Instructions to use whosouravsharma/diffusiondb-sd15-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use whosouravsharma/diffusiondb-sd15-lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("whosouravsharma/diffusiondb-sd15-lora") prompt = "a anthropomorphic lion wizard, diffuse lighting, fantasy, intricate, elegant, highly detailed, lifelike, photorealistic, digital painting, artstation, illustration, concept art, smooth, sharp focus, naturalism, trending on byron's - muse, by greg rutkowski and greg staples" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Update layout for checkpoints/ prefix
Browse files
README.md
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## Repository layout
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samples/
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base/ vanilla SD 1.5, the comparison baseline
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checkpoint-<step>/
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"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
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).to("cuda")
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pipe.load_lora_weights(
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"whosouravsharma/diffusiondb-sd15-lora", subfolder="checkpoint-
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)
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image = pipe(
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## Repository layout
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```
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checkpoints/
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checkpoint-<step>/
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pytorch_lora_weights.safetensors the adapter
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optimizer.pt optimizer + scaler state, for resuming
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state.json step, epoch, hyperparameters
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training/ the scripts that produced all of this
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samples/
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base/ vanilla SD 1.5, the comparison baseline
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checkpoint-<step>/
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"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
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).to("cuda")
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pipe.load_lora_weights(
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"whosouravsharma/diffusiondb-sd15-lora", subfolder="checkpoints/checkpoint-4240"
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)
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image = pipe(
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