Instructions to use Keshabwi66/SmartLugaModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Keshabwi66/SmartLugaModel with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import AutoPipelineForInpainting from diffusers.utils import load_image # switch to "mps" for apple devices pipe = AutoPipelineForInpainting.from_pretrained("Keshabwi66/SmartLugaModel", dtype=torch.float16, device_map="cuda") img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" image = load_image(img_url).resize((1024, 1024)) mask_image = load_image(mask_url).resize((1024, 1024)) prompt = "a tiger sitting on a park bench" generator = torch.Generator(device="cuda").manual_seed(0) image = pipe( prompt=prompt, image=image, mask_image=mask_image, guidance_scale=8.0, num_inference_steps=20, # steps between 15 and 30 work well for us strength=0.99, # make sure to use `strength` below 1.0 generator=generator, ).images[0] - Notebooks
- Google Colab
- Kaggle
Delete unet/requirements.txt
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unet/requirements.txt
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transformers==4.36.2
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torch==2.0.1
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torchvision==0.15.2
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torchaudio==2.0.2
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numpy==1.24.4
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scipy==1.10.1
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scikit-image==0.21.0
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opencv-python==4.7.0.72
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pillow==9.4.0
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diffusers==0.25.0
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transformers==4.36.2
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accelerate==0.26.1
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matplotlib==3.7.4
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tqdm==4.64.1
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config==0.5.1
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einops==0.7.0
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onnxruntime==1.16.2
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basicsr
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av
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fvcore
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cloudpickle
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omegaconf
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pycocotools
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