path

app.py

@@ -15,6 +15,7 @@ from michelangelo.utils.visualizers import html_util
 
 import gradio as gr
 
+from huggingface_hub import snapshot_download
 
 gradio_cached_dir = "./gradio_cached_dir"
 os.makedirs(gradio_cached_dir, exist_ok=True)
@@ -32,7 +33,7 @@ image_model_config_dict = OrderedDict({
         # "config": "./configs/image_cond_diffuser_asl/image-ASLDM-256.yaml",
         # "ckpt_path": "./checkpoints/image_cond_diffuser_asl/image-ASLDM-256.ckpt",
         "config": "/home/user/app/configs/image_cond_diffuser_asl/image-ASLDM-256.yaml",
-        "ckpt_path": "/home/user/app/checkpoints/image_cond_diffuser_asl/image-ASLDM-256.ckpt",
+        "ckpt_path": "image_cond_diffuser_asl/image-ASLDM-256.ckpt",
     },
 })
 
@@ -40,11 +41,13 @@ text_model_config_dict = OrderedDict({
     "ASLDM-256": {
         # "config": "./configs/text_cond_diffuser_asl/text-ASLDM-256.yaml",
         # "ckpt_path": "./checkpoints/text_cond_diffuser_asl/text-ASLDM-256.ckpt",
-        "config": "/home/user/app/configs/text_cond_diffuser_asl/text-ASLDM-256.yaml",
-        "ckpt_path": "/home/user/app/checkpoints/text_cond_diffuser_asl/text-ASLDM-256.ckpt",
+        "config": "./configs/text_cond_diffuser_asl/text-ASLDM-256.yaml",
+        "ckpt_path": "text_cond_diffuser_asl/text-ASLDM-256.ckpt",
     },
 })
 
+model_path = snapshot_download(repo_id="Maikou/Michelangelo/tree/main/checkpoints")
+
 class InferenceModel(object):
     model = None
     name = ""
@@ -115,7 +118,9 @@ def load_model(model_name: str, model_config_dict: dict, inference_model: Infere
         if hasattr(model_config, "model"):
             model_config = model_config.model
 
-        model = instantiate_from_config(model_config, ckpt_path=config_ckpt_path["ckpt_path"])
+        ckpt_path = os.path.join(model_path, config_ckpt_path["ckpt_path"])
+
+        model = instantiate_from_config(model_config, ckpt_path=ckpt_path)
         model = model.to(device)
         model = model.eval()
 
@@ -313,7 +318,7 @@ with gr.Blocks() as app:
     gr.Markdown("2. Note that the Image-conditioned model is trained on multiple 3D datasets like ShapeNet and Objaverse")
     gr.Markdown("3. We provide some examples for you to try. You can also upload images or text as input.")
     gr.Markdown("4. Welcome to share your amazing results with us, and thanks for your interest in our work!")
-    print(os.path.abspath(os.path.dirname(__file__)))     
+    
     with gr.Row():
         with gr.Column():
             
@@ -363,6 +368,8 @@ with gr.Blocks() as app:
            
         img.upload(disable_cache, outputs=cache_dir)
         examples.select(set_cache, outputs=[img, cache_dir])
+        print(os.path.abspath(os.path.dirname(__file__)))     
+        print(model_path)
         print(f'line:404: {cache_dir}')
         btn_generate_img2obj.click(image2mesh, inputs=[img, image_dropdown_models, num_samples,
                                                        guidance_scale, octree_depth],