add duplicate spaces badge

.gitignore

@@ -1,6 +1,5 @@
 cache/
 experiments/
-*.zip
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

Dockerfile

@@ -35,7 +35,7 @@ WORKDIR ${HOME}/app
 
 RUN curl https://pyenv.run | bash
 ENV PATH=${HOME}/.pyenv/shims:${HOME}/.pyenv/bin:${PATH}
-ARG PYTHON_VERSION=3.9.16
+ENV PYTHON_VERSION=3.9.16
 RUN pyenv install ${PYTHON_VERSION} && \
     pyenv global ${PYTHON_VERSION} && \
     pyenv rehash && \
@@ -45,7 +45,7 @@ RUN pip install --no-cache-dir -U torch==1.12.1+cu113 torchvision==0.13.1+cu113
 COPY --chown=1000 requirements.txt /tmp
 RUN pip install --no-cache-dir -r /tmp/requirements.txt
 RUN pip install --no-cache-dir -U kaolin==0.13.0 -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-1.12.1_cu113.html
-RUN pip install --no-cache-dir -U gradio==3.34.0
+RUN pip install --no-cache-dir -U gradio==3.17.1
 
 COPY --chown=1000 . ${HOME}/app
 RUN cd TEXTurePaper && patch -p1 < ../patch

README.md

@@ -6,7 +6,6 @@ colorTo: red
 sdk: docker
 pinned: false
 license: mit
-suggested_hardware: a10g-small
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -2,24 +2,30 @@
 
 from __future__ import annotations
 
-import os
-
 import gradio as gr
 
 from model import Model
 
-DESCRIPTION = '''# [TEXTure](https://github.com/TEXTurePaper/TEXTurePaper)
+DESCRIPTION = '''# TEXTure
+
+This is an unofficial demo for [https://github.com/TEXTurePaper/TEXTurePaper](https://github.com/TEXTurePaper/TEXTurePaper).
 
-- This demo only accepts as input `.obj` files with less than 100,000 faces.
-- Inference takes about 10 minutes on a T4 GPU.
+This demo only accepts as input `.obj` files with less than 100,000 faces.
+
+Inference takes about 10 minutes on a T4 GPU.
 '''
-if (SPACE_ID := os.getenv('SPACE_ID')) is not None:
-    DESCRIPTION += f'\n<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>'
 
 model = Model()
 
 with gr.Blocks(css='style.css') as demo:
     gr.Markdown(DESCRIPTION)
+
+    gr.HTML("""
+<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
+<br/>
+<a href="https://huggingface.co/spaces/hysts/TEXTure?duplicate=true">
+<img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
+<p/>""")
     with gr.Row():
         with gr.Column():
             input_shape = gr.Model3D(label='Input 3D mesh')
@@ -36,15 +42,8 @@ with gr.Blocks(css='style.css') as demo:
                                        step=0.1)
             run_button = gr.Button('Run')
         with gr.Column():
-            progress_text = gr.Text(label='Progress')
-            with gr.Tabs():
-                with gr.TabItem(label='Images from each viewpoint'):
-                    viewpoint_images = gr.Gallery(show_label=False).style(
-                        columns=4, height='auto')
-                with gr.TabItem(label='Result 3D model'):
-                    result_3d_model = gr.Model3D(show_label=False)
-                with gr.TabItem(label='Output mesh file'):
-                    output_file = gr.File(show_label=False)
+            result = gr.Video(label='Result')
+            output_file = gr.File(label='Output mesh file')
     with gr.Row():
         examples = [
             ['shapes/dragon1.obj', 'a photo of a dragon', 0, 7.5],
@@ -61,7 +60,7 @@ with gr.Blocks(css='style.css') as demo:
                         guidance_scale,
                     ],
                     outputs=[
-                        result_3d_model,
+                        result,
                         output_file,
                     ],
                     cache_examples=False)
@@ -74,10 +73,8 @@ with gr.Blocks(css='style.css') as demo:
                          guidance_scale,
                      ],
                      outputs=[
-                         viewpoint_images,
-                         result_3d_model,
+                         result,
                          output_file,
-                         progress_text,
                      ])
 
-demo.queue(max_size=5).launch(debug=True)
+demo.queue(max_size=5).launch()

model.py

@@ -5,10 +5,8 @@ import pathlib
 import shlex
 import subprocess
 import sys
-from typing import Generator, Optional
 
 import gradio as gr
-import trimesh
 
 sys.path.append('TEXTurePaper')
 
@@ -50,10 +48,8 @@ class Model:
         subprocess.run(shlex.split(f'zip -r {out_path} {mesh_dir}'))
         return out_path
 
-    def run(
-        self, shape_path: str, text: str, seed: int, guidance_scale: float
-    ) -> Generator[tuple[list[str], Optional[str], Optional[str], str], None,
-                   None]:
+    def run(self, shape_path: str, text: str, seed: int,
+            guidance_scale: float) -> tuple[str, str]:
         if not shape_path.endswith('.obj'):
             raise gr.Error('The input file is not .obj file.')
         if not self.check_num_faces(shape_path):
@@ -61,34 +57,7 @@ class Model:
 
         config = self.load_config(shape_path, text, seed, guidance_scale)
         trainer = TEXTure(config)
-
-        trainer.mesh_model.train()
-
-        total_steps = len(trainer.dataloaders['train'])
-        for step, data in enumerate(trainer.dataloaders['train'], start=1):
-            trainer.paint_step += 1
-            trainer.paint_viewpoint(data)
-            trainer.evaluate(trainer.dataloaders['val'],
-                             trainer.eval_renders_path)
-            trainer.mesh_model.train()
-
-            sample_image_dir = config.log.exp_dir / 'vis' / 'eval'
-            sample_image_paths = sorted(
-                sample_image_dir.glob(f'step_{trainer.paint_step:05d}_*.jpg'))
-            sample_image_paths = [
-                path.as_posix() for path in sample_image_paths
-            ]
-            yield sample_image_paths, None, None, f'{step}/{total_steps}'
-
-        trainer.mesh_model.change_default_to_median()
-
-        save_dir = trainer.exp_path / 'mesh'
-        save_dir.mkdir(exist_ok=True, parents=True)
-        trainer.mesh_model.export_mesh(save_dir)
-        model_path = save_dir / 'mesh.obj'
-        mesh = trimesh.load(model_path)
-        mesh_path = save_dir / 'mesh.glb'
-        mesh.export(mesh_path, file_type='glb')
-
+        trainer.paint()
+        video_path = config.log.exp_dir / 'results' / 'step_00010_rgb.mp4'
         zip_path = self.zip_results(config.log.exp_dir)
-        yield sample_image_paths, mesh_path.as_posix(), zip_path, 'Done!'
+        return video_path.as_posix(), zip_path

patch

@@ -28,7 +28,7 @@ index 610d2de..e172080 100644
 -            with open('./TOKEN', 'r') as f:
 -                self.token = f.read().replace('\n', '')  # remove the last \n!
 -                logger.info(f'loaded hugging face access token from ./TOKEN!')
-+            self.token = os.getenv('HF_TOKEN')
++            self.token = os.environ['HF_TOKEN']
          except FileNotFoundError as e:
              self.token = True
              logger.warning(

requirements.txt

@@ -1,16 +1,15 @@
-accelerate==0.19.0
-diffusers==0.16.1
-einops==0.6.1
-huggingface-hub==0.15.1
-imageio[ffmpeg,pyav]==2.28.1
-loguru==0.7.0
-matplotlib==3.7.1
+accelerate==0.16.0
+diffusers==0.12.1
+einops==0.6.0
+huggingface-hub==0.12.0
+imageio[ffmpeg,pyav]==2.25.0
+loguru==0.6.0
+matplotlib==3.6.3
 ninja==1.11.1
-opencv-python-headless==4.7.0.72
+opencv-python-headless==4.7.0.68
 pyrallis==0.3.1
 torch==1.12.1
 torchvision==0.13.1
-tqdm==4.65.0
-transformers==4.29.1
-trimesh==3.21.6
+tqdm==4.64.1
+transformers==4.26.0
 xatlas==0.0.7