f16.gguf version has any advantages over the f16 version of safetensor.
Hello, I would like to ask if the f16.gguf version has any advantages over the f16 version of safetensor. Will the inference speed or loading speed be faster?
Go with normal version for now....
Grok 3 result:
Here’s a condensed comparison of flux1-dev-F16.gguf vs flux1-dev.safetensors:
File Format:
flux1-dev-F16.gguf: GGUF (Georgi Gerganov's Unified Format), optimized for compression and compatibility with certain tools (e.g., llama.cpp).
flux1-dev.safetensors: Safetensors, a standard format for PyTorch-based models, widely used in the diffusers library.
Precision: Both are FP16 (16-bit floating-point), offering identical or near-identical quality (99%+ similarity).
Size:
F16.gguf: ~22.2 GB (compressed, slightly smaller due to GGUF format).
.safetensors: ~22.1 GB (uncompressed).
Performance:
F16.gguf: May be slower due to decompression overhead, despite smaller size, unless optimized for specific backends (e.g., stable-diffusion.cpp). VRAM usage is 23 GB.23 GB).
.safetensors: Faster with PyTorch/diffusers (e.g., FluxPipeline), especially on GPU, with similar VRAM (
Use Case:
F16.gguf: Better for low-memory systems or non-PyTorch workflows (e.g., ComfyUI with GGUF nodes).
.safetensors: Ideal for standard diffusers pipelines and broader compatibility.
Summary: Choose .safetensors for speed and ease with FluxPipeline in Python; opt for F16.gguf if you need a compressed format for specific tools or setups with minimal quality trade-off.