Instructions to use hansQAQ/icip_source_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hansQAQ/icip_source_2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hansQAQ/icip_source_2", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 11,613 Bytes
278bf35 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 | import numpy as np
import open3d as o3d
import pymeshlab
import torch
import trimesh
from pymeshlab import Percentage
### Mesh Utils ###
##### read mesh
def read_mesh_from_path(mesh_path):
ms = pymeshlab.MeshSet()
ms.load_new_mesh(mesh_path)
return ms
def mesh_to_meshlab(vertices, faces):
mesh = pymeshlab.Mesh(vertex_matrix=vertices, face_matrix=faces)
ms = pymeshlab.MeshSet()
ms.add_mesh(mesh)
return ms
def meshlab_to_mesh(ms):
m = ms.current_mesh()
return m.vertex_matrix(), m.face_matrix(), m.vertex_normal_matrix()
##### decimation
def decimate_quadric_edge_collapse_with_texture(
ms, targetfacenum=None, preservenormal=True, verbose=False
):
# targetfacenum: int, Target number of faces.
# preservenormal: bool, Preserve the normals of the original mesh.
if verbose:
print("Starting decimation ... ")
m = ms.current_mesh()
if targetfacenum is None:
targetfacenum = int(m.face_number() * 0.5)
if verbose:
print("... Initial face number is %d ... " % m.face_number())
ms.meshing_decimation_quadric_edge_collapse_with_texture(
targetfacenum=targetfacenum, preservenormal=preservenormal
)
if verbose:
print("... Decimated face number is %d ... " % m.face_number())
print("Decimation done!\n ")
def decimate_quadric_edge_collapse(
ms, targetfacenum=None, preservenormal=True, verbose=False
):
# targetfacenum: int, Target number of faces.
# preservenormal: bool, Preserve the normals of the original mesh.
if verbose:
print("Starting decimation ... ")
m = ms.current_mesh()
if targetfacenum is None:
targetfacenum = int(m.face_number() * 0.5)
if verbose:
print("... Initial face number is %d ... " % m.face_number())
ms.meshing_decimation_quadric_edge_collapse(
targetfacenum=targetfacenum, preservenormal=preservenormal
)
if verbose:
print("... Decimated face number is %d ... " % m.face_number())
print("Decimation done!\n ")
##### vertex merge
def merge_close_vertices(ms, threshold=0.0001, verbose=False):
# threshold: float, Merge together all the vertices that are nearer than the specified threshold.
if verbose:
print("Starting merge vertices ... ")
m = ms.current_mesh()
if verbose:
print("... Initial vertex number is %d ... " % m.vertex_number())
ms.meshing_merge_close_vertices(threshold=Percentage(threshold * 100))
if verbose:
print("... Merged vertex number is %d ... " % m.vertex_number())
print("Merge vertices done!\n ")
##### Island Removal
def remove_isolated_pieces(ms, mincomponentsize=25, diameter=None, verbose=False):
# mincomponentsize: Delete isolated connected components composed by a limited number of triangles
# diameter: Delete isolated connected components whose diameter is smaller than the specified constant
if verbose:
print("Starting remove isolated pieces ... ")
m = ms.current_mesh()
if verbose:
print("... Initial face number is %d ... " % m.face_number())
if diameter is None:
ms.meshing_remove_connected_component_by_face_number(
mincomponentsize=mincomponentsize, removeunref=True
)
else:
ms.meshing_remove_connected_component_by_diameter(
mincomponentdiag=Percentage(diameter), removeunref=True
)
if verbose:
print("... Isolated removed face number is %d ... " % m.face_number())
print("Remove isolated pieces done!\n ")
##### hole filling
def fix_hole(ms, maxholesize=30, verbose=False):
# maxholesize: int, Maximum size of the hole to be filled.
if verbose:
print("Starting fix holes ... ")
m = ms.current_mesh()
if verbose:
print("... Initial face number is %d ... " % m.face_number())
ms.meshing_close_holes(maxholesize=maxholesize)
if verbose:
print("... Fixed hole face number is %d ... " % m.face_number())
print("Fix holes done!\n ")
##### repair non manifold edges
def repair_non_manifold(ms, verbose=False):
if verbose:
print("Starting repair non manifold edges ... ")
m = ms.current_mesh()
if verbose:
print("... Initial face number is %d ... " % m.face_number())
ms.meshing_repair_non_manifold_edges()
ms.meshing_repair_non_manifold_vertices(vertdispratio=0.1)
ms.meshing_remove_duplicate_faces()
if verbose:
print("... Fixed non manifold edges face number is %d ... " % m.face_number())
print("Repair non manifold edges done!\n ")
##### laplacian_smooth
def laplacian_smooth(ms, stepsmoothnum=3, verbose=False):
# stepsmoothnum: int, Number of smoothing steps to be performed
if verbose:
print("Starting laplacian smooth ... ")
m = ms.current_mesh()
ms.apply_coord_laplacian_smoothing(stepsmoothnum=stepsmoothnum)
if verbose:
print("Laplacian smooth done!\n ")
##### taubin_smooth
def taubin_smooth(ms, stepsmoothnum=3, verbose=False):
if verbose:
print("Starting Taubin smooth ... ")
m = ms.current_mesh()
ms.apply_coord_taubin_smoothing(stepsmoothnum=stepsmoothnum)
if verbose:
print("Taubin smooth done!\n ")
##### compute_normal
def compute_normal(ms, weightmode="Simple Average", verbose=False):
if verbose:
print("Starting compute_normal_per_vertex ... ")
m = ms.current_mesh()
ms.compute_normal_per_vertex(weightmode=weightmode)
if verbose:
print("compute_normal_per_vertex done!\n ")
### Pre-process Mesh ###
def process_mesh(
vertices,
faces,
threshold=0.0001,
mincomponentRatio=0.02,
targetfacenum=50000,
maxholesize=30,
stepsmoothnum=10,
verbose=False,
):
ms = mesh_to_meshlab(vertices, faces)
### Vertex Merge
merge_close_vertices(ms, threshold=threshold, verbose=verbose)
### Island Removal
faces = ms.current_mesh().face_matrix()
remove_isolated_pieces(
ms, mincomponentsize=int(len(faces) * mincomponentRatio), verbose=verbose
)
### Hole Filling
repair_non_manifold(ms) # repair before fix hole
fix_hole(ms, maxholesize=maxholesize, verbose=verbose)
### Taubin Smoothing
taubin_smooth(ms, stepsmoothnum=stepsmoothnum, verbose=verbose)
vertices, faces, _ = meshlab_to_mesh(ms)
if faces.shape[0] > targetfacenum:
device = o3d.core.Device("CPU:0")
dtype_f = o3d.core.float32
dtype_i = o3d.core.int64
mesh = o3d.t.geometry.TriangleMesh(device)
mesh.vertex.positions = o3d.core.Tensor(
vertices.astype(np.float32), dtype_f, device
)
mesh.triangle.indices = o3d.core.Tensor(faces.astype(np.int64), dtype_i, device)
simplified_mesh = mesh.simplify_quadric_decimation(
target_reduction=1.0 - float(targetfacenum) / faces.shape[0]
)
ms.clear()
vertices = simplified_mesh.vertex.positions.numpy()
faces = simplified_mesh.triangle.indices.numpy()
mesh = pymeshlab.Mesh(vertex_matrix=vertices, face_matrix=faces)
ms.add_mesh(mesh)
### Mesh Simplification/Decimation
# decimate_quadric_edge_collapse(ms, targetfacenum=targetfacenum, verbose=verbose)
taubin_smooth(ms, stepsmoothnum=stepsmoothnum, verbose=verbose)
repair_non_manifold(ms, verbose=verbose)
compute_normal(ms, verbose=verbose)
return meshlab_to_mesh(ms)
### UV Un-Warp ###
def uv_parameterize_uvatlas(
vertices,
faces,
size=1024,
gutter=2.5,
max_stretch=0.1666666716337204,
parallel_partitions=16,
nthreads=0,
):
device = o3d.core.Device("CPU:0")
dtype_f = o3d.core.float32
dtype_i = o3d.core.int64
mesh = o3d.t.geometry.TriangleMesh(device)
mesh.vertex.positions = o3d.core.Tensor(
vertices.astype(np.float32), dtype_f, device
)
mesh.triangle.indices = o3d.core.Tensor(faces.astype(np.int64), dtype_i, device)
mesh.compute_uvatlas(
size=size,
gutter=gutter,
max_stretch=max_stretch,
parallel_partitions=parallel_partitions,
nthreads=nthreads,
)
return mesh.triangle.texture_uvs.numpy() # (#F, 3, 2)
### Pack All ###
def process_raw(mesh_path, save_path, preprocess=True, device="cpu"):
scene = trimesh.load(mesh_path, force="mesh", process=False)
if isinstance(scene, trimesh.Trimesh):
mesh = scene
elif isinstance(scene, trimesh.scene.Scene):
mesh = trimesh.Trimesh()
for obj in scene.geometry.values():
mesh = trimesh.util.concatenate([mesh, obj])
else:
raise ValueError(f"Unknown mesh type at {mesh_path}.")
vertices = mesh.vertices
faces = mesh.faces
mesh_post_process_options = {
"mincomponentRatio": 0.02,
"targetfacenum": 50000,
"maxholesize": 100,
"stepsmoothnum": 10,
"verbose": False,
}
if preprocess:
v_pos, t_pos_idx, normals = process_mesh(
vertices=vertices,
faces=faces,
**mesh_post_process_options,
)
else:
v_pos, t_pos_idx, normals = vertices, faces, mesh.vertex_normals
v_tex_np = (
uv_parameterize_uvatlas(v_pos, t_pos_idx).reshape(-1, 2).astype(np.float32)
)
v_pos = torch.from_numpy(v_pos).to(device=device, dtype=torch.float32)
t_pos_idx = torch.from_numpy(t_pos_idx).to(device=device, dtype=torch.long)
v_tex = torch.from_numpy(v_tex_np).to(device=device, dtype=torch.float32)
normals = torch.from_numpy(normals).to(device=device, dtype=torch.float32)
assert v_tex.shape[0] == t_pos_idx.shape[0] * 3
t_tex_idx = torch.arange(
t_pos_idx.shape[0] * 3,
device=device,
dtype=torch.long,
).reshape(-1, 3)
# uv, index = torch.unique(v_tex, dim=0, return_inverse=True) # 这样实现是2毫秒
# super efficient de-duplication
v_tex_u_uint32 = v_tex_np[..., 0].view(np.uint32)
v_tex_v_uint32 = v_tex_np[..., 1].view(np.uint32)
v_hashed = (v_tex_u_uint32.astype(np.uint64) << 32) | v_tex_v_uint32
v_hashed = torch.from_numpy(v_hashed.view(np.int64)).to(v_pos.device)
t_pos_idx_f3 = torch.arange(
t_pos_idx.shape[0] * 3, device=t_pos_idx.device, dtype=torch.long
).reshape(-1, 3)
v_pos_f3 = v_pos[t_pos_idx].reshape(-1, 3)
normals_f3 = normals[t_pos_idx].reshape(-1, 3)
v_hashed_dedup, inverse_indices = torch.unique(v_hashed, return_inverse=True)
dedup_size, full_size = v_hashed_dedup.shape[0], inverse_indices.shape[0]
indices = torch.scatter_reduce(
torch.full(
[dedup_size],
fill_value=full_size,
device=inverse_indices.device,
dtype=torch.long,
),
index=inverse_indices,
src=torch.arange(full_size, device=inverse_indices.device, dtype=torch.int64),
dim=0,
reduce="amin",
)
v_tex = v_tex[indices]
t_tex_idx = inverse_indices.reshape(-1, 3)
v_pos = v_pos_f3[indices]
normals = normals_f3[indices]
normals = normals.to(dtype=torch.float32, device=device)
# either flip uv or flip texture
# here we flip uv
uv_to_save = v_tex.clone()
uv_to_save[:, 1] = 1.0 - uv_to_save[:, 1]
visual = trimesh.visual.TextureVisuals(uv=uv_to_save.cpu().numpy())
tmesh = trimesh.Trimesh(
vertices=v_pos.cpu().numpy(),
faces=t_tex_idx.cpu().numpy(),
vertex_normals=normals.cpu().numpy(),
visual=visual,
process=False,
)
tmesh.export(save_path)
|