Update README.md

README.md

@@ -1,3 +1,96 @@
 ---
 license: apache-2.0
+language:
+  - en
+datasets:
+  - openvivo/VINS-120K
+base_model: black-forest-labs/FLUX.2-klein-9B
+library_name: diffusers
+pipeline_tag: image-to-image
+metrics:
+  - vins-4keval-plus-plus-overall
+  - vins-4keval-plus-plus-nc
+  - imgedit-bench-score
+  - cie-bench-total
+tags:
+  - image-editing
+  - instruction-based-image-editing
+  - 4k
+  - distillation
+  - dcae
 ---
+
+# BluePixel-Turbo
+
+**Native 4K instruction-based image editing in 4 steps.**
+
+[Project Page](https://tech.vivo.com/) · [Code](https://github.com/vivo/BluePixel) · [Dataset](https://huggingface.co/datasets/openvivo/VINS-120K)
+
+BluePixel-Turbo is the distilled image editing model from **BluePixel: Toward Native 4K Agentic Image Editing**, developed by vivo. It builds on [FLUX.2-klein-9B](https://huggingface.co/black-forest-labs/FLUX.2-klein-9B) with a DCAE-F64 autoencoder for efficient native 4K editing.
+
+- **Native 4K output** without cascaded super-resolution.
+- **4-step inference** through source-anchored target projection (SATP), distilled from a 50-step teacher.
+- **Long-tail editing skills** learned through ACE-MOPD, followed by reinforcement learning for aesthetics and instruction fidelity.
+- **8.7 seconds per 4K edit** on a single Kunlunxin P800 XPU (BF16), as reported on the project page. Latency varies by hardware.
+
+## Quick Start
+
+Use the custom Diffusers-based pipeline provided in the code repository:
+
+```bash
+git clone https://github.com/vivo/BluePixel.git
+cd BluePixel
+pip install -r requirements.txt
+pip install --upgrade diffusers huggingface_hub
+```
+
+Run from the cloned repository in a CUDA environment with BF16 support:
+
+```python
+import torch
+from PIL import Image
+from huggingface_hub import snapshot_download
+from vins_infer import load_pipeline
+
+model_dir = snapshot_download("openvivo/BluePixel-Turbo")
+pipe = load_pipeline(
+    model_dir=model_dir,
+    device="cuda:0",
+    vae_tiling=False,  # Enable to reduce autoencoder memory usage.
+    lora_scale=None,
+    shift=0.0,
+    mu_mode="empirical",
+)
+
+image = Image.open("assets/test_input.jpg").convert("RGB")
+result = pipe(
+    image=image,
+    instruction="Make the sky sunset orange and add warm light.",
+    num_inference_steps=4,
+    guidance_scale=3.0,
+    target_pixels=4096 * 4096,
+    generator=torch.Generator(device="cuda:0").manual_seed(42),
+).images[0]
+result.save("output.png")
+```
+
+The pipeline approximately preserves the input aspect ratio and aligns dimensions to multiples of 64; non-square inputs produce non-square outputs. Use a Diffusers version that includes the FLUX.2 components imported by `vins_infer.py`.
+
+## Benchmarks
+
+Results below are reported for **BluePixel** on the [project page](https://tech.vivo.com/); the page does not assign each quality score specifically to this Turbo checkpoint.
+
+| Benchmark | Metric / category | Result |
+| :--- | :--- | :--- |
+| VINS-4KEval++ | Overall | **8.62**, 2nd of 7 models |
+| VINS-4KEval++ | Non-edited-region consistency (NC) | **9.06** |
+| ImgEdit-Bench | Hybrid / Action | **4.59 / 4.93**, 1st in both |
+| CIE-Bench | Total, before → after ACE-MOPD | **6.24 → 8.87** |
+
+The metadata uses descriptive custom metric identifiers for these measures. See the project page for training details and the broader BluePixel Agent framework.
+
+## Limitations and License
+
+Intended for image editing with English instructions. Outputs may contain artifacts or unintended changes; review results before use.
+
+Released under **Apache-2.0**. See [LICENSE](https://github.com/vivo/BluePixel/blob/main/LICENSE) and [NOTICE](https://github.com/vivo/BluePixel/blob/main/NOTICE).