Instructions to use mltinkerer/fal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mltinkerer/fal 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("mltinkerer/fal") prompt = "mnyk" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
| tags: | |
| - flux | |
| - text-to-image | |
| - lora | |
| - diffusers | |
| - fal | |
| base_model: undefined | |
| instance_prompt: mnyk | |
| license: other | |
| # fal | |
| <Gallery /> | |
| ## Model description | |
| Private model for this character: | |
|  | |
| ## Trigger words | |
| You should use `mnyk` to trigger the image generation. | |
| ## Download model | |
| Weights for this model are available in Safetensors format. | |
| [Download](/mltinkerer/fal/tree/main) them in the Files & versions tab. | |
| ## Training at fal.ai | |
| Training was done using [fal.ai/models/fal-ai/wan-trainer/t2v-14b](https://fal.ai/models/fal-ai/wan-trainer/t2v-14b). | |