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Update app.py
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app.py
CHANGED
@@ -5,7 +5,7 @@ import imageio
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import numpy as np
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import torch
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import rembg
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from PIL import Image
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from torchvision.transforms import v2
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from pytorch_lightning import seed_everything
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from omegaconf import OmegaConf
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@@ -192,35 +192,6 @@ def make3d(images):
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# get triplane
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planes = model.forward_planes(images, input_cameras)
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# # get video
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# chunk_size = 20 if IS_FLEXICUBES else 1
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# render_size = 384
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# frames = []
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# for i in tqdm(range(0, render_cameras.shape[1], chunk_size)):
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# if IS_FLEXICUBES:
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# frame = model.forward_geometry(
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# planes,
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# render_cameras[:, i:i+chunk_size],
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# render_size=render_size,
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# )['img']
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# else:
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# frame = model.synthesizer(
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# planes,
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# cameras=render_cameras[:, i:i+chunk_size],
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# render_size=render_size,
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# )['images_rgb']
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# frames.append(frame)
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# frames = torch.cat(frames, dim=1)
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# images_to_video(
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# frames[0],
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# video_fpath,
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# fps=30,
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# )
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# print(f"Video saved to {video_fpath}")
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# get mesh
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mesh_out = model.extract_mesh(
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planes,
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@@ -239,6 +210,37 @@ def make3d(images):
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return mesh_fpath, mesh_glb_fpath
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_HEADER_ = '''
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<h2><b>Official π€ Gradio Demo</b></h2><h2><a href='https://github.com/TencentARC/InstantMesh' target='_blank'><b>InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models</b></a></h2>
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@@ -277,98 +279,96 @@ If you have any questions, feel free to open a discussion or contact us at <b>bl
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with gr.Blocks() as demo:
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gr.Markdown(
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)
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processed_image = gr.Image(
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label="Processed Image",
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image_mode="RGBA",
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#width=256,
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#height=256,
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type="pil",
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interactive=False
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)
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with gr.Row():
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with gr.Group():
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do_remove_background = gr.Checkbox(
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label="Remove Background", value=True
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)
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label="Sample Steps",
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minimum=30,
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maximum=75,
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value=75,
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step=5
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)
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os.path.join("examples", img_name) for img_name in sorted(os.listdir("examples"))
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],
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inputs=[input_image],
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label="Examples",
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cache_examples=False,
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examples_per_page=16
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)
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with gr.Row():
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with gr.Column():
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mv_show_images = gr.Image(
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label="Generated Multi-views",
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type="pil",
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width=379,
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interactive=False
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)
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# with gr.Column():
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# output_video = gr.Video(
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# label="video", format="mp4",
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# width=379,
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# autoplay=True,
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# interactive=False
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# )
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with gr.Row():
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with gr.Tab("OBJ"):
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output_model_obj = gr.Model3D(
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label="Output Model (OBJ Format)",
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interactive=False,
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)
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gr.Markdown("Note: Downloaded .obj model will be flipped. Export .glb instead or manually flip it before usage.")
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with gr.Tab("GLB"):
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output_model_glb = gr.Model3D(
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label="Output Model (GLB Format)",
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interactive=False,
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)
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gr.Markdown("Note: The model shown here has a darker appearance. Download to get correct results.")
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with gr.Row():
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gr.Markdown('''Try a different <b>seed value</b> if the result is unsatisfying (Default: 42).''')
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gr.Markdown(_CITE_)
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mv_images = gr.State()
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fn=preprocess,
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inputs=[input_image, do_remove_background],
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outputs=[processed_image],
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@@ -376,11 +376,21 @@ with gr.Blocks() as demo:
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fn=generate_mvs,
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inputs=[processed_image, sample_steps, sample_seed],
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outputs=[mv_images, mv_show_images]
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).success(
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fn=make3d,
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inputs=[mv_images],
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outputs=[output_model_obj, output_model_glb]
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)
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import numpy as np
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import torch
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import rembg
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from PIL import Image, ImageDraw, ImageFont
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from torchvision.transforms import v2
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from pytorch_lightning import seed_everything
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from omegaconf import OmegaConf
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# get triplane
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planes = model.forward_planes(images, input_cameras)
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# get mesh
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mesh_out = model.extract_mesh(
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planes,
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return mesh_fpath, mesh_glb_fpath
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# New function to generate 2D pixel art sprites
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def generate_pixel_art(prompt, remove_background=True, sample_steps=75, seed=42):
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"""Generate a pixel art sprite based on the prompt"""
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seed_everything(seed)
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# Create a simple image with text as starting point
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text_img = Image.new('RGB', (512, 512), color=(255, 255, 255))
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draw = ImageDraw.Draw(text_img)
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# Try to load a font, use default if not available
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try:
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font = ImageFont.truetype("Arial", 20)
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except:
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font = ImageFont.load_default()
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# Add prompt as text
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pixel_prompt = f"Pixel art: {prompt}"
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draw.text((10, 10), pixel_prompt, fill=(0, 0, 0), font=font)
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# Process through the pipeline
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processed_img = preprocess(text_img, remove_background)
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# Generate the pixel art
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result_img, _ = generate_mvs(processed_img, sample_steps, seed)
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# Save to a temporary file
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sprite_path = tempfile.NamedTemporaryFile(suffix=".png", delete=False).name
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result_img.save(sprite_path)
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return sprite_path
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_HEADER_ = '''
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<h2><b>Official π€ Gradio Demo</b></h2><h2><a href='https://github.com/TencentARC/InstantMesh' target='_blank'><b>InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models</b></a></h2>
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with gr.Blocks() as demo:
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gr.Markdown("# InstantMesh and Pixel Art Generator")
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with gr.Tab("3D Model Generation"):
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with gr.Row(variant="panel"):
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with gr.Column():
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with gr.Row():
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input_image = gr.Image(
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label="Input Image",
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image_mode="RGBA",
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sources="upload",
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type="pil",
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elem_id="content_image",
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)
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processed_image = gr.Image(
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label="Processed Image",
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image_mode="RGBA",
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type="pil",
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interactive=False
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)
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with gr.Row():
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with gr.Group():
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do_remove_background = gr.Checkbox(
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label="Remove Background", value=True
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)
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sample_seed = gr.Number(value=42, label="Seed Value", precision=0)
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sample_steps = gr.Slider(
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label="Sample Steps",
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minimum=30,
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maximum=75,
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value=75,
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step=5
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)
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with gr.Row():
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submit_3d = gr.Button("Generate 3D Model", elem_id="generate", variant="primary")
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with gr.Column():
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with gr.Row():
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with gr.Column():
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mv_show_images = gr.Image(
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label="Generated Multi-views",
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type="pil",
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width=379,
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interactive=False
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)
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with gr.Row():
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with gr.Tab("OBJ"):
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output_model_obj = gr.Model3D(
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label="Output Model (OBJ Format)",
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interactive=False,
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)
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with gr.Tab("GLB"):
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output_model_glb = gr.Model3D(
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label="Output Model (GLB Format)",
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interactive=False,
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)
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with gr.Tab("Pixel Art Generation"):
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with gr.Row(variant="panel"):
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with gr.Column():
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pixel_prompt = gr.Textbox(
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label="Describe your pixel art sprite",
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placeholder="green cactus snake character, side view, game sprite",
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lines=3
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)
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with gr.Row():
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pixel_bg_remove = gr.Checkbox(label="Remove Background", value=True)
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pixel_seed = gr.Number(value=42, label="Seed Value", precision=0)
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pixel_steps = gr.Slider(
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label="Sample Steps",
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minimum=30,
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maximum=75,
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value=75,
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step=5
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)
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submit_pixel = gr.Button("Generate Pixel Art", variant="primary")
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with gr.Column():
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pixel_output = gr.Image(
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label="Generated Pixel Art Sprite",
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type="pil",
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interactive=False
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)
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# Set up event handlers
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mv_images = gr.State()
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# 3D Model generation flow
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submit_3d.click(fn=check_input_image, inputs=[input_image]).success(
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fn=preprocess,
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inputs=[input_image, do_remove_background],
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outputs=[processed_image],
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fn=generate_mvs,
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inputs=[processed_image, sample_steps, sample_seed],
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outputs=[mv_images, mv_show_images]
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).success(
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fn=make3d,
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inputs=[mv_images],
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outputs=[output_model_obj, output_model_glb]
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)
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# Pixel Art generation flow
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submit_pixel.click(
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fn=generate_pixel_art,
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inputs=[pixel_prompt, pixel_bg_remove, pixel_steps, pixel_seed],
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outputs=[pixel_output]
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)
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# Enable API access
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demo.queue(concurrency_count=1)
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# Launch with API access enabled
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demo.launch(enable_api=True, share=False)
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