FastVideo Interleave Examples

This directory contains a small Python example for the reusable Interleave orchestration helpers. It does not add FastVideo CLI commands or HTTP routes.

Single-Prompt Trace

Run:

FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA \
  python examples/interleave/interleave_single_prompt.py \
    --model-path black-forest-labs/FLUX.2-klein-4B \
    --prompt "a brushed steel espresso machine on a marble counter, morning window light" \
    --output-dir outputs/interleave_single_prompt

The script uses VideoGenerator directly with a fallback single-prompt planner and accept-all critic. It writes an image plus trace.json; the trace records planner/generator/critic attempts and omits base64 image payloads by default.

Planner And Critic

The real InterleaveThinker planner and critic wrappers are integrated through the existing FastVideo training config system:

examples/train/configs/interleave_thinker/planner_sft_lora.yaml
examples/train/configs/interleave_thinker/critic_sft_lora.yaml
examples/train/configs/interleave_thinker/planner_smoke.yaml
examples/train/configs/rl/interleave_thinker/critic_grpo.yaml
examples/train/configs/rl/interleave_thinker/planner_grpo.yaml

Optional Gemini Backends

The RL reward config can use closed-source Google models through lazy wrappers:

fastvideo.train.methods.rl.rewards.GeminiNanoBananaEditScorer generates edits with Nano Banana and scores them with Gemini.
fastvideo.workflow.interleave_thinker.generator.NanoBananaImageGeneratorBackend implements the same image backend protocol as the local FastVideo generator.

Install the optional SDK and provide a key only when using these API backends:

uv pip install -e ".[eval-judge]"
export GEMINI_API_KEY=...

Supported Nano Banana aliases are:

nano-banana -> gemini-2.5-flash-image
nano-banana-pro -> gemini-3-pro-image
nano-banana-2 -> gemini-3.1-flash-image