Spaces:
Running
on
Zero
Running
on
Zero
bug fix
Browse files
gradio_app/gradio_3dgen.py
CHANGED
@@ -11,14 +11,14 @@ from scripts.utils import save_glb_and_video
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# from scripts.multiview_inference import geo_reconstruct
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from scripts.multiview_inference import geo_reconstruct_part1, geo_reconstruct_part2, geo_reconstruct_part3
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@spaces.GPU
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def run_mv(preview_img, input_processing, seed):
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if preview_img.size[0] <= 512:
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preview_img = run_sr_fast([preview_img])[0]
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rgb_pils, front_pil = run_mvprediction(preview_img, remove_bg=input_processing, seed=int(seed)) # 6s
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return rgb_pils, front_pil
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-
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def generate3dv2(preview_img, input_processing, seed, render_video=True, do_refine=True, expansion_weight=0.1, init_type="std"):
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if preview_img is None:
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raise gr.Error("The input image is none!")
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# from scripts.multiview_inference import geo_reconstruct
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from scripts.multiview_inference import geo_reconstruct_part1, geo_reconstruct_part2, geo_reconstruct_part3
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+
@spaces.GPU(duration=100)
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def run_mv(preview_img, input_processing, seed):
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if preview_img.size[0] <= 512:
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preview_img = run_sr_fast([preview_img])[0]
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rgb_pils, front_pil = run_mvprediction(preview_img, remove_bg=input_processing, seed=int(seed)) # 6s
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return rgb_pils, front_pil
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@spaces.GPU(duration=100) # seems split into multiple part will leads to `RuntimeError`, before fix it, still initialize here
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def generate3dv2(preview_img, input_processing, seed, render_video=True, do_refine=True, expansion_weight=0.1, init_type="std"):
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if preview_img is None:
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raise gr.Error("The input image is none!")
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scripts/multiview_inference.py
CHANGED
@@ -104,7 +104,7 @@ def geo_reconstruct(rgb_pils, normal_pils, front_pil, do_refine=False, predict_n
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########################
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import spaces
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-
@spaces.GPU
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def geo_reconstruct_part1(rgb_pils, normal_pils, front_pil, do_refine=False, predict_normal=True, expansion_weight=0.1, init_type="std"):
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if front_pil.size[0] <= 512:
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front_pil = run_sr_fast([front_pil])[0]
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@@ -149,7 +149,7 @@ def geo_reconstruct_part2(vertices, faces):
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meshes = simple_clean_mesh(to_pyml_mesh(vertices, faces), apply_smooth=True, stepsmoothnum=1, apply_sub_divide=True, sub_divide_threshold=0.25)
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return meshes
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@spaces.GPU
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def geo_reconstruct_part3(meshes, img_list):
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meshes = meshes.to("cuda")
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new_meshes = multiview_color_projection(meshes, img_list, resolution=1024, device="cuda", complete_unseen=True, confidence_threshold=0.2, cameras_list = get_cameras_list([0, 90, 180, 270], "cuda", focal=1))
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########################
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import spaces
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@spaces.GPU(duration=100)
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def geo_reconstruct_part1(rgb_pils, normal_pils, front_pil, do_refine=False, predict_normal=True, expansion_weight=0.1, init_type="std"):
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if front_pil.size[0] <= 512:
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front_pil = run_sr_fast([front_pil])[0]
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meshes = simple_clean_mesh(to_pyml_mesh(vertices, faces), apply_smooth=True, stepsmoothnum=1, apply_sub_divide=True, sub_divide_threshold=0.25)
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return meshes
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+
@spaces.GPU(duration=100)
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def geo_reconstruct_part3(meshes, img_list):
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meshes = meshes.to("cuda")
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new_meshes = multiview_color_projection(meshes, img_list, resolution=1024, device="cuda", complete_unseen=True, confidence_threshold=0.2, cameras_list = get_cameras_list([0, 90, 180, 270], "cuda", focal=1))
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