Text-to-Image
Diffusers
flux2
flux2-diffusers
image-to-image
simpletuner
safe-for-work
lora
template:sd-lora
standard
Instructions to use quzo/textf22 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use quzo/textf22 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.2-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("quzo/textf22") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Trained for 0 epochs and 900 steps.
Browse filesTrained with datasets ['text-embed-cache', 'emi-256', 'emi-crop-256', 'emi-512', 'emi-crop-512', 'emi-768', 'emi-crop-768', 'emi-1024', 'emi-crop-1024', 'emi-1440', 'emi-crop-1440']
Learning rate 0.0001, batch size 1, and 1 gradient accumulation steps.
Trained with None prediction type and rescaled_betas_zero_snr=False
Using 'trailing' timestep spacing.
Base model: black-forest-labs/FLUX.2-dev
VAE: black-forest-labs/FLUX.2-dev
pytorch_lora_weights.safetensors
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