Instructions to use Dimension702/flux-dev2pro-fixed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Dimension702/flux-dev2pro-fixed with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Dimension702/flux-dev2pro-fixed", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| library_name: diffusers | |
| pipeline_tag: text-to-image | |
| ## Flux-Dev2Pro | |
| Flux-Dev2Pro finetunes the transformer of Flux-Dev to make LoRA training better. | |
| As discussed in this blog https://medium.com/@zhiwangshi28/why-flux-lora-so-hard-to-train-and-how-to-overcome-it-a0c70bc59eaf, LoRA trained on Flux-Dev often yields bad results, because without guidance distillation the LoRA training is diverged from the original training process. Flux-Dev2Pro recovers Flux-pro from Flux-dev by finetuning the model for many steps. Two epoch of 3M high quality images have been trained. | |
| The LoRA trained on Flux-Dev2pro yields a much better results when being applied on Flux-dev, just like LoRA trained on SDXL and being applied to SDXL-turbo/lightning. | |
| To use this model, run: | |
| ```python | |
| from diffusers import FluxTransformer2DModel | |
| transformer = FluxTransformer2DModel.from_pretrained("ashen0209/Flux-Dev2Pro", torch_dtype=torch.bfloat16) | |
| ``` |