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Flux Fill bug fixing, improvements and support for memory poor GPUs (less than 12GB of VRAM) by Deepbeepmeep.
This fork of Flux Fill (https://github.com/black-forest-labs/flux) is an illustration on how one can set up on an existing model some fast and properly working CPU offloading with very few changes in the core model.
Beside the support for 12 GB VRAM GPU with fast generation of images, I did a few improvements to the Flux Fill tool:
bug fixing
progression bar
automatic resizing of large images
user interface streamlined
Once the installation is done (see instructions below), run the Flux Fill tool with the command:
streamlit run demo_st_fill.py
Configuration
A minimum of 48 GB in your RAM is needed to run this tool.To reduce the RAM requirements to 32 GB and / or run the application even faster (for a small loss in quality), you may download directly a prequantized text encoder by changing the file flux/conditioner.py on line 21 to use a quantized model.
Alternatively if you have 64 GB of RAM, you can increase the quality of the image generation by using a non quantized Flux transformer model by changing the file flux/util.py on 333 to use a non quantized transformer model.
If you have more than 24 GB of VRAM you can set the option profile = 1 on line 88 of file demo_st_fill.py for much faster generations.
Local installation
cd$HOME&& git clone https://github.com/black-forest-labs/flux
cd$HOME/flux
python3.10 -m venv .venv
source .venv/bin/activate
pip install torch==2.5.1 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu124 # or a lower version of Cuda if it is not supported by your system
pip install -e ".[all]"
Cosmos1GP: https://github.com/deepbeepmeep/Cosmos1GP
This application include two models: a text to world generator and a image / video to world (probably the best open source image to video generator).
About
Flux Fill 1.0 GO: flux Inpainting and outpainting starting with 8Gb of VRAM