引言
免费图像生成 API 在 2026 年以 SDXL 与 FLUX 系列为主力。FLUX 在细节与提示词遵循度上明显优于早期 SD，已成为免费出图首选；SDXL 则生态成熟、控制插件丰富。
主要端点
| 服务 |
模型 |
限制 |
| Hugging Face |
FLUX.1-dev / FLUX.1-schnell |
免费层冷启动 |
| Pollinations.ai |
多模型 |
无 Key、无限制 |
| Together AI |
FLUX.1-dev |
用 $5 信用额度 |
| Cloudflare Workers AI |
@cf/stable-diffusion-xl-base-1.0 |
每日免费 neuron 量 |
调用示例
import requests
url = "https://image.pollinations.ai/prompt/a%20cyberpunk%20city%20at%20night"
open("city.png", "wb").write(requests.get(url).content)
from huggingface_hub import InferenceClient
client = InferenceClient(token="hf_...")
img = client.text_to_image(
"a cyberpunk city at night",
model="black-forest-labs/FLUX.1-schnell")
img.save("city.png")
注意事项
FLUX.1-dev 非商用许可，商用选 schnell 或 SDXL；免费 API 排队久需异步队列；Pollinations 稳定性依赖公共服务；高分辨率（>1024）分步 upscale 避免超时。
Introduction
Free image generation APIs in 2026 are dominated by two model families: SDXL and FLUX. FLUX clearly beats early Stable Diffusion on detail and prompt adherence and has become the top free choice for quality; SDXL still wins on ecosystem maturity, with a rich library of ControlNet adapters, LoRAs, and inpainting tools. The right endpoint depends on whether you prioritize zero-config simplicity (Pollinations), model variety (Hugging Face), or production stability (Together, Cloudflare), so this round-up maps each option to the workload it handles best. Licensing is the make-or-break dimension for image generation in particular, because the model's license governs not just your right to use outputs but also your obligation to disclose training data or release derivative weights. FLUX.1-dev's non-commercial clause is easy to miss in a quick prototype and expensive to unwind after launch, so settle the licensing question before you write any integration code.
Major Endpoints
| Service |
Model |
Limits |
| Hugging Face |
black-forest-labs/FLUX.1-dev, FLUX.1-schnell |
Free-tier cold start, shared concurrency |
| Pollinations.ai |
various |
No key, no quota, URL-based |
| Together AI |
black-forest-labs/FLUX.1-dev |
Uses the $5 new-user credit |
| Cloudflare Workers AI |
@cf/stable-diffusion-xl-base-1.0 |
Daily free neuron budget |
Call Example
import requests
url = "https://image.pollinations.ai/prompt/a%20cyberpunk%20city%20at%20night"
open("city.png", "wb").write(requests.get(url).content)
from huggingface_hub import InferenceClient
client = InferenceClient(token="hf_...")
img = client.text_to_image(
"a cyberpunk city at night",
model="black-forest-labs/FLUX.1-schnell")
img.save("city.png")
Caveats
Four practical points guide the choice. First, FLUX.1-dev ships under a non-commercial license — pick FLUX.1-schnell or SDXL for commercial work, or you will inherit a legal risk that surfaces only after launch. Second, free image APIs often queue, so build async queues and progress indicators into the UI rather than blocking on a single request. Third, Pollinations is perfect for fast prototypes but its stability depends on a public service with no SLA. Fourth, for resolutions above 1024, upscale in steps rather than requesting a huge image in one call, which frequently times out. Pollinations supports width, height, seed, and model URL parameters for custom output. A final practical tip: seed your prompts deliberately. Both Pollinations and the Hugging Face endpoints accept a seed parameter, and pinning a seed is the only way to make image generation reproducible for testing, regression checks, and user-facing "regenerate with tweaks" features. Without a seed, the same prompt yields a different image on every call, which makes debugging prompt-engineering changes effectively impossible.