110 lines
3.6 KiB
Python
110 lines
3.6 KiB
Python
"""Smoke test: VAE decoder under GGUF pipeline.
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Builds the pipeline, runs all encoders, initializes the scheduler, executes
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one DIT denoising step, then decodes the resulting latents back to pixel
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frames via the VAE decoder. Validates the full encode→denoise→decode path.
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Run:
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docker compose exec -e DIT_QUANT=gguf-Q4_K_M voice-chat \
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python -m tests.component.test_13_vae_decode
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"""
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from __future__ import annotations
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import os
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import sys
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import torch
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from tests.component._common import ensure_sample_avatar, get_logger, write_bytes
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log = get_logger("test_13")
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DIT_QUANT = os.environ.get("DIT_QUANT", "gguf-Q4_K_M")
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if DIT_QUANT.startswith("gguf-"):
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CONFIG_JSON = "/app/configs/lightx2v/wan22_i2v_gguf_distill.json"
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DIT_REPO = "QuantStack/Wan2.2-I2V-A14B-GGUF"
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else:
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CONFIG_JSON = "/app/configs/lightx2v/wan22_i2v_fp8_distill.json"
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DIT_REPO = "lightx2v/Wan2.2-Distill-Models"
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def run():
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try:
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from server.video_models.wan22 import Wan22Pipeline
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except ImportError as e:
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log.error("Import failed: %s", e)
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sys.exit(0)
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avatar = ensure_sample_avatar()
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log.info("Avatar: %s", avatar)
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log.info("Building pipeline (quant=%s)...", DIT_QUANT)
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pipe = Wan22Pipeline(
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base_repo="Wan-AI/Wan2.2-I2V-A14B",
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dit_repo=DIT_REPO,
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config_json=CONFIG_JSON,
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model_cls="wan2.2_moe_distill",
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resolution=480,
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fps=16,
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dit_quant_scheme=DIT_QUANT,
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t5_quantized=True,
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)
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log.info("Pipeline ready.")
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runner = pipe._runner
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# Set up input_info for a short clip
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from lightx2v.utils.input_info import update_input_info_from_dict
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update_input_info_from_dict(
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pipe._input_info_template,
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{
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"seed": 42,
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"prompt": "a person looking at the camera, natural lighting",
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"negative_prompt": "",
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"image_path": avatar,
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"target_video_length": 17, # 1 second at 16fps + 1
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},
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)
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runner.input_info = pipe._input_info_template
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# 1. Run all encoders (T5 + CLIP + VAE)
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log.info("Running all input encoders (T5 + CLIP + VAE)...")
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runner.inputs = runner.run_input_encoder()
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log.info("Encoder outputs ready.")
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# 2. Initialize run (sets up scheduler, creates noise latents)
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log.info("Initializing run (scheduler.prepare)...")
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runner.init_run()
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log.info("Initial latents: shape=%s dtype=%s",
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runner.model.scheduler.latents.shape,
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runner.model.scheduler.latents.dtype)
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# 3. Single DIT step (so we have realistic latents to decode)
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log.info("Running single DIT step...")
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runner.model.scheduler.step_pre(step_index=0)
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runner.model.infer(runner.inputs)
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runner.model.scheduler.step_post()
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latents = runner.model.scheduler.latents
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log.info("Latents after step: shape=%s dtype=%s", latents.shape, latents.dtype)
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# 4. VAE decode
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log.info("Running VAE decoder...")
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video_out = runner.run_vae_decoder(latents)
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log.info("VAE decoder output type: %s", type(video_out))
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if isinstance(video_out, torch.Tensor):
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log.info("video_out: shape=%s dtype=%s device=%s",
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video_out.shape, video_out.dtype, video_out.device)
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elif isinstance(video_out, list):
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log.info("video_out: list of %d items", len(video_out))
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if len(video_out) > 0 and isinstance(video_out[0], torch.Tensor):
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log.info(" first item: shape=%s dtype=%s", video_out[0].shape, video_out[0].dtype)
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else:
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log.info("video_out: %s", video_out)
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log.info("PASS: VAE decoder succeeded under %s pipeline.", DIT_QUANT)
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if __name__ == "__main__":
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run()
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