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--- |
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base_model: stabilityai/stable-diffusion-xl-base-1.0 |
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library_name: diffusers |
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license: creativeml-openrail-m |
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tags: |
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- stable-diffusion-xl |
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- stable-diffusion-xl-diffusers |
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- text-to-image |
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- diffusers |
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- diffusers-training |
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- lora |
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inference: true |
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widget: |
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- text: monet, a landscape of a snowy mountain region big clouds |
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output: |
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url: images/example_ul824c994.png |
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- text: >- |
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monet, majestic cliffs overlooking a serene ocean, with dramatic rock |
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formations bathed in soft light. The cliffs are painted in shades of green, |
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ochre, and brown, contrasting with the smooth, flowing waves below, |
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capturing the raw, natural beauty of the landscape |
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output: |
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url: images/example_kuktgiadt.png |
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- text: >- |
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monet, mountains far back, street lamps shines with warm yellow colors, |
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black night |
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output: |
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url: images/example_k3nlagsga.png |
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- text: Monet garden scene with colorful flowers and reflections on water |
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output: |
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url: images/example_r2xalzxv0.png |
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datasets: |
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- Aedancodes/monet_dataset |
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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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should probably proofread and complete it, then remove this comment. --> |
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# LoRA text2image fine-tuning |
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These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the Aedancodes/monet_dataset dataset. |
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## Trigger words |
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> [!WARNING] |
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> **Trigger words:** You should use `Monet` to trigger the image generation. |
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## Training details |
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```python |
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resolution=1024*1024 |
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train batch_size = 1 |
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max train steps = 1000 |
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learning rate = 5e-5 |
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lr scheduler = constant |
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mixed precision = fp16 |
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8bit_adam |
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``` |
