README for the Workflow version: parity table, design notes, hf_oauth scopes

README.md

@@ -1,5 +1,5 @@
 ---
-title: MOSS Image
+title: MOSS-Image Workflow
 emoji: 🖼
 colorFrom: blue
 colorTo: indigo
@@ -7,113 +7,82 @@ sdk: gradio
 sdk_version: 6.30.0
 python_version: "3.12"
 app_file: app.py
+hf_oauth: true
+hf_oauth_scopes:
+  - inference-api
+  - write-repos
 pinned: false
 license: apache-2.0
-short_description: Official demo of MOSS-Image.
+tags:
+  - ml-intern
+short_description: MOSS-Image text-to-image as a gr.Workflow node canvas.
 ---
 
-# MOSS-Image Demo
-
-<p align="center">
-  <a href="https://huggingface.co/OpenMOSS-Team/MOSS-Image-Base"><img alt="Hugging Face · MOSS-Image-Base" src="https://img.shields.io/badge/🤗%20Hugging%20Face-MOSS--Image--Base-ffc107?style=flat-square"></a>
-  &nbsp;
-  <a href="https://huggingface.co/OpenMOSS-Team/MOSS-Image-SFT"><img alt="Hugging Face · MOSS-Image-SFT" src="https://img.shields.io/badge/🤗%20Hugging%20Face-MOSS--Image--SFT-ffc107?style=flat-square"></a>
-  &nbsp;
-  <a href="https://modelscope.cn/models/openmoss/MOSS-Image-Base"><img alt="ModelScope · MOSS-Image-Base" src="assets/modelscope-base-badge.svg"></a>
-  &nbsp;
-  <a href="https://modelscope.cn/models/openmoss/MOSS-Image-SFT"><img alt="ModelScope · MOSS-Image-SFT" src="assets/modelscope-sft-badge.svg"></a>
-  &nbsp;
-  <a href="https://github.com/OpenMOSS/MOSS-Image"><img alt="GitHub" src="https://img.shields.io/badge/GitHub-Code-181717?style=flat-square&amp;logo=github&amp;logoColor=white"></a>
-  &nbsp;
-  <a href="https://openmoss.github.io/MOSS-Image/"><img alt="Homepage" src="https://img.shields.io/badge/🌐%20Project-Homepage-2563eb?style=flat-square"></a>
-  &nbsp;
-  <a href="https://github.com/OpenMOSS/MOSS-Image/blob/main/paper/MOSS_Image_Technical_Report.pdf"><img alt="Technical Report" src="https://img.shields.io/badge/📄%20Paper-Technical_Report-b31b1b?style=flat-square"></a>
-  &nbsp;
-  <a href="https://x.com/Open_MOSS"><img alt="Follow OpenMOSS on X" src="https://img.shields.io/badge/Follow-OpenMOSS-000000?style=flat-square&amp;logo=x&amp;logoColor=white"></a>
-  &nbsp;
-  <a href="https://qm.qq.com/q/hphM7VhaDu"><img alt="MOSS-Image QQ 交流群：755535143" src="https://img.shields.io/badge/QQ-755535143-12B7F5?style=flat-square&amp;logo=qq&amp;logoColor=white"></a>
-  &nbsp;
-  <a href="https://applink.feishu.cn/client/chat/chatter/add_by_link?link_token=7a3m2007-815f-409b-9c3d-a4129f5c058e"><img alt="MOSS-Builders Feishu Community" src="assets/feishu-builders-badge.svg"></a>
-  &nbsp;
-  <a href="https://discord.gg/nXFWEyvbb"><img alt="MOSS-Image Discord Community" src="https://img.shields.io/badge/Discord-Community-5865F2?style=flat-square&amp;logo=discord&amp;logoColor=white"></a>
-</p>
-
-Gradio interface for [MOSS-Image](https://github.com/OpenMOSS/MOSS-Image),
-including resolution and aspect-ratio presets, sampling controls, a selectable
-example gallery, advanced guidance controls, and live example/default-prompt updates.
-
-## Model loading
-
-The Space preloads **MOSS-Image-Base** and **MOSS-Image-SFT** with
-`snapshot_download(repo_id, revision=resolved_commit)`, followed by
-`MOSSImagePipeline.from_pretrained(snapshot_path, local_files_only=True, ...)`.
-The Space explicitly loads its bundled pipeline, transformer and scheduler classes
-from `pipeline_moss_image.py` for the ZeroGPU AOT lifecycle. The bundled inference
-implementation follows the current Base pipeline, including negative-branch skipping
-and standard SLG. Progress is reported using the same bridge as the standalone app.
-Weights are downloaded into the Hugging Face cache; no local model directory
-or model weights in this repository are required.
-
-Select **MOSS-Image-Base** or **MOSS-Image-SFT** in the Model dropdown before
-clicking Generate. Base is selected by default. The optional `MOSS_MODEL_ID`
-Space variable changes this initial selection.
-
-Both transformers are loaded at startup. The text encoder, tokenizer and VAE
-are each shared only when their Hub file hashes match at the resolved revisions.
-Components that differ, such as model-specific VAEs, are loaded separately. Each model retains its own
-scheduler, prompt cache and revision metadata. Generation requests run serially;
-changing the dropdown does not change an already submitted request.
-
-While the model repositories are private, configure an `HF_TOKEN` **Space secret**
-with read access to both models. Do not put a token in source files or
-ordinary Space variables. Local development uses the same environment variable
-or your cached Hugging Face login.
-
-## ZeroGPU
-
-Both pipelines are loaded and placed on CUDA before the UI starts. Generation runs
-under `@spaces.GPU(duration=80)`. At startup, a separate GPU allocation exports
-and AOT-compiles the repeated DiT and text-projection blocks with dynamic sequence
-lengths. Both models reuse the compiled graphs with their own weights. Compilation
-is validated against eager block outputs before the UI starts. This uses ZeroGPU
-AOTInductor support, not runtime `torch.compile`; GPU residency is fixed.
-Set the Space variable `MOSS_AOTI=0` to run the eager baseline. Compilation happens
-once per container start and is not part of image generation latency.
-The 80-second budget is scaled to 120 seconds by the current RTX PRO 6000
-ZeroGPU SDK configuration; it is not a measured latency guarantee.
-Large images or batches may require more time or memory than the allocation allows.
-
-Outputs and version-2 parameter JSON files are stored under `outputs/gradio/`. Records
-include the model repository and resolved revision, actual seed, sampling
-parameters, component configuration hashes, execution backend, and software versions. Space storage
-is ephemeral; save generated images you want to keep.
-
-## Local development
+[![Built with ML Intern](https://huggingface.co/datasets/huggingface/badges/resolve/main/built-with-ml-intern-sm.svg)](https://hf.co/chat?mode=ml-intern)
 
-```bash
-pip install -r requirements.txt
-python app.py
-```
+# MOSS-Image Workflow
+
+A [Gradio Workflow](https://www.gradio.app/guides/workflows) (node-canvas) version of the official
+[OpenMOSS-Team/MOSS-Image](https://huggingface.co/spaces/OpenMOSS-Team/MOSS-Image) demo, built on
+`gr.Workflow` in gradio 6.30.0, with the App view styled after the MOSS-Image landing page.
+
+## Feature parity with the official demo
+
+Every sampling control of the original Space is a reference node wired into the
+`MOSS-Image Generate` function node:
+
+| Original demo | Here |
+|---|---|
+| Model dropdown (Base / SFT) | **Model** reference (`base` or `sft`); both checkpoints loaded at startup |
+| Prompt / Negative Prompt | **Prompt** / **Negative Prompt** references (same default text) |
+| Width × Height (presets) | **Width** / **Height** references (multiples of 16) |
+| Steps / CFG / Shift / Seed | **Steps** / **CFG** / **Shift** / **Seed** references (`-1` = random) |
+| Images per prompt | **Images** reference → gallery output |
+| Max text length | **Max Text Length** reference (1–4096) |
+| Skip-layer modes (Off / Negative-branch skipping / Standard SLG) | **Skip Layers** (`22` = negative-branch skipping, empty = off), **SLG Layers**, **SLG Scale**, **Skip Start**, **Skip Stop** references |
+| Example gallery with one-click restore | **Examples strip** under the hero: the original Space's 20 showcase images, hot-linked; clicking one fetches its `parameters.json` and restores prompt, model, size, sampling and SLG settings into the form |
 
-To inspect the UI without downloading weights or allocating a GPU:
+Not ported: showcase add/delete with live refresh (the Workflow App view is
+generated from the graph and has no upload-to-gallery mechanism), and ZeroGPU AOT
+compilation (this app runs the eager backend — set it up like the original if you
+need the extra speed).
+
+## Design
+
+The App view is auto-generated by `gr.Workflow`, so the landing-page look is applied
+through `launch(theme=…, css=…, head=…)`: Inter type, a light gradient canvas, the
+official MOSS-Image lockup (light/dark aware) above the
+"Text-to-Image Generation with Native Layout Control" headline, a top nav with
+model/code links, and a Soft blue theme. The canvas view is untouched.
+
+## Running it
+
+Generation runs on GPU via `@spaces.GPU(duration=80)` per node call, so this Space needs
+**ZeroGPU hardware** (Settings → Hardware). `hf_oauth: true` is enabled so the owner can edit the
+canvas and signed-in visitors spend their own ZeroGPU quota; anonymous visitors fall back to the
+small shared allowance.
+
+Local development:
 
 ```bash
-python app.py --preview --port 7861
+pip install -r requirements.txt
+python app.py                           # full run, needs a CUDA GPU
+MOSS_WORKFLOW_PREVIEW=1 python app.py   # canvas + styling only, no model download
 ```
 
-The UI and size presets follow the MOSS-Image development repository (main at
-`8a3223c`). CFG defaults to 3; advanced settings contain CFG, shift and the
-Off / Negative-branch skipping / Standard SLG modes. Aspect ratios span 1:8–8:1.
-Examples restore their prompts, sampling settings and seed; unknown seeds become
-random (-1). Example files and default prompts refresh every two seconds.
+The bundled `pipeline_moss_image.py` is copied verbatim from the
+[original Space](https://huggingface.co/spaces/OpenMOSS-Team/MOSS-Image) (BSD 3-Clause); model
+weights are Apache-2.0. The lockup artwork is hot-linked from the original Space's assets.
+The form is prefilled with showcase example 007 (the photorealistic Kazusa at
+a Tokyo crosswalk: 1184×1776, 50 steps, CFG 3, shift 5, skip layer 22, seed
+3537712559) so visitors can just hit **Run** and reproduce it exactly. Other
+defaults match the official demo: Euler sampling and negative-branch skipping.
 
-ZeroGPU keeps the two official model choices, startup preloading, matching-component
-sharing and AOT compilation. Memory mode and runtime Compile DiT controls are not
-shown because GPU residency and compilation are managed at startup. The generation
-API returns the gallery and status, matching the standalone interface.
+## API example
 
-## Licenses
+```python
+from gradio_client import Client
 
-Model weights use Apache-2.0. Bundled pipeline code retains its BSD 3-Clause
-license in [LICENSE-CODE](LICENSE-CODE). See [NOTICE.md](NOTICE.md) for component
-attribution and the licenses in `third_party/`.
+client = Client("akhaliq/moss-image-workflow")
+client.view_api()  # exact parameters of /generated_images
+```