import spaces import os, logging, time, argparse, random, tempfile, rembg, shlex, subprocess import gradio as gr import numpy as np import torch from PIL import Image from functools import partial 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 from src.scheduler_perflow import PeRFlowScheduler from diffusers import StableDiffusionPipeline, UNet2DConditionModel def merge_delta_weights_into_unet(pipe, delta_weights, org_alpha = 1.0): unet_weights = pipe.unet.state_dict() for key in delta_weights.keys(): dtype = unet_weights[key].dtype try: unet_weights[key] = org_alpha * unet_weights[key].to(dtype=delta_weights[key].dtype) + delta_weights[key].to(device=unet_weights[key].device) except: unet_weights[key] = unet_weights[key].to(dtype=delta_weights[key].dtype) unet_weights[key] = unet_weights[key].to(dtype) pipe.unet.load_state_dict(unet_weights, strict=True) return pipe def setup_seed(seed): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) torch.backends.cudnn.deterministic = True if torch.cuda.is_available(): device = "cuda:0" else: device = "cpu" ### TripoSR model = TSR.from_pretrained( "stabilityai/TripoSR", config_name="config.yaml", weight_name="model.ckpt", ) # adjust the chunk size to balance between speed and memory usage model.renderer.set_chunk_size(8192) model.to(device) ### PeRFlow-T2I # pipe_t2i = StableDiffusionPipeline.from_pretrained("Lykon/dreamshaper-8", torch_dtype=torch.float16, safety_checker=None) pipe_t2i = StableDiffusionPipeline.from_pretrained("stablediffusionapi/disney-pixar-cartoon", torch_dtype=torch.float16, safety_checker=None) delta_weights = UNet2DConditionModel.from_pretrained("hansyan/piecewise-rectified-flow-delta-weights", torch_dtype=torch.float16, variant="v0-1",).state_dict() pipe_t2i = merge_delta_weights_into_unet(pipe_t2i, delta_weights) pipe_t2i.scheduler = PeRFlowScheduler.from_config(pipe_t2i.scheduler.config, prediction_type="epsilon", num_time_windows=4) pipe_t2i.to('cuda:0', torch.float16) ### gradio rembg_session = rembg.new_session() @spaces.GPU def generate(text, seed): 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 setup_seed(int(seed)) # text = text samples = pipe_t2i( prompt = [text], negative_prompt = ["distorted, blur, low-quality, haze, out of focus"], height = 512, width = 512, # num_inference_steps = 4, # guidance_scale = 4.5, num_inference_steps = 6, guidance_scale = 7, output_type = 'pt', ).images samples = torch.nn.functional.interpolate(samples, size=768, mode='bilinear') samples = samples.squeeze(0).permute(1, 2, 0).cpu().numpy()*255. samples = samples.astype(np.uint8) samples = Image.fromarray(samples[:, :, :3]) image = remove_background(samples, rembg_session) image = resize_foreground(image, 0.85) image = fill_background(image) return image @spaces.GPU def render(image, mc_resolution=256, formats=["obj"]): scene_codes = model(image, device=device) mesh = model.extract_mesh(scene_codes, resolution=mc_resolution)[0] mesh = to_gradio_3d_orientation(mesh) rv = [] for format in formats: mesh_path = tempfile.NamedTemporaryFile(suffix=f".{format}", delete=False) mesh.export(mesh_path.name) rv.append(mesh_path.name) return rv[0] # # warm up # _ = generate("a bird", 42) # layout css = """ h1 { text-align: center; display:block; } h2 { text-align: center; display:block; } h3 { text-align: center; display:block; } """ with gr.Blocks(title="TripoSR", css=css) as interface: gr.Markdown( """ # Instant Text-to-3D Mesh Demo ### [PeRFlow](https://github.com/magic-research/piecewise-rectified-flow)-T2I + [TripoSR](https://github.com/VAST-AI-Research/TripoSR) Two-stage synthesis: 1) generating images by PeRFlow-T2I with 6-step inference; 2) rendering 3D assests. """ ) with gr.Column(): with gr.Row(): output_image = gr.Image(label='Generated Image', height=384,) output_model_obj = gr.Model3D( label="Output 3D Model (OBJ Format)", interactive=False, height=384, ) with gr.Row(): textbox = gr.Textbox(label="Input Prompt", value="a colorful bird") seed = gr.Textbox(label="Random Seed", value=42) # activate textbox.submit( fn=generate, inputs=[textbox, seed], outputs=[output_image], ).success( fn=render, inputs=[output_image], outputs=[output_model_obj], ) seed.submit( fn=generate, inputs=[textbox, seed], outputs=[output_image], ).success( fn=render, inputs=[output_image], outputs=[output_model_obj], ) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--username', type=str, default=None, help='Username for authentication') parser.add_argument('--password', type=str, default=None, help='Password for authentication') parser.add_argument('--port', type=int, default=7860, help='Port to run the server listener on') parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests") parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site") parser.add_argument("--queuesize", type=int, default=1, help="launch gradio queue max_size") args = parser.parse_args() interface.queue(max_size=args.queuesize) interface.launch( auth=(args.username, args.password) if (args.username and args.password) else None, share=args.share, server_name="0.0.0.0" if args.listen else None, server_port=args.port )