zsxkib / step-video-t2v

Generate high-quality videos from text prompts using StepVideo

Cold

Public
91 runs
H100
GitHub
Weights
Paper
License

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

An astronaut discovers a stone monument on the moon with the word 'stepfun' inscribed on it, glowing brightlyAn astronaut discovers a stone monument on the moon with the word 'stepfun' inscribed on it, glowing brightly

Prompt text

Default: "An astronaut on the moon"

negative_prompt

string

Shift + Return to add a new line

dark image, low resolution, bad hands, text, missing fingers, extra fingers, cropped, low quality, grainy, signature, watermark, username, blurrydark image, low resolution, bad hands, text, missing fingers, extra fingers, cropped, low quality, grainy, signature, watermark, username, blurry

Negative prompt

Default: "low resolution, text"

num_inference_steps

integer

(minimum: 1, maximum: 100)

Number of inference steps

Default: 30

cfg_scale

number

(minimum: 1, maximum: 20)

Classifier free guidance scale

Default: 9

num_frames

integer

(minimum: 17, maximum: 204)

Number of frames

Default: 51

Including fps and 2 more...

Run this model in Node.js with one line of code:

npx create-replicate --model=zsxkib/step-video-t2v

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run zsxkib/step-video-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "zsxkib/step-video-t2v:4bc2f604bdc8e0febeeb8a75b813aab3ba4cbdbc1dc0afc1d0f0d62cb4839351",
  {
    input: {
      fps: 25,
      prompt: "An astronaut discovers a stone monument on the moon with the word 'stepfun' inscribed on it, glowing brightly",
      quality: 5,
      cfg_scale: 9,
      num_frames: 51,
      negative_prompt: "dark image, low resolution, bad hands, text, missing fingers, extra fingers, cropped, low quality, grainy, signature, watermark, username, blurry",
      num_inference_steps: 30
    }
  }
);
console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run zsxkib/step-video-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "zsxkib/step-video-t2v:4bc2f604bdc8e0febeeb8a75b813aab3ba4cbdbc1dc0afc1d0f0d62cb4839351",
    input={
        "fps": 25,
        "prompt": "An astronaut discovers a stone monument on the moon with the word 'stepfun' inscribed on it, glowing brightly",
        "quality": 5,
        "cfg_scale": 9,
        "num_frames": 51,
        "negative_prompt": "dark image, low resolution, bad hands, text, missing fingers, extra fingers, cropped, low quality, grainy, signature, watermark, username, blurry",
        "num_inference_steps": 30
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run zsxkib/step-video-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "4bc2f604bdc8e0febeeb8a75b813aab3ba4cbdbc1dc0afc1d0f0d62cb4839351",
    "input": {
      "fps": 25,
      "prompt": "An astronaut discovers a stone monument on the moon with the word \'stepfun\' inscribed on it, glowing brightly",
      "quality": 5,
      "cfg_scale": 9,
      "num_frames": 51,
      "negative_prompt": "dark image, low resolution, bad hands, text, missing fingers, extra fingers, cropped, low quality, grainy, signature, watermark, username, blurry",
      "num_inference_steps": 30
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/zsxkib/step-video-t2v@sha256:4bc2f604bdc8e0febeeb8a75b813aab3ba4cbdbc1dc0afc1d0f0d62cb4839351 \
  -i 'fps=25' \
  -i $'prompt="An astronaut discovers a stone monument on the moon with the word \'stepfun\' inscribed on it, glowing brightly"' \
  -i 'quality=5' \
  -i 'cfg_scale=9' \
  -i 'num_frames=51' \
  -i 'negative_prompt="dark image, low resolution, bad hands, text, missing fingers, extra fingers, cropped, low quality, grainy, signature, watermark, username, blurry"' \
  -i 'num_inference_steps=30'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/zsxkib/step-video-t2v@sha256:4bc2f604bdc8e0febeeb8a75b813aab3ba4cbdbc1dc0afc1d0f0d62cb4839351
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "fps": 25,
      "prompt": "An astronaut discovers a stone monument on the moon with the word \'stepfun\' inscribed on it, glowing brightly",
      "quality": 5,
      "cfg_scale": 9,
      "num_frames": 51,
      "negative_prompt": "dark image, low resolution, bad hands, text, missing fingers, extra fingers, cropped, low quality, grainy, signature, watermark, username, blurry",
      "num_inference_steps": 30
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

{
  "completed_at": "2025-02-25T15:54:10.545543Z",
  "created_at": "2025-02-25T15:49:27.505000Z",
  "data_removed": false,
  "error": null,
  "id": "w6g394w1a5rme0cn7q68qj3ac0",
  "input": {
    "fps": 25,
    "prompt": "An astronaut discovers a stone monument on the moon with the word 'stepfun' inscribed on it, glowing brightly",
    "quality": 5,
    "cfg_scale": 9,
    "num_frames": 51,
    "negative_prompt": "dark image, low resolution, bad hands, text, missing fingers, extra fingers, cropped, low quality, grainy, signature, watermark, username, blurry",
    "num_inference_steps": 30
  },
  "logs": "[predict] Using random seed: 422406473\nStep 1 / 30\nTransformer blocks:   0%|          | 0/48 [00:00<?, ?it/s]\nTransformer blocks:   4%|▍         | 2/48 [00:00<00:02, 19.80it/s]\nTransformer blocks:   8%|▊         | 4/48 [00:00<00:02, 16.82it/s]\nTransformer blocks:  12%|█▎        | 6/48 [00:00<00:02, 15.96it/s]\nTransformer blocks:  17%|█▋        | 8/48 [00:00<00:02, 15.58it/s]\nTransformer blocks:  21%|██        | 10/48 [00:00<00:02, 15.37it/s]\nTransformer blocks:  25%|██▌       | 12/48 [00:00<00:02, 15.20it/s]\nTransformer blocks:  29%|██▉       | 14/48 [00:00<00:02, 15.09it/s]\nTransformer blocks:  33%|███▎      | 16/48 [00:01<00:02, 14.76it/s]\nTransformer blocks:  38%|███▊      | 18/48 [00:01<00:02, 14.76it/s]\nTransformer blocks:  42%|████▏     | 20/48 [00:01<00:01, 14.78it/s]\nTransformer blocks:  46%|████▌     | 22/48 [00:01<00:01, 14.80it/s]\nTransformer blocks:  50%|█████     | 24/48 [00:01<00:01, 14.85it/s]\nTransformer blocks:  54%|█████▍    | 26/48 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35.04it/s]\n[predict] Video saved to video.mp4",
  "metrics": {
    "predict_time": 250.291451803,
    "total_time": 283.040543
  },
  "output": "https://replicate.delivery/xezq/nrHNXu7Otf37DaNAI0QiTe8UqqUnKCvgW6FPHJ7D6Xyim5SUA/video.mp4",
  "started_at": "2025-02-25T15:50:00.254091Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://stream.replicate.com/v1/files/bcwr-mll2vxyygohvz4oyk2rmi4j2c7dcudxaimqbk6ptjdkgjxb7kjya",
    "get": "https://api.replicate.com/v1/predictions/w6g394w1a5rme0cn7q68qj3ac0",
    "cancel": "https://api.replicate.com/v1/predictions/w6g394w1a5rme0cn7q68qj3ac0/cancel"
  },
  "version": "4bc2f604bdc8e0febeeb8a75b813aab3ba4cbdbc1dc0afc1d0f0d62cb4839351"
}

Generated in

4 minutes 10 seconds

Tweak it ShareReport

[predict] Using random seed: 422406473
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[predict] Video saved to video.mp4

Examples

View more examples

Run time and cost

This model runs on Nvidia H100 GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

Step Video T2V - Text to Video Magic ✨

Transform your text descriptions into captivating videos with StepFun’s StepVideo model - now optimized to run on a single GPU! 🚀

About

This model turns your words into fluid, high-quality videos in seconds. Using StepFun’s groundbreaking approach to video generation, it creates remarkably coherent motion and impressive visuals from simple text prompts. 🎬

What makes this implementation special? 🌟

Single GPU power: Unlike the original implementation that required 4 GPUs, this version runs efficiently on just one H100! 💪
FP8 quantization: The diffusion model uses optimized FP8 precision for:
Faster generation on modern hardware 🏎️
Reduced memory footprint 🧠
Quicker creative iterations ⚡

While quantization introduces a slight quality trade-off compared to full-precision models, the speed and accessibility gains make this perfect for most creative projects!

Tips for stunning results 🎯

Be descriptive: “A golden retriever puppy playing with a red ball in a sunny park” works better than “dog playing”
Specify motion: Mention the action you want to see
Adjust frames: More frames = longer video, but might affect per-frame quality
Play with FPS: Higher FPS creates smoother motion
Use negative prompts: Add things you don’t want to see in the “negative prompt” field

Example prompts 💡

“A spaceship landing on a distant planet with two moons in the sky”
“Timelapse of a flower blooming in a lush garden”
“A robot chef preparing a gourmet meal in a futuristic kitchen”
“Waves crashing against a rocky shore during sunset”
“A panda doing kung fu moves in a bamboo forest”

Limitations 🚧

Text rendering isn’t perfect - avoid prompts that require specific text
Very complex scenes might lose some details
Faces can sometimes look a bit uncanny
The quantized version prioritizes speed over absolute quality

Coming soon… 📆

Multi-GPU support for even faster generation
Fine-tuned quality improvements while maintaining speed
Additional creative controls

Credits 🙏

Model adaptation and quantization by @zsakib_ - making high-end video generation accessible on single GPUs.

Based on StepFun’s StepVideo-T2V-Turbo with optimizations for Replicate’s infrastructure.

Happy video creating! 🎥✨