Commit
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8690d62
1
Parent(s):
cc413d0
Refactor generate3d function in inference.py to improve readability by removing unnecessary blank lines. Update requirements.txt to remove gradio and numpy versions for cleaner dependency management.
Browse files- inference.py +1 -5
- requirements.txt +1 -3
inference.py
CHANGED
@@ -7,8 +7,8 @@ import tempfile
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from mesh import Mesh
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import zipfile
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from util.renderer import Renderer
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-
def generate3d(model, rgb, ccm, device):
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model.renderer = Renderer(tet_grid_size=model.tet_grid_size, camera_angle_num=model.camera_angle_num,
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scale=model.input.scale, geo_type = model.geo_type)
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@@ -17,7 +17,6 @@ def generate3d(model, rgb, ccm, device):
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color = color_tri.permute(2,0,1)
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xyz = xyz_tri.permute(2,0,1)
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-
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def get_imgs(color):
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# color : [C, H, W*6]
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color_list = []
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@@ -38,7 +37,6 @@ def generate3d(model, rgb, ccm, device):
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# 3D visualize
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model.eval()
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-
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if model.denoising == True:
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tnew = 20
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tnew = torch.randint(tnew, tnew+1, [triplane.shape[0]], dtype=torch.long, device=triplane.device)
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@@ -53,7 +51,6 @@ def generate3d(model, rgb, ccm, device):
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else:
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triplane_feature2 = model.unet2(triplane)
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-
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with torch.no_grad():
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data_config = {
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'resolution': [1024, 1024],
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@@ -65,7 +62,6 @@ def generate3d(model, rgb, ccm, device):
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data_config['verts'] = verts[0]
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data_config['faces'] = faces
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-
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from kiui.mesh_utils import clean_mesh
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verts, faces = clean_mesh(data_config['verts'].squeeze().cpu().numpy().astype(np.float32), data_config['faces'].squeeze().cpu().numpy().astype(np.int32), repair = False, remesh=True, remesh_size=0.005, remesh_iters=1)
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data_config['verts'] = torch.from_numpy(verts).cuda().contiguous()
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from mesh import Mesh
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import zipfile
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from util.renderer import Renderer
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+
def generate3d(model, rgb, ccm, device):
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model.renderer = Renderer(tet_grid_size=model.tet_grid_size, camera_angle_num=model.camera_angle_num,
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scale=model.input.scale, geo_type = model.geo_type)
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color = color_tri.permute(2,0,1)
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xyz = xyz_tri.permute(2,0,1)
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def get_imgs(color):
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# color : [C, H, W*6]
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color_list = []
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# 3D visualize
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model.eval()
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if model.denoising == True:
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tnew = 20
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tnew = torch.randint(tnew, tnew+1, [triplane.shape[0]], dtype=torch.long, device=triplane.device)
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else:
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triplane_feature2 = model.unet2(triplane)
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with torch.no_grad():
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data_config = {
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'resolution': [1024, 1024],
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data_config['verts'] = verts[0]
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data_config['faces'] = faces
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from kiui.mesh_utils import clean_mesh
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verts, faces = clean_mesh(data_config['verts'].squeeze().cpu().numpy().astype(np.float32), data_config['faces'].squeeze().cpu().numpy().astype(np.int32), repair = False, remesh=True, remesh_size=0.005, remesh_iters=1)
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data_config['verts'] = torch.from_numpy(verts).cuda().contiguous()
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requirements.txt
CHANGED
@@ -1,12 +1,10 @@
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gradio
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pydantic-core==2.22.0
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huggingface-hub==0.19.4
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diffusers==0.24.0
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einops==0.7.0
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Pillow==10.1.0
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transformers==4.27.1
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open-clip-torch==2.7.0
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numpy==1.24.3
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opencv-contrib-python-headless==4.9.0.80
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opencv-python-headless==4.9.0.80
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xformers
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+
gradio
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huggingface-hub==0.19.4
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diffusers==0.24.0
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einops==0.7.0
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Pillow==10.1.0
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transformers==4.27.1
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open-clip-torch==2.7.0
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opencv-contrib-python-headless==4.9.0.80
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opencv-python-headless==4.9.0.80
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xformers
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