Use gradio UI.

Dockerfile

@@ -37,4 +37,4 @@ COPY --chown=user . $HOME/app
 
 RUN python3 download_checkpoints.py
 
-CMD ["streamlit", "run", "--server.enableXsrfProtection", "false", "app.py"]
+CMD ["python", "gradio_app.py"]

gradio_app.py

@@ -0,0 +1,204 @@
+import os
+import torch
+import fire
+import gradio as gr
+from PIL import Image
+from functools import partial
+from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
+
+import cv2
+import time
+import numpy as np
+from rembg import remove
+from segment_anything import sam_model_registry, SamPredictor
+
+_TITLE = '''Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model'''
+_DESCRIPTION = '''
+<div>
+<a style="display:inline-block; margin-left: .5em" href="https://arxiv.org/abs/2310.15110"><img src="https://img.shields.io/badge/2310.15110-f9f7f7?logo=data:image/png;base64,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"></a>
+<a style="display:inline-block; margin-left: .5em" href='https://github.com/SUDO-AI-3D/zero123plus'><img src='https://img.shields.io/github/stars/SUDO-AI-3D/zero123plus?style=social' /></a>
+</div>
+'''
+_GPU_ID = 0
+
+
+if not hasattr(Image, 'Resampling'):
+    Image.Resampling = Image
+
+
+def sam_init():
+    sam_checkpoint = os.path.join(os.path.dirname(__file__), "tmp", "sam_vit_h_4b8939.pth")
+    model_type = "vit_h"
+
+    sam = sam_model_registry[model_type](checkpoint=sam_checkpoint).to(device=f"cuda:{_GPU_ID}")
+    predictor = SamPredictor(sam)
+    return predictor
+
+def sam_segment(predictor, input_image, *bbox_coords):
+    bbox = np.array(bbox_coords)
+    image = np.asarray(input_image)
+
+    start_time = time.time()
+    predictor.set_image(image)
+
+    masks_bbox, scores_bbox, logits_bbox = predictor.predict(
+        box=bbox,
+        multimask_output=True
+    )
+
+    print(f"SAM Time: {time.time() - start_time:.3f}s")
+    out_image = np.zeros((image.shape[0], image.shape[1], 4), dtype=np.uint8)
+    out_image[:, :, :3] = image
+    out_image_bbox = out_image.copy()
+    out_image_bbox[:, :, 3] = masks_bbox[-1].astype(np.uint8) * 255
+    torch.cuda.empty_cache()
+    return Image.fromarray(out_image_bbox, mode='RGBA') 
+
+def expand2square(pil_img, background_color):
+    width, height = pil_img.size
+    if width == height:
+        return pil_img
+    elif width > height:
+        result = Image.new(pil_img.mode, (width, width), background_color)
+        result.paste(pil_img, (0, (width - height) // 2))
+        return result
+    else:
+        result = Image.new(pil_img.mode, (height, height), background_color)
+        result.paste(pil_img, ((height - width) // 2, 0))
+        return result
+
+def preprocess(predictor, input_image, chk_group=None, segment=True, rescale=False):
+    RES = 1024
+    input_image.thumbnail([RES, RES], Image.Resampling.LANCZOS)
+    if chk_group is not None:
+        segment = "Background Removal" in chk_group
+        rescale = "Rescale" in chk_group
+    if segment:
+        image_rem = input_image.convert('RGBA')
+        image_nobg = remove(image_rem, alpha_matting=True)
+        arr = np.asarray(image_nobg)[:,:,-1]
+        x_nonzero = np.nonzero(arr.sum(axis=0))
+        y_nonzero = np.nonzero(arr.sum(axis=1))
+        x_min = int(x_nonzero[0].min())
+        y_min = int(y_nonzero[0].min())
+        x_max = int(x_nonzero[0].max())
+        y_max = int(y_nonzero[0].max())
+        input_image = sam_segment(predictor, input_image.convert('RGB'), x_min, y_min, x_max, y_max)
+    # Rescale and recenter
+    if rescale:
+        image_arr = np.array(input_image)
+        in_w, in_h = image_arr.shape[:2]
+        out_res = min(RES, max(in_w, in_h))
+        ret, mask = cv2.threshold(np.array(input_image.split()[-1]), 0, 255, cv2.THRESH_BINARY)
+        x, y, w, h = cv2.boundingRect(mask)
+        max_size = max(w, h)
+        ratio = 0.75
+        side_len = int(max_size / ratio)
+        padded_image = np.zeros((side_len, side_len, 4), dtype=np.uint8)
+        center = side_len//2
+        padded_image[center-h//2:center-h//2+h, center-w//2:center-w//2+w] = image_arr[y:y+h, x:x+w]
+        rgba = Image.fromarray(padded_image).resize((out_res, out_res), Image.LANCZOS)
+
+        rgba_arr = np.array(rgba) / 255.0
+        rgb = rgba_arr[...,:3] * rgba_arr[...,-1:] + (1 - rgba_arr[...,-1:])
+        input_image = Image.fromarray((rgb * 255).astype(np.uint8))
+    else:
+        input_image = expand2square(input_image, (127, 127, 127, 0))
+    return input_image, input_image.resize((320, 320), Image.Resampling.LANCZOS)
+
+def gen_multiview(pipeline, predictor, input_image, scale_slider, steps_slider, seed, output_processing=False):
+    seed = int(seed)
+    torch.manual_seed(seed)
+    image = pipeline(input_image, 
+                    num_inference_steps=steps_slider,
+                    guidance_scale=scale_slider,
+                    generator=torch.Generator(pipeline.device).manual_seed(seed)).images[0]
+    side_len = image.width//2
+    subimages = [image.crop((x, y, x + side_len, y+side_len)) for y in range(0, image.height, side_len) for x in range(0, image.width, side_len)]
+    if "Background Removal" in output_processing:
+        out_images = []
+        for sub_image in subimages:
+            sub_image, _ = preprocess(predictor, sub_image.convert('RGB'), segment=True, rescale=False)
+            out_images.append(sub_image)
+        return out_images
+    return subimages
+
+
+def run_demo():
+    # Load the pipeline
+    pipeline = DiffusionPipeline.from_pretrained(
+        "sudo-ai/zero123plus-v1.1", custom_pipeline="sudo-ai/zero123plus-pipeline",
+        torch_dtype=torch.float16
+    )
+    # Feel free to tune the scheduler
+    pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
+        pipeline.scheduler.config, timestep_spacing='trailing'
+    )
+    pipeline.to(f'cuda:{_GPU_ID}')
+
+    predictor = sam_init()
+
+    custom_theme = gr.themes.Soft(primary_hue="blue").set(
+                    button_secondary_background_fill="*neutral_100",
+                    button_secondary_background_fill_hover="*neutral_200")
+    custom_css = '''#disp_image {
+        text-align: center; /* Horizontally center the content */
+    }'''
+
+    with gr.Blocks(title=_TITLE, theme=custom_theme, css=custom_css) as demo:
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown('# ' + _TITLE)
+        gr.Markdown(_DESCRIPTION)
+        with gr.Row(variant='panel'):
+            with gr.Column(scale=1):
+                input_image = gr.Image(type='pil', image_mode='RGBA', height=320, label='Input image', tool=None)
+
+                example_folder = os.path.join(os.path.dirname(__file__), "./resources/examples")
+                example_fns = [os.path.join(example_folder, example) for example in os.listdir(example_folder)]
+                gr.Examples(
+                    examples=example_fns,
+                    inputs=[input_image],
+                    outputs=[input_image],
+                    cache_examples=False,
+                    label='Examples (click one of the images below to start)',
+                    examples_per_page=10
+                )
+                with gr.Accordion('Advanced options', open=False):
+                    with gr.Row():
+                        with gr.Column():
+                            input_processing = gr.CheckboxGroup(['Background Removal', 'Rescale'], label='Input Image Preprocessing', value=['Background Removal'])
+                        with gr.Column():
+                            output_processing = gr.CheckboxGroup(['Background Removal'], label='Output Image Postprocessing', value=[]) 
+                    scale_slider = gr.Slider(1, 10, value=4, step=1,
+                                                label='Classifier Free Guidance Scale')
+                    steps_slider = gr.Slider(15, 100, value=75, step=1,
+                                                label='Number of Diffusion Inference Steps',
+                                                info="For general real or synthetic objects, around 28 is enough. For objects with delicate details such as faces (either realistic or illustration), you may need 75 or more steps.")
+                    seed = gr.Number(42, label='Seed')
+                run_btn = gr.Button('Generate', variant='primary', interactive=True)
+            with gr.Column(scale=1):
+                processed_image = gr.Image(type='pil', label="Processed Image", interactive=False, height=320, tool=None, image_mode='RGBA', elem_id="disp_image")
+                processed_image_highres = gr.Image(type='pil', image_mode='RGBA', visible=False, tool=None)
+                with gr.Row():
+                    view_1 = gr.Image(interactive=False, height=240, show_label=False)
+                    view_2 = gr.Image(interactive=False, height=240, show_label=False)
+                    view_3 = gr.Image(interactive=False, height=240, show_label=False)
+                with gr.Row():
+                    view_4 = gr.Image(interactive=False, height=240, show_label=False)
+                    view_5 = gr.Image(interactive=False, height=240, show_label=False)
+                    view_6 = gr.Image(interactive=False, height=240, show_label=False)
+        
+
+        run_btn.click(fn=partial(preprocess, predictor), 
+                        inputs=[input_image, input_processing], 
+                        outputs=[processed_image_highres, processed_image], queue=True
+            ).success(fn=partial(gen_multiview, pipeline, predictor), 
+                        inputs=[processed_image_highres, scale_slider, steps_slider, seed, output_processing],
+                        outputs=[view_1, view_2, view_3, view_4, view_5, view_6])
+        
+        demo.queue().launch(share=True, max_threads=80)
+
+
+if __name__ == '__main__':
+    fire.Fire(run_demo)

requirements.txt

@@ -9,3 +9,5 @@ streamlit==1.22.0
 altair<5
 huggingface_hub
 git+https://github.com/facebookresearch/segment-anything.git
+gradio>=3.50
+fire