Upload folder using huggingface_hub
Browse files- .idea/workspace.xml +1 -1
- app.py +11 -3
.idea/workspace.xml
CHANGED
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@@ -54,7 +54,7 @@
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<option name="number" value="Default" />
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<option name="presentableId" value="Default" />
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<updated>1760056680813</updated>
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-
<workItem from="1760056681869" duration="
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</task>
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<servers />
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</component>
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<option name="number" value="Default" />
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<option name="presentableId" value="Default" />
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<updated>1760056680813</updated>
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+
<workItem from="1760056681869" duration="2999000" />
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</task>
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<servers />
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</component>
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app.py
CHANGED
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@@ -19,7 +19,7 @@ from scripts.camera.visualization.visualize_batch import make_perspective_figure
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from huggingface_hub import snapshot_download
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import os
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-
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repo_id="KangLiao/Puffin",
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repo_type="model",
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#filename="Puffin-Base.pth",
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@@ -27,7 +27,14 @@ local_path = snapshot_download(
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local_dir_use_symlinks=False,
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revision="main",
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)
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-
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NUM = r"[+-]?(?:\d+(?:\.\d+)?|\.\d+)(?:[eE][+-]?\d+)?"
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CAM_PATTERN = re.compile(r"(?:camera parameters.*?:|roll.*?:)\s*("+NUM+r")\s*,\s*("+NUM+r")\s*,\s*("+NUM+r")", re.IGNORECASE|re.DOTALL)
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@@ -46,7 +53,8 @@ config = Config.fromfile(config)
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model = BUILDER.build(config.model).cuda().bfloat16().eval()
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checkpoint_path = "checkpoints/Puffin-Base.pth"
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checkpoint = torch.load(checkpoint_path)
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-
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def fig_to_image(fig):
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buf = io.BytesIO()
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from huggingface_hub import snapshot_download
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import os
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_ = snapshot_download(
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repo_id="KangLiao/Puffin",
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repo_type="model",
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#filename="Puffin-Base.pth",
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local_dir_use_symlinks=False,
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revision="main",
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)
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_ = snapshot_download(
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repo_id="wusize/Puffin",
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repo_type="model",
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#filename="Puffin-Base.pth",
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local_dir="checkpoints/",
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local_dir_use_symlinks=False,
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revision="main",
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)
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NUM = r"[+-]?(?:\d+(?:\.\d+)?|\.\d+)(?:[eE][+-]?\d+)?"
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CAM_PATTERN = re.compile(r"(?:camera parameters.*?:|roll.*?:)\s*("+NUM+r")\s*,\s*("+NUM+r")\s*,\s*("+NUM+r")", re.IGNORECASE|re.DOTALL)
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model = BUILDER.build(config.model).cuda().bfloat16().eval()
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checkpoint_path = "checkpoints/Puffin-Base.pth"
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checkpoint = torch.load(checkpoint_path)
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_ = model.load_state_dict(checkpoint, strict=False)
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_ = model.vae.load_state_dict(torch.load('checkpoints/vae.pth'), strict=True)
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def fig_to_image(fig):
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buf = io.BytesIO()
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