Instructions to use Adminhuggingface/OUTPUTA_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Adminhuggingface/OUTPUTA_2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Adminhuggingface/OUTPUTA_2") 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
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End of training
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README.md
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# LoRA text2image fine-tuning - Adminhuggingface/OUTPUTA_2
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These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the Adminhuggingface/
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# LoRA text2image fine-tuning - Adminhuggingface/OUTPUTA_2
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These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the Adminhuggingface/new_data dataset. You can find some example images in the following.
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