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Davids048andClaude Fable 5.1 1ea7278c00 [refactor]: group model-family settings under pipeline.model

pipeline.model is a tagged union: a mapping with exactly one key, which
names the model family (ltx2, minimax_h3, longcat, or generic) and selects
that family's options class. The former sibling sections pipeline.ltx2,
pipeline.minimax_h3, and pipeline.longcat, and the DiT/VAE architecture
override dicts pipeline.dit and pipeline.vae, live inside the family block.
The parser implements tagged unions as a general mechanism (a dataclass
with a TAGS table); the family is readable as pipeline.model.family.

Resolution validates the tag against the family that the registry chose
for model_path (validate_model_family raises and names the registry class
and the right block) and fills the family's empty block when the input has
none (fill_model_family), so readers never see None. Family-only fills and
derivations (the MiniMax-H3 parallel-VAE environment variables, the LTX-2
tile-size derivation and refine preset copy, the LongCat BSA mirrors) run
only for their family.

Against the 3f6893a0 goldens every PipelineConfig is identical and no
other field differs beyond the allowed categories; family-only settings
are no longer decided on models of another family. LingBot-Video reads
its refiner switch from pipeline.preset_overrides.refine.enabled and loads
the refiner by default when the checkpoint has one, as at 3f6893a0.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CbSuTKA9oX4YsECdcBPAsX

2026-10-05 06:04:17 +00:00

gradio_local_demo_ltx2_3

[refactor]: group model-family settings under pipeline.model

2026-10-05 06:04:17 +00:00

gradio_local_demo_matrixgame2.py

[refactor]: decide every config value before the config freezes, and drop the from_pretrained keywords

2026-10-04 07:14:31 +00:00

gradio_local_demo.py

[refactor]: move examples to generate(request) and from_config

2026-10-03 02:14:36 +00:00

prompts_final.txt

[feat] Add gradio local inference demo (#847 )

2025-10-26 07:01:33 -07:00

README.md

[refactor]: move examples to generate(request) and from_config

2026-10-03 02:14:36 +00:00

README.md

FastVideo Gradio Local Demo

This is a Gradio-based web interface for generating videos using the FastVideo framework. The demo allows users to create videos from text prompts with various customization options.

Overview

The demo uses the FastVideo framework to generate videos based on text prompts. It provides a simple web interface built with Gradio that allows users to:

Enter text prompts to generate videos
Customize video parameters (dimensions, number of frames, etc.)
Use negative prompts to guide the generation process
Set or randomize seeds for reproducibility

Usage

Run the demo with:

python examples/inference/gradio/local/gradio_local_demo.py

This will start a web server at http://0.0.0.0:7860 where you can access the interface.

Model Initialization

This demo initializes a VideoGenerator with the minimum required arguments for inference. Users can seamlessly adjust inference options between generations, including prompts, resolution, video length, without ever needing to reload the model.

Video Generation

The core functionality is in the generate_video function, which:

Processes user inputs
Uses the FastVideo VideoGenerator from earlier to run inference (generator.generate())

Gradio Interface

The interface is built with several components:

A text input for the prompt
A video display for the result
Inference options in a collapsible accordion:
- Height and width sliders
- Number of frames slider
- Guidance scale slider
- Negative prompt options
- Seed controls

Inference Options

Height/Width: Control the resolution of the generated video
Number of Frames: Set how many frames to generate
Guidance Scale: Control how closely the generation follows the prompt
Negative Prompt: Specify what you don't want to see in the video
Seed: Control randomness for reproducible results