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
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
1import torch
2from diffusers import DiffusionPipeline
3
4pipe = DiffusionPipeline.from_pretrained(
5 "blanchon/dc_flux_krea_diffusers",
6 trust_remote_code=True,
7 torch_dtype=torch.bfloat16,
8).to("cuda")
Image Generation Example
1import torch
2from diffusers import DiffusionPipeline
3
4pipe = DiffusionPipeline.from_pretrained(
5 "blanchon/dc_flux_krea_diffusers",
6 trust_remote_code=True,
7 torch_dtype=torch.bfloat16,
8).to("cuda")
9
10prompt = "a tiny astronaut hatching from an egg on mars"
11
12image = pipe(
13 prompt=prompt,
14 width=2048,
15 height=2048,
16 guidance_scale=4.5,
17 num_inference_steps=28,
18 output_type="pil",
19).images[0]
20
21image.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:
- Embedding alignment to bridge the representation gap between latent spaces
- 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:
1@article{he2025dc,
2 title={DC-Gen: Post-Training Diffusion Acceleration with Deeply Compressed Latent Space},
3 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},
4 journal={arXiv preprint arXiv:2509.25180},
5 year={2025}
6}
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
