FP8 quantized variant of Black Forest Labs' FLUX.1 [schnell] model, offering ~2x faster inference with reduced precision while maintaining high-quality image generation in 4 steps
Example outputs coming soon
Details
flux.1-schnell-fp8
Providers & Pricing (1)
FLUX.1 [schnell] FP8 is free to use through Fireworks AI.
fireworks/flux.1-schnell-fp8
Output
Pricing Notes (4)
- • Free to try
- • Normally priced at $0.00035 per inference step
- • FLUX.1 [schnell] uses 4 steps by default, making the effective per-image cost $0.0014
- • FP8 variant uses reduced precision for ~2x faster inference
Provider Performance
Fastest generation through fireworks at 1,769ms median latency with 96.0% success rate.
Aggregated from real API requests over the last 30 days.
Generation Time
Success Rate
Time to First Byte
Provider Rankings
| # | Provider | p50 Gen Time | p95 Gen Time | Success Rate | TTFB (p50) |
|---|---|---|---|---|---|
| 1 | fireworks | 1,769ms | 11,146ms | 96.0% | 962ms |
FLUX.1 [schnell] FP8 API OpenAI-compatible
Integrate FLUX.1 [schnell] FP8 via the Lumenfall OpenAI-compatible API to generate high-resolution images with 2x faster inference speeds using specialized latent flow matching.
https://api.lumenfall.ai/openai/v1
flux.1-schnell-fp8
curl -X POST \ https://api.lumenfall.ai/openai/v1/images/generations \ -H "Authorization: Bearer $LUMENFALL_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "flux.1-schnell-fp8", "prompt": "A serene mountain landscape at sunset", "size": "1024x1024" }'# Response:# { "created": 1234567890, "data": [{ "url": "https://...", "revised_prompt": "..." }] }import OpenAI from 'openai';const client = new OpenAI({ apiKey: 'YOUR_API_KEY', baseURL: 'https://api.lumenfall.ai/openai/v1'});const response = await client.images.generate({ model: 'flux.1-schnell-fp8', prompt: 'A serene mountain landscape at sunset', size: '1024x1024'});// { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }console.log(response.data[0].url);from openai import OpenAIclient = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.lumenfall.ai/openai/v1")response = client.images.generate( model="flux.1-schnell-fp8", prompt="A serene mountain landscape at sunset", size="1024x1024")# { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }print(response.data[0].url)Gallery
View all 1 imagesFLUX.1 [schnell] FP8 FAQ
FLUX.1 [schnell] FP8 is free to use through Lumenfall's unified API.
You can use FLUX.1 [schnell] FP8 through Lumenfall's OpenAI-compatible API. Send requests to the unified endpoint with model ID "flux.1-schnell-fp8". Code examples are available in Python, JavaScript, and cURL.
FLUX.1 [schnell] FP8 is available through Fireworks AI on Lumenfall. Lumenfall automatically routes requests to the best available provider.
FLUX.1 [schnell] FP8 supports images up to 1024x1024 resolution.
Overview
FLUX.1 [schnell] FP8 is a quantized version of Black Forest Labs’ distilled text-to-image model, optimized for maximum inference speed. By utilizing 8-bit floating-point precision, this variant achieves significantly lower latency and reduced memory overhead compared to the standard model. It is specifically designed for high-throughput applications where generating competitive imagery in a handful of steps is the primary requirement.
Strengths
- Generation Speed: Produces usable 1024x1024 images in just 1 to 4 sampling steps, making it one of the fastest high-resolution open-weight models available.
- Standardized Resource Efficiency: The FP8 quantization reduces the VRAM footprint and computational load, allowing for roughly 2x faster inference times compared to the full-precision version without a proportional loss in visual quality.
- Prompt Adherence: Despite the lowered precision and distillation, the model retains the architectural ability to follow complex descriptive prompts and render legible, coherent text within images.
- Output Consistency: It maintains the structural integrity and composition characteristic of the FLUX.1 family, even at extremely low step counts.
Limitations
- Artistic Nuance: Due to the distillation and quantization, it offers less stylistic flexibility and fine-grained detail compared to the [dev] or [pro] iterations of FLUX.1.
- Precision Loss: FP8 quantization can occasionally lead to minor artifacts or less smooth gradients in complex lighting scenarios that would be better handled by 16-bit or 32-bit models.
- Step Sensitivity: The model is strictly tuned for low-step counts; increasing the sampling steps beyond the recommended range usually yields diminishing returns or visual regressions.
Technical Background
FLUX.1 [schnell] is a latent diffusion model based on a flow-based transformer architecture. This specific FP8 variant applies post-training quantization to the model weights, mapping them to 8-bit precision to optimize throughput on modern hardware. The “schnell” version itself is the result of a performance-oriented distillation process, allowing the model to reach a converged image state in a fraction of the time required by standard diffusion processes.
Best For
This model is ideal for real-time applications, rapid prototyping, and high-volume image generation workflows where operational cost and latency are critical. It is a strong choice for “generate-as-you-type” interfaces or large-scale content pipelines that require decent photorealism at minimal compute expense. FLUX.1 [schnell] FP8 is available for testing and integration through Lumenfall’s unified API and interactive playground.
Try FLUX.1 [schnell] FP8 in Playground
Generate images with custom prompts — no API key needed.