import spaces import os import time from os import path from huggingface_hub import hf_hub_download import numpy as np import torch import rembg from PIL import Image from torchvision.transforms import v2 from einops import rearrange from pytorch_lightning import seed_everything from omegaconf import OmegaConf from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler import gradio as gr import shutil import tempfile from src.utils.train_util import instantiate_from_config from src.utils.camera_util import ( FOV_to_intrinsics, get_zero123plus_input_cameras, get_circular_camera_poses, ) from src.utils.mesh_util import save_obj, save_glb from src.utils.infer_util import remove_background, resize_foreground, images_to_video import random import requests import io # Set up cache path cache_path = path.join(path.dirname(path.abspath(__file__)), "models") os.environ["TRANSFORMERS_CACHE"] = cache_path os.environ["HF_HUB_CACHE"] = cache_path os.environ["HF_HOME"] = cache_path huggingface_token = os.getenv("HUGGINGFACE_TOKEN") if not path.exists(cache_path): os.makedirs(cache_path, exist_ok=True) torch.backends.cuda.matmul.allow_tf32 = True class timer: def __init__(self, method_name="timed process"): self.method = method_name def __enter__(self): self.start = time.time() print(f"{self.method} starts") def __exit__(self, exc_type, exc_val, exc_tb): end = time.time() print(f"{self.method} took {str(round(end - self.start, 2))}s") def find_cuda(): cuda_home = os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH') if cuda_home and os.path.exists(cuda_home): return cuda_home nvcc_path = shutil.which('nvcc') if nvcc_path: cuda_path = os.path.dirname(os.path.dirname(nvcc_path)) return cuda_path return None cuda_path = find_cuda() if cuda_path: print(f"CUDA installation found at: {cuda_path}") else: print("CUDA installation not found") API_TOKEN = os.getenv("HUGGINGFACE_TOKEN") headers = {"Authorization": f"Bearer {API_TOKEN}"} timeout = 100 device = 'cuda' # Load 3D generation models config_path = 'configs/instant-mesh-large.yaml' config = OmegaConf.load(config_path) config_name = os.path.basename(config_path).replace('.yaml', '') model_config = config.model_config infer_config = config.infer_config IS_FLEXICUBES = True if config_name.startswith('instant-mesh') else False # Load diffusion model for 3D generation print('Loading diffusion model ...') pipeline = DiffusionPipeline.from_pretrained( "sudo-ai/zero123plus-v1.2", custom_pipeline="zero123plus", torch_dtype=torch.float16, ) pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config( pipeline.scheduler.config, timestep_spacing='trailing' ) # Load custom white-background UNet unet_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename="diffusion_pytorch_model.bin", repo_type="model") state_dict = torch.load(unet_ckpt_path, map_location='cpu') pipeline.unet.load_state_dict(state_dict, strict=True) pipeline = pipeline.to(device) # Load reconstruction model print('Loading reconstruction model ...') model_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename="instant_mesh_large.ckpt", repo_type="model") model = instantiate_from_config(model_config) state_dict = torch.load(model_ckpt_path, map_location='cpu')['state_dict'] state_dict = {k[14:]: v for k, v in state_dict.items() if k.startswith('lrm_generator.') and 'source_camera' not in k} model.load_state_dict(state_dict, strict=True) model = model.to(device) print('Loading Finished!') def get_render_cameras(batch_size=1, M=120, radius=2.5, elevation=10.0, is_flexicubes=False): c2ws = get_circular_camera_poses(M=M, radius=radius, elevation=elevation) if is_flexicubes: cameras = torch.linalg.inv(c2ws) cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1, 1) else: extrinsics = c2ws.flatten(-2) intrinsics = FOV_to_intrinsics(50.0).unsqueeze(0).repeat(M, 1, 1).float().flatten(-2) cameras = torch.cat([extrinsics, intrinsics], dim=-1) cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1) return cameras def preprocess(input_image, do_remove_background): rembg_session = rembg.new_session() if do_remove_background else None if do_remove_background: input_image = remove_background(input_image, rembg_session) input_image = resize_foreground(input_image, 0.85) return input_image @spaces.GPU def generate_mvs(input_image, sample_steps, sample_seed): seed_everything(sample_seed) z123_image = pipeline( input_image, num_inference_steps=sample_steps ).images[0] show_image = np.asarray(z123_image, dtype=np.uint8) show_image = torch.from_numpy(show_image) show_image = rearrange(show_image, '(n h) (m w) c -> (n m) h w c', n=3, m=2) show_image = rearrange(show_image, '(n m) h w c -> (n h) (m w) c', n=2, m=3) show_image = Image.fromarray(show_image.numpy()) return z123_image, show_image @spaces.GPU def make3d(images): global model if IS_FLEXICUBES: model.init_flexicubes_geometry(device, use_renderer=False) model = model.eval() images = np.asarray(images, dtype=np.float32) / 255.0 images = torch.from_numpy(images).permute(2, 0, 1).contiguous().float() images = rearrange(images, 'c (n h) (m w) -> (n m) c h w', n=3, m=2) input_cameras = get_zero123plus_input_cameras(batch_size=1, radius=4.0).to(device) render_cameras = get_render_cameras(batch_size=1, radius=2.5, is_flexicubes=IS_FLEXICUBES).to(device) images = images.unsqueeze(0).to(device) images = v2.functional.resize(images, (320, 320), interpolation=3, antialias=True).clamp(0, 1) mesh_fpath = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False).name mesh_basename = os.path.basename(mesh_fpath).split('.')[0] mesh_dirname = os.path.dirname(mesh_fpath) mesh_glb_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.glb") with torch.no_grad(): planes = model.forward_planes(images, input_cameras) mesh_out = model.extract_mesh( planes, use_texture_map=False, **infer_config, ) vertices, faces, vertex_colors = mesh_out vertices = vertices[:, [1, 2, 0]] save_glb(vertices, faces, vertex_colors, mesh_glb_fpath) save_obj(vertices, faces, vertex_colors, mesh_fpath) return mesh_fpath, mesh_glb_fpath # Remove the FluxPipeline setup and replace with the query function def query(prompt, steps=28, cfg_scale=3.5, randomize_seed=True, seed=-1, width=1024, height=1024): if not prompt: return None lora_id = "gokaygokay/Flux-Game-Assets-LoRA-v2" API_URL = f"https://api-inference.huggingface.co/models/{lora_id}" if randomize_seed: seed = random.randint(1, 4294967296) payload = { "inputs": prompt, "steps": steps, "cfg_scale": cfg_scale, "seed": seed, "parameters": { "width": width, "height": height } } response = requests.post(API_URL, headers=headers, json=payload, timeout=100) if response.status_code != 200: if response.status_code == 503: raise gr.Error("The model is being loaded") raise gr.Error(f"Error {response.status_code}") try: image_bytes = response.content image = Image.open(io.BytesIO(image_bytes)) return image except Exception as e: print(f"Error when trying to open the image: {e}") return None # Update the Gradio interface with gr.Blocks(theme=gr.themes.Soft()) as demo: gr.Markdown( """

Flux Image to 3D Model Generator

""" ) with gr.Row(): with gr.Column(scale=3): prompt = gr.Textbox( label="Your Image Description", placeholder="E.g., A serene landscape with mountains and a lake at sunset", lines=3 ) with gr.Accordion("Advanced Settings", open=False): with gr.Group(): with gr.Row(): height = gr.Slider(label="Height", minimum=256, maximum=1152, step=64, value=1024) width = gr.Slider(label="Width", minimum=256, maximum=1152, step=64, value=1024) with gr.Row(): steps = gr.Slider(label="Inference Steps", minimum=10, maximum=50, step=1, value=28) scales = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=5.0, step=0.1, value=3.5) seed = gr.Number(label="Seed", value=-1, precision=0) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) generate_btn = gr.Button("Generate 3D Model", variant="primary") with gr.Column(scale=4): flux_output = gr.Image(label="Generated Flux Image") mv_show_images = gr.Image(label="Generated Multi-views") with gr.Row(): with gr.Tab("OBJ"): output_model_obj = gr.Model3D(label="Output Model (OBJ Format)") with gr.Tab("GLB"): output_model_glb = gr.Model3D(label="Output Model (GLB Format)") mv_images = gr.State() def process_pipeline(prompt, height, width, steps, scales, seed, randomize_seed): # Generate Flux image using the API prompt_real = f"wbgmsst, {prompt}, white background" flux_image = query(prompt_real, steps, scales, randomize_seed, seed, width, height) if flux_image is None: raise gr.Error("Failed to generate image") processed_image = preprocess(flux_image, do_remove_background=True) mv_images, show_image = generate_mvs(processed_image, steps, seed) obj_path, glb_path = make3d(mv_images) return flux_image, show_image, obj_path, glb_path generate_btn.click( fn=process_pipeline, inputs=[prompt, height, width, steps, scales, seed, randomize_seed], outputs=[flux_output, mv_show_images, output_model_obj, output_model_glb] ) if __name__ == "__main__": demo.queue().launch()