Update app.py

app.py

@@ -1,263 +0,0 @@
-import os
-from PIL import Image
-import torch
-
-from point_e.diffusion.configs import DIFFUSION_CONFIGS, diffusion_from_config
-from point_e.diffusion.sampler import PointCloudSampler
-from point_e.models.download import load_checkpoint
-from point_e.models.configs import MODEL_CONFIGS, model_from_config
-from point_e.util.plotting import plot_point_cloud
-from point_e.util.pc_to_mesh import marching_cubes_mesh
-
-import skimage.measure
-
-from pyntcloud import PyntCloud
-import matplotlib.colors
-import plotly.graph_objs as go
-
-import trimesh
-
-import gradio as gr
-
-
-state = ""
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-def set_state(s):
-    print(s)
-    global state
-    state = s
-
-def get_state():
-    return state
-
-set_state('Creating txt2mesh model...')
-t2m_name = 'base40M-textvec'
-t2m_model = model_from_config(MODEL_CONFIGS[t2m_name], device)
-t2m_model.eval()
-base_diffusion_t2m = diffusion_from_config(DIFFUSION_CONFIGS[t2m_name])
-
-set_state('Downloading txt2mesh checkpoint...')
-t2m_model.load_state_dict(load_checkpoint(t2m_name, device))
-
-
-def load_img2mesh_model(model_name):
-    set_state(f'Creating img2mesh model {model_name}...')
-    i2m_name = model_name
-    i2m_model = model_from_config(MODEL_CONFIGS[i2m_name], device)
-    i2m_model.eval()
-    base_diffusion_i2m = diffusion_from_config(DIFFUSION_CONFIGS[i2m_name])
-
-    set_state(f'Downloading img2mesh checkpoint {model_name}...')
-    i2m_model.load_state_dict(load_checkpoint(i2m_name, device))
-
-    return i2m_model, base_diffusion_i2m
-
-img2mesh_model_name = 'base40M' #'base300M' #'base1B'
-i2m_model, base_diffusion_i2m = load_img2mesh_model(img2mesh_model_name)
-
-
-set_state('Creating upsample model...')
-upsampler_model = model_from_config(MODEL_CONFIGS['upsample'], device)
-upsampler_model.eval()
-upsampler_diffusion = diffusion_from_config(DIFFUSION_CONFIGS['upsample'])
-
-set_state('Downloading upsampler checkpoint...')
-upsampler_model.load_state_dict(load_checkpoint('upsample', device))
-
-set_state('Creating SDF model...')
-sdf_name = 'sdf'
-sdf_model = model_from_config(MODEL_CONFIGS[sdf_name], device)
-sdf_model.eval()
-
-set_state('Loading SDF model...')
-sdf_model.load_state_dict(load_checkpoint(sdf_name, device))
-
-stable_diffusion = gr.Blocks.load(name="spaces/runwayml/stable-diffusion-v1-5")
-
-
-set_state('')
-
-def get_sampler(model_name, txt2obj, guidance_scale):
-
-    global img2mesh_model_name
-    global base_diffusion_i2m
-    global i2m_model
-    if model_name != img2mesh_model_name:
-        img2mesh_model_name = model_name
-        i2m_model, base_diffusion_i2m = load_img2mesh_model(model_name)
-
-    return PointCloudSampler(
-            device=device,
-            models=[t2m_model if txt2obj else i2m_model, upsampler_model],
-            diffusions=[base_diffusion_t2m if txt2obj else base_diffusion_i2m, upsampler_diffusion],
-            num_points=[1024, 4096 - 1024],
-            aux_channels=['R', 'G', 'B'],
-            guidance_scale=[guidance_scale, 0.0 if txt2obj else guidance_scale],
-            model_kwargs_key_filter=('texts', '') if txt2obj else ("*",)
-        )
-
-def generate_txt2img(prompt):
-
-    prompt = f"“a 3d rendering of {prompt}, full view, white background"
-    gallery_dir = stable_diffusion(prompt, fn_index=2)
-    imgs = [os.path.join(gallery_dir, img) for img in os.listdir(gallery_dir) if os.path.splitext(img)[1] == '.jpg']
-
-    return imgs[0], gr.update(visible=True)
-
-def generate_3D(input, model_name='base40M', guidance_scale=3.0, grid_size=32):
-
-    set_state('Entered generate function...')
-
-    if isinstance(input, Image.Image):
-        input = prepare_img(input)
-
-    # if input is a string, it's a text prompt
-    sampler = get_sampler(model_name, txt2obj=True if isinstance(input, str) else False, guidance_scale=guidance_scale)
-
-    # Produce a sample from the model.
-    set_state('Sampling...')
-    samples = None
-    kw_args = dict(texts=[input]) if isinstance(input, str) else dict(images=[input])
-    for x in sampler.sample_batch_progressive(batch_size=1, model_kwargs=kw_args):
-        samples = x
-
-    set_state('Converting to point cloud...')
-    pc = sampler.output_to_point_clouds(samples)[0]
-
-    set_state('Saving point cloud...')
-    with open("point_cloud.ply", "wb") as f:
-        pc.write_ply(f)
-
-    set_state('Converting to mesh...')
-    save_ply(pc, 'mesh.ply', grid_size)
-
-    set_state('')
-
-    return pc_to_plot(pc), ply_to_obj('mesh.ply', '3d_model.obj'), gr.update(value=['3d_model.obj', 'mesh.ply', 'point_cloud.ply'], visible=True)
-
-def prepare_img(img):
-
-    w, h = img.size
-    if w > h:
-        img = img.crop((w - h) / 2, 0, w - (w - h) / 2, h)
-    else:
-        img = img.crop((0, (h - w) / 2, w, h - (h - w) / 2))
-
-    # resize to 256x256
-    img = img.resize((256, 256))
-    
-    return img
-
-def pc_to_plot(pc):
-
-    return go.Figure(
-        data=[
-            go.Scatter3d(
-                x=pc.coords[:,0], y=pc.coords[:,1], z=pc.coords[:,2], 
-                mode='markers',
-                marker=dict(
-                  size=2,
-                  color=['rgb({},{},{})'.format(r,g,b) for r,g,b in zip(pc.channels["R"], pc.channels["G"], pc.channels["B"])],
-              )
-            )
-        ],
-        layout=dict(
-            scene=dict(xaxis=dict(visible=False), yaxis=dict(visible=False), zaxis=dict(visible=False))
-        ),
-    )
-
-def ply_to_obj(ply_file, obj_file):
-    mesh = trimesh.load(ply_file)
-    mesh.export(obj_file)
-
-    return obj_file
-
-def save_ply(pc, file_name, grid_size):
-
-    # Produce a mesh (with vertex colors)
-    mesh = marching_cubes_mesh(
-        pc=pc,
-        model=sdf_model,
-        batch_size=4096,
-        grid_size=grid_size, # increase to 128 for resolution used in evals
-        fill_vertex_channels=True,
-        progress=True,
-    )
-
-    # Write the mesh to a PLY file to import into some other program.
-    with open(file_name, 'wb') as f:
-        mesh.write_ply(f)
-
-
-with gr.Blocks() as app:
-    gr.Markdown("## Point-E text-to-3D Demo")
-    gr.Markdown("This is a demo for [Point-E: A System for Generating 3D Point Clouds from Complex Prompts](https://arxiv.org/abs/2212.08751) by OpenAI. Check out the [GitHub repo](https://github.com/openai/point-e) for more information.")
-    gr.HTML("""To skip the queue you can duplicate this space:
-            <br><a href="https://huggingface.co/spaces/anzorq/point-e_demo?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a>
-            <br>Don't forget to change space hardware to <b>GPU</b> after duplicating it.""")
-    
-    with gr.Row():
-        with gr.Column():
-            with gr.Tab("Text to 3D"):
-                prompt = gr.Textbox(label="Prompt", placeholder="A cactus in a pot")
-                btn_generate_txt2obj = gr.Button(value="Generate")
-            
-            with gr.Tab("Image to 3D"):
-                img = gr.Image(label="Image")
-                gr.Markdown("Best results with images of 3D objects with no shadows on a white background.")
-                btn_generate_img2obj = gr.Button(value="Generate")
-
-            with gr.Tab("Text to Image to 3D"):
-                gr.Markdown("Generate an image with Stable Diffusion, then convert it to 3D. Just enter the object you want to generate.")
-                prompt_sd = gr.Textbox(label="Prompt", placeholder="a 3d rendering of [your prompt], full view, white background")
-                btn_generate_txt2sd = gr.Button(value="Generate image")
-                img_sd = gr.Image(label="Image")
-                btn_generate_sd2obj = gr.Button(value="Convert to 3D", visible=False)
-
-            with gr.Accordion("Advanced settings", open=False):
-                dropdown_models = gr.Dropdown(label="Model", value="base40M", choices=["base40M", "base300M"]) #, "base1B"])
-                guidance_scale = gr.Slider(label="Guidance scale", value=3.0, minimum=3.0, maximum=10.0, step=0.1)
-                grid_size = gr.Slider(label="Grid size (for .obj 3D model)", value=32, minimum=16, maximum=128, step=16)
-
-        with gr.Column():
-            plot = gr.Plot(label="Point cloud")
-            # btn_pc_to_obj = gr.Button(value="Convert to OBJ", visible=False)
-            model_3d = gr.Model3D(value=None)
-            file_out = gr.File(label="Files", visible=False)
-            
-            # state_info = state_info = gr.Textbox(label="State", show_label=False).style(container=False)
-
-
-        # inputs = [dropdown_models, prompt, img, guidance_scale, grid_size]
-        outputs = [plot, model_3d, file_out]
-
-        prompt.submit(generate_3D, inputs=[prompt, dropdown_models, guidance_scale, grid_size], outputs=outputs)
-        btn_generate_txt2obj.click(generate_3D, inputs=[prompt, dropdown_models, guidance_scale, grid_size], outputs=outputs)
-        
-        btn_generate_img2obj.click(generate_3D, inputs=[img, dropdown_models, guidance_scale, grid_size], outputs=outputs)
-
-        prompt_sd.submit(generate_txt2img, inputs=prompt_sd, outputs=[img_sd, btn_generate_sd2obj])
-        btn_generate_txt2sd.click(generate_txt2img, inputs=prompt_sd, outputs=[img_sd, btn_generate_sd2obj], queue=False)
-        btn_generate_sd2obj.click(generate_3D, inputs=[img, dropdown_models, guidance_scale, grid_size], outputs=outputs)
-
-        # btn_pc_to_obj.click(ply_to_obj, inputs=plot, outputs=[model_3d, file_out])
-
-
-
-
-    # app.load(get_state, inputs=[], outputs=state_info, every=0.5, show_progress=False)
-
-    gr.HTML("""
-    <br><br>
-    <div style="border-top: 1px solid #303030;">
-      <br>
-      <p>Space by:<br>
-      <a href="https://twitter.com/hahahahohohe"><img src="https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social" alt="Twitter Follow"></a><br>
-      <a href="https://github.com/qunash"><img alt="GitHub followers" src="https://img.shields.io/github/followers/qunash?style=social" alt="Github Follow"></a></p><br>
-      <a href="https://www.buymeacoffee.com/anzorq" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 30px !important;width: 102px !important;" ></a><br><br>
-      <p><img src="https://visitor-badge.glitch.me/badge?page_id=anzorq.point-e_demo" alt="visitors"></p>
-    </div>
-    """)
-
-app.queue(max_size=250, concurrency_count=6).launch()