Instructions to use ming-yang/Monet_Style with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ming-yang/Monet_Style 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-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ming-yang/Monet_Style") prompt = "MonetStyle rendering of a hot air balloon festival, emphasizing colorful balloons against a vibrant sky." image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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README.md
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- text: MonetStyle urban rain scene, showcasing people walking on wet city streets
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output:
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url: images/7.png
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base_model: stabilityai/stable-diffusion-xl-base-1.0
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instance_prompt: MonetStyle
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license: creativeml-openrail-m
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- text: MonetStyle urban rain scene, showcasing people walking on wet city streets
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output:
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url: images/7.png
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- text: MonetStyle capture of a high-speed train in motion through a lush valley, with a focus on natural beauty and fluidity.
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output:
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url: images/3.png
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base_model: stabilityai/stable-diffusion-xl-base-1.0
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instance_prompt: MonetStyle
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license: creativeml-openrail-m
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