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app.py

@@ -1,299 +1,228 @@
-# Not ready to use yet
-import spaces
-import argparse
-import numpy as np
-import gradio as gr
-from omegaconf import OmegaConf
-import torch
-from PIL import Image
-import PIL
-from pipelines import TwoStagePipeline
-from huggingface_hub import hf_hub_download
-import os
-import rembg
-from typing import Any
-import json
-import os
-import json
-import argparse
-
-from model import CRM
-from inference import generate3d
-
-pipeline = None
-rembg_session = rembg.new_session()
-
-
-def expand_to_square(image, bg_color=(0, 0, 0, 0)):
-    # expand image to 1:1
-    width, height = image.size
-    if width == height:
-        return image
-    new_size = (max(width, height), max(width, height))
-    new_image = Image.new("RGBA", new_size, bg_color)
-    paste_position = ((new_size[0] - width) // 2, (new_size[1] - height) // 2)
-    new_image.paste(image, paste_position)
-    return new_image
-
-def check_input_image(input_image):
-    if input_image is None:
-        raise gr.Error("No image uploaded!")
-
-
-def remove_background(
-    image: PIL.Image.Image,
-    rembg_session: Any = None,
-    force: bool = False,
-    **rembg_kwargs,
-) -> PIL.Image.Image:
-    do_remove = True
-    if image.mode == "RGBA" and image.getextrema()[3][0] < 255:
-        # explain why current do not rm bg
-        print("alhpa channl not enpty, skip remove background, using alpha channel as mask")
-        background = Image.new("RGBA", image.size, (0, 0, 0, 0))
-        image = Image.alpha_composite(background, image)
-        do_remove = False
-    do_remove = do_remove or force
-    if do_remove:
-        image = rembg.remove(image, session=rembg_session, **rembg_kwargs)
-    return image
-
-def do_resize_content(original_image: Image, scale_rate):
-    # resize image content wile retain the original image size
-    if scale_rate != 1:
-        # Calculate the new size after rescaling
-        new_size = tuple(int(dim * scale_rate) for dim in original_image.size)
-        # Resize the image while maintaining the aspect ratio
-        resized_image = original_image.resize(new_size)
-        # Create a new image with the original size and black background
-        padded_image = Image.new("RGBA", original_image.size, (0, 0, 0, 0))
-        paste_position = ((original_image.width - resized_image.width) // 2, (original_image.height - resized_image.height) // 2)
-        padded_image.paste(resized_image, paste_position)
-        return padded_image
-    else:
-        return original_image
-
-def add_background(image, bg_color=(255, 255, 255)):
-    # given an RGBA image, alpha channel is used as mask to add background color
-    background = Image.new("RGBA", image.size, bg_color)
-    return Image.alpha_composite(background, image)
-
-
-def preprocess_image(image, background_choice, foreground_ratio, backgroud_color):
-    """
-    input image is a pil image in RGBA, return RGB image
-    """
-    print(background_choice)
-    if background_choice == "Alpha as mask":
-        background = Image.new("RGBA", image.size, (0, 0, 0, 0))
-        image = Image.alpha_composite(background, image)
-    else:
-        image = remove_background(image, rembg_session, force=True)
-    image = do_resize_content(image, foreground_ratio)
-    image = expand_to_square(image)
-    image = add_background(image, backgroud_color)
-    return image.convert("RGB")
-
-@spaces.GPU
-def gen_image(input_image, seed, scale, step):
-    global pipeline, model, args
-    pipeline.set_seed(seed)
-    rt_dict = pipeline(input_image, scale=scale, step=step)
-    stage1_images = rt_dict["stage1_images"]
-    stage2_images = rt_dict["stage2_images"]
-    np_imgs = np.concatenate(stage1_images, 1)
-    np_xyzs = np.concatenate(stage2_images, 1)
-
-    glb_path = generate3d(model, np_imgs, np_xyzs, args.device)
-    return Image.fromarray(np_imgs), Image.fromarray(np_xyzs), glb_path#, obj_path
-
-
-parser = argparse.ArgumentParser()
-parser.add_argument(
-    "--stage1_config",
-    type=str,
-    default="configs/nf7_v3_SNR_rd_size_stroke.yaml",
-    help="config for stage1",
-)
-parser.add_argument(
-    "--stage2_config",
-    type=str,
-    default="configs/stage2-v2-snr.yaml",
-    help="config for stage2",
-)
-
-parser.add_argument("--device", type=str, default="cuda")
-args = parser.parse_args()
-
-crm_path = hf_hub_download(repo_id="Zhengyi/CRM", filename="CRM.pth")
-specs = json.load(open("configs/specs_objaverse_total.json"))
-model = CRM(specs)
-model.load_state_dict(torch.load(crm_path, map_location="cpu"), strict=False)
-model = model.to(args.device)
-
-stage1_config = OmegaConf.load(args.stage1_config).config
-stage2_config = OmegaConf.load(args.stage2_config).config
-stage2_sampler_config = stage2_config.sampler
-stage1_sampler_config = stage1_config.sampler
-
-stage1_model_config = stage1_config.models
-stage2_model_config = stage2_config.models
-
-xyz_path = hf_hub_download(repo_id="Zhengyi/CRM", filename="ccm-diffusion.pth")
-pixel_path = hf_hub_download(repo_id="Zhengyi/CRM", filename="pixel-diffusion.pth")
-stage1_model_config.resume = pixel_path
-stage2_model_config.resume = xyz_path
-
-pipeline = TwoStagePipeline(
-    stage1_model_config,
-    stage2_model_config,
-    stage1_sampler_config,
-    stage2_sampler_config,
-    device=args.device,
-    dtype=torch.float32
-)
-
-_DESCRIPTION = '''
-* Our [official implementation](https://github.com/thu-ml/CRM) uses UV texture instead of vertex color. It has better texture than this online demo.
-* Project page of CRM: https://ml.cs.tsinghua.edu.cn/~zhengyi/CRM/
-* If you find the output unsatisfying, try using different seeds:)
-'''
-
-with gr.Blocks() as demo:
-    gr.Markdown("# CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model")
-    gr.Markdown(_DESCRIPTION)
-    with gr.Row():
-        with gr.Column():
-            with gr.Row():
-                image_input = gr.Image(
-                    label="Image input",
-                    image_mode="RGBA",
-                    sources="upload",
-                    type="pil",
-                )
-                processed_image = gr.Image(label="Processed Image", interactive=False, type="pil", image_mode="RGB")
-            with gr.Row():
-                with gr.Column():
-                    with gr.Row():
-                        background_choice = gr.Radio([
-                                "Alpha as mask",
-                                "Auto Remove background"
-                            ], value="Auto Remove background",
-                            label="backgroud choice")
-                        # do_remove_background = gr.Checkbox(label=, value=True)
-                        # force_remove = gr.Checkbox(label=, value=False)
-                    back_groud_color = gr.ColorPicker(label="Background Color", value="#7F7F7F", interactive=False)
-                    foreground_ratio = gr.Slider(
-                        label="Foreground Ratio",
-                        minimum=0.5,
-                        maximum=1.0,
-                        value=1.0,
-                        step=0.05,
-                    )
-
-                with gr.Column():
-                    seed = gr.Number(value=1234, label="seed", precision=0)
-                    guidance_scale = gr.Number(value=5.5, minimum=3, maximum=10, label="guidance_scale")
-                    step = gr.Number(value=30, minimum=30, maximum=100, label="sample steps", precision=0)
-            text_button = gr.Button("Generate 3D shape")
-            gr.Examples(
-                examples=[os.path.join("examples", i) for i in os.listdir("examples")],
-                inputs=[image_input],
-                examples_per_page = 20,
-            )
-        with gr.Column():
-            image_output = gr.Image(interactive=False, label="Output RGB image")
-            xyz_ouput = gr.Image(interactive=False, label="Output CCM image")
-
-            output_model = gr.Model3D(
-                label="Output OBJ",
-                interactive=False,
-            )
-            gr.Markdown("Note: Ensure that the input image is correctly pre-processed into a grey background, otherwise the results will be unpredictable.")
-
-    inputs = [
-        processed_image,
-        seed,
-        guidance_scale,
-        step,
-    ]
-    outputs = [
-        image_output,
-        xyz_ouput,
-        output_model,
-        # output_obj,
-    ]
-
-
-    text_button.click(fn=check_input_image, inputs=[image_input]).success(
-        fn=preprocess_image,
-        inputs=[image_input, background_choice, foreground_ratio, back_groud_color],
-        outputs=[processed_image],
-    ).success(
-        fn=gen_image,
-        inputs=inputs,
-        outputs=outputs,
-    )
-    demo.queue().launch()
-
-
-
-# import torch
-# import gradio as gr
-# import requests
-# import os
-
-# # Download model weights from Hugging Face model repo (if not already present)
-# model_repo = "Mariam-Elz/CRM"  # Your Hugging Face model repo
-
-# model_files = {
-#     "ccm-diffusion.pth": "ccm-diffusion.pth",
-#     "pixel-diffusion.pth": "pixel-diffusion.pth",
-#     "CRM.pth": "CRM.pth",
-# }
-
-
-# os.makedirs("models", exist_ok=True)
-
-
-
-# for filename, output_path in model_files.items():
-#     file_path = f"models/{output_path}"
-#     if not os.path.exists(file_path):
-#         url = f"https://huggingface.co/{model_repo}/resolve/main/{filename}"
-#         print(f"Downloading {filename}...")
-#         response = requests.get(url)
-#         with open(file_path, "wb") as f:
-#             f.write(response.content)
-
-# # Load model (This part depends on how the model is defined)
-# device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# def load_model():
-#     model_path = "models/CRM.pth"
-#     model = torch.load(model_path, map_location=device)
-#     model.eval()
-#     return model
-
-# model = load_model()
-
-# # Define inference function
-# def infer(image):
-#     """Process input image and return a reconstructed image."""
-#     with torch.no_grad():
-#         # Assuming model expects a tensor input
-#         image_tensor = torch.tensor(image).to(device)
-#         output = model(image_tensor)
-#         return output.cpu().numpy()
-
-# # Create Gradio UI
-# demo = gr.Interface(
-#     fn=infer,
-#     inputs=gr.Image(type="numpy"),
-#     outputs=gr.Image(type="numpy"),
-#     title="Convolutional Reconstruction Model",
-#     description="Upload an image to get the reconstructed output."
-# )
-
-# if __name__ == "__main__":
-#     demo.launch()
+# Not ready to use yet
+import argparse
+import numpy as np
+import gradio as gr
+from omegaconf import OmegaConf
+import torch
+from PIL import Image
+import PIL
+from pipelines import TwoStagePipeline
+from huggingface_hub import hf_hub_download
+import os
+import rembg
+from typing import Any
+import json
+import os
+import json
+import argparse
+
+from model import CRM
+from inference import generate3d
+
+pipeline = None
+rembg_session = rembg.new_session()
+
+
+def expand_to_square(image, bg_color=(0, 0, 0, 0)):
+    # expand image to 1:1
+    width, height = image.size
+    if width == height:
+        return image
+    new_size = (max(width, height), max(width, height))
+    new_image = Image.new("RGBA", new_size, bg_color)
+    paste_position = ((new_size[0] - width) // 2, (new_size[1] - height) // 2)
+    new_image.paste(image, paste_position)
+    return new_image
+
+def check_input_image(input_image):
+    if input_image is None:
+        raise gr.Error("No image uploaded!")
+
+
+def remove_background(
+    image: PIL.Image.Image,
+    rembg_session = None,
+    force: bool = False,
+    **rembg_kwargs,
+) -> PIL.Image.Image:
+    do_remove = True
+    if image.mode == "RGBA" and image.getextrema()[3][0] < 255:
+        # explain why current do not rm bg
+        print("alhpa channl not enpty, skip remove background, using alpha channel as mask")
+        background = Image.new("RGBA", image.size, (0, 0, 0, 0))
+        image = Image.alpha_composite(background, image)
+        do_remove = False
+    do_remove = do_remove or force
+    if do_remove:
+        image = rembg.remove(image, session=rembg_session, **rembg_kwargs)
+    return image
+
+def do_resize_content(original_image: Image, scale_rate):
+    # resize image content wile retain the original image size
+    if scale_rate != 1:
+        # Calculate the new size after rescaling
+        new_size = tuple(int(dim * scale_rate) for dim in original_image.size)
+        # Resize the image while maintaining the aspect ratio
+        resized_image = original_image.resize(new_size)
+        # Create a new image with the original size and black background
+        padded_image = Image.new("RGBA", original_image.size, (0, 0, 0, 0))
+        paste_position = ((original_image.width - resized_image.width) // 2, (original_image.height - resized_image.height) // 2)
+        padded_image.paste(resized_image, paste_position)
+        return padded_image
+    else:
+        return original_image
+
+def add_background(image, bg_color=(255, 255, 255)):
+    # given an RGBA image, alpha channel is used as mask to add background color
+    background = Image.new("RGBA", image.size, bg_color)
+    return Image.alpha_composite(background, image)
+
+
+def preprocess_image(image, background_choice, foreground_ratio, backgroud_color):
+    """
+    input image is a pil image in RGBA, return RGB image
+    """
+    print(background_choice)
+    if background_choice == "Alpha as mask":
+        background = Image.new("RGBA", image.size, (0, 0, 0, 0))
+        image = Image.alpha_composite(background, image)
+    else:
+        image = remove_background(image, rembg_session, force_remove=True)
+    image = do_resize_content(image, foreground_ratio)
+    image = expand_to_square(image)
+    image = add_background(image, backgroud_color)
+    return image.convert("RGB")
+
+
+def gen_image(input_image, seed, scale, step):
+    global pipeline, model, args
+    pipeline.set_seed(seed)
+    rt_dict = pipeline(input_image, scale=scale, step=step)
+    stage1_images = rt_dict["stage1_images"]
+    stage2_images = rt_dict["stage2_images"]
+    np_imgs = np.concatenate(stage1_images, 1)
+    np_xyzs = np.concatenate(stage2_images, 1)
+
+    glb_path, obj_path = generate3d(model, np_imgs, np_xyzs, args.device)
+    return Image.fromarray(np_imgs), Image.fromarray(np_xyzs), glb_path, obj_path
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--stage1_config",
+    type=str,
+    default="configs/nf7_v3_SNR_rd_size_stroke.yaml",
+    help="config for stage1",
+)
+parser.add_argument(
+    "--stage2_config",
+    type=str,
+    default="configs/stage2-v2-snr.yaml",
+    help="config for stage2",
+)
+
+parser.add_argument("--device", type=str, default="cuda")
+args = parser.parse_args()
+
+crm_path = hf_hub_download(repo_id="Zhengyi/CRM", filename="CRM.pth")
+specs = json.load(open("configs/specs_objaverse_total.json"))
+model = CRM(specs).to(args.device)
+model.load_state_dict(torch.load(crm_path, map_location = args.device), strict=False)
+
+stage1_config = OmegaConf.load(args.stage1_config).config
+stage2_config = OmegaConf.load(args.stage2_config).config
+stage2_sampler_config = stage2_config.sampler
+stage1_sampler_config = stage1_config.sampler
+
+stage1_model_config = stage1_config.models
+stage2_model_config = stage2_config.models
+
+xyz_path = hf_hub_download(repo_id="Zhengyi/CRM", filename="ccm-diffusion.pth")
+pixel_path = hf_hub_download(repo_id="Zhengyi/CRM", filename="pixel-diffusion.pth")
+stage1_model_config.resume = pixel_path
+stage2_model_config.resume = xyz_path
+
+pipeline = TwoStagePipeline(
+    stage1_model_config,
+    stage2_model_config,
+    stage1_sampler_config,
+    stage2_sampler_config,
+    device=args.device,
+    dtype=torch.float16
+)
+
+with gr.Blocks() as demo:
+    gr.Markdown("# CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model")
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                image_input = gr.Image(
+                    label="Image input",
+                    image_mode="RGBA",
+                    sources="upload",
+                    type="pil",
+                )
+                processed_image = gr.Image(label="Processed Image", interactive=False, type="pil", image_mode="RGB")
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        background_choice = gr.Radio([
+                                "Alpha as mask",
+                                "Auto Remove background"
+                            ], value="Auto Remove background",
+                            label="backgroud choice")
+                        # do_remove_background = gr.Checkbox(label=, value=True)
+                        # force_remove = gr.Checkbox(label=, value=False)
+                    back_groud_color = gr.ColorPicker(label="Background Color", value="#7F7F7F", interactive=False)
+                    foreground_ratio = gr.Slider(
+                        label="Foreground Ratio",
+                        minimum=0.5,
+                        maximum=1.0,
+                        value=1.0,
+                        step=0.05,
+                    )
+
+                with gr.Column():
+                    seed = gr.Number(value=1234, label="seed", precision=0)
+                    guidance_scale = gr.Number(value=5.5, minimum=3, maximum=10, label="guidance_scale")
+                    step = gr.Number(value=50, minimum=30, maximum=100, label="sample steps", precision=0)
+            text_button = gr.Button("Generate 3D shape")
+            gr.Examples(
+                examples=[os.path.join("examples", i) for i in os.listdir("examples")],
+                inputs=[image_input],
+            )
+        with gr.Column():
+            image_output = gr.Image(interactive=False, label="Output RGB image")
+            xyz_ouput = gr.Image(interactive=False, label="Output CCM image")
+
+            output_model = gr.Model3D(
+                label="Output GLB",
+                interactive=False,
+            )
+            gr.Markdown("Note: The GLB model shown here has a darker lighting and enlarged UV seams. Download for correct results.")
+            output_obj = gr.File(interactive=False, label="Output OBJ")
+
+    inputs = [
+        processed_image,
+        seed,
+        guidance_scale,
+        step,
+    ]
+    outputs = [
+        image_output,
+        xyz_ouput,
+        output_model,
+        output_obj,
+    ]
+
+
+    text_button.click(fn=check_input_image, inputs=[image_input]).success(
+        fn=preprocess_image,
+        inputs=[image_input, background_choice, foreground_ratio, back_groud_color],
+        outputs=[processed_image],
+    ).success(
+        fn=gen_image,
+        inputs=inputs,
+        outputs=outputs,
+    )
+    demo.queue().launch()