lucataco / demofusion-enhance

Image to Image enhancer using DemoFusion

  • Public
  • 10.4K runs
  • A100 (80GB)
  • GitHub
  • Paper
  • License

Input

image
*file

Input image

integer

Scale factor for input image

Default: 2

string
Shift + Return to add a new line

Input prompt

Default: "A high resolution photo"

boolean

Select to use auto-generated CLIP prompt instead of using the above custom prompt

Default: false

string
Shift + Return to add a new line

Input Negative Prompt

Default: "blurry, ugly, duplicate, poorly drawn, deformed, mosaic"

integer
(minimum: 1, maximum: 500)

Number of denoising steps

Default: 40

number
(minimum: 1, maximum: 50)

Scale for classifier-free guidance

Default: 8.5

integer

The batch size for multiple denoising paths

Default: 16

integer

The stride of moving local patches

Default: 64

number

Control the strength of skip-residual

Default: 3

number

Control the strength of dilated sampling

Default: 1

number

Control the strength of the Gaussian filter

Default: 1

number

The standard value of the Gaussian filter

Default: 0.8

boolean

Use multiple decoders

Default: false

integer

Random seed. Leave blank to randomize the seed

Output

We were unable to load these images. Please make sure the URLs are valid.

{
  "input": "https://replicate.delivery/pbxt/K10dBMR5HkR2twsOHUImt6hch8oGy4AuSXnaQT70vayk5OVk/red-flower.jpg",
  "outut": "https://replicate.delivery/pbxt/6Yq3KtwejJ3WPC8vsv7518ujHlFtcOafJl9S4GeJ1ShihpAkA/out-2.png"
}
Generated in

Run time and cost

This model costs approximately $0.42 to run on Replicate, or 2 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 A100 (80GB) GPU hardware. Predictions typically complete within 6 minutes.

Readme

This is a Cog implementation of the Huggingface space: Enhance-This-DemoFusion-SDXL

DemoFusion enables higher-resolution image generation. You can upload an initial image and prompt to generate an enhanced version.