Qwen Image Edit Plus Texture Extract Serverless API
Extract seamless, tileable textures from photographs.
POST /v2/qwen-image-edit-plus-texture-extract · submit + poll 1# pip install "segmind>=1.1.0"
2# export SEGMIND_API_KEY="YOUR_API_KEY"
3import segmind
4
5# Async (v2): submit to the queue and block until COMPLETED.
6# run() returns the final result dict (600s deadline, 1.0s poll by default).
7result = segmind.run(
8 "qwen-image-edit-plus-texture-extract",
9 prompt="Extract stone texture from the wall. Extract into a texture image.",
10 image_1="https://segmind-resources.s3.amazonaws.com/input/73db8f48-bbbb-43e9-8758-ed99c88c3654-08-scaled.jpg",
11 lora="texture_extract",
12 aspect_ratio="1:1",
13 seed=87568756,
14 image_format="webp",
15 quality=95,
16 base64=False,
17)
18print(result["status"]) # COMPLETED
19print(result.get("output")) # model output (e.g. media URL)
20print(result["metrics"]["inference_time"]) # server compute seconds
21
22# --- Or submit + poll manually (track request_id, control the cadence) ---
23from segmind import SegmindClient, InferenceFailed, InferenceTimeout
24
25client = SegmindClient() # reads SEGMIND_API_KEY
26payload = {
27 "prompt": "Extract stone texture from the wall. Extract into a texture image.",
28 "image_1": "https://segmind-resources.s3.amazonaws.com/input/73db8f48-bbbb-43e9-8758-ed99c88c3654-08-scaled.jpg",
29 "lora": "texture_extract",
30 "aspect_ratio": "1:1",
31 "seed": 87568756,
32 "image_format": "webp",
33 "quality": 95,
34 "base64": False,
35}
36job = client.submit_async("qwen-image-edit-plus-texture-extract", **payload)
37print(job.request_id) # available immediately
38try:
39 result = job.wait(timeout=600, interval=1.0)
40except InferenceTimeout as e:
41 print("still running:", e.request_id)
42except InferenceFailed as e:
43 print("failed:", e.detail) 1# pip install "segmind>=1.1.0"
2# export SEGMIND_API_KEY="YOUR_API_KEY"
3import segmind
4
5# Async (v2): submit to the queue and block until COMPLETED.
6# run() returns the final result dict (600s deadline, 1.0s poll by default).
7result = segmind.run(
8 "qwen-image-edit-plus-texture-extract",
9 prompt="Extract stone texture from the wall. Extract into a texture image.",
10 image_1="https://segmind-resources.s3.amazonaws.com/input/73db8f48-bbbb-43e9-8758-ed99c88c3654-08-scaled.jpg",
11 lora="texture_extract",
12 aspect_ratio="1:1",
13 seed=87568756,
14 image_format="webp",
15 quality=95,
16 base64=False,
17)
18print(result["status"]) # COMPLETED
19print(result.get("output")) # model output (e.g. media URL)
20print(result["metrics"]["inference_time"]) # server compute seconds
21
22# --- Or submit + poll manually (track request_id, control the cadence) ---
23from segmind import SegmindClient, InferenceFailed, InferenceTimeout
24
25client = SegmindClient() # reads SEGMIND_API_KEY
26payload = {
27 "prompt": "Extract stone texture from the wall. Extract into a texture image.",
28 "image_1": "https://segmind-resources.s3.amazonaws.com/input/73db8f48-bbbb-43e9-8758-ed99c88c3654-08-scaled.jpg",
29 "lora": "texture_extract",
30 "aspect_ratio": "1:1",
31 "seed": 87568756,
32 "image_format": "webp",
33 "quality": 95,
34 "base64": False,
35}
36job = client.submit_async("qwen-image-edit-plus-texture-extract", **payload)
37print(job.request_id) # available immediately
38try:
39 result = job.wait(timeout=600, interval=1.0)
40except InferenceTimeout as e:
41 print("still running:", e.request_id)
42except InferenceFailed as e:
43 print("failed:", e.detail)API Endpoint
https://api.segmind.com/v1/qwen-image-edit-plus-texture-extractParameters
promptrequiredstringDescribe the desired image. Use specific details for tailored outputs, like 'sunset over mountains' for nature scenes.
aspect_ratiooptionalstringSet output aspect ratio. Use 'match_input_image' for consistent dimensions or select ratios like '16:9' for widescreen.
"1:1""1:1""2:3""3:2""3:4""4:3""4:5""5:4""9:16""16:9""21:9"+1 morebase64optionalbooleanGet output as a base64 string. Use for easy web embedding.
falseimage_1optionalstring (uri)Upload a base image. Use landscape images for scenery edits, portraits for fashion edits.
image_2optionalstring (uri)Add a secondary image for blending. Ideal for combining images like cityscapes with skies.
nullimage_3optionalstring (uri)Use a tertiary image for texture layering. Choose textures like rust or water for creative effects.
nullimage_formatoptionalstringChoose image output format. 'webp' is efficient; choose 'jpeg' or 'png' for compatibility.
"webp""jpeg""png""webp"loraoptionalstringSelect a LoRA model for texture extraction. Use 'texture_extract' for detailed texture effects.
"texture_extract""texture_extract"lora_2_urloptionalstringInclude an additional LoRA model URL. Useful for specialized effects like artistic filters.
nulllora_3_urloptionalstringInclude another LoRA model URL. Opt for unique URLs for intricate design blending.
nullqualityoptionalintegerSet image quality. Use '95' for high quality and '80' for web optimization.
95Range: 1 - 100seedoptionalintegerSet a seed for reproducible results. Use -1 for random outcomes.
87568756Range: -1 - 2147483647Response Type
Returns: Image
Asynchronous requests (v2)
Use Async for video, long-running (>~60s), or high-concurrency workloads; Sync is simplest for fast image & LLM calls. Async submits a request and you poll it to completion.
- 1
POST /v2/qwen-image-edit-plus-texture-extractSubmit — returns request_id, status_url, response_url
- 2
GET /v2/requests/{id}/statusPoll — until COMPLETED or FAILED
- 3
GET /v2/requests/{id}Result — final response body
Status states
- A FAILED request is served as HTTP 422 — the body still carries the error detail.
- An unknown or expired request_id returns HTTP 404.
- Results are retained for 1 hour, then expire.
- Content / RAI blocks surface as FAILED, not a separate state.
- Track completion by polling the status endpoint.
Common Error Codes
The API returns standard HTTP status codes. Detailed error messages are provided in the response body.
Bad Request
Invalid parameters or request format
Unauthorized
Missing or invalid API key
Forbidden
Insufficient permissions
Not Found
Model or endpoint not found
Insufficient Credits
Not enough credits to process request
Rate Limited
Too many requests
Server Error
Internal server error
Bad Gateway
Service temporarily unavailable
Timeout
Request timed out