58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
import os
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from torch import device
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from .download_utils import load_file_from_url
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from ..model.modules.flow_comp_raft import RAFT_bi
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from ..model.recurrent_flow_completion import RecurrentFlowCompleteNet
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from ..model.propainter import InpaintGenerator
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pretrain_model_url = "https://github.com/sczhou/ProPainter/releases/download/v0.1.0/"
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def download_model(model_url: str, model_name: str) -> str:
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"""Downloads a model from a URL and returns the local path to the downloaded model."""
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return load_file_from_url(
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url=os.path.join(model_url, model_name),
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model_dir="custom_nodes/ComfyUI_ProPainter_Nodes/weights",
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progress=True,
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file_name=None,
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)
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def load_raft_model(device: device) -> RAFT_bi:
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"""Loads the RAFT bi-directional model."""
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model_path = download_model(pretrain_model_url, "raft-things.pth")
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raft_model = RAFT_bi(model_path, device)
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return raft_model
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def load_recurrent_flow_model(device: device) -> RecurrentFlowCompleteNet:
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"""Loads the Recurrent Flow Completion Network model."""
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model_path = download_model(pretrain_model_url, "recurrent_flow_completion.pth")
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flow_model = RecurrentFlowCompleteNet(model_path)
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for p in flow_model.parameters():
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p.requires_grad = False
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flow_model.to(device)
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flow_model.eval()
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return flow_model
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def load_inpaint_model(device: device) -> InpaintGenerator:
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"""Loads the Inpaint Generator model."""
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model_path = download_model(pretrain_model_url, "ProPainter.pth")
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inpaint_model = InpaintGenerator(model_path=model_path).to(device)
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inpaint_model.eval()
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return inpaint_model
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def initialize_models(
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device: device,
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) -> tuple[RAFT_bi, RecurrentFlowCompleteNet, InpaintGenerator]:
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"Return initialized inference models."
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raft_model = load_raft_model(device)
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flow_model = load_recurrent_flow_model(device)
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inpaint_model = load_inpaint_model(device)
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return raft_model, flow_model, inpaint_model
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