Update README.md

README.md

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 ---
-license: apache-2.0
+license: other
+license_name: rift-non-commercial-license-v1.0
+license_link: https://huggingface.co/Rift-ai/Rift.1-decoder/blob/main/LICENSE.md
+language:
+- en
+pipeline_tag: image-to-image
+library_name: diffusers
+tags:
+- text-to-image
+- image-editing
+- flux
+- diffusion-single-file
+- vae
+- decoder
+- rift
+- rift-ai
 ---
+
+![Comparison Panel](./comparison_panel.jpeg)
+
+`Rift.1-decoder` is a FLUX-compatible VAE decoder made for the Rift model line. It is designed as a **drop-in decoder component** for compatible FLUX.2-style Diffusers pipelines that use `AutoencoderKLFlux2`. The encoder path remains compatible with FLUX-style latent encoding, while the decoder has been trained as the Rift image reconstruction component.
+
+The exported Diffusers runtime class remains `AutoencoderKLFlux2` for loader compatibility. The model metadata identifies the architecture as `Rift1Decoder` with model type `rift1_decoder`.
+
+# **Key Features**
+
+1. **FLUX-compatible decoder interface** using `AutoencoderKLFlux2`.
+2. **Rift1Decoder metadata** in `config.json` for clear model identity.
+3. **32 latent channels** for compatibility with FLUX.2-style latent spaces.
+4. **512px reconstruction training** with edge and frequency losses for sharper detail retention.
+5. **Single-file artifacts included** for decoder-focused workflows:
+   - `diffusion_pytorch_model.safetensors`
+   - `full_encoder_small_decoder.safetensors`
+   - `small_decoder.safetensors`
+6. Released under the **Rift Non-Commercial License v1.0**.
+
+Compatible target pipeline family:
+
+- FLUX.2-style Diffusers pipelines using `AutoencoderKLFlux2`
+- [FLUX.2-klein-4B](https://huggingface.co/black-forest-labs/FLUX.2-klein-4B)
+- [FLUX.2-klein-9B](https://huggingface.co/black-forest-labs/FLUX.2-klein-9B)
+- [FLUX.2-klein-9b-kv](https://huggingface.co/black-forest-labs/FLUX.2-klein-9b-kv)
+- [FLUX.2-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev)
+
+# **Comparison**
+
+| Reference Decoder | Rift1Decoder |
+|:---:|:---:|
+| ![Reference Decoder](./compare_full_decoder.png) | ![Rift1Decoder](./compare_small_decoder.png) |
+
+# **Detail View**
+
+![Detail Zoom](./detail_zoom.jpeg)
+
+# **Usage**
+
+```shell
+pip install git+https://github.com/huggingface/diffusers.git transformers accelerate torch
+```
+
+```python
+import torch
+from diffusers import AutoencoderKLFlux2
+
+vae = AutoencoderKLFlux2.from_pretrained(
+    "Rift-ai/Rift.1-decoder",
+    torch_dtype=torch.bfloat16,
+)
+```
+
+If using a compatible FLUX.2 pipeline, pass this VAE when loading the pipeline:
+
+```python
+import torch
+from diffusers import Flux2KleinPipeline, AutoencoderKLFlux2
+
+device = "cuda"
+dtype = torch.bfloat16
+
+vae = AutoencoderKLFlux2.from_pretrained("Rift-ai/Rift.1-decoder", torch_dtype=dtype)
+pipe = Flux2KleinPipeline.from_pretrained(
+    "black-forest-labs/FLUX.2-klein-4B",
+    vae=vae,
+    torch_dtype=dtype,
+)
+pipe.enable_model_cpu_offload()
+
+prompt = "A black cat holding a sign that says 'hello world' in typewriter font"
+image = pipe(
+    prompt=prompt,
+    height=1024,
+    width=1024,
+    guidance_scale=1.0,
+    num_inference_steps=4,
+    generator=torch.Generator(device=device).manual_seed(0),
+).images[0]
+image.save("rift-decoder-output.png")
+```
+
+---
+
+# **Artifact Files**
+
+| File | Purpose |
+|:---|:---|
+| `config.json` | Diffusers config with Rift metadata |
+| `diffusion_pytorch_model.safetensors` | Standard Diffusers weights |
+| `full_encoder_small_decoder.safetensors` | Full autoencoder-format weights |
+| `small_decoder.safetensors` | Decoder-only and post-quant-conv weights |
+| `comparison_panel.jpeg` | Full reference/Rift comparison |
+| `compare_full_decoder.png` | Reference decoder reconstruction sample |
+| `compare_small_decoder.png` | Rift decoder reconstruction sample |
+| `detail_zoom.jpeg` | Zoomed detail comparison |
+| `editing.jpg` | Additional visual sample |
+
+---
+
+# **Limitations**
+
+- This repository contains a VAE decoder component, not a complete text-to-image model.
+- Visual quality depends on the surrounding diffusion model, scheduler, prompt, latent distribution, and inference settings.
+- The decoder may introduce color shifts, texture smoothing, edge artifacts, or small structural artifacts.
+- Text rendered in generated images may be inaccurate or distorted.
+- Prompt following is handled primarily by the surrounding generation pipeline, not the VAE decoder alone.
+- This model should be evaluated visually and quantitatively before production use.
+
+# **Out-of-Scope Use**
+
+This model and its derivatives may not be used outside the scope of the Rift Non-Commercial License v1.0, including for unlawful, fraudulent, defamatory, abusive, exploitative, privacy-invasive, or otherwise harmful purposes.
+
+---
+
+# **Responsible AI Development**
+
+Rift.1-decoder should be evaluated as part of a complete image generation or image reconstruction system. A decoder can affect visual fidelity and artifacts, but safety behavior also depends on the text encoder, diffusion transformer, prompt filters, data pipeline, deployment environment, and downstream product policy.
+
+Users are responsible for applying appropriate safeguards, content review, watermarking or provenance notices where required, and compliance with applicable law.
+
+---
+
+# **License**
+
+This model is licensed under the [Rift Non-Commercial License v1.0](./LICENSE.md).
+
+# **Trademarks & IP**
+
+This project may contain trademarks or references to third-party projects, products, or services. Use of Rift, Rift-ai, or associated marks in modified versions of this project must not imply sponsorship, endorsement, approval, or official status unless explicitly authorized. Third-party trademarks, intellectual property, and logos remain subject to their respective owners' policies.