Model reference · open weights
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.
Summary of the StanfordNeuroAILab/psi0_5 model card, 2026-10-01
What it is
| Released by | StanfordNeuroAILab |
|---|---|
| Released | 2026-05-17 |
| VRAM | 3.3 GB for the weights |
What it runs on
| Card | Weights | Memory |
|---|---|---|
| RTX 3060 12 GB | fits | 11.6 GB |
| RTX 4060 Ti 16 GB | fits | 15.4 GB |
| RTX 3090 24 GB | fits | 23.4 GB |
| RTX 4090 24 GB | fits | 23.4 GB |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From the model card
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.
| Prompt | What PSI Does |
|---|---|
rgb0->rgb1 | continue a scene one frame forward |
rgb0->f01,rgb1 | imagine motion and render the next frame |
rgb0,f01->f01,rgb1 | densify a sparse flow prompt, then render |
rgb0,d0,f01->f01,d1,rgb1 | use depth and motion to predict flow, depth, and RGB |
rgb0,c01->rgb1 | synthesize a new camera view |
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")
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,
)
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,
)
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
Rent a machine by the hour. How to run this model is on its model card.