https://github.com/deepfates/repflix-studios
Scripts for generating large amounts of videos with fine-tuned models on Replicate
https://github.com/deepfates/repflix-studios
Last synced: about 1 year ago
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Scripts for generating large amounts of videos with fine-tuned models on Replicate
- Host: GitHub
- URL: https://github.com/deepfates/repflix-studios
- Owner: deepfates
- License: mit
- Created: 2025-01-30T00:55:01.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-30T17:05:40.000Z (about 1 year ago)
- Last Synced: 2025-03-30T01:35:50.131Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 18.6 KB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Repflix Studios
Generate and download videos with the same prompts across a bunch of different fine-tuned HunyuanVideo models on Replicate.
This toolkit lets you:
- Generate videos with different style models
- Sweep across multiple parameters to explore variations
- Download results before they expire
- Create pre-generated parameter grids for web exploration
## Setup
1. Install `uv`:
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```
2. Set your Replicate API token:
```bash
export REPLICATE_API_TOKEN=your_token_here
```
That's it! Each script has its dependencies defined inline, so `uv` will handle everything else automatically.
## Usage
### 1. Generate Individual Videos
For quick tests and single videos, use `generate_video.py`:
```bash
uv run generate_video.py "A TOK style video of a cat playing with yarn"
```
Sweep across parameters to explore variations:
```bash
# Sweep lora_strength and guidance_scale
uv run generate_video.py "A TOK style video of a cat" \
--param1 lora_strength --param2 guidance_scale
# Custom parameter ranges
uv run generate_video.py "A TOK style video of a cat" \
--param1 steps --param1-start 25 --param1-end 50
```
### 2. Generate Parameter Grid
To create a complete exploration space across models and parameters, use `generate_grid.py`. This script:
- Takes a set of prompts
- Generates videos for all combinations of:
- 9 different style models
- 3 key parameters (lora_strength, guidance_scale, steps)
- 3 values per parameter
- Records prediction IDs and metadata for later retrieval
- Perfect for creating pre-generated content for web exploration
```bash
uv run generate_grid.py
```
### 3. Download Grid Results
After generating the parameter grid, use `download_grid.py` to:
- Download all generated videos before they expire
- Organize them in a CDN-friendly directory structure
- Create a complete exploration space for web interfaces
```bash
uv run download_grid.py
```
Use `--dry-run` to preview download paths:
```bash
uv run download_grid.py --dry-run
```
Videos are saved to `public/videos/` organized by model and parameters, ready for web serving.
### Key Parameters
These parameters have the most impact on video style and quality:
- `lora_strength`: Controls style adaptation (0.5-1.5 recommended)
- `guidance_scale`: Controls prompt adherence (5-8 recommended)
- `steps`: Number of denoising steps (25-50 recommended)
Less critical parameters:
- `num_frames`: Frames to generate (16-64 recommended)
- `frame_rate`: Frames per second (8-24 recommended)
## Notes
- The scripts use "TOK" in prompts as a placeholder - it's automatically replaced with the appropriate trigger word for each model
- Generated videos are temporarily stored on Replicate and should be downloaded promptly
- The grid generation workflow creates a complete exploration space for web interfaces
- The download script creates a CDN-friendly directory structure based on model and parameters
## Models
The script includes several Hunyuan models with different styles:
- Dune
- Pixar
- Arcane
- La La Land
- Twin Peaks
- Pulp Fiction
- Cowboy Bebop
- The Grand Budapest Hotel
- Spider-Man: Into the Spider-Verse
More models are available but commented out in the code.
## Requirements
- Python 3.11 or higher (installed automatically by `uv` if needed)
- Replicate API token
Each script has its own dependencies defined at the top of the file. The main dependencies are:
- replicate
- tqdm
- numpy
## License
MIT