README.md

---
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
- diffusion-single-file
- decoder
---

![Comparison Panel](./comparison_panel.jpeg)

`Rift.1-decoder` is a VAE decoder made for the Rift model line. It is designed as a **drop-in decoder component** for compatible Diffusers image pipelines that use `AutoencoderKLFlux2`. 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. **Diffusers decoder interface** using `AutoencoderKLFlux2`.
2. **Rift1Decoder metadata** in `config.json` for clear model identity.
3. **32 latent channels** for compatible image 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:

- Diffusers image pipelines using `AutoencoderKLFlux2`

# **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 image pipeline, pass this VAE when loading the pipeline:

```python
import torch
from diffusers import AutoencoderKLFlux2

device = "cuda"
dtype = torch.bfloat16

vae = AutoencoderKLFlux2.from_pretrained(
    "Rift-ai/Rift.1-decoder",
    torch_dtype=dtype,
).to(device)

# Pass `vae=vae` into a compatible Diffusers image pipeline.
```

---

# **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.