# Qwen-Image-Edit-2511

> Qwen/Qwen-Image-Edit-2511

This file is intended for AI coding agents wiring an application to this specific Chutes model.

## Important URLs

- Model page: https://chutes.ai/app/chute/vonkaiser-qwen-image-edit-2511
- Model llms.txt: https://chutes.ai/app/chute/vonkaiser-qwen-image-edit-2511/llms.txt
- Model OpenAPI 3.1 spec (this chute, callable): https://chutes.ai/app/chute/vonkaiser-qwen-image-edit-2511/openapi.json
- Global Chutes llms.txt: https://chutes.ai/llms.txt
- Full Chutes docs export: https://chutes.ai/llms-full.txt
- Management API OpenAPI (account/billing/keys, NOT this model): https://api.chutes.ai/openapi.json

## Model Identifiers

- Name: `Qwen-Image-Edit-2511`
- Chute ID: `968bd316-f8da-52f0-a314-c985467934d6`
- Slug: `vonkaiser-qwen-image-edit-2511`
- Owner: `vonkaiser`

## Authentication

- Use `Authorization: Bearer $CHUTES_API_KEY` for inference calls.
- Send JSON request bodies with `Content-Type: application/json` unless the endpoint documentation says otherwise.
- The request body is FLAT — send the request fields at the top level (no `input_args`/`args` wrapper).
- This model is served on its own host (`https://vonkaiser-qwen-image-edit-2511.chutes.ai`). Call the endpoint paths listed below directly.
- For base64 media fields, send raw base64 strings in API requests. The web playground may use temporary Blob upload references internally for large files before proxying to Chutes.

## Playground Notes

- Large reference images are uploaded through temporary Blob storage automatically.
- Keep width and height at 1024 while testing; larger values cost more and may be slower.

## Endpoints

### 1. POST /generate

- Base URL: `https://vonkaiser-qwen-image-edit-2511.chutes.ai`
- Output content type: `image/jpeg`
- Playground note: Upload one to three reference images, then describe the exact edit rather than the whole final image.

Request fields:

- `prompt` (string, optional) Default: "make the background a tropical beach scene".

Example call:

```bash
# NOTE: replace the image_b64s placeholder(s) with real base64 data first.
curl -X POST "https://vonkaiser-qwen-image-edit-2511.chutes.ai/generate" \
  -H "Authorization: Bearer $CHUTES_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "prompt": "a serene mountain lake at sunset",
  "seed": 42,
  "width": 1024,
  "height": 1024,
  "image_b64s": [
    "<base64-encoded image>"
  ],
  "true_cfg_scale": 1,
  "negative_prompt": "",
  "num_inference_steps": 4
}' \
  --output output.jpg
```

Playground presets:

- Product background swap
- Precise color edit

## Model Facts

- Architecture: QwenImageTransformer2DModel diffusion transformer (60 layers, 24 heads, head dim 128) with a Qwen vision-language text encoder; diffusers QwenImageEditPlusPipeline
- Modalities: text, image in → image out
- License: Apache-2.0
- Released: 2025-12
- Task: Instruction-based image editing (image-to-image)
- Reference images: 1-3 input images per request on this chute
- Languages: English and Chinese editing instructions
- Improvements over 2509: Reduced image drift, better character and multi-person consistency, built-in popular LoRA effects, industrial design and geometric reasoning (per model card)
- Output format on Chutes: image/jpeg
- Hugging Face: https://huggingface.co/Qwen/Qwen-Image-Edit-2511

## Recommended Parameters

- `true_cfg_scale`: 4.0 (model-card example setting; the chute defaults to 1 for its fast 4-step serving profile)
- `num_inference_steps`: 40 (model-card example; the chute defaults to 4 steps for low latency)

## Best For

- Instruction-based edits: change backgrounds, materials, colors, or lighting while preserving the subject
- Identity-preserving character edits and multi-person composites from separate photos
- Product and industrial design variations (material swaps, batch design mockups)
- E-commerce imagery: background swaps and precise color edits from reference shots
- Bilingual (English/Chinese) edit instructions

Not ideal for:

- Pure text-to-image generation from scratch (use Qwen-Image-2512 or Z-Image Turbo instead)
- Pixel-exact edits with hard guarantees that untouched regions stay byte-identical; diffusion editing regenerates the whole frame
- Very high step-count quality sweeps on this chute; it is tuned as a fast 4-step serving profile

## FAQ

### How many input images can I send?

One to three reference images per request, as raw base64 strings in the image_b64s array. Multi-image input is how you compose subjects from separate photos, such as merging two portraits into one group shot.

### Why does the chute default to 4 steps when the model card uses 40?

The chute is configured as a low-latency serving profile: 4 inference steps with true_cfg_scale 1. The model card's example uses 40 steps with true_cfg_scale 4.0. Both fields are request parameters, so you can raise them for higher-fidelity edits at the cost of latency and compute.

### Does it keep the parts of the image I didn't ask to change?

That is the design goal, and the 2511 release specifically targets reduced image drift and improved consistency versus 2509. It is still a generative model, though: the whole frame is regenerated, so untouched regions are visually preserved rather than byte-identical.

### What license is Qwen-Image-Edit-2511 under?

Apache-2.0, per the model card's license section and the Hugging Face repo metadata. Commercial use, modification, and redistribution are permitted under standard Apache terms.

### Can I write edit instructions in Chinese?

Yes. The model is trained for English and Chinese instructions, and the model card demonstrates bilingual editing.

### How do I call it from code?

POST flat JSON to https://vonkaiser-qwen-image-edit-2511.chutes.ai/generate with an Authorization: Bearer header. Base64-encode your input image(s) into image_b64s, set prompt to the edit instruction, and save the response body as a JPEG.

### Is this a text-to-image model?

No, it is an editing model: it expects at least one reference image and an instruction describing the change. For generation from scratch use a text-to-image chute such as Qwen-Image-2512 or Z-Image Turbo.


## Model Guide & Sources

- Full model guide: https://chutes.ai/docs/models/vonkaiser-qwen-image-edit-2511
- Source: https://huggingface.co/Qwen/Qwen-Image-Edit-2511
- Source: https://chutes.ai/app/chute/vonkaiser-qwen-image-edit-2511/llms.txt

## Agent Integration Checklist

- Pick the endpoint path that matches the desired task.
- Set `CHUTES_API_KEY` in the server-side environment only.
- Validate required fields before sending requests.
- For media models, keep file upload, base64 conversion, and output preview/download handling explicit in the app UI.