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