README.md

---
library_name: diffusers
pipeline_tag: text-to-image
tags:
  - text-to-image
  - image-generation
  - flux
  - dc-gen
  - diffusers
base_model:
  - dc-ai/dc_flux_2K4K
  - black-forest-labs/FLUX.1-Krea-dev
---

# blanchon/dc_flux_krea_diffusers

**Diffusers-compatible port of DC-Gen-FLUX (Krea)** for efficient high-resolution text-to-image generation (2K / 4K).

This repository repackages the original **DC-Gen FLUX.1-Krea checkpoint** into a 🧨 **Diffusers** `DiffusionPipeline`, enabling standard Diffusers workflows while preserving the behavior and performance of the upstream model.

---

## Model Details

### Model Description

**FLUX.1 DC-Gen Krea [dev]** is a DC-Gen–adapted FLUX.1-Krea checkpoint that replaces the original FLUX VAE with a **deeply compressed DC-AE latent space**.  
Using **embedding alignment** followed by **lightweight LoRA fine-tuning**, DC-Gen enables much faster native **2K / 4K image generation** while preserving the base model’s realism and text-rendering quality.

This repository does **not** retrain the model. It only provides a **Diffusers port** of the upstream checkpoint for easier inference and deployment.

- **DC-Gen method & model:** NVIDIA DC-Gen team  
  (Wenkun He*, Yuchao Gu*, Junyu Chen*, Dongyun Zou, Yujun Lin, Zhekai Zhang, Haocheng Xi, Muyang Li, Ligeng Zhu, Jincheng Yu, Junsong Chen, Enze Xie, Song Han, Han Cai)
- **Diffusers port:** @blanchon
- **Model type:** Text-to-image diffusion (FLUX family, rectified flow transformer)
- **License:** FLUX.1 [dev] **Non-Commercial License** (same as upstream)
- **Upstream checkpoint:** `dc-ai/dc_flux_2K4K`
- **Base model family:** `black-forest-labs/FLUX.1-Krea-dev`

---

## Model Sources

- **DC-Gen project:** https://github.com/dc-ai-projects/DC-Gen  
- **DC-Gen homepage:** https://hanlab.mit.edu/projects/dc-gen  
- **Paper:** https://arxiv.org/abs/2509.25180  
- **Upstream checkpoint:** https://huggingface.co/dc-ai/dc_flux_2K4K  
- **FLUX.1-Krea base model:** https://huggingface.co/black-forest-labs/FLUX.1-Krea-dev  

---

## Uses

### Direct Use

- High-resolution text-to-image generation (1024 → 4096 px)
- Diffusers-based inference, demos, and deployment
- Research on efficient latent-space diffusion and high-resolution synthesis

### Downstream Use

- Further research or finetuning **only if compliant with the upstream license**
- Integration into non-commercial creative or research tools

### Out-of-Scope Use

- Commercial usage (not permitted by the FLUX.1-dev license)
- Illegal, harmful, or deceptive content generation

---

## Bias, Risks, and Limitations

- The model may reproduce societal biases present in its training data.
- High-resolution generation is GPU- and VRAM-intensive.
- Outputs are not guaranteed to be factual or safe without moderation.
- This repo does not introduce new safety mechanisms beyond those of the base model.

### Recommendations

- Review the FLUX.1-dev non-commercial license carefully before use.
- Apply standard content filtering and safety practices in downstream applications.
- Expect memory usage to scale significantly with resolution.

---

## How to Get Started with the Model

### Minimal Load

```python
import torch
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained(
    "blanchon/dc_flux_krea_diffusers",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
).to("cuda")
````

### Image Generation Example

```python
import torch
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained(
    "blanchon/dc_flux_krea_diffusers",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
).to("cuda")

prompt = "a tiny astronaut hatching from an egg on mars"

image = pipe(
    prompt=prompt,
    width=2048,
    height=2048,
    guidance_scale=4.5,
    num_inference_steps=28,
    output_type="pil",
).images[0]

image.save("dc_flux_krea.png")
```

For reproducible results, pass a seeded `torch.Generator(device="cuda")`.

---

## Training Details

### Training Data

This repository does **not** introduce new training data.

According to the DC-Gen paper, post-training uses **synthetic data generated from the base model** to adapt it to a deeply compressed latent space.

### Training Procedure

DC-Gen applies:

1. **Embedding alignment** to bridge the representation gap between latent spaces
2. **LoRA fine-tuning** to recover base-model quality

See the DC-Gen paper for full methodological details.

---

## Evaluation

This repository does not add new evaluation results.

All reported quality, throughput, and latency benchmarks originate from the DC-Gen technical report.

---

## Technical Specifications

### Architecture

* FLUX-family text-to-image diffusion model
* Rectified flow transformer
* Deeply compressed DC-AE latent space (DC-Gen)

### Hardware Requirements

* CUDA-capable GPU strongly recommended
* 2K/4K generation requires substantial VRAM (≥24 GB recommended)

---

## Citation

If you use this model in research, please cite:

```bibtex
@article{he2025dc,
  title={DC-Gen: Post-Training Diffusion Acceleration with Deeply Compressed Latent Space},
  author={He, Wenkun and Gu, Yuchao and Chen, Junyu and Zou, Dongyun and Lin, Yujun and Zhang, Zhekai and Xi, Haocheng and Li, Muyang and Zhu, Ligeng and Yu, Jincheng and others},
  journal={arXiv preprint arXiv:2509.25180},
  year={2025}
}
```

---

## Model Card Authors

* **DC-Gen research & model:** DC-Gen team (NVIDIA)
* **Diffusers port & model card:** @blanchon

## Model Card Contact

* For research questions: see the DC-Gen project page
* For Diffusers port issues: use the Hugging Face Discussions tab