import os import shutil import tempfile import gradio as gr import numpy as np import rembg import spaces import torch from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler, StableDiffusionXLPipeline, EulerDiscreteScheduler from einops import rearrange from huggingface_hub import hf_hub_download from omegaconf import OmegaConf from PIL import Image from pytorch_lightning import seed_everything from torchvision.transforms import v2 from safetensors.torch import load_file from src.utils.camera_util import (FOV_to_intrinsics, get_circular_camera_poses, get_zero123plus_input_cameras) from src.utils.infer_util import (remove_background, resize_foreground) from src.utils.mesh_util import save_glb, save_obj from src.utils.train_util import instantiate_from_config 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 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 check_input_image(input_image): if input_image is None: raise gr.Error("No image selected!") 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 print(mesh_fpath) 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) print(f"Mesh saved to {mesh_fpath}") return mesh_fpath, mesh_glb_fpath @spaces.GPU def generate_image(prompt): checkpoint = "sdxl_lightning_8step_unet.safetensors" num_inference_steps = 8 pipe.scheduler = EulerDiscreteScheduler.from_config( pipe.scheduler.config, timestep_spacing="trailing") pipe.unet.load_state_dict( load_file(hf_hub_download(repo, checkpoint), device="cuda")) results = pipe( prompt, num_inference_steps=num_inference_steps, guidance_scale=0) return results.images[0] # Configuration cuda_path = find_cuda() 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 = config_name.startswith('instant-mesh') device = torch.device('cuda') # Load diffusion model 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' ) 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) # Load StableDiffusionXL model base = "stabilityai/stable-diffusion-xl-base-1.0" repo = "ByteDance/SDXL-Lightning" pipe = StableDiffusionXLPipeline.from_pretrained( base, torch_dtype=torch.float16, variant="fp16").to("cuda") print('Loading Finished!') # Gradio UI with gr.Blocks() as demo: with gr.Group(): with gr.Row(): prompt = gr.Textbox(label='Enter your prompt (English)', scale=8) submit_prompt = gr.Button( scale=1, variant='primary', label='Generate Image') img = gr.Image(label='SDXL-Lightning Generated Image') with gr.Row(variant="panel"): with gr.Column(): with gr.Row(): input_image = gr.Image( label="Input Image", image_mode="RGBA", sources="upload", type="pil", elem_id="content_image", ) processed_image = gr.Image( label="Processed Image", image_mode="RGBA", type="pil", interactive=False ) with gr.Row(): with gr.Group(): do_remove_background = gr.Checkbox( label="Remove Background", value=True) sample_seed = gr.Number( value=42, label="Seed Value", precision=0) sample_steps = gr.Slider( label="Sample Steps", minimum=30, maximum=75, value=75, step=5) with gr.Row(): submit_mesh = gr.Button( "Generate", elem_id="generate", variant="primary") with gr.Row(variant="panel"): gr.Examples( examples=[os.path.join("examples", img_name) for img_name in sorted(os.listdir("examples"))], inputs=[input_image], label="Examples", cache_examples=False, examples_per_page=16 ) with gr.Column(): with gr.Row(): with gr.Column(): mv_show_images = gr.Image( label="Generated Multi-views", type="pil", width=379, interactive=False ) with gr.Row(): with gr.Tab("OBJ"): output_model_obj = gr.Model3D( label="Output Model (OBJ Format)", interactive=False, ) gr.Markdown( "Note: Downloaded .obj model will be flipped. 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(): gr.Markdown( '''Try a different seed value if the result is unsatisfying (Default: 42).''') mv_images = gr.State() submit_prompt.click(fn=generate_image, inputs=[prompt], outputs=img) submit_mesh.click(fn=check_input_image, inputs=[input_image]).success( fn=preprocess, inputs=[input_image, do_remove_background], outputs=[processed_image], ).success( fn=generate_mvs, inputs=[processed_image, sample_steps, sample_seed], outputs=[mv_images, mv_show_images] ).success( fn=make3d, inputs=[mv_images], outputs=[output_model_obj, output_model_glb] ) demo.launch()