|
||||
await fsPromises.rename(convertedResultPath, convertedFilePath);
|
||||
console.log(`Converted image saved to: ${convertedFilePath}`);
|
||||
} else {
|
||||
console.error(`Failed to convert image for ${originalImageFileName}`);
|
||||
}
|
||||
|
||||
} else {
|
||||
console.error(`Failed to generate image for prompt: ${finalPrompt}`);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
main().catch(console.error);
|
||||
74
src/tools/generateVideoFromInput.ts
Normal file
74
src/tools/generateVideoFromInput.ts
Normal file
@ -0,0 +1,74 @@
|
||||
import * as fs from 'fs/promises';
|
||||
import * as path from 'path';
|
||||
import { generateVideo } from '../lib/video-generator';
|
||||
import dotenv from 'dotenv';
|
||||
|
||||
dotenv.config();
|
||||
|
||||
const inputFolder = './input';
|
||||
const prompt = "a girl making a cute pose, static camera";
|
||||
b
|
||||
async function main() {
|
||||
try {
|
||||
const files = await fs.readdir(inputFolder);
|
||||
const pngFiles = files.filter(file => path.extname(file).toLowerCase() === '.png');
|
||||
|
||||
if (pngFiles.length === 0) {
|
||||
console.log('No PNG files found in the input folder.');
|
||||
return;
|
||||
}
|
||||
|
||||
const comfyBaseUrl = process.env.SERVER2_COMFY_BASE_URL;
|
||||
const comfyOutputDir = process.env.SERVER2_COMFY_OUTPUT_DIR;
|
||||
|
||||
if (!comfyBaseUrl || !comfyOutputDir) {
|
||||
console.error('Please define SERVER1_COMFY_BASE_URL and SERVER1_COMFY_OUTPUT_DIR in your .env file.');
|
||||
return;
|
||||
}
|
||||
|
||||
const comfyInputDir = comfyOutputDir.replace('output', 'input');
|
||||
|
||||
|
||||
for (const file of pngFiles) {
|
||||
const inputImagePath = path.join(inputFolder, file);
|
||||
const comfyInputImagePath = path.join(comfyInputDir, file);
|
||||
|
||||
console.log(`Processing ${file}...`);
|
||||
|
||||
try {
|
||||
await fs.access(inputImagePath);
|
||||
// Copy file to comfy input directory
|
||||
await fs.copyFile(inputImagePath, comfyInputImagePath);
|
||||
console.log(`Copied ${file} to ComfyUI input directory.`);
|
||||
} catch (e) {
|
||||
console.error(`Error copying file ${file}:`, e);
|
||||
continue;
|
||||
}
|
||||
|
||||
|
||||
const newFileName = `${path.parse(file).name}.mp4`;
|
||||
try {
|
||||
const generatedVideoPath = await generateVideo(
|
||||
prompt,
|
||||
file, // Pass only the filename as per instructions
|
||||
newFileName,
|
||||
comfyBaseUrl,
|
||||
comfyOutputDir
|
||||
);
|
||||
console.log(`Successfully generated video for ${file} at: ${generatedVideoPath}`);
|
||||
} catch (e: any) {
|
||||
if (e.code === 'ECONNREFUSED' || e.code === 'ETIMEDOUT') {
|
||||
console.error(`\nError: Connection to ComfyUI server at ${comfyBaseUrl} failed.`);
|
||||
console.error('Please ensure the ComfyUI server is running and accessible.');
|
||||
break;
|
||||
} else {
|
||||
console.error(`An error occurred while generating video for ${file}:`, e);
|
||||
}
|
||||
}
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('An unexpected error occurred:', error);
|
||||
}
|
||||
}
|
||||
|
||||
main();
|
||||
179
src/tools/pinterest_cloth_extraction.ts
Normal file
179
src/tools/pinterest_cloth_extraction.ts
Normal file
@ -0,0 +1,179 @@
|
||||
import dotenv from 'dotenv';
|
||||
import * as fs from 'fs/promises';
|
||||
import * as path from 'path';
|
||||
import { logger } from '../lib/logger';
|
||||
import { getPinUrlFromPinterest, downloadImageFromPin } from '../lib/pinterest';
|
||||
import { convertImage, extractCloth } from '../lib/image-converter';
|
||||
dotenv.config();
|
||||
|
||||
const KEYWORDS = [
|
||||
'teen skirt outfit',
|
||||
'teen casual cute outfit',
|
||||
'cute outfit',
|
||||
'summer dress',
|
||||
'elegant dress',
|
||||
'bohemian dress',
|
||||
'ball gown dress',
|
||||
'cosplay outfit',
|
||||
'vintage skirt outfit',
|
||||
'casual skirt outfit',
|
||||
'harajuku outfit',
|
||||
'pop colorful outfit',
|
||||
'cute colorful outfit',
|
||||
'japanese idol outfit',
|
||||
'folk dress outfit',
|
||||
];
|
||||
const TARGET_COUNT = Number(process.env.IMAGE_COUNT || 20);
|
||||
const PROMPT =
|
||||
`Change pose of the person in the image1 to stantind in front of camera, with a smile, full body visible, wearing a fashionable outfit suitable for a casual day out. The background should be a white. Ensure the lighting is bright and natural, highlighting the details of the outfit and the person's features.
|
||||
`;
|
||||
|
||||
const PROMPT2 =
|
||||
`Extract clothes from image1, put it on the light gray backgroud
|
||||
`;
|
||||
|
||||
type ServerCfg = { baseUrl: string; outputDir: string; inputDir: string };
|
||||
|
||||
function getServerConfig(): ServerCfg {
|
||||
const candidates = [
|
||||
//{ baseUrl: process.env.SERVER1_COMFY_BASE_URL, outputDir: process.env.SERVER1_COMFY_OUTPUT_DIR },
|
||||
{ baseUrl: process.env.SERVER2_COMFY_BASE_URL, outputDir: process.env.SERVER2_COMFY_OUTPUT_DIR },
|
||||
].filter((s): s is { baseUrl: string; outputDir: string } => !!s.baseUrl && !!s.outputDir);
|
||||
|
||||
if (candidates.length === 0) {
|
||||
throw new Error(
|
||||
'No ComfyUI server configured. Please set SERVER1_COMFY_BASE_URL/OUTPUT_DIR or SERVER2_COMFY_BASE_URL/OUTPUT_DIR in .env'
|
||||
);
|
||||
}
|
||||
const chosen = candidates[0];
|
||||
const inputDir = chosen.outputDir.replace('output', 'input');
|
||||
return { ...chosen, inputDir };
|
||||
}
|
||||
|
||||
function sleep(ms: number) {
|
||||
return new Promise((res) => setTimeout(res, ms));
|
||||
}
|
||||
|
||||
async function ensureDir(p: string) {
|
||||
try {
|
||||
await fs.mkdir(p, { recursive: true });
|
||||
} catch {
|
||||
// ignore
|
||||
}
|
||||
}
|
||||
|
||||
async function collectImages(keyword: string, total: number): Promise<string[]> {
|
||||
const results: string[] = [];
|
||||
// ensure local download dir exists (pinterest.ts also ensures it, but harmless here)
|
||||
await ensureDir(path.join(process.cwd(), 'download'));
|
||||
|
||||
while (results.length < total) {
|
||||
try {
|
||||
const pinUrl = await getPinUrlFromPinterest(keyword);
|
||||
if (!pinUrl) {
|
||||
logger.warn('No pin URL found, retrying...');
|
||||
await sleep(1500);
|
||||
continue;
|
||||
}
|
||||
const remaining = total - results.length;
|
||||
// attempt to grab up to 5 per pin to reduce churn
|
||||
const batchTarget = Math.min(5, remaining);
|
||||
const imgs = await downloadImageFromPin(pinUrl, batchTarget);
|
||||
if (imgs && imgs.length > 0) {
|
||||
results.push(...imgs);
|
||||
logger.info(`Downloaded ${imgs.length} image(s) from ${pinUrl}.Progress: ${results.length}/${total}`);
|
||||
} else {
|
||||
logger.warn(`Pin yielded no downloadable images: ${pinUrl}`);
|
||||
}
|
||||
await sleep(1000 + Math.random() * 1000);
|
||||
} catch (err) {
|
||||
logger.error('Error while collecting images:', err);
|
||||
await sleep(2000);
|
||||
}
|
||||
}
|
||||
return results.slice(0, total);
|
||||
}
|
||||
|
||||
async function processImages(imagePaths: string[], server: ServerCfg) {
|
||||
await ensureDir(server.inputDir);
|
||||
|
||||
for (const localImagePath of imagePaths) {
|
||||
const baseName = path.basename(localImagePath);
|
||||
const serverInputPath = path.join(server.inputDir, baseName);
|
||||
|
||||
try {
|
||||
// copy source image into ComfyUI input dir so workflow node can find it by filename
|
||||
await fs.copyFile(localImagePath, serverInputPath);
|
||||
logger.info(`Copied ${localImagePath} -> ${serverInputPath}`);
|
||||
|
||||
// Run conversion (sequential to avoid output race conditions)
|
||||
const generatedPath1 = await extractCloth(
|
||||
PROMPT2,
|
||||
baseName,
|
||||
server.baseUrl,
|
||||
server.outputDir,
|
||||
{ width: 720, height: 1280 } // portrait
|
||||
);
|
||||
/*
|
||||
await fs.copyFile(generatedPath1, serverInputPath);
|
||||
const baseName2 = path.basename(localImagePath);
|
||||
|
||||
const generatedPath2 = await extractCloth(
|
||||
PROMPT2,
|
||||
baseName2,
|
||||
server.baseUrl,
|
||||
server.outputDir,
|
||||
{ width: 720, height: 1280 } // portrait
|
||||
);
|
||||
*/
|
||||
logger.info(`Generated image: ${generatedPath1}`);
|
||||
} catch (err) {
|
||||
logger.error(`Failed to convert ${localImagePath}:`, err);
|
||||
} finally {
|
||||
// cleanup both server input copy and local downloaded file
|
||||
try {
|
||||
await fs.unlink(serverInputPath);
|
||||
} catch { }
|
||||
try {
|
||||
await fs.unlink(localImagePath);
|
||||
} catch { }
|
||||
await sleep(500);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
async function main() {
|
||||
const server = getServerConfig();
|
||||
|
||||
// Infinite loop as requested
|
||||
while (true) {
|
||||
|
||||
for (const KEYWORD of KEYWORDS) {
|
||||
logger.info(`Starting Pinterest image conversion loop for keyword: "${KEYWORD}" (target ${TARGET_COUNT})`);
|
||||
try {
|
||||
const images = await collectImages(KEYWORD, TARGET_COUNT);
|
||||
logger.info(`Collected ${images.length} image(s). Starting conversion...`);
|
||||
await processImages(images, server);
|
||||
logger.info('Iteration completed. Sleeping before next cycle...');
|
||||
} catch (err) {
|
||||
logger.error('Iteration failed:', err);
|
||||
}
|
||||
|
||||
// brief pause between iterations
|
||||
await sleep(10000);
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
process.on('unhandledRejection', (reason) => {
|
||||
logger.error('UnhandledRejection:', reason);
|
||||
});
|
||||
|
||||
process.on('uncaughtException', (err) => {
|
||||
logger.error('UncaughtException:', err);
|
||||
});
|
||||
|
||||
main().catch((e) => {
|
||||
logger.error('Fatal error:', e);
|
||||
});
|
||||
247
src/tools/pinterest_collect_image_convert.ts
Normal file
247
src/tools/pinterest_collect_image_convert.ts
Normal file
@ -0,0 +1,247 @@
|
||||
import dotenv from 'dotenv';
|
||||
import * as fs from 'fs/promises';
|
||||
import * as path from 'path';
|
||||
import { logger } from '../lib/logger';
|
||||
import { getPinUrlFromPinterest, downloadImageFromPin } from '../lib/pinterest';
|
||||
import { generateImage } from '../lib/image-generator-face';
|
||||
import { callLMStudioAPIWithFile } from '../lib/lmstudio';
|
||||
|
||||
dotenv.config();
|
||||
|
||||
const MODE: "keywords" | "pinIds" = "keywords";
|
||||
|
||||
const KEYWORDS = [
|
||||
'a girl in scary forest',
|
||||
'a girl in graveyard',
|
||||
''];
|
||||
|
||||
const pinIDs = [
|
||||
"22377329393970367",
|
||||
"18999629674210230",
|
||||
"3166662232983784",
|
||||
"291537775902572408",
|
||||
"2744449769232655",
|
||||
"9429480465939847",
|
||||
"34058540926328062",
|
||||
"1071153092617107265",
|
||||
"6825836928646465",
|
||||
"1407443629997072",
|
||||
"333407178685095962",
|
||||
"15833036184288417",
|
||||
"6825836928284784",
|
||||
"2181499815469509",
|
||||
"199706564723106062",
|
||||
"1759287348280571",
|
||||
"56083957854040032",
|
||||
"3025924743999802",
|
||||
"2955556001576084",
|
||||
"1407443627212889",
|
||||
"836965911982723974",
|
||||
"97460779431981493",
|
||||
"282600945363725869",
|
||||
"/59532026387104913",
|
||||
"70437490453979",
|
||||
"152489137384620437",
|
||||
"50947039528553588",
|
||||
"73042825197955754",
|
||||
"624593042089280419",
|
||||
"351912466315529",
|
||||
"624030092104188250",
|
||||
"21673641951379251",
|
||||
"27021666506512503",
|
||||
"3377768467678091",
|
||||
"985231163409578",
|
||||
"17240411068654164"
|
||||
|
||||
]
|
||||
const TARGET_COUNT = Number(process.env.IMAGE_COUNT || 20);
|
||||
const PROMPT =
|
||||
`
|
||||
change camera angle to fullbody shot from image1,
|
||||
change background to light gray with faing gradient,
|
||||
change clothes to shite sports bra and shite cotton short pants
|
||||
`;
|
||||
const LmStudioPrompt = `
|
||||
describe the image in 50 words including, scene, lighting, describe character(s), add some beautiful accent like light, fog, starts, lamps, whatever suits with scene.
|
||||
|
||||
then return in this format
|
||||
{"prompt":""}
|
||||
`;
|
||||
|
||||
type ServerCfg = { baseUrl: string; outputDir: string; inputDir: string };
|
||||
|
||||
function getServerConfig(): ServerCfg {
|
||||
const candidates = [
|
||||
//{ baseUrl: process.env.SERVER1_COMFY_BASE_URL, outputDir: process.env.SERVER1_COMFY_OUTPUT_DIR },
|
||||
{ baseUrl: process.env.SERVER2_COMFY_BASE_URL, outputDir: process.env.SERVER2_COMFY_OUTPUT_DIR },
|
||||
].filter((s): s is { baseUrl: string; outputDir: string } => !!s.baseUrl && !!s.outputDir);
|
||||
|
||||
if (candidates.length === 0) {
|
||||
throw new Error(
|
||||
'No ComfyUI server configured. Please set SERVER1_COMFY_BASE_URL/OUTPUT_DIR or SERVER2_COMFY_BASE_URL/OUTPUT_DIR in .env'
|
||||
);
|
||||
}
|
||||
const chosen = candidates[0];
|
||||
const inputDir = chosen.outputDir.replace('output', 'input');
|
||||
return { ...chosen, inputDir };
|
||||
}
|
||||
|
||||
function sleep(ms: number) {
|
||||
return new Promise((res) => setTimeout(res, ms));
|
||||
}
|
||||
|
||||
async function ensureDir(p: string) {
|
||||
try {
|
||||
await fs.mkdir(p, { recursive: true });
|
||||
} catch {
|
||||
// ignore
|
||||
}
|
||||
}
|
||||
|
||||
async function collectImages(keyword: string, total: number): Promise<string[]> {
|
||||
const results: string[] = [];
|
||||
// ensure local download dir exists (pinterest.ts also ensures it, but harmless here)
|
||||
await ensureDir(path.join(process.cwd(), 'download'));
|
||||
|
||||
if (MODE == "keywords") {
|
||||
while (results.length < total) {
|
||||
try {
|
||||
const pinUrl = await getPinUrlFromPinterest(keyword);
|
||||
if (!pinUrl) {
|
||||
logger.warn('No pin URL found, retrying...');
|
||||
await sleep(1500);
|
||||
return [];
|
||||
}
|
||||
const remaining = total - results.length;
|
||||
// attempt to grab up to 5 per pin to reduce churn
|
||||
const batchTarget = Math.min(5, remaining);
|
||||
const imgs = await downloadImageFromPin(pinUrl, batchTarget);
|
||||
if (imgs && imgs.length > 0) {
|
||||
results.push(...imgs);
|
||||
logger.info(`Downloaded ${imgs.length} image(s) from ${pinUrl}.Progress: ${results.length}/${total}`);
|
||||
} else {
|
||||
logger.warn(`Pin yielded no downloadable images: ${pinUrl}`);
|
||||
}
|
||||
await sleep(1000 + Math.random() * 1000);
|
||||
} catch (err) {
|
||||
logger.error('Error while collecting images:', err);
|
||||
await sleep(2000);
|
||||
}
|
||||
}
|
||||
} else if (MODE == "pinIds") {
|
||||
while (results.length < total) {
|
||||
const shuffledPinIds = pinIDs.slice().sort(() => 0.5 - Math.random());
|
||||
const pinUrl = `https://www.pinterest.com/pin/${shuffledPinIds[0]}`;
|
||||
|
||||
try {
|
||||
const remaining = total - results.length;
|
||||
// attempt to grab up to 5 per pin to reduce churn
|
||||
const batchTarget = Math.min(5, remaining);
|
||||
const imgs = await downloadImageFromPin(pinUrl, batchTarget);
|
||||
if (imgs && imgs.length > 0) {
|
||||
results.push(...imgs);
|
||||
logger.info(`Downloaded ${imgs.length} image(s) from ${pinUrl}.Progress: ${results.length}/${total}`);
|
||||
} else {
|
||||
logger.warn(`Pin yielded no downloadable images: ${pinUrl}`);
|
||||
}
|
||||
await sleep(1000 + Math.random() * 1000);
|
||||
} catch (err) {
|
||||
logger.error('Error while collecting images:', err);
|
||||
await sleep(2000);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return results.slice(0, total);
|
||||
}
|
||||
|
||||
let imageIndex = 0;
|
||||
async function processImages(imagePaths: string[], server: ServerCfg) {
|
||||
await ensureDir(server.inputDir);
|
||||
|
||||
for (const localImagePath of imagePaths) {
|
||||
const response = await callLMStudioAPIWithFile(localImagePath, LmStudioPrompt);
|
||||
const prompt = response.prompt;
|
||||
const baseName = path.basename(localImagePath);
|
||||
const serverInputPath = path.join(server.inputDir, baseName);
|
||||
|
||||
try {
|
||||
// copy source image into ComfyUI input dir so workflow node can find it by filename
|
||||
await fs.copyFile(localImagePath, serverInputPath);
|
||||
logger.info(`Copied ${localImagePath} -> ${serverInputPath}`);
|
||||
|
||||
// Run conversion (sequential to avoid output race conditions)
|
||||
const generatedPath = await generateImage(
|
||||
`ultra realistic photo, ${prompt}`,
|
||||
baseName,
|
||||
`monster_${imageIndex}.png`,
|
||||
server.baseUrl,
|
||||
server.outputDir,
|
||||
{ width: 1280, height: 720 } // portrait
|
||||
);
|
||||
logger.info(`Generated image: ${generatedPath}`);
|
||||
imageIndex++;
|
||||
} catch (err) {
|
||||
logger.error(`Failed to convert ${localImagePath}:`, err);
|
||||
} finally {
|
||||
// cleanup both server input copy and local downloaded file
|
||||
try {
|
||||
await fs.unlink(serverInputPath);
|
||||
} catch { }
|
||||
try {
|
||||
await fs.unlink(localImagePath);
|
||||
} catch { }
|
||||
await sleep(500);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
async function main() {
|
||||
const server = getServerConfig();
|
||||
const files = await fs.readdir(path.join(process.cwd(), 'generated'));
|
||||
const monsterFiles = files.filter((f) => f.startsWith('monster_'));
|
||||
if (monsterFiles.length > 0) {
|
||||
const latestFile = monsterFiles.sort().pop();
|
||||
if (latestFile) {
|
||||
const latestIndex = parseInt(latestFile.replace('monster_', '').replace('.png', ''));
|
||||
imageIndex = latestIndex + 1;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// Infinite loop as requested
|
||||
while (true) {
|
||||
|
||||
for (const KEYWORD of KEYWORDS) {
|
||||
logger.info(`Starting Pinterest image conversion loop for keyword: "${KEYWORD}" (target ${TARGET_COUNT})`);
|
||||
try {
|
||||
const images = await collectImages(KEYWORD, TARGET_COUNT);
|
||||
logger.info(`Collected ${images.length} image(s). Starting conversion...`);
|
||||
|
||||
if (images.length == 0)
|
||||
continue;
|
||||
|
||||
await processImages(images, server);
|
||||
logger.info('Iteration completed. Sleeping before next cycle...');
|
||||
} catch (err) {
|
||||
logger.error('Iteration failed:', err);
|
||||
}
|
||||
|
||||
// brief pause between iterations
|
||||
await sleep(10000);
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
process.on('unhandledRejection', (reason) => {
|
||||
logger.error('UnhandledRejection:', reason);
|
||||
});
|
||||
|
||||
process.on('uncaughtException', (err) => {
|
||||
logger.error('UncaughtException:', err);
|
||||
});
|
||||
|
||||
main().catch((e) => {
|
||||
logger.error('Fatal error:', e);
|
||||
});
|
||||
156
src/tools/vton_generator.ts
Normal file
156
src/tools/vton_generator.ts
Normal file
@ -0,0 +1,156 @@
|
||||
import * as fs from 'fs';
|
||||
import * as path from 'path';
|
||||
import { convertImageVton, convertImage } from '../lib/image-converter';
|
||||
import * as dotenv from 'dotenv';
|
||||
import sharp from 'sharp';
|
||||
|
||||
dotenv.config();
|
||||
|
||||
const clothesDir = 'D:\\projects\\random_video_maker\\input';
|
||||
const outputDir = 'generated';
|
||||
|
||||
const comfyBaseUrl = process.env.SERVER1_COMFY_BASE_URL;
|
||||
const comfyOutputDir = process.env.SERVER1_COMFY_OUTPUT_DIR;
|
||||
|
||||
function getNextIndex(directory: string): number {
|
||||
if (!fs.existsSync(directory)) {
|
||||
fs.mkdirSync(directory, { recursive: true });
|
||||
return 0;
|
||||
}
|
||||
const dirs = fs.readdirSync(directory, { withFileTypes: true })
|
||||
.filter(dirent => dirent.isDirectory())
|
||||
.map(dirent => dirent.name);
|
||||
|
||||
const vtonDirs = dirs.filter(dir => dir.startsWith('vton_'));
|
||||
if (vtonDirs.length === 0) {
|
||||
return 0;
|
||||
}
|
||||
const indices = vtonDirs.map(dir => {
|
||||
const match = dir.match(/vton_(\d+)/);
|
||||
return match ? parseInt(match[1], 10) : -1;
|
||||
});
|
||||
return Math.max(...indices) + 1;
|
||||
}
|
||||
|
||||
function getRandomFile(directory: string): string {
|
||||
const files = fs.readdirSync(directory).filter(file => /\.(jpg|png|jpeg)$/i.test(file));
|
||||
if (files.length === 0) {
|
||||
throw new Error(`No image files found in directory: ${directory}`);
|
||||
}
|
||||
const randomFile = files[Math.floor(Math.random() * files.length)];
|
||||
return path.join(directory, randomFile);
|
||||
}
|
||||
|
||||
async function generateVtonImages() {
|
||||
if (!comfyBaseUrl || !comfyOutputDir) {
|
||||
throw new Error("ComfyUI URL or Output Directory is not set in environment variables.");
|
||||
}
|
||||
|
||||
let index = getNextIndex(outputDir);
|
||||
|
||||
const comfyInputDir = comfyOutputDir.replace("output", "input");
|
||||
|
||||
while (true) { // Infinite loop
|
||||
const iterationDir = path.join(outputDir, `vton_${index}`);
|
||||
fs.mkdirSync(iterationDir, { recursive: true });
|
||||
|
||||
try {
|
||||
const personOrigPath = getRandomFile(clothesDir);
|
||||
const clothOrigPath = getRandomFile(clothesDir);
|
||||
|
||||
fs.copyFileSync(personOrigPath, path.join(iterationDir, '1-personOrig.png'));
|
||||
fs.copyFileSync(clothOrigPath, path.join(iterationDir, '3-clothOrig.png'));
|
||||
|
||||
const personOrigFileName = path.basename(personOrigPath);
|
||||
const clothOrigFileName = path.basename(clothOrigPath);
|
||||
|
||||
fs.copyFileSync(personOrigPath, path.join(comfyInputDir, personOrigFileName));
|
||||
fs.copyFileSync(clothOrigPath, path.join(comfyInputDir, clothOrigFileName));
|
||||
|
||||
console.log(`Processing person: ${personOrigPath}, cloth: ${clothOrigPath}`);
|
||||
|
||||
const cleanePersonImagePath = await convertImage("请把姿势改成站立的，转换成全身照片。去掉衣服，只保留白色运动文胸和白色短裤。双脚保持赤脚。背景为浅灰色。", personOrigFileName, comfyBaseUrl, comfyOutputDir, { width: 720, height: 1280 });
|
||||
fs.copyFileSync(cleanePersonImagePath, path.join(iterationDir, '2-personCleaned.png'));
|
||||
const cleanedPersonFileName = path.basename(cleanePersonImagePath);
|
||||
fs.copyFileSync(cleanePersonImagePath, path.join(comfyInputDir, cleanedPersonFileName));
|
||||
|
||||
const cleanedClothImagePath = await convertImage("请将图1中的上衣、下装和配饰分别提取出来，放到同一个浅灰色的背景上。", clothOrigFileName, comfyBaseUrl, comfyOutputDir, { width: 720, height: 1280 });
|
||||
fs.copyFileSync(cleanedClothImagePath, path.join(iterationDir, '4-clothCleaned.png'));
|
||||
const cleanedClothFileName = path.basename(cleanedClothImagePath);
|
||||
fs.copyFileSync(cleanedClothImagePath, path.join(comfyInputDir, cleanedClothFileName));
|
||||
|
||||
const outputFilename = `vton_final_${index}.png`;
|
||||
const generatedImagePath = await convertImageVton(cleanedPersonFileName, cleanedClothFileName, outputFilename, comfyBaseUrl, comfyOutputDir, { width: 720, height: 1280 });
|
||||
|
||||
if (generatedImagePath) {
|
||||
fs.copyFileSync(generatedImagePath, path.join(iterationDir, '5-finalResult.png'));
|
||||
console.log(`Generated image saved to ${generatedImagePath}`);
|
||||
|
||||
// --- Create composite image ---
|
||||
const imagePaths = [
|
||||
path.join(iterationDir, '1-personOrig.png'),
|
||||
path.join(iterationDir, '3-clothOrig.png'),
|
||||
path.join(iterationDir, '2-personCleaned.png'),
|
||||
path.join(iterationDir, '4-clothCleaned.png'),
|
||||
path.join(iterationDir, '5-finalResult.png')
|
||||
];
|
||||
|
||||
const resizedImages = [];
|
||||
let totalWidth = 10; // Initial left margin
|
||||
const resizedHeight = 720;
|
||||
|
||||
for (const imagePath of imagePaths) {
|
||||
const image = sharp(imagePath);
|
||||
const metadata = await image.metadata();
|
||||
if (!metadata.width || !metadata.height) {
|
||||
throw new Error(`Could not get metadata for image ${imagePath}`);
|
||||
}
|
||||
const resizedWidth = Math.round((metadata.width / metadata.height) * resizedHeight);
|
||||
|
||||
const resizedImageBuffer = await image.resize(resizedWidth, resizedHeight).toBuffer();
|
||||
resizedImages.push({
|
||||
buffer: resizedImageBuffer,
|
||||
width: resizedWidth
|
||||
});
|
||||
totalWidth += resizedWidth + 10; // Add image width and right margin
|
||||
}
|
||||
|
||||
const compositeOps = [];
|
||||
let currentLeft = 10; // Start with left margin
|
||||
|
||||
for (const img of resizedImages) {
|
||||
compositeOps.push({
|
||||
input: img.buffer,
|
||||
top: 10, // 10px top margin
|
||||
left: currentLeft
|
||||
});
|
||||
currentLeft += img.width + 10; // Move to the next position
|
||||
}
|
||||
|
||||
await sharp({
|
||||
create: {
|
||||
width: totalWidth,
|
||||
height: 740,
|
||||
channels: 4,
|
||||
background: { r: 255, g: 255, b: 255, alpha: 1 }
|
||||
}
|
||||
})
|
||||
.composite(compositeOps)
|
||||
.toFile(path.join(iterationDir, 'process.png'));
|
||||
|
||||
console.log(`Generated composite image process.png in ${iterationDir}`);
|
||||
// --- End of composite image creation ---
|
||||
|
||||
index++;
|
||||
} else {
|
||||
console.error(`Failed to generate image for index ${index}`);
|
||||
}
|
||||
} catch (error) {
|
||||
console.error("An error occurred during image generation:", error);
|
||||
// Optional: wait for a bit before retrying to avoid spamming errors
|
||||
await new Promise(resolve => setTimeout(resolve, 5000));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
generateVtonImages().catch(console.error);
|
||||
538
src/vton/handback_portrait.ts
Normal file
538
src/vton/handback_portrait.ts
Normal file
@ -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 };
|
||||
Reference in New Issue
Block a user