Black Forest Labs' 12 billion parameter distilled image generation model optimized for speed, capable of generating high-quality images in just 4 inference steps
Details
flux.1-schnell
Starting from
Prices shown are in USD
See all providersProviders & Pricing (3)
FLUX.1 [schnell] is available from 3 providers, with per-image pricing starting at $0.003 through fal.ai.
fal/flux.1-schnell
replicate/flux.1-schnell
runware/flux.1-schnell
Output
Pricing Notes (1)
- • Pricing copied from fal.ai - verify with Runware official pricing
FLUX.1 [schnell] API OpenAI-compatible
Access FLUX.1 [schnell] via Lumenfall’s OpenAI-compatible API to generate high-resolution images using a streamlined 4-step inference process tailored for low-latency applications.
https://api.lumenfall.ai/openai/v1
flux.1-schnell
Code Examples
Text to Image
/v1/images/generationscurl -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", "prompt": "", "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', prompt: '', 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", prompt="", size="1024x1024")# { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }print(response.data[0].url)Parameter Reference
Core Parameters
| Parameter | Type | Description | Modes |
|---|---|---|---|
prompt
|
string | Required. Text prompt for image generation |
T2I
|
Size & Layout
| Parameter | Type | Description | Modes |
|---|---|---|---|
size
|
string |
Image dimensions as WxH pixels (e.g. "1024x1024") or aspect ratio (e.g. "16:9")
WxH determines both shape and scale (aspect_ratio and resolution are ignored when size is provided). W:H format is equivalent to aspect_ratio.
|
T2I
|
aspect_ratio
|
string |
Aspect ratio of the output image (e.g. "16:9", "1:1")
Controls shape independently of scale. Use with resolution to control both. If size is also provided, size takes precedence. Any ratio is accepted and mapped to the nearest supported value.
|
T2I
|
resolution
|
string |
Output resolution tier (e.g. "1K", "4K")
Controls scale independently of shape. Higher tiers produce larger images and cost more. If size is also provided, size takes precedence for scale. Any tier is accepted and mapped to the nearest supported value.
|
T2I
|
size
Exact pixel dimensions
"1920x1080"
aspect_ratio
Shape only, default scale
"16:9"
resolution
Scale tier, preserves shape
"1K"
Priority when combined
size is most specific and always wins. aspect_ratio and resolution control shape and scale independently.
How matching works
7:1 on a model with
4:1 and 8:1,
you get 8:1.
0.5K 1K 2K 4K)
or megapixel tiers (0.25 1).
If the exact tier isn't available, you get the nearest one.
Output & Format
| Parameter | Type | Description | Modes |
|---|---|---|---|
response_format
|
string |
How to return the image
url
b64_json
Default:
"url" |
T2I
|
output_format
|
string |
Output image format
png
jpeg
gif
webp
avif
Gateway converts to requested format if provider doesn't support it natively.
|
T2I
|
output_compression
|
integer | Compression level for lossy formats (JPEG, WebP, AVIF) |
T2I
|
n
|
integer |
Number of images to generate
Default:
1Gateway generates multiple images in parallel even if provider only supports 1.
|
T2I
|
Parameter Normalization
How we handle parameters across different providers
Not every provider speaks the same language. When you send a parameter, we handle it in one of four ways depending on what the model supports:
| Behavior | What happens | Example |
|---|---|---|
passthrough |
Sent as-is to the provider | style, quality |
renamed |
Same value, mapped to the field name the provider expects | prompt |
converted |
Transformed to the provider's native format | size |
emulated |
Works even if the provider has no concept of it | n, response_format |
Parameters we don't recognize pass straight through to the upstream API, so provider-specific options still work.
Gallery
View all 2 imagesFLUX.1 [schnell] FAQ
How much does FLUX.1 [schnell] cost?
FLUX.1 [schnell] starts at $0.003 per image through Lumenfall. Pricing varies by provider. Lumenfall does not add any markup to provider pricing.
How do I use FLUX.1 [schnell] via API?
You can use FLUX.1 [schnell] through Lumenfall's OpenAI-compatible API. Send requests to the unified endpoint with model ID "flux.1-schnell". Code examples are available in Python, JavaScript, and cURL.
Which providers offer FLUX.1 [schnell]?
FLUX.1 [schnell] is available through fal.ai, Replicate, and Runware on Lumenfall. Lumenfall automatically routes requests to the best available provider.
What is the maximum resolution for FLUX.1 [schnell]?
FLUX.1 [schnell] supports images up to 1024x1024 resolution.
Overview
FLUX.1 [schnell] is a 12 billion parameter text-to-image model developed by Black Forest Labs, designed specifically for low-latency image generation. As the distilled version of the FLUX.1 family, it produces high-fidelity visuals in significantly fewer steps than traditional diffusion models. It is an open-weights model that balances large-scale parameter density with extreme inference efficiency.
Strengths
- Inference Speed: Capable of generating high-resolution images in as few as 1 to 4 inference steps, making it one of the fastest high-parameter models currently available.
- Text Rendering: High accuracy in rendering legible, correctly spelled text within generated images, a common failure point for many latent diffusion models.
- Anatomical Precision: Maintains strong structural integrity in complex subjects, such as human hands and limbs, even at low step counts.
- Prompt Adherence: Follows complex, multi-part descriptive prompts closely, ensuring that specific spatial relationships and attributes defined in the text are reflected in the output.
Limitations
- Photorealistic Detail: While the model is fast, it may lack the fine-grained texture and lighting nuances found in the [pro] or [dev] versions of FLUX.1 which use more inference steps.
- Stylistic Range: The distillation process can sometimes lead to a slightly more uniform “digital” aesthetic compared to non-distilled models that allow for more creative variance.
- Compositional Fixedness: Due to the low step count, the model has less “time” to refine compositions during sampling, which can occasionally lead to artifacts in highly crowded scenes.
Technical Background
The model is built on a 12-billion parameter architecture that utilizes flow matching, a generative modeling technique that simplifies the path from noise to data. The [schnell] variant (German for “fast”) uses latent adversarial diffusion distillation to compress the capabilities of the larger FLUX.1 base into a regime that requires minimal sampling iterations. This architecture allows it to bypass the traditional requirement of 20-50 steps common in standard diffusion pipelines.
Best For
FLUX.1 [schnell] is ideal for real-time applications, rapid prototyping, and high-volume generation tasks where cost and speed are prioritized over maximum artistic refinement. It is particularly effective for generating UI mockups, social media assets, and iterative design brainstorming. You can experiment with FLUX.1 [schnell] using Lumenfall’s unified API and playground to compare its performance against slower, more intensive image models.
Try FLUX.1 [schnell] in Playground
Generate images with custom prompts — no API key needed.