Instructions to use Cossale/frames with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Cossale/frames 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.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Cossale/frames") prompt = "a road leading to a mountain in a night, visible moon and stars. FRM$" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
| tags: | |
| - text-to-image | |
| - flux | |
| - lora | |
| - diffusers | |
| - template:sd-lora | |
| - fluxgym | |
| base_model: black-forest-labs/FLUX.1-dev | |
| instance_prompt: FRM$ | |
| license: other | |
| license_name: flux-1-dev-non-commercial-license | |
| license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md | |
| widget: | |
| - text: a road leading to a mountain in a night, visible moon and stars. FRM$ | |
| output: | |
| url: images/example_du3zlevlr.png | |
| - text: >- | |
| a snowy mountain with lavander haze over the horizon, distant mountain, | |
| evening time, birds. FRM$ | |
| output: | |
| url: images/example_ajesrotih.png | |
| - text: >- | |
| a mountain range with a large mountain in center, dusk, no sun, forest, | |
| pink dominated image. FRM$ | |
| output: | |
| url: images/example_m0sl2j6rp.png | |
| # Frames | |
| A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) | |
| <Gallery /> | |
| ## Trigger words | |
| You should use `FRM$` to trigger the image generation. | |
| ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. | |
| Weights for this model are available in Safetensors format. | |