lightweight-ai/m_q_e_t
Public
514
runs
Run lightweight-ai/m_q_e_t 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 |
|---|---|---|---|
| init_image |
string
|
수정 또는 인페인팅할 원본 이미지
|
|
| prompt |
string
|
|
이미지 수정을 위한 텍스트 프롬프트
|
| mask_image |
string
|
인페인팅을 위한 마스크 이미지. 흰색 영역은 수정되고, 검은색 영역은 유지됩니다.
|
|
| negative_prompt |
string
|
ugly, blurry, poor quality
|
생성 결과에서 제외할 요소 (예: 저화질, 흐림)
|
| lang |
None
|
en
|
언어 선택 ('en', 'zh'). 선택된 언어에 따라 긍정적 프롬프트가 자동으로 추가됩니다.
|
| num_inference_steps |
integer
|
28
Min: 1 Max: 200 |
디퓨전 스텝 수. 높을수록 품질이 향상되지만 속도가 느려집니다.
|
| true_cfg_scale |
number
|
4
Min: 1 Max: 20 |
프롬프트 충실도. 높을수록 프롬프트 내용을 강하게 따릅니다.
|
| aspect_ratio |
None
|
original
|
출력 이미지의 화면 비율. 'original' 선택 시 원본 크기 유지.
|
| width |
integer
|
출력 이미지의 너비. 16의 배수로 자동 조정됩니다.
|
|
| height |
integer
|
출력 이미지의 높이. 16의 배수로 자동 조정됩니다.
|
|
| lora |
string
|
LoRA 모델의 URL (.safetensors 형식).
|
|
| lora_scale |
number
|
1
|
LoRA 적용 강도.
|
| seed |
integer
|
42
|
랜덤 시드. 동일한 시드는 동일한 결과를 보장합니다.
|
{
"type": "object",
"title": "Input",
"required": [
"init_image"
],
"properties": {
"lang": {
"enum": [
"en",
"zh"
],
"type": "string",
"title": "lang",
"description": "\uc5b8\uc5b4 \uc120\ud0dd ('en', 'zh'). \uc120\ud0dd\ub41c \uc5b8\uc5b4\uc5d0 \ub530\ub77c \uae0d\uc815\uc801 \ud504\ub86c\ud504\ud2b8\uac00 \uc790\ub3d9\uc73c\ub85c \ucd94\uac00\ub429\ub2c8\ub2e4.",
"default": "en",
"x-order": 4
},
"lora": {
"type": "string",
"title": "Lora",
"x-order": 10,
"description": "LoRA \ubaa8\ub378\uc758 URL (.safetensors \ud615\uc2dd)."
},
"seed": {
"type": "integer",
"title": "Seed",
"default": 42,
"x-order": 12,
"description": "\ub79c\ub364 \uc2dc\ub4dc. \ub3d9\uc77c\ud55c \uc2dc\ub4dc\ub294 \ub3d9\uc77c\ud55c \uacb0\uacfc\ub97c \ubcf4\uc7a5\ud569\ub2c8\ub2e4."
},
"width": {
"type": "integer",
"title": "Width",
"x-order": 8,
"description": "\ucd9c\ub825 \uc774\ubbf8\uc9c0\uc758 \ub108\ube44. 16\uc758 \ubc30\uc218\ub85c \uc790\ub3d9 \uc870\uc815\ub429\ub2c8\ub2e4."
},
"height": {
"type": "integer",
"title": "Height",
"x-order": 9,
"description": "\ucd9c\ub825 \uc774\ubbf8\uc9c0\uc758 \ub192\uc774. 16\uc758 \ubc30\uc218\ub85c \uc790\ub3d9 \uc870\uc815\ub429\ub2c8\ub2e4."
},
"prompt": {
"type": "string",
"title": "Prompt",
"default": "",
"x-order": 1,
"description": "\uc774\ubbf8\uc9c0 \uc218\uc815\uc744 \uc704\ud55c \ud14d\uc2a4\ud2b8 \ud504\ub86c\ud504\ud2b8"
},
"init_image": {
"type": "string",
"title": "Init Image",
"format": "uri",
"x-order": 0,
"description": "\uc218\uc815 \ub610\ub294 \uc778\ud398\uc778\ud305\ud560 \uc6d0\ubcf8 \uc774\ubbf8\uc9c0"
},
"lora_scale": {
"type": "number",
"title": "Lora Scale",
"default": 1,
"x-order": 11,
"description": "LoRA \uc801\uc6a9 \uac15\ub3c4."
},
"mask_image": {
"type": "string",
"title": "Mask Image",
"format": "uri",
"x-order": 2,
"description": "\uc778\ud398\uc778\ud305\uc744 \uc704\ud55c \ub9c8\uc2a4\ud06c \uc774\ubbf8\uc9c0. \ud770\uc0c9 \uc601\uc5ed\uc740 \uc218\uc815\ub418\uace0, \uac80\uc740\uc0c9 \uc601\uc5ed\uc740 \uc720\uc9c0\ub429\ub2c8\ub2e4."
},
"aspect_ratio": {
"enum": [
"1:1",
"16:9",
"9:16",
"4:3",
"3:4",
"3:2",
"2:3",
"original"
],
"type": "string",
"title": "aspect_ratio",
"description": "\ucd9c\ub825 \uc774\ubbf8\uc9c0\uc758 \ud654\uba74 \ube44\uc728. 'original' \uc120\ud0dd \uc2dc \uc6d0\ubcf8 \ud06c\uae30 \uc720\uc9c0.",
"default": "original",
"x-order": 7
},
"true_cfg_scale": {
"type": "number",
"title": "True Cfg Scale",
"default": 4,
"maximum": 20,
"minimum": 1,
"x-order": 6,
"description": "\ud504\ub86c\ud504\ud2b8 \ucda9\uc2e4\ub3c4. \ub192\uc744\uc218\ub85d \ud504\ub86c\ud504\ud2b8 \ub0b4\uc6a9\uc744 \uac15\ud558\uac8c \ub530\ub985\ub2c8\ub2e4."
},
"negative_prompt": {
"type": "string",
"title": "Negative Prompt",
"default": "ugly, blurry, poor quality",
"x-order": 3,
"description": "\uc0dd\uc131 \uacb0\uacfc\uc5d0\uc11c \uc81c\uc678\ud560 \uc694\uc18c (\uc608: \uc800\ud654\uc9c8, \ud750\ub9bc)"
},
"num_inference_steps": {
"type": "integer",
"title": "Num Inference Steps",
"default": 28,
"maximum": 200,
"minimum": 1,
"x-order": 5,
"description": "\ub514\ud4e8\uc804 \uc2a4\ud15d \uc218. \ub192\uc744\uc218\ub85d \ud488\uc9c8\uc774 \ud5a5\uc0c1\ub418\uc9c0\ub9cc \uc18d\ub3c4\uac00 \ub290\ub824\uc9d1\ub2c8\ub2e4."
}
}
}
Output schema
The shape of the response you’ll get when you run this model with an API.
Schema
{
"type": "string",
"title": "Output",
"format": "uri"
}