Very nice :) (Love your Vader)
I add some issues with their script and switched to (https://github.com/huggingface/diffusers/tree/main/examples/text_to_image). I've adapted their script and did the same thing for Magic Card and One Piece Character if that interests you https://github.com/YaYaB/finetune-diffusion

Thanks, wish I knew about that repository before!

Out of curiosity did you you have to pay to host your demos on HuggingFace? I looked around for some free options with GPUs but only found Google Colab which isn't very convenient for Gradio apps.

Nop you can host your demo without paying (for the moment I suppose) however you'll have a cpu (very low inference time). If you upload your model to HuggingFace feel free to copy paste the space and modify the verbatim + the model path for your repo (https://huggingface.co/spaces/YaYaB/text-to-onepiece). It is merely a copy paste of the one made by lambdalabs, did not get time to make something more personal ^^

Looks like you were lucky enough to be granted some A10G GPUs :)

Thanks for the tips, I uploaded it here https://huggingface.co/spaces/Gazoche/text-to-gundam

It's running on CPU so very slow, but easier to use than Colab at least.

Yep ^^

Impressive, how big is the dataset? Huggingface says n<2k which seems incredibly small.

Also, what is an individual sample point? A gundam image and it's name?

Thanks! There's 1565 images in the datasaset. The original Pokemon project used an even smaller one (less than 1K images).

Each row is a gundam image + a text description. The original project used BLIP to auto-caption the images but that didn't really work for this dataset so instead I asked BLIP to only describe the colors and inserted them into a generic description: "A robot, humanoid, futuristic, <colors>". One could likely get better results with more fine-grained captions.