bytedance / hyper-flux-8step

Hyper FLUX 8-step by ByteDance

  • Public
  • 5.7M runs
  • H100
  • GitHub
  • Weights
  • Paper
  • License

Input

*string
Shift + Return to add a new line

Prompt for generated image

string

Aspect ratio for the generated image. The size will always be 1 megapixel, i.e. 1024x1024 if aspect ratio is 1:1. To use arbitrary width and height, set aspect ratio to 'custom'.

Default: "1:1"

integer
(minimum: 256, maximum: 1440)

Width of the generated image. Optional, only used when aspect_ratio=custom. Must be a multiple of 16 (if it's not, it will be rounded to nearest multiple of 16)

integer
(minimum: 256, maximum: 1440)

Height of the generated image. Optional, only used when aspect_ratio=custom. Must be a multiple of 16 (if it's not, it will be rounded to nearest multiple of 16)

integer
(minimum: 1, maximum: 4)

Number of images to output.

Default: 1

integer
(minimum: 1, maximum: 30)

Number of inference steps

Default: 8

number
(minimum: 0, maximum: 10)

Guidance scale for the diffusion process

Default: 3.5

integer

Random seed. Set for reproducible generation

string

Format of the output images

Default: "webp"

integer
(minimum: 0, maximum: 100)

Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs

Default: 80

boolean

This model’s safety checker can’t be disabled when running on the website. Learn more about platform safety on Replicate.

Disable safety checker for generated images. This feature is only available through the API. See [https://replicate.com/docs/how-does-replicate-work#safety](https://replicate.com/docs/how-does-replicate-work#safety)

Default: false

Output

output
Generated in

Run time and cost

This model costs approximately $0.011 to run on Replicate, or 90 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia H100 GPU hardware. Predictions typically complete within 7 seconds.

Readme

About

This is a Cog implementation of the ByteDance/Hyper-SD Flux.1-Dev 8-step LoRA

News

Our 8-steps and 16-steps FLUX.1-dev-related LoRAs are available now! We recommend LoRA scales around 0.125 that is adaptive with training and guidance scale could be kept on 3.5. Lower step LoRAs would be coming soon.

Hyper-SD

Official Repository of the paper: Hyper-SD.

Project Page: https://hyper-sd.github.io/

Try our Hugging Face demos:

Hyper-SD Scribble demo host on 🤗 scribble

Hyper-SDXL One-step Text-to-Image demo host on 🤗 T2I

Introduction

Hyper-SD is one of the new State-of-the-Art diffusion model acceleration techniques. In this repository, we release the models distilled from FLUX.1-dev, SD3-Medium, SDXL Base 1.0 and Stable-Diffusion v1-5

Checkpoints

  • Hyper-FLUX.1-dev-Nsteps-lora.safetensors: Lora checkpoint, for FLUX.1-dev-related models.
  • Hyper-SD3-Nsteps-CFG-lora.safetensors: Lora checkpoint, for SD3-related models.
  • Hyper-SDXL-Nstep-lora.safetensors: Lora checkpoint, for SDXL-related models.
  • Hyper-SD15-Nstep-lora.safetensors: Lora checkpoint, for SD1.5-related models.
  • Hyper-SDXL-1step-unet.safetensors: Unet checkpoint distilled from SDXL-Base.

Citation

@misc{ren2024hypersd,
      title={Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis}, 
      author={Yuxi Ren and Xin Xia and Yanzuo Lu and Jiacheng Zhang and Jie Wu and Pan Xie and Xing Wang and Xuefeng Xiao},
      year={2024},
      eprint={2404.13686},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Licensing and commercial use

If you generate images on Replicate with FLUX.1 models and their fine-tunes, then you can use the images commercially.

If you download the weights off Replicate and generate images on your own computer, you can’t use the images commercially.