Jacklu0831/procreate-diffusion-frank-gehry
ProCreate checkpoint: Frank Gehry
A Stable Diffusion 1.5 UNet fine-tuned for 2,000 steps on the Frank Gehry category of FSCG-8 (50 caption-image pairs of Frank Gehry architecture). It is one of eight checkpoints released with ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative Generation (ECCV 2024).
ProCreate is a sampling method that pushes generated images away from the reference images, so a model fine-tuned on a few examples produces novel samples in the same style instead of replicating its training data.
Paper · Project page · Code · Dataset
What this repository contains
Only the fine-tuned UNet (UNet2DConditionModel, diffusers format). Load it into a Stable Diffusion 1.5 pipeline:
import torch
from diffusers import StableDiffusionPipeline, UNet2DConditionModel
pipe = StableDiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16
).to("cuda")
pipe.unet = UNet2DConditionModel.from_pretrained(
"Jacklu0831/procreate-diffusion-frank-gehry", torch_dtype=torch.float16
).to("cuda")
image = pipe("a twisting tall apartment building, designed by Frank Gehry").images[0]This samples from the fine-tuned model directly. To sample with ProCreate, use src/inference.py in the code repository, which downloads this checkpoint automatically:
python src/inference.py \
--dataset_dir few-shot-creative-generation-8/frank_gehry \
--unet_ckpt_dir Jacklu0831/procreate-diffusion-frank-gehry \
--prompt "a twisting tall apartment building, designed by Frank Gehry"License
The weights are a derivative of Stable Diffusion 1.5 and are released under the CreativeML Open RAIL-M license.
Citation
@inproceedings{lu2024procreate,
title = {ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative Generation},
author = {Lu, Jack and Teehan, Ryan and Ren, Mengye},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2024}
}