Instructions to use IsaacAkintaro/class_conditioned_training_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use IsaacAkintaro/class_conditioned_training_output with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("IsaacAkintaro/class_conditioned_training_output", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Epoch 1
Browse files
logs/train_example/events.out.tfevents.1725881650.MSI.4432.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:53e9753825b9214e1d4f34232ca10824da9cae7994972e72de71ab1012232f42
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size 6282
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samples/0000.png
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training_metrics.csv
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epoch,train_loss,val_loss,learning_rate,psnr,ssim,lpips
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1,0.7206861972808838,0.7251554819253775,1.02e-05,14.525068553193073,0.09988017,1.0393900045981774
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