docs: add model card with vLLM serve guide

README.md

@@ -1,3 +1,147 @@
----
-license: mit
----
+---
+base_model: Tongyi-MAI/Z-Image-Turbo
+library_name: diffusers
+tags:
+  - diffusers
+  - text-to-image
+  - anime
+  - art-style
+  - z-image
+  - fuliji
+  - lora-merged
+license: apache-2.0
+language:
+  - zh
+  - en
+---
+
+# Z-Image-Turbo × Fuliji — Merged Model
+
+**Z-Image Turbo with Fuliji artist LoRA baked in.** The LoRA weights have been permanently merged into the base transformer via `merge_and_unload()`, so no PEFT dependency is needed at inference time.
+
+> **Want the standalone LoRA adapter instead?**
+> Use [DownFlow/Z-Image-Turbo-Fuli-LoRA](https://huggingface.co/DownFlow/Z-Image-Turbo-Fuli-LoRA) to apply the adapter on top of any Z-Image-Turbo checkpoint.
+
+---
+
+## What This Is
+
+This model is [Tongyi-MAI/Z-Image-Turbo](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo) (an 8-step flow-matching image generation model) fine-tuned with a LoRA trained on art from 8 Chinese anime/illustration artists in the [DownFlow/fuliji](https://huggingface.co/datasets/DownFlow/fuliji) dataset.
+
+Trigger the artist style by prepending `by <artist>,` to your prompt.
+
+---
+
+## Quick Start (Python)
+
+```bash
+pip install diffusers transformers accelerate safetensors
+```
+
+```python
+import torch
+from diffusers import DiffusionPipeline
+
+pipe = DiffusionPipeline.from_pretrained(
+    "DownFlow/Z-Image-Turbo-Fuli",
+    torch_dtype=torch.bfloat16,
+).to("cuda")
+
+image = pipe(
+    prompt="by 蠢沫沫, 1girl, solo, smile, soft lighting",
+    num_inference_steps=8,
+    guidance_scale=0.0,   # Z-Image Turbo uses CFG=0
+    height=512,
+    width=512,
+).images[0]
+
+image.save("output.png")
+```
+
+---
+
+## Serving with vLLM
+
+vLLM (≥ 0.8) can serve this model via an OpenAI-compatible `/v1/images/generations` endpoint.
+
+### 1 — Start the server
+
+```bash
+pip install "vllm>=0.8.0"
+
+vllm serve DownFlow/Z-Image-Turbo-Fuli \
+    --task generate \
+    --dtype bfloat16 \
+    --max-model-len 512 \
+    --port 8000
+```
+
+### 2 — Generate via curl
+
+```bash
+curl http://localhost:8000/v1/images/generations \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "DownFlow/Z-Image-Turbo-Fuli",
+    "prompt": "by 蠢沫沫, 1girl, smile, soft watercolour style",
+    "n": 1,
+    "size": "512x512"
+  }'
+```
+
+### 3 — Generate via OpenAI Python SDK
+
+```python
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")
+
+response = client.images.generate(
+    model="DownFlow/Z-Image-Turbo-Fuli",
+    prompt="by 年年, 1girl, white dress, cherry blossoms",
+    n=1,
+    size="512x512",
+)
+print(response.data[0].url)
+```
+
+---
+
+## Artist Trigger Tokens
+
+Prepend `by <artist>, ` at the start of your prompt.
+
+| Token | Training images |
+|---|---|
+| `萌芽儿o0` | 30 |
+| `年年` | 26 |
+| `封疆疆v` | 26 |
+| `焖焖碳` | 26 |
+| `星之迟迟` | 25 |
+| `蠢沫沫` | 23 |
+| `雨波HaneAme` | 23 |
+| `清水由乃` | 21 |
+
+---
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| Base model | `Tongyi-MAI/Z-Image-Turbo` |
+| Fine-tuning method | LoRA rank=32, alpha=32 — merged into weights |
+| Target modules | `to_q`, `to_k`, `to_v`, `w1`, `w2`, `w3` |
+| Training steps | 3 000 (EMA decay=0.9999) |
+| Training resolution | 512 × 512 |
+| Inference steps | 8 |
+| CFG scale | 0.0 (CFG-free) |
+| Precision | bfloat16 |
+| Dataset | [DownFlow/fuliji](https://huggingface.co/datasets/DownFlow/fuliji) (8 artists, ~200 images) |
+
+---
+
+## Related
+
+- [DownFlow/Z-Image-Turbo-Fuli-LoRA](https://huggingface.co/DownFlow/Z-Image-Turbo-Fuli-LoRA) — standalone LoRA adapter
+- [DownFlow/fuliji](https://huggingface.co/datasets/DownFlow/fuliji) — training dataset
+- [Tongyi-MAI/Z-Image-Turbo](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo) — base model