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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.whl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,10 +1,11 @@
 ---
-title: Text To 3D
-emoji: 🚀
-colorFrom: green
-colorTo: gray
+title: TripoSR
+emoji: 🐳
+colorFrom: gray
+colorTo: red
 sdk: gradio
-sdk_version: 4.41.0
+sdk_version: 4.20.1
+python_version: 3.10.13
 app_file: app.py
 pinned: false
 license: mit

app.py

@@ -0,0 +1,182 @@
+import logging
+import os
+import boto3
+import json
+import shlex
+import subprocess
+import tempfile
+import time
+import base64
+import gradio as gr
+import numpy as np
+import rembg
+import spaces
+import torch
+from PIL import Image
+from functools import partial
+import io
+
+# s3 = boto3.client(
+#     's3',
+#     aws_access_key_id="AKIAZW3QSPMIH4RF42UA",
+#     aws_secret_access_key="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",
+#     region_name='us-east-1'
+# )
+
+subprocess.run(shlex.split('pip install wheel/torchmcubes-0.1.0-cp310-cp310-linux_x86_64.whl'))
+
+from tsr.system import TSR
+from tsr.utils import remove_background, resize_foreground, to_gradio_3d_orientation
+
+
+HEADER = """FRAME AI"""
+
+if torch.cuda.is_available():
+    device = "cuda:0"
+else:
+    device = "cpu"
+
+model = TSR.from_pretrained(
+    "stabilityai/TripoSR",
+    config_name="config.yaml",
+    weight_name="model.ckpt",
+)
+model.renderer.set_chunk_size(131072)
+model.to(device)
+
+rembg_session = rembg.new_session()
+
+def generate_image_from_text(pos_prompt):
+    # bedrock_runtime = boto3.client(region_name = 'us-east-1', service_name='bedrock-runtime')
+    bedrock_runtime = boto3.client(service_name='bedrock-runtime', aws_access_key_id = "AKIAZW3QSPMIH4RF42UA", aws_secret_access_key = "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", region_name='us-east-1')
+    parameters = {'text_prompts': [{'text':pos_prompt, 'weight':1},
+                                    {'text': """Blurry, unnatural, ugly, pixelated obscure, dull, artifacts, duplicate, bad quality, low resolution, cropped, out of frame, out of focus""", 'weight': -1}],
+                  'cfg_scale': 7, 'seed': 0, 'samples': 1}
+    request_body = json.dumps(parameters)
+    response = bedrock_runtime.invoke_model(body=request_body,modelId = 'stability.stable-diffusion-xl-v1')
+    response_body = json.loads(response.get('body').read())
+    base64_image_data = base64.b64decode(response_body['artifacts'][0]['base64'])
+    
+    return Image.open(io.BytesIO(base64_image_data))
+
+def check_input_image(input_image):
+    if input_image is None:
+        raise gr.Error("No image uploaded!")
+
+def preprocess(input_image, do_remove_background, foreground_ratio):
+    def fill_background(image):
+        image = np.array(image).astype(np.float32) / 255.0
+        image = image[:, :, :3] * image[:, :, 3:4] + (1 - image[:, :, 3:4]) * 0.5
+        image = Image.fromarray((image * 255.0).astype(np.uint8))
+        return image
+
+    if do_remove_background:
+        image = input_image.convert("RGB")
+        image = remove_background(image, rembg_session)
+        image = resize_foreground(image, foreground_ratio)
+        image = fill_background(image)
+    else:
+        image = input_image
+        if image.mode == "RGBA":
+            image = fill_background(image)
+    return image
+
+@spaces.GPU
+def generate(image, mc_resolution, formats=["obj", "glb"]):
+    scene_codes = model(image, device=device)
+    mesh = model.extract_mesh(scene_codes, resolution=mc_resolution)[0]
+    mesh = to_gradio_3d_orientation(mesh)
+
+    mesh_path_glb = tempfile.NamedTemporaryFile(suffix=f".glb", delete=False)
+    mesh.export(mesh_path_glb.name)
+
+    mesh_path_obj = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False)
+    mesh.apply_scale([-1, 1, 1])  # Otherwise the visualized .obj will be flipped
+    mesh.export(mesh_path_obj.name)
+    
+    return mesh_path_obj.name, mesh_path_glb.name
+
+def run_example(text_prompt, do_remove_background, foreground_ratio, mc_resolution):
+    # Step 1: Generate the image from text prompt
+    image_pil = generate_image_from_text(text_prompt)
+    
+    # Step 2: Preprocess the image
+    preprocessed = preprocess(image_pil, do_remove_background, foreground_ratio)
+    
+    # Step 3: Generate the 3D model
+    mesh_name_obj, mesh_name_glb = generate(preprocessed, mc_resolution, ["obj", "glb"])
+    
+    return preprocessed, mesh_name_obj, mesh_name_glb
+
+with gr.Blocks() as demo:
+    gr.Markdown(HEADER)
+    with gr.Row(variant="panel"):
+        with gr.Column():
+            with gr.Row():
+                text_prompt = gr.Textbox(
+                    label="Text Prompt",
+                    placeholder="Enter a text prompt for image generation"
+                )
+                input_image = gr.Image(
+                    label="Generated Image",
+                    image_mode="RGBA",
+                    sources="upload",
+                    type="pil",
+                    elem_id="content_image",
+                    visible=False  # Hidden since we generate the image from text
+                )
+                processed_image = gr.Image(label="Processed Image", interactive=False)
+            with gr.Row():
+                with gr.Group():
+                    do_remove_background = gr.Checkbox(
+                        label="Remove Background", value=True
+                    )
+                    foreground_ratio = gr.Slider(
+                        label="Foreground Ratio",
+                        minimum=0.5,
+                        maximum=1.0,
+                        value=0.85,
+                        step=0.05,
+                    )
+                    mc_resolution = gr.Slider(
+                        label="Marching Cubes Resolution",
+                        minimum=32,
+                        maximum=320,
+                        value=256,
+                        step=32
+                     )
+            with gr.Row():
+                submit = gr.Button("Generate", elem_id="generate", variant="primary")
+        with gr.Column():
+            with gr.Tab("OBJ"):
+                output_model_obj = gr.Model3D(
+                    label="Output Model (OBJ Format)",
+                    interactive=False,
+                )
+                gr.Markdown("Note: Downloaded object will be flipped in case of .obj export. Export .glb instead or manually flip it before usage.")
+            with gr.Tab("GLB"):
+                output_model_glb = gr.Model3D(
+                    label="Output Model (GLB Format)",
+                    interactive=False,
+                )
+                gr.Markdown("Note: The model shown here has a darker appearance. Download to get correct results.")
+    with gr.Row(variant="panel"):
+        gr.Examples(
+            examples=[
+                os.path.join("examples", img_name) for img_name in sorted(os.listdir("examples"))
+            ],
+            inputs=[text_prompt],
+            outputs=[processed_image, output_model_obj, output_model_glb],
+            cache_examples=True,
+            fn=partial(run_example, do_remove_background=True, foreground_ratio=0.85, mc_resolution=256),
+            label="Examples",
+            examples_per_page=20
+        )
+    submit.click(fn=check_input_image, inputs=[text_prompt]).success(
+        fn=run_example,
+        inputs=[text_prompt, do_remove_background, foreground_ratio, mc_resolution],
+        outputs=[processed_image, output_model_obj, output_model_glb],
+    )
+
+demo.queue(max_size=10)
+demo.launch()

requirements.txt

@@ -0,0 +1,10 @@
+omegaconf==2.3.0
+Pillow==10.1.0
+einops==0.7.0
+torch==2.0.1
+transformers==4.35.0
+trimesh==4.0.5
+rembg
+huggingface-hub
+gradio
+boto3