| pipeline_tag: text-to-image | |
| # Work in progress | |
| Similar hacking to our opendiffusionai/stablediffusionxl_t5 model | |
| But, with SD1.5 of course. | |
| Why do this? | |
| Because coming up with a usable finetuning script for SDXL is turning out | |
| to be a pain in the rear. So I thought I might regress to the theoretically | |
| easier experiment. | |
| # Precision | |
| Note that the unet is only bf16 at this time | |
| # Usage | |
| You can use it with the sample code in [demo.py](demo.py) | |
| Note that it will give you an image of SOMETHING... | |
| however, it is sort of random output at this point. | |
| The unet needs to be retrained to get things to match up. | |
| # Sample | |
| here's how random the output looks. | |
| (its equivalent to putting random strings into an sd1.5 prompt I'd guess) | |
| Prompt: "a misty Tokyo alley at night" | |
|  | |