working ok

tests/README.md

@@ -9,25 +9,45 @@ python -m pytest tests/unit -v
 ```
 
 These exercise pure logic: config parsing, prompt derivation, LoRA spec
-parsing, frame-length fitting, library round-robin selection. They do not
-touch CUDA, Wan2.2, MuseTalk, or ffmpeg. Safe to run on Windows, outside
-Docker, without any models installed.
+parsing, frame-length fitting, library round-robin selection, the
+pipeline's video branch, and ffmpeg mux argument shaping. They do not
+touch CUDA, Wan2.2, MuseTalk, or a real ffmpeg binary. Safe to run on
+Windows, outside Docker, without any models installed.
+
+Current unit files:
+
+- `test_video_config.py` — `VideoConfig.from_dict` round-trip, LoRA target validation
+- `test_video_engine_logic.py` — prompt derivation, library cursor, frame fitting
+- `test_pipeline_video_branch.py` — pipeline takes the video path iff engine is ready
+- `test_musetalk_fit_frames.py` — frame-length adjustment to match audio duration
+- `test_muxer_ffmpeg.py` — ffmpeg command construction
 
 ## Component tests — slow, GPU-required, run inside Docker
 
-Each script in `tests/component/` exercises one subsystem end-to-end against
-the real models. They are ordered to match the implementation phases:
+Each script in `tests/component/` exercises one subsystem end-to-end
+against the real models. The numbered prefix reflects the implementation
+phase each script gates, and also serves as a reasonable run order when
+debugging a fresh environment:
 
 | Script | Phase | Tests |
 |---|---|---|
 | `test_01_video_skeleton.py` | 1 | VideoEngine loads, config gate respected |
 | `test_02_wan22_loras.py` | 2 | Wan2.2 pipeline loads, LoRA stack applies |
-| `test_03_idle_clip.py` | 3 | set_avatar → idle MP4, written to disk for eyeballing |
+| `test_03_idle_clip.py` | 3 | `set_avatar` → idle MP4, written to disk for eyeballing |
 | `test_04_library_prebake.py` | 4 | library mode pre-bakes N base clips |
 | `test_05_musetalk_lipsync.py` | 5 | MuseTalk lip-sync on library frames + ffmpeg mux |
 | `test_06_reflective.py` | 6 | reflective mode: fresh Wan2.2 per reply |
 | `test_07_endpoints.py` | 7 | HTTP endpoints return sane responses |
-| `test_08_lora_reload.py` | 8 | /api/reload-loras swaps LoRAs live |
+| `test_08_lora_reload.py` | 8 | `/api/reload-loras` swaps LoRAs live |
+| `test_09_gguf_generate.py` | 9 | GGUF-quantised DIT end-to-end I2V generation |
+| `test_10_t5_encode.py` | 10 | T5 encoder (optionally fp8-quantised) on CUDA |
+| `test_11_image_encode.py` | 11 | Avatar image → VAE latent path |
+| `test_12_dit_single_step.py` | 12 | Single DIT step on the loaded expert(s) |
+| `test_13_vae_decode.py` | 13 | VAE decode back to RGB frames |
+
+Tests 09-13 are focused on the GGUF + Blackwell (SM120) path and are how
+new quant schemes / attention backends get validated before wiring them
+into the full pipeline.
 
 Run one:
 
@@ -36,7 +56,7 @@ Run one:
 docker compose exec voice-chat python -m tests.component.test_03_idle_clip
 ```
 
-Run all (slow, ~20+ minutes on 5090):
+Run all (slow, ~20+ minutes on a 5090):
 
 ```
 docker compose exec voice-chat python -m tests.component.run_all