Fixing Spaces Issues

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
-
 import os
+
 # Import constants
 import numpy as np
 import torch
@@ -18,7 +18,7 @@ import random
 #import accelerate 
 from transformers import AutoTokenizer , DPTImageProcessor, DPTForDepthEstimation
 from pathlib import Path
-import open3d as o3d
+
 import logging
 #logging.getLogger("transformers.modeling_utils").setLevel(logging.ERROR)
 import gc
@@ -68,12 +68,7 @@ from utils.excluded_colors import (
 # from utils.ai_generator import (
 #     generate_ai_image,
 # )
-from utils.version_info import (
-    versions_html,
-    #initialize_cuda,
-    #release_torch_resources,
-    #get_torch_info
-)
+
 from utils.lora_details import (
     upd_prompt_notes,
     split_prompt_precisely,
@@ -89,11 +84,13 @@ PIPELINE_CLASSES = {
 }
 
 import spaces
-#-------------- ------------------------------------------------MODEL INITIALIZATION------------------------------------------------------------#
-# Load models once during module import
-image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large",)
-depth_model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large", ignore_mismatched_sizes=True)
 
+from utils.version_info import (
+    versions_html,
+    #initialize_cuda,
+    #release_torch_resources,
+    #get_torch_info
+)
 
 input_image_palette = []
 current_prerendered_image = gr.State("./images/images/Beeuty-1.png")
@@ -303,11 +300,11 @@ class Condition(object):
         type_id = torch.ones_like(ids[:, :1]) * self.type_id
         return tokens, ids, type_id
 
-@spaces.GPU(duration=140, progress=gr.Progress(track_tqdm=True))
-def generate_image(pipe, generate_params, progress=gr.Progress(track_tqdm=True)):
-    return pipe(**generate_params)
+# @spaces.GPU(duration=140, progress=gr.Progress(track_tqdm=True))
+# def generate_image(pipe, generate_params, progress=gr.Progress(track_tqdm=True)):
+#     return pipe(**generate_params)
 
-@spaces.GPU()
+#@spaces.GPU(duration=140, progress=gr.Progress(track_tqdm=True))
 def generate_image_lowmem(
     text,
     neg_prompt=None,
@@ -509,7 +506,7 @@ def generate_image_lowmem(
         generate_params = {k: v for k, v in generate_params.items() if v is not None}
         print(f"generate_params: {generate_params}")
         # Generate the image
-        result = generate_image(pipe,generate_params)
+        result = pipe(**generate_params) #generate_image(pipe,generate_params)
         image = result.images[0]
         # Clean up
         del result
@@ -606,7 +603,7 @@ def generate_ai_image_local (
         #gc.collect()
         return None
 
-#@spaces.GPU(duration=140)
+#@spaces.GPU(duration=140,progress=gr.Progress(track_tqdm=True))
 def generate_input_image_click(map_option, prompt_textbox_value, negative_prompt_textbox_value, model_textbox_value, randomize_seed=True, seed=None, use_conditioned_image=False, strength=0.5, image_format="16:9", scale_factor=(8/3), progress=gr.Progress(track_tqdm=True)):
     if randomize_seed:
         seed = random.randint(0, constants.MAX_SEED)
@@ -661,7 +658,7 @@ def generate_input_image_click(map_option, prompt_textbox_value, negative_prompt
         upscaled_image.save(tmp_upscaled.name, format="PNG")
         constants.temp_files.append(tmp_upscaled.name)
         print(f"Upscaled image saved to {tmp_upscaled.name}")
-    #gc.collect()
+    gc.collect()
     # Return the path of the upscaled image
     return tmp_upscaled.name
 
@@ -698,6 +695,10 @@ def add_border(image, mask_width, mask_height, blank_color):
 
 
 ################################## DEPTH ESTIMATION ##################################
+#-------------- ------------------------------------------------MODEL INITIALIZATION------------------------------------------------------------#
+# Load models once during module import
+image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large",)
+depth_model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large", ignore_mismatched_sizes=True)
 
 def create_3d_obj(rgb_image, raw_depth, image_path, depth=10, z_scale=200):
     """
@@ -713,6 +714,7 @@ def create_3d_obj(rgb_image, raw_depth, image_path, depth=10, z_scale=200):
     Returns:
         str: The file path to the saved GLTF model.
     """
+    import open3d as o3d
     # Normalize the depth image
     depth_image = ((raw_depth - raw_depth.min()) / (raw_depth.max() - raw_depth.min()) * 255).astype("uint8")
     depth_o3d = o3d.geometry.Image(depth_image)
@@ -787,7 +789,6 @@ def create_3d_obj(rgb_image, raw_depth, image_path, depth=10, z_scale=200):
     o3d.io.write_triangle_mesh(gltf_path, mesh_crop, write_triangle_uvs=True)
     return gltf_path
 
-@spaces.GPU()
 def depth_process_image(image_path, resized_width=800, z_scale=208):
     """
     Processes the input image to generate a depth map and a 3D mesh reconstruction.
@@ -798,6 +799,7 @@ def depth_process_image(image_path, resized_width=800, z_scale=208):
     Returns:
         list: A list containing the depth image, 3D mesh reconstruction, and GLTF file path.
     """
+
     image_path = Path(image_path)
     if not image_path.exists():
         raise ValueError("Image file not found")

requirements.txt

@@ -15,7 +15,7 @@ invisible_watermark
 # ==0.0.27.post2 --index-url https://download.pytorch.org/whl/cu118/xformers-0.0.27.post2%2Bcu118-cp310-cp310-manylinux2014_x86_64.whl#sha256=b3cdeeb9eae4547805ab8c3c645ac2fa9c6da85b46c039d9befa117e9f6f22fe
 
 #generic Torch versions
-#--extra-index-url https://download.pytorch.org/whl/cu124
+--extra-index-url https://download.pytorch.org/whl/cu124
 torch
 torchvision
 #xformers #==0.0.29.post3