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FoundationVision’s Infinity is a text-to-image model you can try on Replicate through a hosted form or call from code. For a first image, open the Infinity model page, enter a prompt, and run it. The page currently lists four inputs: prompt, guidance_scale, tau, and seed. Its schema and examples may change, so use the live page as the authority for current settings.
What is the Infinity model?
Infinity is FoundationVision’s text-to-image image-generation model. The project describes its method as bitwise visual autoregressive modeling for high-resolution image synthesis: it predicts image tokens in a bitwise framework, using an infinite-vocabulary tokenizer and bitwise self-correction. Those are descriptions of the model’s approach, not guarantees that every generated image will be accurate or that text inside an image will render correctly.
Replicate hosts a public version of Infinity with a browser interface and API examples. That makes the hosted route the simplest way to try it without setting up the model locally.
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- Open the Replicate Infinity page and find the live input form.
- Enter a prompt describing the subject and scene you want. The page’s displayed example prompt is “alien spaceship enterprise.” Treat it as an example, not a required format.
- Run the prediction and review the returned image. If you want to experiment, change one listed control at a time so you can see what that change does.
- If you want to reproduce a result, record its seed along with the prompt and other settings. The seed field may be left blank for a random seed, according to the current page.
What do the listed inputs control?
The model page currently displays these fields and defaults. Hosted schemas can change, so check the live form before relying on a field or default.
#1 Best Overall
| Input | Current page information | Practical use |
|---|---|---|
prompt |
Displayed example: “alien spaceship enterprise.” | Describe the image you want the model to create. |
guidance_scale |
Displayed default: 3. | Try a different value only as an experiment; the available information does not establish a setting that guarantees a better result. |
tau |
Displayed default: 0.5. | Change it separately from other inputs when exploring its effect. |
seed |
May be left blank for a random seed. | Keep track of the seed when reproducibility matters. |
How to call Infinity from code
The Replicate model page provides examples for Node.js, Python, and HTTP requests. Follow the example for your chosen environment rather than copying an old snippet: each invocation identifies a specific model version, and the hosted schema or version can change.
- Open the model page’s API examples and choose Node.js, Python, or HTTP.
- Install the client required by the example, if you are using a client library.
- Create and use a Replicate API token as shown in the example. Keep the token private; do not put it in public source code or share it in a prompt.
- Pass the inputs shown in the current example, then save or otherwise handle the image returned by the prediction.
Check the live page again when updating an integration. In particular, confirm the version identifier and input names rather than assuming they are permanent.
What FoundationVision reports about speed and benchmarks
FoundationVision’s project README reports that Infinity generated a 1024×1024 image in 0.8 seconds “without extra optimization” and describes that as 2.6 times faster than SD3-Medium. These are the project authors’ reported figures, not an independent or universal benchmark; the README’s cited excerpt does not provide a publication date.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThe same README lists the following benchmark entries. They describe reported evaluations, not what a particular prompt or user will achieve. The README notes that daggered results use a prompt rewriter.
| Model | Resolution listed | GenEval | DPG | HPSv2.1 |
|---|---|---|---|---|
| Infinity-2B | 1024 | 0.69 / 0.73 | 83.5 | 32.2 |
| Infinity-8B | 1024 | 0.79 | 86.6 | Not listed in the README |
Hosted Replicate use or local inference?
Use the hosted page if your goal is to try the model or integrate it through Replicate’s API. FoundationVision also documents local inference using Docker, but that route requires downloading model weights and FLAN-T5-XL, building and running a container with NVIDIA GPU access, and running the reproduction script. It involves substantially more setup than using the hosted form.
The reviewed official instructions do not state a minimum GPU model or VRAM requirement. They therefore do not establish whether a particular computer is sufficient. The project labels its repository MIT licensed, but that license label alone should not be treated as a blanket conclusion about the rights or restrictions for model outputs, training data, or third-party uses.
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