Open source FLUX.1 - schnell - DF11 model: Reduce GPU memory usage by 30%, accurate calculation without pressure!

FLUX.1 Schnell DF11

Developed by DFloat11

FLUX.1-schnell model using DFloat11 lossless compression format, reducing VRAM usage by 30% while maintaining 100% accuracy

Image Generation #Lossless Compression #VRAM Optimization #GPU Real-time Decompression

Downloads 2,043

Release Time : 5/7/2025

Model Overview

Text-to-image model compressed with DFloat11 format, achieving lossless compression through hardware-aware algorithms, ideal for deployment in VRAM-constrained environments

Model Features

Lossless Compression

Achieves 70% volume reduction with DFloat11 format, producing binary-identical outputs to the original model

Hardware Acceleration

Supports real-time GPU decompression, eliminating CPU decompression or host-device data transfer overhead

VRAM Optimization

Weights remain compressed in VRAM during inference, reducing GPU memory usage by approximately 30%

Model Capabilities

Text-to-Image Generation

High-quality Image Synthesis

Fast Inference (4-step sampling)

Use Cases

Creative Design

Concept Art Generation

Rapidly generates futuristic scene concepts from text descriptions

Example output includes cityscapes with flying cars and neon lights

Content Creation

Social Media Content

Quickly generates accompanying images for social media

🚀 DFloat11 Compressed Model: `black-forest-labs/FLUX.1-schnell`

This project presents a losslessly compressed version of the black-forest-labs/FLUX.1-schnell model. By leveraging the custom DFloat11 format, it achieves significant GPU memory savings while maintaining the same output as the original BFloat16 model.

Property	Details
Base Model	black-forest-labs/FLUX.1-schnell
Base Model Relation	quantized
Pipeline Tag	text-to-image
Tags	dfloat11, df11, lossless compression, 70% size, 100% accuracy

🚀 Quick Start

This is a losslessly compressed version of black-forest-labs/FLUX.1-schnell using our custom DFloat11 format. The outputs of this compressed model are bit-for-bit identical to the original BFloat16 model, while reducing GPU memory consumption by approximately 30%.

✨ Features

How It Works

DFloat11 compresses model weights using Huffman coding of BFloat16 exponent bits, combined with hardware-aware algorithmic designs that enable efficient on-the-fly decompression directly on the GPU. During inference, the weights remain compressed in GPU memory and are decompressed just before matrix multiplications, then immediately discarded after use to minimize memory footprint.

Key benefits:

No CPU decompression or host-device data transfer --- all operations are handled entirely on the GPU.
DFloat11 is much faster than CPU-offloading approaches, enabling practical deployment in memory-constrained environments.
The compression is fully lossless, guaranteeing that the model's outputs are bit-for-bit identical to those of the original model.

📦 Installation

Install the DFloat11 pip package (installs the CUDA kernel automatically; requires a CUDA-compatible GPU and PyTorch installed):
```
pip install dfloat11[cuda12]
# or if you have CUDA version 11:
# pip install dfloat11[cuda11]
```

💻 Usage Examples

Basic Usage

import torch
from diffusers import FluxPipeline
from dfloat11 import DFloat11Model

pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()

DFloat11Model.from_pretrained('DFloat11/FLUX.1-schnell-DF11', device='cpu', bfloat16_model=pipe.transformer)

prompt = "A futuristic cityscape at sunset, with flying cars, neon lights, and reflective water canals"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
    generator=torch.Generator("cpu").manual_seed(0)
).images[0]
image.save("flux-schnell.png")