Instructions to use xin0920/trained-sd3-sae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xin0920/trained-sd3-sae with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("xin0920/trained-sd3-sae", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
End of training
Browse files- README.md +7 -0
- checkpoint-1000/optimizer.bin +3 -0
- checkpoint-1000/random_states_0.pkl +3 -0
- checkpoint-1000/scaler.pt +3 -0
- checkpoint-1000/scheduler.bin +3 -0
- checkpoint-500/optimizer.bin +3 -0
- checkpoint-500/random_states_0.pkl +3 -0
- checkpoint-500/scaler.pt +3 -0
- checkpoint-500/scheduler.bin +3 -0
- pytorch_sae_weights.safetensors +1 -1
README.md
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- template:sd-sae
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- sd3
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---
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<!-- This model card has been generated automatically according to the information the training script had access to. You
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- template:sd-sae
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- sd3
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- sd3-diffusers
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- text-to-image
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---
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<!-- This model card has been generated automatically according to the information the training script had access to. You
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checkpoint-1000/optimizer.bin
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pytorch_sae_weights.safetensors
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