zf-kbot/seedvr2-0304
Run zf-kbot/seedvr2-0304 with an API
Use one of our client libraries to get started quickly. Clicking on a library will take you to the Playground tab where you can tweak different inputs, see the results, and copy the corresponding code to use in your own project.
Input schema
The fields you can use to run this model with an API. If you don't give a value for a field its default value will be used.
| Field | Type | Default value | Description |
|---|---|---|---|
| input_image |
string
|
输入图像
|
|
| target_megapixels |
number
|
2
Min: 2 Max: 100 |
目标像素（百万像素）
|
| ttp_switch |
boolean
|
False
|
是否分片
|
| tiles_per_row |
integer
|
3
Min: 3 Max: 10 |
每行分片数量
|
| short_pixels_after_tile |
integer
|
1024
Min: 512 Max: 4096 |
分片压缩后短边
|
| upscale_multiple_after_tile |
number
|
2
Min: 1 Max: 4 |
分片压缩放大比例
|
| output_ext |
None
|
.jpg
|
输出图像格式
|
| quality |
integer
|
95
Min: 1 Max: 100 |
输出图像质量 (1-100)
|
| png_compress_level |
integer
|
1
Max: 9 |
PNG 压缩级别 (0-9，仅 PNG 生效；0 最快，9 体积更小)
|
| seed |
integer
|
0
|
随机种子。0 表示随机生成。
|
| max_resolution |
integer
|
0
Max: 16384 |
最大边长限制（像素）。0 表示无限制。用于防止极端宽高比导致显存溢出。
|
| color_correction |
None
|
lab
|
色彩校正方法。
• lab: LAB 色彩空间匹配（推荐，最高保真度）
• wavelet: 小波频率色彩迁移，保留细节
• wavelet_adaptive: 小波 + 自适应饱和度校正
• hsv: HSV 色调条件饱和度匹配
• adain: AdaIN 风格迁移
• none: 不校正
|
| input_noise_scale |
number
|
0
Max: 1 |
输入噪声注入（0.0-1.0）。可减少某些伪影。0 表示禁用。
|
| latent_noise_scale |
number
|
0
Max: 1 |
潜在空间噪声注入（0.0-1.0）。可柔化细节。0 表示禁用。
|
| debug |
boolean
|
False
|
启用调试输出（显示详细日志、内存使用、计时信息）
|
{
"type": "object",
"title": "Input",
"required": [
"input_image"
],
"properties": {
"seed": {
"type": "integer",
"title": "Seed",
"default": 0,
"minimum": 0,
"x-order": 9,
"description": "\u968f\u673a\u79cd\u5b50\u30020 \u8868\u793a\u968f\u673a\u751f\u6210\u3002"
},
"debug": {
"type": "boolean",
"title": "Debug",
"default": false,
"x-order": 14,
"description": "\u542f\u7528\u8c03\u8bd5\u8f93\u51fa\uff08\u663e\u793a\u8be6\u7ec6\u65e5\u5fd7\u3001\u5185\u5b58\u4f7f\u7528\u3001\u8ba1\u65f6\u4fe1\u606f\uff09"
},
"quality": {
"type": "integer",
"title": "Quality",
"default": 95,
"maximum": 100,
"minimum": 1,
"x-order": 7,
"description": "\u8f93\u51fa\u56fe\u50cf\u8d28\u91cf (1-100)"
},
"output_ext": {
"enum": [
".png",
".jpg"
],
"type": "string",
"title": "output_ext",
"description": "\u8f93\u51fa\u56fe\u50cf\u683c\u5f0f",
"default": ".jpg",
"x-order": 6
},
"ttp_switch": {
"type": "boolean",
"title": "Ttp Switch",
"default": false,
"x-order": 2,
"description": "\u662f\u5426\u5206\u7247"
},
"input_image": {
"type": "string",
"title": "Input Image",
"format": "uri",
"x-order": 0,
"description": "\u8f93\u5165\u56fe\u50cf"
},
"tiles_per_row": {
"type": "integer",
"title": "Tiles Per Row",
"default": 3,
"maximum": 10,
"minimum": 3,
"x-order": 3,
"description": "\u6bcf\u884c\u5206\u7247\u6570\u91cf"
},
"max_resolution": {
"type": "integer",
"title": "Max Resolution",
"default": 0,
"maximum": 16384,
"minimum": 0,
"x-order": 10,
"description": "\u6700\u5927\u8fb9\u957f\u9650\u5236\uff08\u50cf\u7d20\uff09\u30020 \u8868\u793a\u65e0\u9650\u5236\u3002\u7528\u4e8e\u9632\u6b62\u6781\u7aef\u5bbd\u9ad8\u6bd4\u5bfc\u81f4\u663e\u5b58\u6ea2\u51fa\u3002"
},
"color_correction": {
"enum": [
"lab",
"wavelet",
"wavelet_adaptive",
"hsv",
"adain",
"none"
],
"type": "string",
"title": "color_correction",
"description": "\u8272\u5f69\u6821\u6b63\u65b9\u6cd5\u3002\n\u2022 lab: LAB \u8272\u5f69\u7a7a\u95f4\u5339\u914d\uff08\u63a8\u8350\uff0c\u6700\u9ad8\u4fdd\u771f\u5ea6\uff09\n\u2022 wavelet: \u5c0f\u6ce2\u9891\u7387\u8272\u5f69\u8fc1\u79fb\uff0c\u4fdd\u7559\u7ec6\u8282\n\u2022 wavelet_adaptive: \u5c0f\u6ce2 + \u81ea\u9002\u5e94\u9971\u548c\u5ea6\u6821\u6b63\n\u2022 hsv: HSV \u8272\u8c03\u6761\u4ef6\u9971\u548c\u5ea6\u5339\u914d\n\u2022 adain: AdaIN \u98ce\u683c\u8fc1\u79fb\n\u2022 none: \u4e0d\u6821\u6b63",
"default": "lab",
"x-order": 11
},
"input_noise_scale": {
"type": "number",
"title": "Input Noise Scale",
"default": 0,
"maximum": 1,
"minimum": 0,
"x-order": 12,
"description": "\u8f93\u5165\u566a\u58f0\u6ce8\u5165\uff080.0-1.0\uff09\u3002\u53ef\u51cf\u5c11\u67d0\u4e9b\u4f2a\u5f71\u30020 \u8868\u793a\u7981\u7528\u3002"
},
"target_megapixels": {
"type": "number",
"title": "Target Megapixels",
"default": 2,
"maximum": 100,
"minimum": 2,
"x-order": 1,
"description": "\u76ee\u6807\u50cf\u7d20\uff08\u767e\u4e07\u50cf\u7d20\uff09"
},
"latent_noise_scale": {
"type": "number",
"title": "Latent Noise Scale",
"default": 0,
"maximum": 1,
"minimum": 0,
"x-order": 13,
"description": "\u6f5c\u5728\u7a7a\u95f4\u566a\u58f0\u6ce8\u5165\uff080.0-1.0\uff09\u3002\u53ef\u67d4\u5316\u7ec6\u8282\u30020 \u8868\u793a\u7981\u7528\u3002"
},
"png_compress_level": {
"type": "integer",
"title": "Png Compress Level",
"default": 1,
"maximum": 9,
"minimum": 0,
"x-order": 8,
"description": "PNG \u538b\u7f29\u7ea7\u522b (0-9\uff0c\u4ec5 PNG \u751f\u6548\uff1b0 \u6700\u5feb\uff0c9 \u4f53\u79ef\u66f4\u5c0f)"
},
"short_pixels_after_tile": {
"type": "integer",
"title": "Short Pixels After Tile",
"default": 1024,
"maximum": 4096,
"minimum": 512,
"x-order": 4,
"description": "\u5206\u7247\u538b\u7f29\u540e\u77ed\u8fb9"
},
"upscale_multiple_after_tile": {
"type": "number",
"title": "Upscale Multiple After Tile",
"default": 2,
"maximum": 4,
"minimum": 1,
"x-order": 5,
"description": "\u5206\u7247\u538b\u7f29\u653e\u5927\u6bd4\u4f8b"
}
}
}
Output schema
The shape of the response you’ll get when you run this model with an API.
{
"type": "string",
"title": "Output",
"format": "uri"
}