Instructions to use Jemmo/ProtonX50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Jemmo/ProtonX50 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("Jemmo/ProtonX50") prompt = "A sleek, red x50style car drifts around a corner on a racetrack, leaving a trail of smoke behind it. The car's low, aerodynamic design is emphasized as it takes the turn at high speed, with its tires gripping the asphalt. The night background shows a racetrack with safety barriers and a distant cityscape under a night cloudy sky, which is lit by the setting moon, casting a warm glow over the scene. The motion blur and smoke create a dynamic sense of speed and intensity, capturing the thrill of high-performance racing. reflection." 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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output:
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url: https://cdn-uploads.huggingface.co/production/uploads/64088f5db6a334f53e1fe69f/FCoM-m8spJv80OXVrFQYq.png
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base_model: black-forest-labs/FLUX.1-dev
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instance_prompt:
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# ProtonX50
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output:
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url: https://cdn-uploads.huggingface.co/production/uploads/64088f5db6a334f53e1fe69f/FCoM-m8spJv80OXVrFQYq.png
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base_model: black-forest-labs/FLUX.1-dev
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instance_prompt: x50style
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# ProtonX50
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