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Update api/ltx/ltx_utils.py
Browse files- api/ltx/ltx_utils.py +7 -2
api/ltx/ltx_utils.py
CHANGED
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@@ -75,17 +75,22 @@ def build_ltx_pipeline_on_cpu(config: Dict) -> Tuple[LTXVideoPipeline, Optional[
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t0 = time.perf_counter()
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logging.info("Building LTX pipeline on CPU...")
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if not ckpt_path.is_file():
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raise FileNotFoundError(f"Main checkpoint file not found: {ckpt_path}")
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with safe_open(ckpt_path, framework="pt") as f:
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metadata = f.metadata() or {}
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config_str = metadata.get("config", "{}")
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configs = json.loads(config_str)
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allowed_inference_steps = configs.get("allowed_inference_steps")
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vae = CausalVideoAutoencoder.from_pretrained(ckpt_path).to("cpu")
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transformer = Transformer3DModel.from_pretrained(ckpt_path).to("cpu")
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scheduler = RectifiedFlowScheduler.from_pretrained(ckpt_path)
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t0 = time.perf_counter()
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logging.info("Building LTX pipeline on CPU...")
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ckpt_path = hf_hub_download(repo_id=LTX_REPO_ID, filename=config["checkpoint_path"], cache_dir=CACHE_DIR)
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if not ckpt_path.is_file():
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raise FileNotFoundError(f"Main checkpoint file not found: {ckpt_path}")
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with safe_open(ckpt_path, framework="pt") as f:
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metadata = f.metadata() or {}
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config_str = metadata.get("config", "{}")
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configs = json.loads(config_str)
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allowed_inference_steps = configs.get("allowed_inference_steps")
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vae = CausalVideoAutoencoder.from_pretrained(ckpt_path).to("cpu")
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transformer = Transformer3DModel.from_pretrained(ckpt_path).to("cpu")
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scheduler = RectifiedFlowScheduler.from_pretrained(ckpt_path)
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