Instructions to use glowskeleton/fal_training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use glowskeleton/fal_training 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("glowskeleton/fal_training") prompt = "rx_product" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from glowskeleton/fal_training: direct link, hf CLI and curl.
- Browser
- Download file 730 Bytes
-
https://huggingface.co/glowskeleton/fal_training/resolve/main/README.md
- Command line
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hf download hf://glowskeleton/fal_training/README.md
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curl -L -o README.md https://huggingface.co/glowskeleton/fal_training/resolve/main/README.md
730 Bytes
metadata
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: rx_product
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
fal_training
Model description
Trigger words
You should use rx_product 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.