Instructions to use rikiwi/AVE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rikiwi/AVE with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("rikiwi/AVE") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Add generated example
Browse filesGenerated example for model rikiwi/AVE.
Prompt: -
- README.md +3 -0
- images/example_aivm89lqq.png +3 -0
README.md
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- text: '-'
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output:
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url: images/example_nwarhjj3y.png
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base_model: black-forest-labs/FLUX.1-dev
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instance_prompt: No red
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license: apache-2.0
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- text: '-'
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output:
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url: images/example_nwarhjj3y.png
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- text: '-'
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output:
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url: images/example_aivm89lqq.png
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base_model: black-forest-labs/FLUX.1-dev
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instance_prompt: No red
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license: apache-2.0
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images/example_aivm89lqq.png
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Git LFS Details
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