Instructions to use CabalResearch/NoobAI-V-pred-to-Flow-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CabalResearch/NoobAI-V-pred-to-Flow-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("CabalResearch/NoobAI-V-pred-to-Flow-LoRA", 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
V-pred to Flow LoRA
A LoRA-based conversion training, based on NoobAI V-pred 1.0. Allows v-pred models to utilize flow-based prediction.
You can find basic workflow alongside models.
Possible Benefits
- Reduction in color bias
- Ability to use flow-specific features and samplers
Possible Downsides
- Can desaturate too much
Caveats
- Results are model-dependent, your mileage may vary.
- Is only a LoRA adaptation, will not provide full Flow benefits.
Support
If you wish to support our continuous effort of making waifus 0.2% better, you can do it here:
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Model tree for CabalResearch/NoobAI-V-pred-to-Flow-LoRA
Base model
Laxhar/noobai-XL-1.0 Finetuned
Laxhar/noobai-XL-Vpred-0.5 Finetuned
Laxhar/noobai-XL-Vpred-0.6 Finetuned
Laxhar/noobai-XL-Vpred-0.65 Finetuned
Laxhar/noobai-XL-Vpred-0.75 Finetuned
Laxhar/noobai-XL-Vpred-1.0