infer_api_bk.py

import spaces
from PIL import Image

import io
import argparse
import os
import random
import tempfile
from typing import Dict, Optional, Tuple
from omegaconf import OmegaConf
import numpy as np

import torch

from diffusers import AutoencoderKL, DDIMScheduler
from diffusers.utils import check_min_version
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer, CLIPImageProcessor, CLIPVisionModelWithProjection
from torchvision import transforms

from canonicalize.models.unet_mv2d_condition import UNetMV2DConditionModel
from canonicalize.models.unet_mv2d_ref import UNetMV2DRefModel
from canonicalize.pipeline_canonicalize import CanonicalizationPipeline
from einops import rearrange
from torchvision.utils import save_image
import json
import cv2

import onnxruntime as rt
from huggingface_hub.file_download import hf_hub_download
from huggingface_hub import list_repo_files
from rm_anime_bg.cli import get_mask, SCALE

import argparse
import os
import cv2
import glob
import numpy as np
import matplotlib.pyplot as plt
from typing import Dict, Optional,  List
from omegaconf import OmegaConf, DictConfig
from PIL import Image
from pathlib import Path
from dataclasses import dataclass
from typing import Dict
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import torchvision.transforms.functional as TF
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from torchvision.utils import make_grid, save_image
from accelerate.utils import set_seed
from tqdm.auto import tqdm
from einops import rearrange, repeat
from multiview.pipeline_multiclass import StableUnCLIPImg2ImgPipeline

import os
import imageio
import numpy as np
import torch
import cv2
import glob
import matplotlib.pyplot as plt
from PIL import Image
from torchvision.transforms import v2
from pytorch_lightning import seed_everything
from omegaconf import OmegaConf
from tqdm import tqdm

from slrm.utils.train_util import instantiate_from_config
from slrm.utils.camera_util import (
    FOV_to_intrinsics, 
    get_circular_camera_poses,
)
from slrm.utils.mesh_util import save_obj, save_glb
from slrm.utils.infer_util import images_to_video

import cv2
import numpy as np
import os
import trimesh
import argparse
import torch
import scipy
from PIL import Image

from refine.mesh_refine import geo_refine
from refine.func import make_star_cameras_orthographic
from refine.render import NormalsRenderer, calc_vertex_normals

import pytorch3d
from pytorch3d.structures import Meshes
from sklearn.neighbors import KDTree

from segment_anything import SamAutomaticMaskGenerator, sam_model_registry

check_min_version("0.24.0")
weight_dtype = torch.float16
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
VIEWS = ['front', 'front_right', 'right', 'back', 'left', 'front_left']


@spaces.GPU
def set_seed(seed):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)


session_infer_path = hf_hub_download(
    repo_id="skytnt/anime-seg", filename="isnetis.onnx",
)
providers: list[str] = ["CPUExecutionProvider"]
if "CUDAExecutionProvider" in rt.get_available_providers():
    providers = ["CUDAExecutionProvider"]

bkg_remover_session_infer = rt.InferenceSession(
    session_infer_path, providers=providers,
)

@spaces.GPU
def remove_background(
    img: np.ndarray,
    alpha_min: float,
    alpha_max: float,
) -> list:
    img = np.array(img)
    mask = get_mask(bkg_remover_session_infer, img)
    mask[mask < alpha_min] = 0.0
    mask[mask > alpha_max] = 1.0
    img_after = (mask * img).astype(np.uint8)
    mask = (mask * SCALE).astype(np.uint8)
    img_after = np.concatenate([img_after, mask], axis=2, dtype=np.uint8)
    return Image.fromarray(img_after)


def process_image(image, totensor, width, height):
    assert image.mode == "RGBA"

    # Find non-transparent pixels
    non_transparent = np.nonzero(np.array(image)[..., 3])
    min_x, max_x = non_transparent[1].min(), non_transparent[1].max()
    min_y, max_y = non_transparent[0].min(), non_transparent[0].max()    
    image = image.crop((min_x, min_y, max_x, max_y))

    # paste to center
    max_dim = max(image.width, image.height)
    max_height = int(max_dim * 1.2)
    max_width = int(max_dim / (height/width) * 1.2)
    new_image = Image.new("RGBA", (max_width, max_height))
    left = (max_width - image.width) // 2
    top = (max_height - image.height) // 2
    new_image.paste(image, (left, top))

    image = new_image.resize((width, height), resample=Image.BICUBIC)
    image = np.array(image)
    image = image.astype(np.float32) / 255.
    assert image.shape[-1] == 4  # RGBA
    alpha = image[..., 3:4]
    bg_color = np.array([1., 1., 1.], dtype=np.float32)
    image = image[..., :3] * alpha + bg_color * (1 - alpha)
    return totensor(image)


@spaces.GPU
@torch.no_grad()
def inference(validation_pipeline, input_image, vae, feature_extractor, image_encoder, unet, ref_unet, tokenizer,
              text_encoder, pretrained_model_path, validation, val_width, val_height, unet_condition_type,
              use_noise=True, noise_d=256, crop=False, seed=100, timestep=20):
    set_seed(seed)
    generator = torch.Generator(device=device).manual_seed(seed)
    
    totensor = transforms.ToTensor()

    prompts = "high quality, best quality"
    prompt_ids = tokenizer(
        prompts, max_length=tokenizer.model_max_length, padding="max_length", truncation=True,
        return_tensors="pt"
    ).input_ids[0]

    # (B*Nv, 3, H, W)
    B = 1
    if input_image.mode != "RGBA":
        # remove background
        input_image = remove_background(input_image, 0.1, 0.9)
    imgs_in = process_image(input_image, totensor, val_width, val_height)
    imgs_in = rearrange(imgs_in.unsqueeze(0).unsqueeze(0), "B Nv C H W -> (B Nv) C H W")

    with torch.autocast('cuda' if torch.cuda.is_available() else 'cpu', dtype=weight_dtype):
        imgs_in = imgs_in.to(device=device)
        # B*Nv images
        out = validation_pipeline(prompt=prompts, image=imgs_in.to(weight_dtype), generator=generator, 
                                  num_inference_steps=timestep, prompt_ids=prompt_ids, 
                                  height=val_height, width=val_width, unet_condition_type=unet_condition_type, 
                                  use_noise=use_noise, **validation,)
        out = rearrange(out, "B C f H W -> (B f) C H W", f=1)

    print("OUT!!!!!!")

    img_buf = io.BytesIO()
    save_image(out[0], img_buf, format='PNG')
    img_buf.seek(0)
    img = Image.open(img_buf)

    print("OUT2!!!!!!")

    torch.cuda.empty_cache()
    return img


######### Multi View Part #############
weight_dtype = torch.float16
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

def tensor_to_numpy(tensor):
    return tensor.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()


@dataclass
class TestConfig:
    pretrained_model_name_or_path: str
    pretrained_unet_path:Optional[str]
    revision: Optional[str]
    validation_dataset: Dict
    save_dir: str
    seed: Optional[int]
    validation_batch_size: int
    dataloader_num_workers: int
    save_mode: str
    local_rank: int

    pipe_kwargs: Dict
    pipe_validation_kwargs: Dict
    unet_from_pretrained_kwargs: Dict
    validation_grid_nrow: int
    camera_embedding_lr_mult: float

    num_views: int
    camera_embedding_type: str

    pred_type: str
    regress_elevation: bool
    enable_xformers_memory_efficient_attention: bool

    cond_on_normals: bool
    cond_on_colors: bool
    
    regress_elevation: bool
    regress_focal_length: bool
    


def convert_to_numpy(tensor):
    return tensor.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()

def save_image(tensor):
    ndarr = convert_to_numpy(tensor)
    return save_image_numpy(ndarr)

def save_image_numpy(ndarr):
    im = Image.fromarray(ndarr)
    # pad to square
    if im.size[0] != im.size[1]:
        size = max(im.size)
        new_im = Image.new("RGB", (size, size))
        # set to white
        new_im.paste((255, 255, 255), (0, 0, size, size))
        new_im.paste(im, ((size - im.size[0]) // 2, (size - im.size[1]) // 2))
        im = new_im
    # resize to 1024x1024
    im = im.resize((1024, 1024), Image.LANCZOS)
    return im

@spaces.GPU
def run_multiview_infer(data, pipeline, cfg: TestConfig, num_levels=3):
    if cfg.seed is None:
        generator = None
    else:
        generator = torch.Generator(device=pipeline.unet.device).manual_seed(cfg.seed)
    
    images_cond = []
    results = {}

    torch.cuda.empty_cache()
    images_cond.append(data['image_cond_rgb'][:, 0].cuda()) 
    imgs_in = torch.cat([data['image_cond_rgb']]*2, dim=0).cuda()
    num_views = imgs_in.shape[1]
    imgs_in = rearrange(imgs_in, "B Nv C H W -> (B Nv) C H W")# (B*Nv, 3, H, W)

    target_h, target_w = imgs_in.shape[-2], imgs_in.shape[-1]

    normal_prompt_embeddings, clr_prompt_embeddings = data['normal_prompt_embeddings'].cuda(), data['color_prompt_embeddings'].cuda()
    prompt_embeddings = torch.cat([normal_prompt_embeddings, clr_prompt_embeddings], dim=0)
    prompt_embeddings = rearrange(prompt_embeddings, "B Nv N C -> (B Nv) N C")

    # B*Nv images
    unet_out = pipeline(
        imgs_in, None, prompt_embeds=prompt_embeddings,
        generator=generator, guidance_scale=3.0, output_type='pt', num_images_per_prompt=1,
        height=cfg.height, width=cfg.width,
        num_inference_steps=40, eta=1.0,
        num_levels=num_levels,
    )

    for level in range(num_levels):
        out = unet_out[level].images
        bsz = out.shape[0] // 2

        normals_pred = out[:bsz]
        images_pred = out[bsz:]

        if num_levels == 2:
            results[level+1] = {'normals': [], 'images': []}
        else:
            results[level] = {'normals': [], 'images': []}

        for i in range(bsz//num_views):
            img_in_ = images_cond[-1][i].to(out.device)
            for j in range(num_views):
                view = VIEWS[j]
                idx = i*num_views + j
                normal = normals_pred[idx]
                color = images_pred[idx]

                ## save color and normal---------------------
                new_normal = save_image(normal)
                new_color = save_image(color)

                if num_levels == 2:
                    results[level+1]['normals'].append(new_normal)
                    results[level+1]['images'].append(new_color)
                else:
                    results[level]['normals'].append(new_normal)
                    results[level]['images'].append(new_color)

    torch.cuda.empty_cache()    
    return results

@spaces.GPU
def load_multiview_pipeline(cfg):
    pipeline = StableUnCLIPImg2ImgPipeline.from_pretrained(
        cfg.pretrained_path,
        torch_dtype=torch.float16,)
    pipeline.unet.enable_xformers_memory_efficient_attention()
    if torch.cuda.is_available():
        pipeline.to(device)
    return pipeline


class InferAPI:
    def __init__(self,
                 canonical_configs,
                 multiview_configs,
                 slrm_configs,
                 refine_configs):
        self.canonical_configs = canonical_configs
        self.multiview_configs = multiview_configs
        self.slrm_configs = slrm_configs
        self.refine_configs = refine_configs

        repo_id = "hyz317/StdGEN"
        all_files = list_repo_files(repo_id, revision="main")
        for file in all_files:
            if os.path.exists(file):
                continue
            hf_hub_download(repo_id, file, local_dir="./ckpt")

        self.canonical_infer = InferCanonicalAPI(self.canonical_configs)
        # self.multiview_infer = InferMultiviewAPI(self.multiview_configs)
        # self.slrm_infer = InferSlrmAPI(self.slrm_configs)
        # self.refine_infer = InferRefineAPI(self.refine_configs)

    def genStage1(self, img, seed):
        return self.canonical_infer.gen(img, seed)

    def genStage2(self, img, seed, num_levels):
        return self.multiview_infer.gen(img, seed, num_levels)

    def genStage3(self, img):
        return self.slrm_infer.gen(img)

    def genStage4(self, meshes, imgs):
        return self.refine_infer.refine(meshes, imgs)


############## Refine ##############
def fix_vert_color_glb(mesh_path):
    from pygltflib import GLTF2, Material, PbrMetallicRoughness
    obj1 = GLTF2().load(mesh_path)
    obj1.meshes[0].primitives[0].material = 0
    obj1.materials.append(Material(
        pbrMetallicRoughness = PbrMetallicRoughness(
            baseColorFactor = [1.0, 1.0, 1.0, 1.0],
            metallicFactor = 0.,
            roughnessFactor = 1.0,
        ),
        emissiveFactor = [0.0, 0.0, 0.0],
        doubleSided = True,
    ))
    obj1.save(mesh_path)


def srgb_to_linear(c_srgb):
    c_linear = np.where(c_srgb <= 0.04045, c_srgb / 12.92, ((c_srgb + 0.055) / 1.055) ** 2.4)
    return c_linear.clip(0, 1.)


def save_py3dmesh_with_trimesh_fast(meshes: Meshes, save_glb_path, apply_sRGB_to_LinearRGB=True):
    # convert from pytorch3d meshes to trimesh mesh
    vertices = meshes.verts_packed().cpu().float().numpy()
    triangles = meshes.faces_packed().cpu().long().numpy()
    np_color = meshes.textures.verts_features_packed().cpu().float().numpy()
    if save_glb_path.endswith(".glb"):
        # rotate 180 along +Y
        vertices[:, [0, 2]] = -vertices[:, [0, 2]]

    if apply_sRGB_to_LinearRGB:
        np_color = srgb_to_linear(np_color)
    assert vertices.shape[0] == np_color.shape[0]
    assert np_color.shape[1] == 3
    assert 0 <= np_color.min() and np_color.max() <= 1.001, f"min={np_color.min()}, max={np_color.max()}"
    np_color = np.clip(np_color, 0, 1)
    mesh = trimesh.Trimesh(vertices=vertices, faces=triangles, vertex_colors=np_color)
    mesh.remove_unreferenced_vertices()
    # save mesh
    mesh.export(save_glb_path)
    if save_glb_path.endswith(".glb"):
        fix_vert_color_glb(save_glb_path)
    print(f"saving to {save_glb_path}")


def calc_horizontal_offset(target_img, source_img):
    target_mask = target_img.astype(np.float32).sum(axis=-1) > 750
    source_mask = source_img.astype(np.float32).sum(axis=-1) > 750
    best_offset = -114514
    for offset in range(-200, 200):
        offset_mask = np.roll(source_mask, offset, axis=1)
        overlap = (target_mask & offset_mask).sum()
        if overlap > best_offset:
            best_offset = overlap
            best_offset_value = offset
    return best_offset_value


def calc_horizontal_offset2(target_mask, source_img):
    source_mask = source_img.astype(np.float32).sum(axis=-1) > 750
    best_offset = -114514
    for offset in range(-200, 200):
        offset_mask = np.roll(source_mask, offset, axis=1)
        overlap = (target_mask & offset_mask).sum()
        if overlap > best_offset:
            best_offset = overlap
            best_offset_value = offset
    return best_offset_value


@spaces.GPU
def get_distract_mask(generator, color_0, color_1, normal_0=None, normal_1=None, thres=0.25, ratio=0.50, outside_thres=0.10, outside_ratio=0.20):
    distract_area = np.abs(color_0 - color_1).sum(axis=-1) > thres
    if normal_0 is not None and normal_1 is not None:
        distract_area |= np.abs(normal_0 - normal_1).sum(axis=-1) > thres
    labeled_array, num_features = scipy.ndimage.label(distract_area)
    results = []

    random_sampled_points = []

    for i in range(num_features + 1):
        if np.sum(labeled_array == i) > 1000 and np.sum(labeled_array == i) < 100000:
            results.append((i, np.sum(labeled_array == i)))
            # random sample a point in the area
            points = np.argwhere(labeled_array == i)
            random_sampled_points.append(points[np.random.randint(0, points.shape[0])])

    results = sorted(results, key=lambda x: x[1], reverse=True)  # [1:]
    distract_mask = np.zeros_like(distract_area)
    distract_bbox = np.zeros_like(distract_area)
    for i, _ in results:
        distract_mask |= labeled_array == i
        bbox = np.argwhere(labeled_array == i)
        min_x, min_y = bbox.min(axis=0)
        max_x, max_y = bbox.max(axis=0)
        distract_bbox[min_x:max_x, min_y:max_y] = 1

    points = np.array(random_sampled_points)[:, ::-1]
    labels = np.ones(len(points), dtype=np.int32)

    masks = generator.generate((color_1 * 255).astype(np.uint8))

    outside_area = np.abs(color_0 - color_1).sum(axis=-1) < outside_thres

    final_mask = np.zeros_like(distract_mask)
    for iii, mask in enumerate(masks):
        mask['segmentation'] = cv2.resize(mask['segmentation'].astype(np.float32), (1024, 1024)) > 0.5
        intersection = np.logical_and(mask['segmentation'], distract_mask).sum()
        total = mask['segmentation'].sum()
        iou = intersection / total
        outside_intersection = np.logical_and(mask['segmentation'], outside_area).sum()
        outside_total = mask['segmentation'].sum()
        outside_iou = outside_intersection / outside_total
        if iou > ratio and outside_iou < outside_ratio:
            final_mask |= mask['segmentation']

    # calculate coverage
    intersection = np.logical_and(final_mask, distract_mask).sum()
    total = distract_mask.sum()
    coverage = intersection / total

    if coverage < 0.8:
        # use original distract mask
        final_mask = (distract_mask.copy() * 255).astype(np.uint8)
        final_mask = cv2.dilate(final_mask, np.ones((3, 3), np.uint8), iterations=3)
        labeled_array_dilate, num_features_dilate = scipy.ndimage.label(final_mask)
        for i in range(num_features_dilate + 1):
            if np.sum(labeled_array_dilate == i) < 200:
                final_mask[labeled_array_dilate == i] = 255

        final_mask = cv2.erode(final_mask, np.ones((3, 3), np.uint8), iterations=3)
        final_mask = final_mask > 127

    return distract_mask, distract_bbox, random_sampled_points, final_mask


class InferRefineAPI:
    @spaces.GPU
    def __init__(self, config):
        self.sam = sam_model_registry["vit_h"](checkpoint="./ckpt/sam_vit_h_4b8939.pth").cuda()
        self.generator = SamAutomaticMaskGenerator(
            model=self.sam,
            points_per_side=64,
            pred_iou_thresh=0.80,
            stability_score_thresh=0.92,
            crop_n_layers=1,
            crop_n_points_downscale_factor=2,
            min_mask_region_area=100,
        )
        self.outside_ratio = 0.20

    @spaces.GPU
    def refine(self, meshes, imgs):
        fixed_v, fixed_f, fixed_t = None, None, None
        flow_vert, flow_vector = None, None
        last_colors, last_normals = None, None
        last_front_color, last_front_normal = None, None
        distract_mask = None

        mv, proj = make_star_cameras_orthographic(8, 1, r=1.2)
        mv = mv[[4, 3, 2, 0, 6, 5]]        
        renderer = NormalsRenderer(mv,proj,(1024,1024))

        results = []

        for name_idx, level in zip([2, 0, 1], [2, 1, 0]):
            mesh = trimesh.load(meshes[name_idx])
            new_mesh = mesh.split(only_watertight=False)
            new_mesh = [ j for j in new_mesh if len(j.vertices) >= 300 ]
            mesh = trimesh.Scene(new_mesh).dump(concatenate=True)
            mesh_v, mesh_f = mesh.vertices, mesh.faces

            if last_colors is None:
                images = renderer.render(
                    torch.tensor(mesh_v, device='cuda').float(),
                    torch.ones_like(torch.from_numpy(mesh_v), device='cuda').float(),
                    torch.tensor(mesh_f, device='cuda'),
                )
                mask = (images[..., 3] < 0.9).cpu().numpy()

            colors, normals = [], []
            for i in range(6):
                color = np.array(imgs[level]['images'][i])
                normal = np.array(imgs[level]['normals'][i])

                if last_colors is not None:
                    offset = calc_horizontal_offset(np.array(last_colors[i]), color)
                    # print('offset', i, offset)
                else:
                    offset = calc_horizontal_offset2(mask[i], color)
                    # print('init offset', i, offset)

                if offset != 0:
                    color = np.roll(color, offset, axis=1)
                    normal = np.roll(normal, offset, axis=1)

                color = Image.fromarray(color)
                normal = Image.fromarray(normal)
                colors.append(color)
                normals.append(normal)

            if last_front_color is not None and level == 0:
                original_mask, distract_bbox, _, distract_mask = get_distract_mask(self.generator, last_front_color, np.array(colors[0]).astype(np.float32) / 255.0, outside_ratio=self.outside_ratio)
            else:  
                distract_mask = None
                distract_bbox = None

            last_front_color = np.array(colors[0]).astype(np.float32) / 255.0
            last_front_normal = np.array(normals[0]).astype(np.float32) / 255.0

            if last_colors is None:
                from copy import deepcopy
                last_colors, last_normals = deepcopy(colors), deepcopy(normals)

            # my mesh flow weight by nearest vertexs
            if fixed_v is not None and fixed_f is not None and level == 1:
                t = trimesh.Trimesh(vertices=mesh_v, faces=mesh_f)

                fixed_v_cpu = fixed_v.cpu().numpy()
                kdtree_anchor = KDTree(fixed_v_cpu)
                kdtree_mesh_v = KDTree(mesh_v)
                _, idx_anchor = kdtree_anchor.query(mesh_v, k=1)
                _, idx_mesh_v = kdtree_mesh_v.query(mesh_v, k=25)
                idx_anchor = idx_anchor.squeeze()
                neighbors = torch.tensor(mesh_v).cuda()[idx_mesh_v]  # V, 25, 3
                # calculate the distances neighbors [V, 25, 3]; mesh_v [V, 3] -> [V, 25]
                neighbor_dists = torch.norm(neighbors - torch.tensor(mesh_v).cuda()[:, None], dim=-1)
                neighbor_dists[neighbor_dists > 0.06] = 114514.
                neighbor_weights = torch.exp(-neighbor_dists * 1.)
                neighbor_weights = neighbor_weights / neighbor_weights.sum(dim=1, keepdim=True)
                anchors = fixed_v[idx_anchor]  # V, 3
                anchor_normals = calc_vertex_normals(fixed_v, fixed_f)[idx_anchor]  # V, 3
                dis_anchor = torch.clamp(((anchors - torch.tensor(mesh_v).cuda()) * anchor_normals).sum(-1), min=0) + 0.01
                vec_anchor = dis_anchor[:, None] * anchor_normals  # V, 3
                vec_anchor = vec_anchor[idx_mesh_v]  # V, 25, 3
                weighted_vec_anchor = (vec_anchor * neighbor_weights[:, :, None]).sum(1)  # V, 3
                mesh_v += weighted_vec_anchor.cpu().numpy()

                t = trimesh.Trimesh(vertices=mesh_v, faces=mesh_f)

            mesh_v = torch.tensor(mesh_v, device='cuda', dtype=torch.float32)
            mesh_f = torch.tensor(mesh_f, device='cuda')

            new_mesh, simp_v, simp_f = geo_refine(mesh_v, mesh_f, colors, normals, fixed_v=fixed_v, fixed_f=fixed_f, distract_mask=distract_mask, distract_bbox=distract_bbox)

            # my mesh flow weight by nearest vertexs
            try:
                if fixed_v is not None and fixed_f is not None and level != 0:
                    new_mesh_v = new_mesh.verts_packed().cpu().numpy()

                    fixed_v_cpu = fixed_v.cpu().numpy()
                    kdtree_anchor = KDTree(fixed_v_cpu)
                    kdtree_mesh_v = KDTree(new_mesh_v)
                    _, idx_anchor = kdtree_anchor.query(new_mesh_v, k=1)
                    _, idx_mesh_v = kdtree_mesh_v.query(new_mesh_v, k=25)
                    idx_anchor = idx_anchor.squeeze()
                    neighbors = torch.tensor(new_mesh_v).cuda()[idx_mesh_v]  # V, 25, 3
                    # calculate the distances neighbors [V, 25, 3]; new_mesh_v [V, 3] -> [V, 25]
                    neighbor_dists = torch.norm(neighbors - torch.tensor(new_mesh_v).cuda()[:, None], dim=-1)
                    neighbor_dists[neighbor_dists > 0.06] = 114514.
                    neighbor_weights = torch.exp(-neighbor_dists * 1.)
                    neighbor_weights = neighbor_weights / neighbor_weights.sum(dim=1, keepdim=True)
                    anchors = fixed_v[idx_anchor]  # V, 3
                    anchor_normals = calc_vertex_normals(fixed_v, fixed_f)[idx_anchor]  # V, 3
                    dis_anchor = torch.clamp(((anchors - torch.tensor(new_mesh_v).cuda()) * anchor_normals).sum(-1), min=0) + 0.01
                    vec_anchor = dis_anchor[:, None] * anchor_normals  # V, 3
                    vec_anchor = vec_anchor[idx_mesh_v]  # V, 25, 3
                    weighted_vec_anchor = (vec_anchor * neighbor_weights[:, :, None]).sum(1)  # V, 3
                    new_mesh_v += weighted_vec_anchor.cpu().numpy()

                    # replace new_mesh verts with new_mesh_v
                    new_mesh = Meshes(verts=[torch.tensor(new_mesh_v, device='cuda')], faces=new_mesh.faces_list(), textures=new_mesh.textures)

            except Exception as e:
                pass

            notsimp_v, notsimp_f, notsimp_t = new_mesh.verts_packed(), new_mesh.faces_packed(), new_mesh.textures.verts_features_packed()

            if fixed_v is None:
                fixed_v, fixed_f = simp_v, simp_f
                complete_v, complete_f, complete_t = notsimp_v, notsimp_f, notsimp_t
            else:
                fixed_f = torch.cat([fixed_f, simp_f + fixed_v.shape[0]], dim=0)
                fixed_v = torch.cat([fixed_v, simp_v], dim=0)
                
                complete_f = torch.cat([complete_f, notsimp_f + complete_v.shape[0]], dim=0)
                complete_v = torch.cat([complete_v, notsimp_v], dim=0)
                complete_t = torch.cat([complete_t, notsimp_t], dim=0)
            
            if level == 2:
                new_mesh = Meshes(verts=[new_mesh.verts_packed()], faces=[new_mesh.faces_packed()], textures=pytorch3d.renderer.mesh.textures.TexturesVertex(verts_features=[torch.ones_like(new_mesh.textures.verts_features_packed(), device=new_mesh.verts_packed().device)*0.5]))

            save_py3dmesh_with_trimesh_fast(new_mesh, meshes[name_idx].replace('.obj', '_refined.obj'), apply_sRGB_to_LinearRGB=False)
            results.append(meshes[name_idx].replace('.obj', '_refined.obj'))

        # save whole mesh
        save_py3dmesh_with_trimesh_fast(Meshes(verts=[complete_v], faces=[complete_f], textures=pytorch3d.renderer.mesh.textures.TexturesVertex(verts_features=[complete_t])), meshes[name_idx].replace('.obj', '_refined_whole.obj'), apply_sRGB_to_LinearRGB=False)
        results.append(meshes[name_idx].replace('.obj', '_refined_whole.obj'))

        return results


class InferSlrmAPI:
    @spaces.GPU
    def __init__(self, config):
        self.config_path = config['config_path']
        self.config = OmegaConf.load(self.config_path)
        self.config_name = os.path.basename(self.config_path).replace('.yaml', '')
        self.model_config = self.config.model_config
        self.infer_config = self.config.infer_config
        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
        self.model = instantiate_from_config(self.model_config)
        state_dict = torch.load(self.infer_config.model_path, map_location='cpu')
        self.model.load_state_dict(state_dict, strict=False)
        self.model = self.model.to(self.device)
        self.model.init_flexicubes_geometry(self.device, fovy=30.0, is_ortho=self.model.is_ortho)
        self.model = self.model.eval()

    @spaces.GPU
    def gen(self, imgs):
        imgs = [ cv2.imread(img[0])[:, :, ::-1] for img in imgs ]
        imgs = np.stack(imgs, axis=0).astype(np.float32) / 255.0
        imgs = torch.from_numpy(np.array(imgs)).permute(0, 3, 1, 2).contiguous().float()   # (6, 3, 1024, 1024)
        mesh_glb_fpaths = self.make3d(imgs)
        return mesh_glb_fpaths[1:4] + mesh_glb_fpaths[0:1]

    @spaces.GPU
    def make3d(self, images):
        input_cameras = torch.tensor(np.load('slrm/cameras.npy')).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)

        with torch.no_grad():
            # get triplane
            planes = self.model.forward_planes(images, input_cameras.float())

            # get mesh
            mesh_glb_fpaths = []
            for j in range(4):
                mesh_glb_fpath = self.make_mesh(mesh_fpath.replace(mesh_fpath[-4:], f'_{j}{mesh_fpath[-4:]}'), planes, level=[0, 3, 4, 2][j])
                mesh_glb_fpaths.append(mesh_glb_fpath)

        return mesh_glb_fpaths

    @spaces.GPU
    def make_mesh(self, mesh_fpath, planes, level=None):
        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():
            # get mesh
            mesh_out = self.model.extract_mesh(
                planes,
                use_texture_map=False,
                levels=torch.tensor([level]).to(device),
                **self.infer_config,
            )

            vertices, faces, vertex_colors = mesh_out
            vertices = vertices[:, [1, 2, 0]]

            if level == 2:
                # fill all vertex_colors with 127
                vertex_colors = np.ones_like(vertex_colors) * 127
            
            save_obj(vertices, faces, vertex_colors, mesh_fpath)

        return mesh_fpath

class InferMultiviewAPI:
    def __init__(self, config):
        parser = argparse.ArgumentParser()
        parser.add_argument("--seed", type=int, default=42)
        parser.add_argument("--num_views", type=int, default=6)
        parser.add_argument("--num_levels", type=int, default=3)
        parser.add_argument("--pretrained_path", type=str, default='./ckpt/StdGEN-multiview-1024')
        parser.add_argument("--height", type=int, default=1024)
        parser.add_argument("--width", type=int, default=576)
        self.cfg = parser.parse_args()
        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
        self.pipeline = load_multiview_pipeline(self.cfg)
        self.results = {}
        if torch.cuda.is_available():
            self.pipeline.to(device)

        self.image_transforms = [transforms.Resize(int(max(self.cfg.height, self.cfg.width))),
                                 transforms.CenterCrop((self.cfg.height, self.cfg.width)),
                                 transforms.ToTensor(),
                                 transforms.Lambda(lambda x: x * 2. - 1),
                                 ]
        self.image_transforms = transforms.Compose(self.image_transforms)

        prompt_embeds_path = './multiview/fixed_prompt_embeds_6view'
        self.normal_text_embeds = torch.load(f'{prompt_embeds_path}/normal_embeds.pt')
        self.color_text_embeds = torch.load(f'{prompt_embeds_path}/clr_embeds.pt')
        self.total_views = self.cfg.num_views


    def process_im(self, im):
        im = self.image_transforms(im)
        return im

    def gen(self, img, seed, num_levels):
        set_seed(seed)
        data = {}

        cond_im_rgb = self.process_im(img)
        cond_im_rgb = torch.stack([cond_im_rgb] * self.total_views, dim=0)
        data["image_cond_rgb"] = cond_im_rgb[None, ...]
        data["normal_prompt_embeddings"] = self.normal_text_embeds[None, ...]
        data["color_prompt_embeddings"] = self.color_text_embeds[None, ...]

        results = run_multiview_infer(data, self.pipeline, self.cfg, num_levels=num_levels)
        for k in results:
            self.results[k] = results[k]
        return results


class InferCanonicalAPI:
    def __init__(self, config):
        self.config = config
        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

        self.config_path = config['config_path']
        self.loaded_config = OmegaConf.load(self.config_path)

        self.setup(**self.loaded_config)

    def setup(self,
        validation: Dict,
        pretrained_model_path: str,
        local_crossattn: bool = True,
        unet_from_pretrained_kwargs=None,
        unet_condition_type=None,
        use_noise=True,
        noise_d=256,
        timestep: int = 40,
        width_input: int = 640,
        height_input: int = 1024,
    ):
        self.width_input = width_input
        self.height_input = height_input
        self.timestep = timestep
        self.use_noise = use_noise
        self.noise_d = noise_d
        self.validation = validation
        self.unet_condition_type = unet_condition_type
        self.pretrained_model_path = pretrained_model_path
        
        self.tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
        self.text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder")
        self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(pretrained_model_path, subfolder="image_encoder")
        self.feature_extractor = CLIPImageProcessor()
        self.vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae")
        self.unet = UNetMV2DConditionModel.from_pretrained_2d(pretrained_model_path, subfolder="unet", local_crossattn=local_crossattn, **unet_from_pretrained_kwargs)
        self.ref_unet = UNetMV2DRefModel.from_pretrained_2d(pretrained_model_path, subfolder="ref_unet", local_crossattn=local_crossattn, **unet_from_pretrained_kwargs)

        self.text_encoder.to(device, dtype=weight_dtype)
        self.image_encoder.to(device, dtype=weight_dtype)
        self.vae.to(device, dtype=weight_dtype)
        self.ref_unet.to(device, dtype=weight_dtype)
        self.unet.to(device, dtype=weight_dtype)

        self.vae.requires_grad_(False)
        self.ref_unet.requires_grad_(False)
        self.unet.requires_grad_(False)

        self.noise_scheduler = DDIMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler-zerosnr")
        self.validation_pipeline = CanonicalizationPipeline(
            vae=self.vae, text_encoder=self.text_encoder, tokenizer=self.tokenizer, unet=self.unet, ref_unet=self.ref_unet,feature_extractor=self.feature_extractor,image_encoder=self.image_encoder,
            scheduler=self.noise_scheduler
        )
        self.validation_pipeline.set_progress_bar_config(disable=True)

    def canonicalize(self, image, seed):
        return inference(
            self.validation_pipeline, image, self.vae, self.feature_extractor, self.image_encoder, self.unet, self.ref_unet, self.tokenizer, self.text_encoder,
            self.pretrained_model_path, self.validation, self.width_input, self.height_input, self.unet_condition_type,
            use_noise=self.use_noise, noise_d=self.noise_d, crop=True, seed=seed, timestep=self.timestep
        )

    def gen(self, img_input, seed=0):
        if np.array(img_input).shape[-1] == 4 and np.array(img_input)[..., 3].min() == 255:
            # convert to RGB
            img_input = img_input.convert("RGB")
        img_output = self.canonicalize(img_input, seed)
        
        max_dim = max(img_output.width, img_output.height)
        new_image = Image.new("RGBA", (max_dim, max_dim))
        left = (max_dim - img_output.width) // 2
        top = (max_dim - img_output.height) // 2
        new_image.paste(img_output, (left, top))

        return new_image