Instructions to use Alisson990/thumbnail-lora-flux2-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Alisson990/thumbnail-lora-flux2-dev 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("Alisson990/thumbnail-lora-flux2-dev") 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
| base_model: black-forest-labs/FLUX.2-dev | |
| tags: [lora, flux, flux2, youtube-thumbnails, diffusers] | |
| license: other | |
| license_name: flux-dev-non-commercial-license | |
| library_name: diffusers | |
| pipeline_tag: text-to-image | |
| # YouTube Thumbnail LoRA -- FLUX.2-dev | |
| | Parametro | Valor | | |
| |-----------|-------| | |
| | Base Model | black-forest-labs/FLUX.2-dev | | |
| | Dataset | Alisson990/snn_compute | | |
| | Steps | 3000 | | |
| | LoRA Rank | 32 | | |
| | LR | 0.0001 | | |
| | Resolucao | 1280x720 | | |
| | Batch efetivo | 8 | | |