Instructions to use pcuenq/debug-training-output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use pcuenq/debug-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("pcuenq/debug-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 0
Browse files
.gitignore
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logs/train_unconditional/1666004573.9911475/events.out.tfevents.1666004573.deep-hack.2524472.1
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oid sha256:c0ce98846c791ad14f3dfa1c24606c42071608cd497c21f279097df565a6f7b0
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logs/train_unconditional/events.out.tfevents.1666004567.deep-hack.2524472.0
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oid sha256:14f320401e034081736a0de271ccf4b73d06e034a6d315fd299b3742f3196104
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size 174219
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