---
title: Workshop
emoji: 🛠️
colorFrom: yellow
colorTo: red
sdk: docker
pinned: false
license: mit
short_description: LoRA pipeline configurator for Wan 2.2 I2V
---

# Workshop

Configure a **Wan 2.2 I2V Lightning 14B** base pipeline **or the
Qwen-Image-Edit-2511 LoRA Studio** (multi-image examples, T2I edit), attach
LoRAs from your reference library (`00000tt/wan22-loras`), label example
image + prompt pairs, and publish a **private** self-contained Gradio Space.

## Templates

1. **Wan 2.2 I2V Lightning 14B (AoT FP8)** — video generation; LoRA pulled from
   the warehouse on the first generate (cached after). `templates/wan22_i2v/`.
2. **Qwen-Image-Edit-2511 LoRAs Fast** — image editing; the proven
   `Qwen-Image-Edit-2511-LoRAs-Fast` recipe (Rapid-AIO transformer, FA-attn3,
   lazy LoRA loading, 20+ LoRAs). Examples support **multiple images per
   preset** (lighting/style transfer). `templates/qwen_image_edit_2511/`.

## Workflow

1. **Vault** — the Space's `HF_TOKEN` secret auto-loads. A manual token is only
   a fallback.
2. **Model & pipeline** — pick the base pipeline (Wan 2.2 I2V Lightning 14B,
   FP8 + int8 text-encoder, 6 steps default).
3. **LoRA reference library** — checkbox the `.safetensors` you want attached;
   drag new ones into the *Reference library parts* panel to upload them to the
   warehouse.
4. **Generation settings** — steps, guidance, duration stay as defaults in the
   published Space.
5. **Examples** — label image + prompt pairs; they are baked into the published
   Space's presets.
6. **Publish** — creates a Space under `00000tt`, **private by default**. Flip
   visibility to public only when you want it visible.

## Assistant

The *Workshop Assistant* talks through the Hugging Face Inference API using the
same `HF_TOKEN` secret. It can list the warehouse, set pipeline config, publish
private Spaces, flip visibility — and research the Hub: ask it to *find similar
models* and it will search Spaces, rank them by likes, fetch the target
`app.py`, extract that workflow (pipeline, base model, steps, guidance, fps,
frames, LoRA use, quantization), and diff it against your configured pipeline
so you can decide whether to match it or read their source. It also carries a
**gold-standard rubric** distilled from your proven reference Space
(`wan2-2-fp8da-aoti-preview-nsfw`) plus diffusers best practice — say
*"diagnose mine"* and it grades every Space you've published as
Perfect / Solid / Needs polish / Unreliable with a per-check pass/fail list
(base resolves, right pipeline, dtype, quantization with fallback, VRAM cleanup,
resolution, seeds, frame math, LoRA token flow, valid examples, negative-prompt,
controls, `@spaces.GPU`, no leaked secrets) and proposes add-ons. All driven by
small JSON tool calls at the end of its replies, parsed and executed by the
backend; a rule-based fallback answers when the LLM is unavailable.

## Develop locally

```bash
npm install && npm run dev   # frontend dev server (proxies /api to :7860)
uvicorn server:app --port 7860
```