Instructions to use JohanP/leonmarchand3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JohanP/leonmarchand3 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("JohanP/leonmarchand3") prompt = "Portrait of LNMRCHND in suit. Front view, medium shot, cinematic, 4k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Add generated example
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by JohanP - opened
- README.md +9 -4
- images/example_f3wejfuab.png +0 -0
README.md
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license: apache-2.0
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language:
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tags:
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base_model: black-forest-labs/FLUX.1-dev
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pipeline_tag: text-to-image
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instance_prompt: LNMRCHND
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---
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LoRas For Léon Marchand. Use LNMRCHND for keyword
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license: apache-2.0
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language:
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- en
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tags:
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- flux
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- diffusers
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- lora
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base_model: black-forest-labs/FLUX.1-dev
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pipeline_tag: text-to-image
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instance_prompt: LNMRCHND
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widget:
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- text: Portrait of LNMRCHND in suit. Front view, medium shot, cinematic, 4k
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output:
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url: images/example_f3wejfuab.png
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---
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LoRas For Léon Marchand. Use LNMRCHND for keyword
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images/example_f3wejfuab.png
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