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()