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Remove unnecessary Space token dependency
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# Agent guidance
## Project
This repository is a Hugging Face Gradio Space for image editing with:
- `Qwen/Qwen-Image-Edit-2511`
- a four-step Rapid-AIO transformer
- a custom Qwen pipeline under `qwenimage/`
- optional FA3 attention acceleration
The base model, accelerated transformer, and Diffusers dependency are pinned to
exact revisions. Update those pins deliberately and test the deployed Space
before merging.
The transformer and text encoder are dynamically quantized to FP8 before the
pipeline is moved to its runtime device. Keep that ordering: the unquantized
BF16 pipeline is too large for the ZeroGPU transfer path.
Keep output sizing automatic and preserve the minimum input-side validation.
Exposing low output dimensions or accepting thumbnail-sized sources can produce
tiled/grid-like artifacts that look like a model failure.
## Privacy boundary
Image inference runs inside the Space. Do not add application code that sends
prompts, input images, generated images, or generation parameters to another
service without explicit user consent and prominent UI/README disclosure.
Do not print prompt text or image contents to application logs. Let Gradio
manage temporary files and keep `delete_cache` enabled. The pinned model
repositories are public; do not add an account token or other Space secret
without a documented requirement and least-privilege review.
## Model-loading boundary
The four-step UI must use the matching accelerated transformer. Do not silently
fall back to the standard Qwen transformer while retaining a four-step default;
that produces broken or misleading output. A clear startup failure is safer
than silently changing model behavior.
## Validation
Before deployment:
```bash
python3 -m compileall -q .
git diff --check
```
After deployment, verify:
- the Space reaches `RUNNING`
- a harmless synthetic edit succeeds
- prompts do not appear in application logs
- no unexpected application-level outbound upload paths exist
The Space uses ZeroGPU and requires Hugging Face infrastructure for model
downloads, builds, uploads, and inference hosting.