flux1 dev bnb nf4

Introduction
FLUX1-DEV-BNB-NF4 is a model hosted on Hugging Face, developed by lllyasviel. It is designed to improve upon previous versions by offering enhanced quantization and precision, aimed at optimizing performance and reducing computational overhead during inference.

Architecture

• Main Model: Utilizes bnb-nf4, with two versions:
  • V1: chunk 64 norm in nf4.
  • V2: chunk 64 norm in float32, offering higher precision.
• T5xxl: Operates in fp8e4m3fn precision.
• CLIP-L: Operates in fp16 precision.
• VAE: Operates in bf16 precision.

Training
Version 2 (V2) has been optimized by quantizing in a more efficient manner, removing the second stage of double quantization. This version is 0.5 GB larger due to storing chunk 64 norm in full precision float32 but offers reduced computational overhead by eliminating the need for on-the-fly decompression.

Guide: Running Locally

1. Setup Environment:

   • Ensure Python and necessary libraries (e.g., PyTorch) are installed.
   • Clone the repository from Hugging Face or GitHub.

2. Download Model:

   • Download the model files from the Hugging Face model card.

3. Load Model:

   • Use the appropriate library (e.g., transformers for NLP models) to load the model in your Python environment.

4. Run Inference:

   • Prepare input data in the required format.
   • Execute the model inference script.

5. Suggested Cloud GPUs:

   • Consider using cloud platforms like AWS, Google Cloud, or Azure that provide GPU instances to enhance computational efficiency, especially for larger models like FLUX1-DEV-BNB-NF4.

License
The model is released under the flux-1-dev-non-commercial-license. For more details, refer to the license document.