Image-to-Video
Diffusers
English
robotics
video-generation
world-model
multiview
robot-manipulation
simulation
Instructions to use qiyuan-robot/PrimeSim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use qiyuan-robot/PrimeSim with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("qiyuan-robot/PrimeSim", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
PrimeSim commited on
Commit ·
2b213fa
1
Parent(s): b8000e9
Point at the repo demos for the posttrain checkpoint
Browse files
README.md
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@@ -34,6 +34,9 @@ it leads four of five WorldSimProbe suites and posts the best overall EWMBench s
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| `primesim_base.safetensors` | the midtrained model -- start here to fine-tune |
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| `primesim_posttrain_g2.safetensors` | stage 4, causal few-step model -- use for inference |
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## Requirements
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**These weights are not self-contained.** The text encoder, VAE and tokenizer come
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| `primesim_base.safetensors` | the midtrained model -- start here to fine-tune |
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| `primesim_posttrain_g2.safetensors` | stage 4, causal few-step model -- use for inference |
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Run `primesim_posttrain_g2` on the demos in the
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[repository](https://github.com/PrimebotTech/PrimeSim) to see what posttraining adds.
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## Requirements
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**These weights are not self-contained.** The text encoder, VAE and tokenizer come
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