Instructions to use ppbrown/f8c32-alpha-p4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ppbrown/f8c32-alpha-p4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ppbrown/f8c32-alpha-p4", 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
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README.md
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@@ -18,7 +18,7 @@ Results from utility "calculate_loss.py"
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image l1 rawvgg edge lap
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P1 step_010000/vae_sample.webp 0.2104 7.4103 0.2934 0.0729
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P1 step_070000/vae_sample.webp 0.0153 1.1987 0.0686 0.0385
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(LR 1e-5, lpips weight 0.1 lap 0.02 edge_l1_weight 0.1)
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P2 step_960000/vae_sample.webp 0.0121 0.7109 0.0535 0.0355
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image l1 rawvgg edge lap
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P1 step_010000/vae_sample.webp 0.2104 7.4103 0.2934 0.0729
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P1 step_070000/vae_sample.webp 0.0153 1.1987 0.0686 0.0385
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(LR 1e-5, lpips weight 0.1 lap 0.02 [NO RAWVGG!!] edge_l1_weight 0.1)
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P2 step_960000/vae_sample.webp 0.0121 0.7109 0.0535 0.0355
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