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usamaehsan /mage-flow-edit-turbo:a930c359
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 |
|---|---|---|---|
| image |
string
|
Image to edit.
|
|
| prompt |
string
|
An instruction describing the edit.
|
|
| width |
integer
|
1024
Max: 2048 |
Output width. Must be a multiple of 16. Set 0 to keep the input's own size (capped by max_size).
|
| height |
integer
|
1024
Max: 2048 |
Output height. Must be a multiple of 16. Set 0 to keep the input's own size (capped by max_size).
|
| max_size |
integer
|
1024
Min: 256 Max: 2048 |
Long-edge cap used only when width/height are 0.
|
| steps |
integer
|
4
Min: 1 Max: 50 |
Denoise steps. This is the 4-step Turbo checkpoint; above ~4 costs more without improving much.
|
| cfg |
number
|
1
Min: 1 Max: 10 |
Guidance. Turbo is distilled for 1.0 (no CFG); raising it doubles the work per step.
|
| seed |
integer
|
-1
|
Random seed. -1 for random.
|
| debug_timing |
boolean
|
False
|
Log a per-stage time breakdown. Adds GPU syncs, so it slightly inflates the total - use for profiling only.
|
| vl_cond_long_edge |
integer
|
384
Min: 128 Max: 768 |
Long edge the source image is resized to for the text encoder's conditioning pass. 384 matches training.
|
| content_gate |
boolean
|
True
|
Run the model's built-in content gate. It is a full Qwen3-VL generation on every edit (~1s of billed GPU), so turn it off only when the caller already moderates the prompt and the source image upstream.
|
| gate_long_edge |
integer
|
0
Max: 1024 |
Long edge the source image is resized to for the mandatory content gate. 0 = full resolution.
|
| gate_max_tokens |
integer
|
192
Min: 16 Max: 192 |
Token budget for the content gate's verdict. The gate is a greedy VL generation, so this caps its decode loop.
|
| output_format |
None
|
webp
|
Output image format.
|
| output_quality |
integer
|
90
Min: 1 Max: 100 |
Compression quality for WebP and JPEG.
|
Output schema
The shape of the response you’ll get when you run this model with an API.
{'format': 'uri', 'title': 'Output', 'type': 'string'}