Instructions to use egodevRJM/ryanm-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use egodevRJM/ryanm-lora 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("egodevRJM/ryanm-lora") prompt = "ryan" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from egodevRJM/ryanm-lora: direct link, hf CLI and curl.
- Browser
- Download file 728 Bytes
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https://huggingface.co/egodevRJM/ryanm-lora/resolve/main/README.md
- Command line
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hf download hf://egodevRJM/ryanm-lora/README.md
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curl -L -o README.md https://huggingface.co/egodevRJM/ryanm-lora/resolve/main/README.md
728 Bytes
metadata
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: ryan
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
ryanm lora
Model description
A lora of my face
Trigger words
You should use ryan to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
Training at fal.ai
Training was done using fal.ai/models/fal-ai/flux-lora-fast-training.