Instructions to use bartar/prci with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bartar/prci 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("bartar/prci") prompt = "Photorealistic photo of prci, gray nissan qashqai, standing on a grey brick pavement, trees and blue sky in the background. Taken by phone" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Bartosz commited on
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README.md
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# Photorealistic Car Images
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A Flux LoRA trained locally on dataset of various low/midrange cars close-up photos with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
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You can use it to get better quality of car photos with realistic reflections, and more realistic backgrounds.
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<Gallery />
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# Photorealistic Car Images
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A Flux LoRA trained locally on dataset of various low/midrange cars close-up photos with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
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You can use it to get better quality of car photos with realistic reflections, and more realistic backgrounds.
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<Gallery />
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