Instructions to use vdumanov/suteev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vdumanov/suteev with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("vdumanov/suteev") prompt = "unconditional (blank prompt)" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Commit History
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Model card auto-generated by SimpleTuner
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Trained for 4 epochs and 666 steps.
Verified signatureTrained with datasets ['text-embeds', 'suteev'] Learning rate 0.0004, batch size 1, and 1 gradient accumulation steps. Used DDPM noise scheduler for training with epsilon prediction type and rescaled_betas_zero_snr=False Using 'trailing' timestep spacing. Base model: stabilityai/stable-diffusion-3-medium-diffusers VAE: None82adb09 -
Validation images auto-generated by SimpleTuner
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Model card auto-generated by SimpleTuner
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Trained for 3 epochs and 500 steps.
Verified signatureTrained with datasets ['text-embeds', 'suteev'] Learning rate 0.0004, batch size 1, and 1 gradient accumulation steps. Used DDPM noise scheduler for training with epsilon prediction type and rescaled_betas_zero_snr=False Using 'trailing' timestep spacing. Base model: stabilityai/stable-diffusion-3-medium-diffusers VAE: Nonede1df62 -
Validation images auto-generated by SimpleTuner
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initial commit
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