One prompt → 30s cinematic reel. End-to-end on a single AMD Instinct MI300X. Built solo for the AMD Developer Hackathon, May 2026.

flowchart LR
    P([prompt]) --> D[Director Agent<br/>Qwen3.5-35B]
    D --> CM[Character Masters<br/>FLUX.2 klein]
    CM --> KF[Per-shot Keyframes<br/>FLUX.2 reference]
    KF --> AN[Animation<br/>Wan2.2-I2V-A14B]
    AN --> VC{Vision Critic<br/>Qwen3.5-35B}
    VC -- score &lt; 7 --> AN
    VC -- score ≥ 7 --> MX[ffmpeg mix]
    D --> MU[Music<br/>ACE-Step v1]
    D --> VO[Narration<br/>Kokoro-82M, 9 lang]
    MU --> MX
    VO --> MX
    MX --> O([30s mp4])

    classDef llm fill:#a78bfa,color:#020617,stroke:#7c3aed
    classDef diff fill:#f472b6,color:#020617,stroke:#db2777
    classDef vid fill:#fbbf24,color:#020617,stroke:#d97706
    classDef io fill:#94a3b8,color:#020617,stroke:#475569
    class D,VC llm
    class CM,KF diff
    class AN vid
    class MU,VO,MX io

The Director also doubles as the Vision Critic — same Qwen3.5-35B checkpoint, two roles, reloaded between phases. 192 GB HBM3 is what lets four different architectures share one card.

python generate.py --prompt "a young woman walks through neon-lit Tokyo at night and meets two friends" --out outputs/demo --critic

~45 minutes later you get outputs/demo/reel_final.mp4 - six 5-second shots, character-consistent, with music and per-shot voice-over, mixed.

Eight stages, all on the same GPU:

1. Director - Qwen3.5-35B-A3B via vLLM. Plans 6 shots, character portraits, music brief, per-shot VO script, and the language to narrate in.
2. Masters - FLUX.2 [klein] 4B text-to-image, one canonical frame per character.
3. Per-shot keyframes - same klein, reference editing, conditioned on the master.
4. Animation - Wan2.2-I2V-A14B with FBCache (lossless 2x) + selective torch.compile. FLF2V mode on cut: false continuation arcs locks identity at both ends.
5. Vision critic - Qwen3.5 re-loads, scores 4 frames per clip with structured labels (STYLIZED_AI_LOOK, CHARACTER_DRIFT, CAMERA_IGNORED, ...). Bumps seed and re-renders if overall < 7. Up to 3 attempts.
6. Music - ACE-Step v1 3.5B, 30s instrumental from the brief.
7. Voice-over - Kokoro-82M, 9 languages, one wav per shot, ffmpeg adelays them onto the music bed at clip-start offsets.
8. Mix - ffmpeg concat + lanczos upscale + loudnorm.

The Director also doubles as the vision critic. Same checkpoint, two roles.

Outputs are commercially usable. No NC weights anywhere.

192 GB HBM3 lets four very different architectures share one card sequentially. On 24 GB consumer hardware you'd need 4-5 separate machines.

Each phase unloads cleanly via gc.collect() + torch.cuda.empty_cache() before the next one loads. Director runs in-process for planning then dels itself before Wan2.2 loads, otherwise OOM.

Tested inside rocm/vllm-dev:nightly_main_20260506 (vLLM 0.20.2rc1, torch 2.10, ROCm 7.2 in-container) on AMD Developer Cloud.

# inside the rocm container
pip install -r requirements.txt --no-deps
export STUDIOMI_AITER_FP8=0
export VLLM_ROCM_USE_AITER=1
python generate.py --prompt "your reel idea" --out outputs/myreel --critic

ROCm env is set in generate.py before any torch import - PYTORCH_HIP_ALLOC_CONF=expandable_segments:True, TORCH_BLAS_PREFER_HIPBLASLT=1, MIOPEN_FIND_MODE=FAST, GPU_MAX_HW_QUEUES=2, HIP_FORCE_DEV_KERNARG=1, HSA_ENABLE_SDMA=0. If you change those after import you get a silent allocator-profile mismatch and lose ~3 GB.

Stage routing via env vars (default cuda:0 for everything):

STUDIOMI_GPU_FLUX=cuda:1 STUDIOMI_GPU_WAN=cuda:0 python generate.py ...

Tested only on single-MI300X. Hooks are there for 2+ card setups.

STUDIO_API_TOKEN=secret uvicorn server:app --host 0.0.0.0 --port 8000

POST /jobs with {"prompt": "...", "use_critic": true} returns {"job_id"}. GET /jobs/{id}/stream is an SSE feed of every stage event (plan, masters, keyframes, clip render, critic verdicts, music, VO, final). Granular artifact endpoints under /jobs/{id}/{plan,master,keyframe,clip,music,vo,video}.

End-to-end: 25.9 min → 10.4 min per 720p clip on a single MI300X.

• AITER FP8 on Wan2.2 - gemm_a8w8_CK segfaults on the cross-attn shape (M=512, K=4096, N=5120) on ROCm 7.0; closed standalone on 7.2 but still crashes inside the full pipeline graph (matches ROCm/aiter#2187). Code stays behind STUDIOMI_AITER_FP8=1 env flag; production ships BF16.
• MagCache - diffusers 0.38 calibration-step counter doesn't fire on Wan2.2's dual-transformer schedule.
• cache-dit + TaylorSeer - slower than baseline FBCache on ROCm.
• Wan2.2-Lightning I2V LoRA - V1 only (Aug 2025), V2.0 phased-DMD never landed for I2V; quality drop on hero shots vs full-step.
• AITER FA3 - JIT compile for 81×1280×704 attention never finishes.
• torch.compile(mode="max-autotune", fullgraph=True) - Dynamo error on Wan2.2 (diffusers#12728).
• channels_last - Wan2.2 transformer is rank-5; channels_last is rank-4 only.

generate.py        cli entry, runs the pipeline
director.py        director agent + vision critic (one Qwen3.5-35B serves both)
utils.py           pipeline core: masters, keyframes, render_clips, music, vo, mix
aiter_linear.py    fp8 nn.Linear drop-in for wan2.2 transformer (off by default)
events.py          stage event emit (jsonl + EVENT:: stdout marker)
server.py          fastapi wrapper with sse + per-artifact endpoints
app.py             gradio app: showcase + stub generate
incidents.md       running journal of failures, root causes, fixes
benchmarks/        wan2.2 speedup table on mi300x
space/             slim showcase-only gradio app for huggingface space

incidents.md is honest. Headless violinist and AITER segfault are in there.

MIT for project code. All upstream models are Apache 2.0 / MIT - see the stack table.