Instructions to use codecandy/antiblur with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use codecandy/antiblur 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("codecandy/antiblur") prompt = "a young college student, walking on the street, campus background, photography" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| tags: | |
| - text-to-image | |
| - stable-diffusion | |
| - lora | |
| - diffusers | |
| - image-generation | |
| - flux | |
| - safetensors | |
| widget: | |
| - text: >- | |
| a young college student, walking on the street, campus background, | |
| photography | |
| output: | |
| url: images/2f82e6b1e5969d70a9044c19975bcdcca06b0f251d14f9c2c6095fa6.jpg | |
| - text: a young woman, New York City | |
| output: | |
| url: images/340c1ae6709f56f3d8176848653dcade93d2b5b8ade662da167ef818.jpg | |
| - text: >- | |
| happy stunning girl with long dark hair, wearing blue clothes, playing | |
| guitar, a beautiful field of flowers, colorful flowers everywhere, hills in | |
| the background | |
| output: | |
| url: images/ec9a40eed46e8d17d3db1560a6543c6e6be9ebe1e41ecd5d137c01e0.jpg | |
| base_model: black-forest-labs/FLUX.1-dev | |
| instance_prompt: null | |
| 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 | |
| # FLUX.1-dev-LoRA-AntiBlur | |
| This is a functional LoRA trained on FLUX.1-dev for deep DoF (Anti-Blur🔥) by [Vadim_Fedenko](https://www.shakker.ai/userpage/1f90018d803d4045b8dec4d627915098/publish) on [Shakker AI](https://www.shakker.ai/modelinfo/5c3fa3f1d5034e63be325196eae0b4f6?from=search). | |
| It may not be fancy, but it works. | |
| <div class="container"> | |
| <img src="./poster.jpg" width="1024"/> | |
| </div> | |
| <!-- ## Showcases | |
| <Gallery /> --> | |
| ## Comparison | |
| The following example shows a simple comparison with FLUX.1-dev under the same parameter setting. | |
| <div class="container"> | |
| <img src="./compare1.png" width="1024"/> | |
| </div> | |
| It is worth noting that this LoRA has very little damage to image quality while enhancing the depth of field, and can be used together with other components, such as ControlNet. We regard it as a basic functional LoRA. | |
| <div class="container"> | |
| <img src="./compare2.png" width="1024"/> | |
| </div> | |
| ## Trigger words | |
| The trigger word is not required. The recommended scale is `1.0` to `1.5` in diffusers. | |
| ## Inference | |
| ```python | |
| import torch | |
| from diffusers import FluxPipeline | |
| pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16) | |
| pipe.load_lora_weights("Shakker-Labs/FLUX.1-dev-LoRA-AntiBlur", weight_name="FLUX-dev-lora-AntiBlur.safetensors") | |
| pipe.fuse_lora(lora_scale=1.5) | |
| pipe.to("cuda") | |
| prompt = "a young college student, walking on the street, campus background, photography" | |
| image = pipe(prompt, | |
| num_inference_steps=24, | |
| guidance_scale=3.5, | |
| width=768, height=1024, | |
| ).images[0] | |
| image.save(f"example.png") | |
| ``` | |
| ## Online Inference | |
| You can also run this model at [Shakker AI](https://www.shakker.ai/modelinfo/5c3fa3f1d5034e63be325196eae0b4f6?from=search), where we provide an online interface to generate images. | |
| ## Acknowledgements | |
| This model is trained by our copyrighted users [Vadim_Fedenko](https://www.shakker.ai/userpage/1f90018d803d4045b8dec4d627915098/publish). We release this model under permissions. The model follows [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). | |