update

.gitignore

@@ -1,2 +1,4 @@
 __pycache__
-data
+data
+examples
+.gradio

app_3d.py

@@ -0,0 +1,21 @@
+import gradio as gr
+import os
+
+def load_mesh(mesh_file_name):
+    return mesh_file_name
+
+demo = gr.Interface(
+    fn=load_mesh,
+    inputs=gr.Model3D(),
+    outputs=gr.Model3D(
+            clear_color=(255.0, 0.0, 0.0, 0.0),  label="3D Model", display_mode="wireframe"),
+    examples=[
+        [os.path.join(os.path.dirname(__file__), "examples/bunny/mesh.obj")],
+        [os.path.join(os.path.dirname(__file__), "examples/monkey/mesh.obj")],
+        [os.path.join(os.path.dirname(__file__), "examples/Bunny.obj")],
+    ],
+    cache_examples=True
+)
+
+if __name__ == "__main__":
+    demo.launch()

app_texnet.py

@@ -1,6 +1,11 @@
 #!/usr/bin/env python
 
+import os
+import shutil
+import tempfile
 import gradio as gr
+from PIL import Image
+import numpy as np
 
 from settings import (
     DEFAULT_IMAGE_RESOLUTION,
@@ -11,6 +16,77 @@ from settings import (
 )
 from utils import randomize_seed_fn
 
+# ---- helper to build a quick textured copy of the mesh ---------------
+def apply_texture(src_mesh:str, texture:str, tag:str)->str:
+    """
+    Writes a copy of `src_mesh` and tiny .mtl that points to `texture`.
+    Returns the new OBJ/GLB path for viewing.
+    """
+    tmp_dir = tempfile.mkdtemp()
+    mesh_copy = os.path.join(tmp_dir, f"{tag}.obj")
+    mtl_name  = f"{tag}.mtl"
+
+    # copy geometry
+    shutil.copy(src_mesh, mesh_copy)
+
+    # write minimal MTL
+    with open(os.path.join(tmp_dir, mtl_name), "w") as f:
+        f.write(f"newmtl material_0\nmap_Kd {os.path.basename(texture)}\n")
+
+    # ensure texture lives next to OBJ
+    shutil.copy(texture, os.path.join(tmp_dir, os.path.basename(texture)))
+
+    # patch OBJ to reference our new MTL
+    with open(mesh_copy, "r+") as f:
+        lines = f.readlines()
+        if not lines[0].startswith("mtllib"):
+            lines.insert(0, f"mtllib {mtl_name}\n")
+            f.seek(0); f.writelines(lines)
+
+    return mesh_copy
+
+def image_to_temp_path(img_like, tag):
+    """
+    Convert various image-like objects (str, PIL.Image, list, tuple) to temp PNG path.
+    Returns the path to the saved image file.
+    """
+    # Handle tuple or list input
+    if isinstance(img_like, (list, tuple)):
+        if len(img_like) == 0:
+            raise ValueError("Empty image list/tuple.")
+        img_like = img_like[0]
+
+    # If it's already a file path
+    if isinstance(img_like, str):
+        return img_like
+
+    # If it's a PIL Image
+    if isinstance(img_like, Image.Image):
+        temp_path = os.path.join(tempfile.mkdtemp(), f"{tag}.png")
+        img_like.save(temp_path)
+        return temp_path
+
+    # if it's numpy array
+    if isinstance(img_like, np.ndarray):
+        temp_path = os.path.join(tempfile.mkdtemp(), f"{tag}.png")
+        img_like = Image.fromarray(img_like)
+        img_like.save(temp_path)
+        return temp_path
+
+    raise ValueError(f"Expected PIL.Image, str, list, or tuple — got {type(img_like)}")
+
+def show_mesh(which, mesh, inp, coarse, fine):
+    """Switch the displayed texture based on dropdown change."""
+    print()
+    tex_map = {
+        "Input": image_to_temp_path(inp, "input"),
+        "Coarse": coarse[0] if isinstance(coarse, tuple) else coarse,
+        "Fine": fine[0] if isinstance(fine, tuple) else fine,
+    }
+    texture_path = tex_map[which]
+    return apply_texture(mesh, texture_path, which.lower())
+# ----------------------------------------------------------------------
+
 
 def create_demo(process):
     with gr.Blocks() as demo:
@@ -39,7 +115,14 @@ def create_demo(process):
                         value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
                     )
             with gr.Column():
-                result = gr.Gallery(label="Output", show_label=False, columns=2, object_fit="scale-down")
+                result_coarse = gr.Gallery(label="Output Coarse", show_label=True, columns=2, object_fit="scale-down")
+                result_fine = gr.Gallery(label="Output Fine", show_label=True, columns=2, object_fit="scale-down")
+                # mesh_viewer = gr.Model3D(label="Textured Mesh", clear_color=[0, 0, 0, 0], value="examples/monkey/mesh.obj")
+
+        # radio buttons let the user toggle which texture to view
+        # texture_choice = gr.Radio(["Input", "Coarse", "Fine"], label="Preview texture", value="Input")
+        # mesh_path_state = gr.State("examples/bunny/mesh.obj")
+
         inputs = [
             image,
             prompt,
@@ -51,6 +134,8 @@ def create_demo(process):
             guidance_scale,
             seed,
         ]
+        
+        # first call → run diffusion / texture network
         prompt.submit(
             fn=randomize_seed_fn,
             inputs=[seed, randomize_seed],
@@ -60,19 +145,25 @@ def create_demo(process):
         ).then(
             fn=process,
             inputs=inputs,
-            outputs=result,
+            outputs=[result_coarse, result_fine],
             api_name="canny",
             concurrency_id="main",
         )
+        # .then(
+        #     fn=show_mesh,
+        #     inputs=[texture_choice, mesh_path_state, image, result_coarse, result_fine],
+        #     outputs=mesh_viewer,
+        #     queue=False,
+        #     api_name=False,
+        # )
 
-        # TODO: setup several example images
         gr.Examples(
             fn=process,
             inputs=inputs,
-            outputs=result,
+            outputs=[result_coarse, result_fine],
             examples=[
                 [
-                    "examples/uv_normal.png", 
+                    "examples/bunny/uv_normal.png", # /dgxusers/Users/jyang/project/ObjectReal/data/control/preprocess/bunny/uv_normal/fused.png
                     "feather", 
                     a_prompt.value, 
                     n_prompt.value,
@@ -82,6 +173,17 @@ def create_demo(process):
                     guidance_scale.value,
                     seed.value,
                 ],
+                [
+                    "examples/monkey/uv_normal.png", # /dgxusers/Users/jyang/project/ObjectReal/data/control/preprocess/monkey/uv_normal/fused.png
+                    "wood", 
+                    a_prompt.value, 
+                    n_prompt.value,
+                    num_samples.value,
+                    image_resolution.value,
+                    num_steps.value,
+                    guidance_scale.value,
+                    seed.value,
+                ],
             ],
         )
     return demo

model.py

@@ -220,7 +220,7 @@ class Model:
         # restore the original pipe
         self.pipe = self.pipe_backup
 
-        return [control_image, *results_fine, control_image, *results_coarse,]
+        return [*results_coarse], [*results_fine]
 
     # @spaces.GPU #[uncomment to use ZeroGPU]
     @torch.inference_mode()