Davids048andClaude Opus 5.5
4117696a4f
- Docs, AGENTS.md files, and skills describe ResolvedGeneratorConfig, resolve_inference_config, typed YAML, and --config plus dotted overrides for training and preprocessing instead of FastVideoArgs and flat flags. - The local tests build resolved configs (or small fakes that expose typed paths) instead of FastVideoArgs; the LoRA extraction scripts build pipelines with from_pretrained(model_path, resolved_config=...). - The mixkit README's NVFP4 snippet uses from_config with engine.quantization.transformer_quant (the from_pretrained keyword it used is rejected). Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
146 lines
4.5 KiB
Markdown
146 lines
4.5 KiB
Markdown
# Configuration
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## Multi-GPU Setup
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FastVideo automatically distributes the generation process when multiple GPUs are specified:
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```python
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# Will use 4 GPUs in parallel for faster generation
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generator = VideoGenerator.from_pretrained(
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"Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
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num_gpus=4,
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)
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```
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One node uses the multiprocessing executor (`execution_backend: mp`, the
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default). Two machines — for example two DGX Sparks, one GPU each — need Ray:
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```yaml
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generator:
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engine:
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num_gpus: 2
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execution_backend: ray
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parallelism:
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sp_size: 2
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```
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Set `RAY_ADDRESS` and `FASTVIDEO_HOST_IP` to the interconnect IPs, not Wi-Fi.
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The FastH3 example selects Ray automatically when `RAY_ADDRESS` is set. Full
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bring-up: [Pair two NVIDIA DGX Sparks](../getting_started/installation/spark_pair.md).
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## Customizing Generation
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`VideoGenerator.from_pretrained` accepts only common engine and offload
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keywords, such as `num_gpus` and `dit_cpu_offload`. The full list is
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`fastvideo.api.compat.FROM_PRETRAINED_KWARGS`. Pass every other startup
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setting to `VideoGenerator.from_config` at its config path, and pass
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generation settings to `VideoGenerator.generate` as a request:
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```python
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from fastvideo import VideoGenerator
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def main():
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model_name = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
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# Create the generator
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generator = VideoGenerator.from_config({
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"model_path": model_name,
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"engine": {
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"num_gpus": 1,
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"offload": {"dit_layerwise": True},
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"precision": {"vae": "fp16"},
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},
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})
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# Generate video with custom parameters
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prompt = "A beautiful sunset over a calm ocean, with gentle waves."
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video = generator.generate({
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"prompt": prompt,
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"sampling": {
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# How many frames to generate
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"num_frames": 45,
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# Video resolution (width, height)
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"width": 1024,
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"height": 576,
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# How many steps we denoise the video (higher = better quality, slower generation)
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"num_inference_steps": 30,
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# How strongly the video conforms to the prompt (higher = more faithful to prompt)
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"guidance_scale": 7.5,
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# Random seed for reproducibility
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"seed": 42, # Optional, leave unset for random results
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},
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"output": {
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"output_path": "my_videos/", # Controls where videos are saved
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"save_video": True,
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},
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})
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# If return_frames=True, frames are available in video.frames
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print(f"Generated {len(video.frames)} frames")
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if __name__ == '__main__':
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main()
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```
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## JSON/YAML Config Files (CLI)
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The inference CLI is config-first. Use an explicit subcommand with `--config`,
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then apply optional dotted overrides on top, matching the training CLI style.
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By default, CLI generation uses `return_frames=false` unless you set
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`request.output.return_frames: true` in config or via a dotted override.
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```bash
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fastvideo generate --config config.yaml
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```
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Example nested config:
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```yaml
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generator:
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model_path: FastVideo/FastHunyuan-diffusers
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engine:
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num_gpus: 2
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parallelism:
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sp_size: 2
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request:
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prompt: A capybara relaxing in a hammock
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sampling:
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num_frames: 45
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height: 720
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width: 1280
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num_inference_steps: 6
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seed: 1024
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output:
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output_path: outputs/
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```
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Override individual values from the CLI with dotted paths:
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```bash
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fastvideo generate --config config.yaml --request.sampling.seed 42
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```
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## Where a Value Came From
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FastVideo resolves the generator config once at startup and records the source of every value: the input config
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(`input`; `explicit` tells whether you wrote the value or it is the schema default), a `FASTVIDEO_*` environment
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variable, the model's defaults, or a derived value. A worker's device policy and values read from checkpoint files
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are recorded too.
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```python
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generator = VideoGenerator.from_config(config)
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generator.resolved_config.provenance("engine.parallelism.sp_size")
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# PathProvenance(path='engine.parallelism.sp_size', value=2, source='derive_parallel_sizes', ...)
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result = generator.generate(request)
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result.resolved_request.provenance("sampling.num_frames")
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# PathProvenance(..., value=81, source='fill_sampling_defaults[preset wan_t2v_1_3b]', explicit=False)
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```
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`resolved_config.provenance_table()` lists every path. After startup, `resolved_config` is read-only; a later
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change is a recorded override (`resolved_config.with_override(source, values)`) that returns a new object.
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## Performance Optimization
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For configuring optimizations, please see our [optimizations guide](optimizations.md)
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