Instructions to use mattbasedow/dollyforward-ltx23 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mattbasedow/dollyforward-ltx23 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Lightricks/LTX-2.3-fp8", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("mattbasedow/dollyforward-ltx23") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
| tags: | |
| - text-to-image | |
| - lora | |
| - diffusers | |
| - template:diffusion-lora | |
| widget: | |
| - output: | |
| url: images/18100 Karen Drive Tarzana (1).jpg | |
| text: '-' | |
| base_model: Lightricks/LTX-2.3-fp8 | |
| instance_prompt: DOLLYFORWARD | |
| license: apache-2.0 | |
| # DOLLYFORWARD LTX-2.3 | |
| <Gallery /> | |
| ## Model description | |
| LTX-2.3 LoRA trained to produce dolly forward camera movement. Use trigger word DOLLYFORWARD in your prompt. | |
| ## Trigger words | |
| You should use `DOLLYFORWARD` to trigger the image generation. | |
| ## Download model | |
| [Download](/mattbasedow/dollyforward-ltx23/tree/main) them in the Files & versions tab. | |