Instructions to use developercomp99/quizmint-style with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use developercomp99/quizmint-style with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("undefined", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("developercomp99/quizmint-style") prompt = "quizmint_style" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("undefined", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("developercomp99/quizmint-style")
prompt = "quizmint_style"
image = pipe(prompt).images[0]quizmint style
Model description
Quiz-specific mint style LoRA, trained on diverse subject set for quiz image generation
Trigger words
You should use quizmint_style 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-2-klein-9b-base-trainer.
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