cjwbw / stable-diffusion-v2

sd-v2 with diffusers, test version!

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Output

Examples

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Run time and cost

This model costs approximately $0.10 to run on Replicate, or 10 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 75 seconds. The predict time for this model varies significantly based on the inputs.

Readme

weights from: https://huggingface.co/stabilityai/stable-diffusion-2, fp32 version code for the demo: https://github.com/chenxwh/cog-stable-diffusion/tree/sd-v2

TEST version!

This stable-diffusion-2 model is resumed from stable-diffusion-2-base (512-base-ema.ckpt) and trained for 150k steps using a v-objective on the same dataset. Resumed for another 140k steps on 768x768 images.

Compared to the mainline stable-diffusion https://replicate.com/stability-ai/stable-diffusion, the differences are: - seems only DDIM, K_EULER, and DPMSolverMultistep schedulers work properly - removed image inpainting pipeline - Default size set 768X768 - less num_output is allowed to fit to on V100