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LUT/plasma.cube

@@ -0,0 +1,258 @@
+TITLE "Colormap plasma"
+LUT_1D_SIZE 256
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LUT/prism.cube

@@ -0,0 +1,258 @@
+TITLE "Colormap prism"
+LUT_1D_SIZE 256
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+0.0051334 0.6770625 0.3295914
+0.0000000 0.4963115 0.5859728
+0.0000000 0.3045949 0.8037184
+0.0000000 0.1145534 0.9684715
+0.0301581 0.0000000 1.0000000
+0.1508926 0.0000000 1.0000000
+0.3058541 0.0000000 1.0000000
+0.4848254 0.0000000 0.9457798
+0.6760059 0.0000000 0.7715638
+0.8667905 0.0000000 0.5464753
+1.0000000 0.0000000 0.2853554
+1.0000000 0.0000000 0.0054208
+1.0000000 0.0000000 0.0000000
+1.0000000 0.1260862 0.0000000
+1.0000000 0.3166027 0.0000000
+1.0000000 0.5080026 0.0000000
+1.0000000 0.6876660 0.0000000
+1.0000000 0.8437470 0.0000000
+1.0000000 0.9659545 0.0000000
+0.8925165 1.0000000 0.0000000
+0.7027915 1.0000000 0.0000000
+0.5109044 1.0000000 0.0000000
+0.3295071 0.9982549 0.0000000

app.py

@@ -32,7 +32,11 @@ import logging
 import gc
 
 # Import functions from modules
-from utils.file_utils import cleanup_temp_files, get_file_parts
+from utils.file_utils import (
+    cleanup_temp_files, 
+    get_file_parts, 
+    generate_permalink_from_urls
+)
 
 from utils.color_utils import (
     hex_to_rgb,
@@ -800,7 +804,7 @@ def load_trellis_model():
     loaded = False
     if TRELLIS_PIPELINE == None:
         try:
-            TRELLIS_PIPELINE = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
+            TRELLIS_PIPELINE = TrellisImageTo3DPipeline.from_pretrained("jetx/TRELLIS-image-large")
             TRELLIS_PIPELINE.cuda()
             # Preload with a dummy image to finalize initialization
             try:
@@ -1143,6 +1147,33 @@ def extract_gaussian(state: dict, req: gr.Request, progress=gr.Progress(track_tq
     torch.cuda.ipc_collect()
     return gaussian_path, gaussian_path
 
+def update_permalink(glb, gaussian, depth_out, depth_src_file):
+    """
+    Determines the smaller file (glb or gaussian) in bytes and uses that file along with
+    the depth output and the file used to generate the 3D model (depth_src_file) to
+    create a permalink URL.
+    
+    Parameters:
+        glb (str): Path to the extracted GLB file.
+        gaussian (str): Path to the extracted Gaussian file.
+        depth_out (str): File path for the depth image.
+        depth_src_file (str): File path for the image used to generate the 3D model.
+    
+    Returns:
+        tuple: A tuple of (update object for permalink_row, permalink URL string).
+    """
+    file_candidates = {}
+    if glb and os.path.exists(glb):
+        file_candidates[glb] = os.path.getsize(glb)
+    if gaussian and os.path.exists(gaussian):
+        file_candidates[gaussian] = os.path.getsize(gaussian)
+
+    if not file_candidates:
+        return gr.update(visible=False), ""
+    
+    smallest_file = min(file_candidates, key=file_candidates.get)
+    permalink = generate_permalink_from_urls(smallest_file, depth_out, depth_src_file)
+    return gr.update(visible=True), permalink
 
 @spaces.GPU()
 def getVersions():
@@ -1564,6 +1595,9 @@ with gr.Blocks(css_paths="style_20250314.css", title=title, theme='Surn/beeuty',
                             gr.Markdown("""
                             ### Files over 10 MB may not display in the 3D model viewer
                             """, elem_id="file_size_info", elem_classes="intro" )
+                            with gr.Row(visible=False) as permalink_row:
+                                permalink_button = gr.Button("View in Permalink", elem_classes="solid small centered", variant="secondary")
+                                permalink_text = gr.Textbox(label="Permalink", elem_classes="solid small centered")
 
     is_multiimage = gr.State(False)
     output_buf = gr.State()
@@ -1811,6 +1845,10 @@ with gr.Blocks(css_paths="style_20250314.css", title=title, theme='Surn/beeuty',
     ).then(
         lambda: gr.Button(interactive=True),
         outputs=[glb_file]
+    ).then(
+        fn=update_permalink,
+        inputs=[glb_file, gaussian_file, depth_output, ddd_image_path],  # ddd_image_path now holds the source file
+        outputs=[permalink_row, permalink_text]
     )
 
     extract_gaussian_btn.click(
@@ -1820,6 +1858,10 @@ with gr.Blocks(css_paths="style_20250314.css", title=title, theme='Surn/beeuty',
     ).then(
         lambda: gr.Button(interactive=True),
         outputs=[gaussian_file]
+    ).then(
+        fn=update_permalink,
+        inputs=[glb_file, gaussian_file, depth_output, ddd_image_path],  # Use the actual depth source file here
+        outputs=[permalink_row, permalink_text]
     )
 
 if __name__ == "__main__":
@@ -1854,7 +1896,7 @@ if __name__ == "__main__":
     # image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large")
     # depth_model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large", ignore_mismatched_sizes=True)
     if constants.IS_SHARED_SPACE:
-        TRELLIS_PIPELINE = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
+        TRELLIS_PIPELINE = TrellisImageTo3DPipeline.from_pretrained("jetx/TRELLIS-image-large")
         TRELLIS_PIPELINE.to(device)
         try:
            TRELLIS_PIPELINE.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)), 512, True)    # Preload rembg

requirements.txt

@@ -19,7 +19,7 @@ sentencepiece
 --extra-index-url https://download.pytorch.org/whl/cu124
 torch
 torchvision
-xformers #==0.0.29.post3
+xformers==0.0.29.post3
 
 # Other dependencies
 Haishoku
@@ -65,13 +65,13 @@ xatlas==0.0.9
 pyvista==0.44.2
 pymeshfix==0.17.0
 igraph==0.11.8
-git+https://github.com/EasternJournalist/utils3d.git@9a4eb15e4021b67b12c460c7057d642626897ec8
+git+https://github.com/EasternJournalist/utils3d.git@3913c65d81e05e47b9f367250cf8c0f7462a0900
 spconv-cu124==2.3.8
 gradio_litmodel3d==0.0.1
 #linux only
 https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.3/flash_attn-2.7.3+cu12torch2.6cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
-https://huggingface.co/spaces/JeffreyXiang/TRELLIS/resolve/main/wheels/diff_gaussian_rasterization-0.0.0-cp310-cp310-linux_x86_64.whl?download=true
-https://huggingface.co/spaces/JeffreyXiang/TRELLIS/resolve/main/wheels/nvdiffrast-0.3.3-cp310-cp310-linux_x86_64.whl?download=true
+https://huggingface.co/spaces/Surn/HexaGrid/resolve/main/wheels/diff_gaussian_rasterization-0.0.0-cp310-cp310-linux_x86_64.whl?download=true
+https://huggingface.co/spaces/Surn/Hexagrid/resolve/main/wheels/nvdiffrast-0.3.3-cp310-cp310-linux_x86_64.whl?download=true
 #Windows only
 #https://huggingface.co/spaces/Surn/HexaGrid/main/wheels/flash_attn-2.7.4.post1-cp312-cp312-win_amd64.whl?download=true
 #https://huggingface.co/spaces/Surn/HexaGrid/main/wheels/diff_gaussian_rasterization-0.0.0-cp312-cp312-win_amd64.whl?download=true

utils/file_utils.py

@@ -103,4 +103,23 @@ def get_unique_file_path(directory, filename, file_ext, counter=0):
         return get_unique_file_path(directory, filename, file_ext, counter + 1)
 
 # Example usage:
-# new_file_path = get_unique_file_path(video_dir, title_file_name, video_new_ext)
+# new_file_path = get_unique_file_path(video_dir, title_file_name, video_new_ext)
+
+def generate_permalink_from_urls(model_url, hm_url, img_url, permalink_viewer_url="surn-3d-viewer.hf.space"):
+    """
+    Constructs and returns a permalink URL with query string parameters for the viewer.
+    Each parameter is passed separately so that the image positions remain consistent.
+    
+    Parameters:
+        model_url (str): Processed URL for the 3D model.
+        hm_url (str): Processed URL for the height map image.
+        img_url (str): Processed URL for the main image.
+        permalink_viewer_url (str): The base viewer URL.
+    
+    Returns:
+        str: The generated permalink URL.
+    """
+    import urllib.parse
+    params = {"3d": model_url, "hm": hm_url, "image": img_url}
+    query_str = urllib.parse.urlencode(params)
+    return f"https://{permalink_viewer_url}/?{query_str}"