Instructions to use hf-internal-testing/sd-model-finetuned-lora-t4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hf-internal-testing/sd-model-finetuned-lora-t4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("hf-internal-testing/sd-model-finetuned-lora-t4") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
LoRA text2image fine-tuning - https://huggingface.co/sayakpaul/sd-model-finetuned-lora-t4
These are LoRA adaption weights for https://huggingface.co/sayakpaul/sd-model-finetuned-lora-t4. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.
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