Instructions to use Veetance/Flux1-fusion-nf4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Veetance/Flux1-fusion-nf4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Veetance/Flux1-fusion-nf4", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
FLUX.1-Fusion | Debloated
This repository contains the FLUX.1 Fusion weights, debloated and optimized for the Asset Editor ecosystem.
π Model Details
- Architecture: FLUX.1 (Dev/Schnell Hybrid)
- Quantization: FP8 / NF4 (Project Dependent)
- Primary Use: High-quality asset generation with enhanced spatial control.
π Integration
Optimized for the Asset Editor Leviathan/Zerodrag pipelines.
π Links
- Core Engine: Veetance Asset Editor
- Triage & Development: Asset Editor GitHub
Note: This is a specialized manifold optimized for the Veetance sovereign resource governor.
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