import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("mobled37/vae-model-finetuned", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Text-to-image finetuning - mobled37/vae-model-finetuned
This pipeline was finetuned from None on the vipseg dataset. Below are some example images generated with the finetuned pipeline using the following prompts: Nothing:
Training info
These are the key hyperparameters used during training:
- Epochs: 1000
- Learning rate: 1.92e-05
- Batch size: 64
- Gradient accumulation steps: 2
- Image resolution: 30
- Mixed-precision: fp16
More information on all the CLI arguments and the environment are available on your wandb run page.
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