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
Checkpoint at step 3000
Browse files
checkpoint-3000/optimizer.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:59cf5879fd838bc2e598ec80006cc3e53f97b82a129a011b3487a5b556bcb38e
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size 51235083
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checkpoint-3000/pytorch_lora_weights.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:4329f5d23dde71dffc71a8dade83f33b92abd989ecd3915945725794a289dc44
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size 25548064
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checkpoint-3000/state.json
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{
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"step": 3000,
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"epoch": 7,
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"base_model": "runwayml/stable-diffusion-v1-5",
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"lora_rank": 32,
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"lora_alpha": 32,
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"resolution": 512,
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"scaling_factor": 0.18215,
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"learning_rate": 0.0001,
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"batch_size": 8,
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"grad_accum": 4,
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"caption_dropout": 0.1,
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"latents": "whosouravsharma/text-to-image-diffusiondb-2M@latents-512"
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}
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