FLUX.1 Dev (GGUF) vs FLUX.1 Schnell (GGUF)
Side-by-side comparison of hardware requirements, quantization options, and specifications to help you choose the right model for your device.
Black Forest Labs
FLUX.1 Dev (GGUF)
12B params
Image GenerationBlack Forest Labs
FLUX.1 Schnell (GGUF)
12B params
Image GenerationSpecifications Comparison
| Spec | FLUX.1 Dev (GGUF) | FLUX.1 Schnell (GGUF) |
|---|---|---|
| Parameters | 12B | 12B |
| Architecture | rectified-flow | rectified-flow |
| License | flux-1-dev-non-commercial | Apache 2.0 |
| Context Length | N/A | N/A |
| Category | Image Generation | Image Generation |
| Author | Black Forest Labs | Black Forest Labs |
| HF Downloads | 715.7K | 573.8K |
| VRAM Range | 14 - 14 GB | 14 - 14 GB |
| Quantizations | 1 options | 1 options |
| Best Quality Score | 100% | 90% |
Quantization Options
FLUX.1 Dev (GGUF)
FLUX.1 Schnell (GGUF)
In-depth comparison
FLUX.1 Dev (GGUF) is the better choice for most users due to its higher quality score of 100%, making it ideal for detailed and high-quality image generation. However, FLUX.1 Schnell (GGUF) is a better pick if you need faster generation times, especially in 1-4 steps.
When to choose FLUX.1 Dev (GGUF)
FLUX.1 Dev (GGUF) is the better choice when you prioritize the highest possible image quality and detail. It is particularly useful for professional artists and designers who need to create visually stunning images from textual descriptions. The model's 100% quality score and ability to handle 20-50 steps ensure that the final output is both precise and contextually rich, making it suitable for projects where visual fidelity is paramount.
When to choose FLUX.1 Schnell (GGUF)
FLUX.1 Schnell (GGUF) is the better choice when you need fast image generation, especially in scenarios where time is of the essence. With its 1-4 step generation process, it can produce high-quality images quickly, making it ideal for rapid prototyping, quick design iterations, or real-time applications. The model's 90% quality score is still impressive and can be sufficient for many creative tasks, especially when speed is a critical factor.
Quality
FLUX.1 Dev (GGUF) outperforms FLUX.1 Schnell (GGUF) in terms of output quality, with a best quality score of 100% compared to 90%. Both models have the same 12B parameters and rectified-flow architecture, but FLUX.1 Dev (GGUF) benefits from a longer generation process (20-50 steps), allowing it to produce more detailed and refined images. FLUX.1 Schnell (GGUF), while still delivering state-of-the-art quality, is optimized for speed over absolute quality.
Performance & hardware fit
Both models require a minimum of 14.0GB VRAM, but FLUX.1 Dev (GGUF) is recommended for systems with 24GB+ RAM, while FLUX.1 Schnell (GGUF) needs 16GB+ RAM. FLUX.1 Dev (GGUF) is slower, taking 20-50 steps to generate images, whereas FLUX.1 Schnell (GGUF) is much faster, completing generation in just 1-4 steps. This makes FLUX.1 Schnell (GGUF) more suitable for users with limited time or those who need rapid iterations.
Use-case fit
| coding | Tie | Neither model is specifically designed for coding tasks, so neither has a clear advantage in this use case. |
| creative writing | FLUX.1 Dev (GGUF) | FLUX.1 Dev (GGUF) is better suited for creative writing as it can generate more detailed and high-quality images to visualize complex ideas, enhancing the writer's creative process. |
| RAG / retrieval | Tie | Both models are primarily designed for image generation and do not have features specifically tailored for RAG or retrieval tasks. |
| agent / tool use | FLUX.1 Schnell (GGUF) | FLUX.1 Schnell (GGUF) is faster and more efficient, making it a better choice for agents or tools that require quick image generation for real-time applications. |
| running on consumer GPU (8-12GB) | FLUX.1 Schnell (GGUF) | FLUX.1 Schnell (GGUF) requires less VRAM (16GB+) compared to FLUX.1 Dev (GGUF) (24GB+), making it more feasible to run on consumer GPUs with 8-12GB VRAM. |
| long context (16K+) | FLUX.1 Dev (GGUF) | FLUX.1 Dev (GGUF) handles longer generation processes (20-50 steps), which may be more suitable for generating images from long contextual descriptions, although the exact context length is not specified for either model. |
FLUX.1 Dev (GGUF) is the better choice for most users due to its superior image quality and detail, making it ideal for professional and high-fidelity applications. However, FLUX.1 Schnell (GGUF) is the preferred option for users who need fast image generation, especially in real-time or rapid prototyping scenarios.