Instructions to use uwcc/simon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use uwcc/simon 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("uwcc/simon") prompt = "A church in a field on a sunny day, [trigger] style." image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| tags: | |
| - text-to-image | |
| - flux | |
| - lora | |
| - diffusers | |
| - template:sd-lora | |
| - ai-toolkit | |
| widget: | |
| - text: A church in a field on a sunny day, [trigger] style. | |
| output: | |
| url: samples/1726699838182__000002000_0.jpg | |
| - text: A seal plays with a ball on the beach, [trigger] style. | |
| output: | |
| url: samples/1726699856681__000002000_1.jpg | |
| - text: A clown at the circus rides on a zebra, [trigger] style. | |
| output: | |
| url: samples/1726699875170__000002000_2.jpg | |
| - text: '[trigger]' | |
| output: | |
| url: samples/1726699893655__000002000_3.jpg | |
| base_model: black-forest-labs/FLUX.1-dev | |
| instance_prompt: simon | |
| 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 | |
| # simon | |
| Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) | |
| <Gallery /> | |
| ## Trigger words | |
| You should use `simon` to trigger the image generation. | |
| ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc. | |
| Weights for this model are available in Safetensors format. | |
| [Download](/uwcc/simon/tree/main) them in the Files & versions tab. | |
| ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) | |
| ```py | |
| from diffusers import AutoPipelineForText2Image | |
| import torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda') | |
| pipeline.load_lora_weights('uwcc/simon', weight_name='simon') | |
| image = pipeline('A church in a field on a sunny day, [trigger] style.').images[0] | |
| image.save("my_image.png") | |
| ``` | |
| For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) | |