Upload external/MV-Adapter/scripts/inference_ig2mv_sd.py with huggingface_hub

external/MV-Adapter/scripts/inference_ig2mv_sd.py

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+import argparse
+
+import numpy as np
+import torch
+from diffusers import AutoencoderKL, DDPMScheduler, LCMScheduler, UNet2DConditionModel
+from PIL import Image
+from torchvision import transforms
+from tqdm import tqdm
+from transformers import AutoModelForImageSegmentation
+
+from mvadapter.models.attention_processor import DecoupledMVRowColSelfAttnProcessor2_0
+from mvadapter.pipelines.pipeline_mvadapter_i2mv_sd import MVAdapterI2MVSDPipeline
+from mvadapter.schedulers.scheduling_shift_snr import ShiftSNRScheduler
+from mvadapter.utils import make_image_grid, tensor_to_image
+from mvadapter.utils.mesh_utils import (
+    NVDiffRastContextWrapper,
+    get_orthogonal_camera,
+    load_mesh,
+    render,
+)
+
+
+def prepare_pipeline(
+    base_model,
+    vae_model,
+    unet_model,
+    lora_model,
+    adapter_path,
+    scheduler,
+    num_views,
+    device,
+    dtype,
+):
+    # Load vae and unet if provided
+    pipe_kwargs = {}
+    if vae_model is not None:
+        pipe_kwargs["vae"] = AutoencoderKL.from_pretrained(vae_model)
+    if unet_model is not None:
+        pipe_kwargs["unet"] = UNet2DConditionModel.from_pretrained(unet_model)
+
+    # Prepare pipeline
+    pipe: MVAdapterI2MVSDPipeline
+    pipe = MVAdapterI2MVSDPipeline.from_pretrained(base_model, **pipe_kwargs)
+
+    # Load scheduler if provided
+    scheduler_class = None
+    if scheduler == "ddpm":
+        scheduler_class = DDPMScheduler
+    elif scheduler == "lcm":
+        scheduler_class = LCMScheduler
+
+    pipe.scheduler = ShiftSNRScheduler.from_scheduler(
+        pipe.scheduler,
+        shift_mode="interpolated",
+        shift_scale=8.0,
+        scheduler_class=scheduler_class,
+    )
+    pipe.init_custom_adapter(
+        num_views=num_views, self_attn_processor=DecoupledMVRowColSelfAttnProcessor2_0
+    )
+    pipe.load_custom_adapter(
+        adapter_path, weight_name="mvadapter_ig2mv_sd21.safetensors"
+    )
+
+    pipe.to(device=device, dtype=dtype)
+    pipe.cond_encoder.to(device=device, dtype=dtype)
+
+    # load lora if provided
+    if lora_model is not None:
+        model_, name_ = lora_model.rsplit("/", 1)
+        pipe.load_lora_weights(model_, weight_name=name_)
+
+    # vae slicing for lower memory usage
+    pipe.enable_vae_slicing()
+
+    return pipe
+
+
+def remove_bg(image, net, transform, device):
+    image_size = image.size
+    input_images = transform(image).unsqueeze(0).to(device)
+    with torch.no_grad():
+        preds = net(input_images)[-1].sigmoid().cpu()
+    pred = preds[0].squeeze()
+    pred_pil = transforms.ToPILImage()(pred)
+    mask = pred_pil.resize(image_size)
+    image.putalpha(mask)
+    return image
+
+
+def preprocess_image(image: Image.Image, height, width):
+    image = np.array(image)
+    alpha = image[..., 3] > 0
+    H, W = alpha.shape
+    # get the bounding box of alpha
+    y, x = np.where(alpha)
+    y0, y1 = max(y.min() - 1, 0), min(y.max() + 1, H)
+    x0, x1 = max(x.min() - 1, 0), min(x.max() + 1, W)
+    image_center = image[y0:y1, x0:x1]
+    # resize the longer side to H * 0.9
+    H, W, _ = image_center.shape
+    if H > W:
+        W = int(W * (height * 0.9) / H)
+        H = int(height * 0.9)
+    else:
+        H = int(H * (width * 0.9) / W)
+        W = int(width * 0.9)
+    image_center = np.array(Image.fromarray(image_center).resize((W, H)))
+    # pad to H, W
+    start_h = (height - H) // 2
+    start_w = (width - W) // 2
+    image = np.zeros((height, width, 4), dtype=np.uint8)
+    image[start_h : start_h + H, start_w : start_w + W] = image_center
+    image = image.astype(np.float32) / 255.0
+    image = image[:, :, :3] * image[:, :, 3:4] + (1 - image[:, :, 3:4]) * 0.5
+    image = (image * 255).clip(0, 255).astype(np.uint8)
+    image = Image.fromarray(image)
+
+    return image
+
+
+def run_pipeline(
+    pipe,
+    mesh_path,
+    num_views,
+    text,
+    image,
+    height,
+    width,
+    num_inference_steps,
+    guidance_scale,
+    seed,
+    remove_bg_fn=None,
+    reference_conditioning_scale=1.0,
+    negative_prompt="watermark, ugly, deformed, noisy, blurry, low contrast",
+    lora_scale=1.0,
+    device="cuda",
+):
+    # Prepare cameras
+    cameras = get_orthogonal_camera(
+        elevation_deg=[0, 0, 0, 0, 89.99, -89.99],
+        distance=[1.8] * num_views,
+        left=-0.55,
+        right=0.55,
+        bottom=-0.55,
+        top=0.55,
+        azimuth_deg=[x - 90 for x in [0, 90, 180, 270, 180, 180]],
+        device=device,
+    )
+    ctx = NVDiffRastContextWrapper(device=device)
+
+    mesh = load_mesh(mesh_path, rescale=True, device=device)
+    render_out = render(
+        ctx,
+        mesh,
+        cameras,
+        height=height,
+        width=width,
+        render_attr=False,
+        normal_background=0.0,
+    )
+    pos_images = tensor_to_image((render_out.pos + 0.5).clamp(0, 1), batched=True)
+    normal_images = tensor_to_image(
+        (render_out.normal / 2 + 0.5).clamp(0, 1), batched=True
+    )
+    control_images = (
+        torch.cat(
+            [
+                (render_out.pos + 0.5).clamp(0, 1),
+                (render_out.normal / 2 + 0.5).clamp(0, 1),
+            ],
+            dim=-1,
+        )
+        .permute(0, 3, 1, 2)
+        .to(device)
+    )
+
+    # Prepare image
+    reference_image = Image.open(image) if isinstance(image, str) else image
+    if remove_bg_fn is not None:
+        reference_image = remove_bg_fn(reference_image)
+        reference_image = preprocess_image(reference_image, height, width)
+    elif reference_image.mode == "RGBA":
+        reference_image = preprocess_image(reference_image, height, width)
+
+    pipe_kwargs = {}
+    if seed != -1 and isinstance(seed, int):
+        pipe_kwargs["generator"] = torch.Generator(device=device).manual_seed(seed)
+
+    images = pipe(
+        text,
+        height=height,
+        width=width,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        num_images_per_prompt=num_views,
+        control_image=control_images,
+        control_conditioning_scale=1.0,
+        reference_image=reference_image,
+        reference_conditioning_scale=reference_conditioning_scale,
+        negative_prompt=negative_prompt,
+        cross_attention_kwargs={"scale": lora_scale},
+        **pipe_kwargs,
+    ).images
+
+    return images, pos_images, normal_images, reference_image
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # Models
+    parser.add_argument(
+        "--base_model", type=str, default="stabilityai/stable-diffusion-2-1-base"
+    )
+    parser.add_argument("--vae_model", type=str, default=None)
+    parser.add_argument("--unet_model", type=str, default=None)
+    parser.add_argument("--scheduler", type=str, default=None)
+    parser.add_argument("--lora_model", type=str, default=None)
+    parser.add_argument("--adapter_path", type=str, default="huanngzh/mv-adapter")
+    parser.add_argument("--num_views", type=int, default=6)
+    # Device
+    parser.add_argument("--device", type=str, default="cuda")
+    # Inference
+    parser.add_argument("--mesh", type=str, required=True)
+    parser.add_argument("--image", type=str, required=True)
+    parser.add_argument("--text", type=str, required=False, default="high quality")
+    parser.add_argument("--num_inference_steps", type=int, default=50)
+    parser.add_argument("--guidance_scale", type=float, default=3.0)
+    parser.add_argument("--seed", type=int, default=-1)
+    parser.add_argument("--lora_scale", type=float, default=1.0)
+    parser.add_argument("--reference_conditioning_scale", type=float, default=1.0)
+    parser.add_argument(
+        "--negative_prompt",
+        type=str,
+        default="watermark, ugly, deformed, noisy, blurry, low contrast",
+    )
+    parser.add_argument("--output", type=str, default="output.png")
+    # Extra
+    parser.add_argument("--remove_bg", action="store_true", help="Remove background")
+    args = parser.parse_args()
+
+    pipe = prepare_pipeline(
+        base_model=args.base_model,
+        vae_model=args.vae_model,
+        unet_model=args.unet_model,
+        lora_model=args.lora_model,
+        adapter_path=args.adapter_path,
+        scheduler=args.scheduler,
+        num_views=args.num_views,
+        device=args.device,
+        dtype=torch.float16,
+    )
+
+    if args.remove_bg:
+        birefnet = AutoModelForImageSegmentation.from_pretrained(
+            "ZhengPeng7/BiRefNet", trust_remote_code=True
+        )
+        birefnet.to(args.device)
+        transform_image = transforms.Compose(
+            [
+                transforms.Resize((1024, 1024)),
+                transforms.ToTensor(),
+                transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+            ]
+        )
+        remove_bg_fn = lambda x: remove_bg(x, birefnet, transform_image, args.device)
+    else:
+        remove_bg_fn = None
+
+    images, pos_images, normal_images, reference_image = run_pipeline(
+        pipe,
+        mesh_path=args.mesh,
+        num_views=args.num_views,
+        text=args.text,
+        image=args.image,
+        height=512,
+        width=512,
+        num_inference_steps=args.num_inference_steps,
+        guidance_scale=args.guidance_scale,
+        seed=args.seed,
+        lora_scale=args.lora_scale,
+        reference_conditioning_scale=args.reference_conditioning_scale,
+        negative_prompt=args.negative_prompt,
+        device=args.device,
+        remove_bg_fn=remove_bg_fn,
+    )
+    make_image_grid(images, rows=1).save(args.output)
+    make_image_grid(pos_images, rows=1).save(args.output.rsplit(".", 1)[0] + "_pos.png")
+    make_image_grid(normal_images, rows=1).save(
+        args.output.rsplit(".", 1)[0] + "_nor.png"
+    )
+    reference_image.save(args.output.rsplit(".", 1)[0] + "_reference.png")