Model reference · open weights

psi0_5

Video StanfordNeuroAILab Image→video 1 build Its own licence terms 998 dl/mo

psi0_5 is an open-weight video model from StanfordNeuroAILab. psi0_5 (BF16) weighs 3.3 GB; the smallest configuration that runs it is RTX 3060 12 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • PSI is an image-to-video model developed by StanfordNeuroAILab that functions as a controllable physical world model.
  • It accepts prompts including images, motion, depth, camera pose, or partial future states to complete missing pieces of a physical scene.
  • The model is described as modestly sized and has not undergone post-training, with a license listed as other.

Summary of the StanfordNeuroAILab/psi0_5 model card, 2026-10-01

What it is

Released byStanfordNeuroAILab
Released2026-05-17
VRAM3.3 GB for the weights

What it runs on

Memory and cards for psi0_5 (BF16)

3.3 GBweights, file size
537 MBruntime overhead
CardWeightsMemory
RTX 3060 12 GBfits11.6 GB
RTX 4060 Ti 16 GBfits15.4 GB
RTX 3090 24 GBfits23.4 GB
RTX 4090 24 GBfits23.4 GB
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

From the model card

What StanfordNeuroAILab says about psi0_5

Read the model card

A richly controllable physical world model

Prompt PSI with images, motion, depth, camera pose, or partial future states, and ask it to complete the missing pieces of a physical scene.

PSI treats visual prediction as a promptable modeling problem. A prompt can be as simple as rgb0->rgb1, or it can include explicit control handles such as optical flow, depth, camera motion, and partially specified future frames. The same predictor handles all of these notations.

What You Can Prompt

PromptWhat PSI Does
rgb0->rgb1continue a scene one frame forward
rgb0->f01,rgb1imagine motion and render the next frame
rgb0,f01->f01,rgb1densify a sparse flow prompt, then render
rgb0,d0,f01->f01,d1,rgb1use depth and motion to predict flow, depth, and RGB
rgb0,c01->rgb1synthesize a new camera view

Quick Start

from PIL import Image
from transformers import AutoModel

predictor = AutoModel.from_pretrained(
    "StanfordNeuroAILab/psi0_5",
    trust_remote_code=True,
    device="cuda:0",
)
rgb1 = predictor.generate("rgb0->rgb1", rgb0=Image.open("scene.png"))
rgb1.save("scene_next.png")

A Sparse Motion Prompt

f01 = predictor.sparse_flow_prompt([((70, 221), (168, 221))], rgb0.size)

dense_flow, rgb1 = predictor.generate(
    "rgb0,f01->f01,rgb1",
    rgb0=rgb0,
    f01=f01,
    num_seq_patches=256,
)

Novel View Synthesis

camera = {
    "fov_x": 60.0,
    "fov_y": 60.0,
    "euler_angles": [0.0, -0.12, 0.0],
    "translation": [0.10, 0.0, 0.04],
}

rgb1 = predictor.generate(
    "rgb0,c01->rgb1",
    rgb0=Image.open("coffee_mug_000.png"),
    c01=camera,
)

More Examples

The full usage guide includes sparse flow construction, depth/flow prompting, camera-conditioned NVS, visual statistics, and scriptable demos:

docs/usage.md

The release gallery shows many prompt patterns in action:

https://neuroailab.github.io/psi-website/blog/psi-generations.html

PSIv0.5 is a modestly sized model that has not undergone any post-training yet. Some of its rollouts diverge. We recommend unrestricted sampling for flow prediction and top_p=0.9, top_k=1000 for RGB rendering. Correct prompting can significantly improve generations, and simple harnesses such as those in the provided Gradio app can be used to steer the model much more effectively. We believe this direction has great potential for scaling to create even more comprehensive models of the world while maintaining this highly controllable API.

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

Running it yourself

Run it on a rented GPU

Rent a machine by the hour. How to run this model is on its model card.

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