https://replicate.com/tommoore515/material_stable_diffusion
tommoore515
/
material_stable_diffusion
Stable diffusion fork for generating tileable outputs
Input
Input prompt
Default: ""
Width of output image. Maximum size is 1024x768 or 768x1024 because of memory limits
Default: 512
Height of output image. Maximum size is 1024x768 or 768x1024 because of memory limits
Default: 512
Inital image to generate variations of. Will be resized to the specified width and height
Black and white image to use as mask for inpainting over init_image. Black pixels are inpainted and white pixels are preserved. Experimental feature, tends to work better with prompt strength of 0.5-0.7
Prompt strength when using init image. 1.0 corresponds to full destruction of information in init image
Default: 0.8
Number of images to output
Default: 1
Number of denoising steps
Default: 50
Scale for classifier-free guidance
Default: 7.5
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Output
This example was created by a different version, tommoore515/material_stable_diffusion:56f26876.
Examples
Run time and cost
This model costs approximately $0.016 to run on Replicate, or 62 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 L40S GPU hardware. Predictions typically complete within 17 seconds. The predict time for this model varies significantly based on the inputs.
Readme
Stable diffusion fork for generating tileable outputs.
The model uses a circular convolution, so the model sees the image as if all the parallel edges were connected. Like a tube, but in both directions to form a torus:
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