Get a GPU with more than VRAM and it will work, no need replacing as lower quantized versions will not output same quality/cohesive images

@LancerMaster Recommend how much GB?

I have a 4090 with 24GB of VRAM and that does not seem to be enough. When I try to load the model to my cuda device, I always get:

torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 90.00 MiB. GPU 0 has a total capacity of 23.62 GiB of which 94.94 MiB is free. Process 2033 has 488.00 MiB memory in use. 

but the values of RAM it says it is trying to allocate and that are free varies. What is the minimum VRAM needed for this model?

Running FLUX.1-dev Image Generation with Memory Optimization on my Nvidia GTX 1070 8GB GPU

This guide explains how to run the FLUX.1-dev image generation model with various memory optimizations to handle GPU memory constraints.

Setup and Imports

import os
os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'
import torch
from diffusers import FluxPipeline

The first lines set up our environment:

• Setting PYTORCH_CUDA_ALLOC_CONF helps prevent memory fragmentation
• We import PyTorch and the FluxPipeline from the diffusers library

Pipeline Configuration

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    torch_dtype=torch.bfloat16,
    use_safetensors=True
)

Here we configure the pipeline with several optimizations:

• torch_dtype=torch.bfloat16 uses 16-bit precision to reduce memory usage
• use_safetensors=True enables more efficient model loading

Memory Optimizations

torch.cuda.empty_cache()
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()

These lines implement three key memory-saving techniques:

• empty_cache() clears unused CUDA memory
• enable_attention_slicing() processes attention in smaller chunks
• enable_sequential_cpu_offload() moves unused model components to CPU

Image Generation

prompt = "A cat holding a sign that says hello world"
image = pipe(
    prompt,
    height=160,
    width=160,
    guidance_scale=3.5,
    num_inference_steps=50,
    max_sequence_length=512,
    generator=torch.Generator("cpu").manual_seed(0)
).images[0]

The generation parameters are configured for memory efficiency:

• Small image dimensions (160x160) to minimize memory usage
• guidance_scale=3.5 controls how closely the image follows the prompt
• num_inference_steps=50 determines generation quality
• max_sequence_length=512 limits the prompt token length
• Setting a manual seed ensures reproducible results

Saving the Result

image.save("flux-dev.png")

Finally, we save the generated image to a PNG file.

Memory Usage Tips

If you're still experiencing memory issues, you can try:

• Further reducing image dimensions
• Decreasing the number of inference steps (try 30-40)
• Lowering the max_sequence_length if using shorter prompts
• Adjusting the guidance_scale (lower values use less memory)

Complete Code

Here's the complete code block for easy copying:

import os
os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'
import torch
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    torch_dtype=torch.bfloat16,
    use_safetensors=True
)

# Memory optimizations
torch.cuda.empty_cache()
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()

prompt = "A cat holding a sign that says hello world"
image = pipe(
    prompt,
    height=160,
    width=160,
    guidance_scale=3.5,
    num_inference_steps=50,
    max_sequence_length=512,
    generator=torch.Generator("cpu").manual_seed(0)
).images[0]

image.save("flux-dev.png")