local variable 'num_keep_views' referenced before assignment

[vvubbalubba]

Im trying to run mvedit by python, and getting this error on the last step - runner.run_zero123plus1_2_to_mesh(seed, img_segm, *args):

---

UnboundLocalError Traceback (most recent call last) Cell In[15], line 1 ----> 1 glb_path = runner.run_zero123plus1_2_to_mesh(42, img_segm, *args)

File ~/shares/SR004.nfs2/fominaav/3D/MVEdit/lib/apis/mvedit.py:49, in _api_wrapper..wrapper(*args, *kwargs) 47 torch.set_grad_enabled(False) 48 torch.backends.cuda.matmul.allow_tf32 = True ---> 49 ret = func(args, **kwargs) 50 gc.collect() 51 if self.empty_cache:

File ~/shares/SR004.nfs2/fominaav/3D/MVEdit/lib/apis/mvedit.py:841, in MVEditRunner.run_zero123plus1_2_to_mesh(self, seed, in_img, cache_dir, *args, *kwargs) 838 intrinsics = torch.cat([in_intrinsics[None, :], intrinsics[None, :].expand(camera_poses.size(0), -1)], dim=0) 839 camera_poses = torch.cat([in_pose[None, :3], camera_poses], dim=0) --> 841 out_mesh, ingp_states = self.proc_nerf_mesh( 842 pipe, seed, nerf_mesh_kwargs, superres_kwargs, init_images=init_images, normals=init_normals, 843 camera_poses=camera_poses, intrinsics=intrinsics, intrinsics_size=intrinsics_size, 844 cam_weights=[2.0] + [1.1, 0.95, 0.9, 0.85, 1.0, 1.05] 6, seg_padding=96, 845 keep_views=[0], ip_adapter=self.ip_adapter, use_reference=True, use_normal=True) 847 if superres_kwargs['do_superres']: 848 self.load_stable_diffusion(superres_kwargs['checkpoint'])

File ~/shares/SR004.nfs2/fominaav/3D/MVEdit/lib/apis/mvedit.py:447, in MVEditRunner.proc_nerf_mesh(self, pipe, seed, nerf_mesh_kwargs, superres_kwargs, front_azi, camera_poses, use_reference, use_normal, kwargs) 443 set_random_seed(seed, deterministic=True) 444 prompts = nerf_mesh_kwargs['prompt'] if front_azi is None \ 445 else [join_prompts(nerf_mesh_kwargs['prompt'], view_prompt) 446 for view_prompt in view_prompts(camera_poses, front_azi)] --> 447 out_mesh, ingp_states = pipe( 448 prompt=prompts, 449 negative_prompt=nerf_mesh_kwargs['negative_prompt'], 450 camera_poses=camera_poses, 451 use_reference=use_reference, 452 use_normal=use_normal, 453 guidance_scale=nerf_mesh_kwargs['cfg_scale'], 454 num_inference_steps=nerf_mesh_kwargs['steps'], 455 denoising_strength=None if nerf_mesh_kwargs['random_init'] else nerf_mesh_kwargs['denoising_strength'], 456 patch_size=nerf_mesh_kwargs['patch_size'], 457 patch_bs=nerf_mesh_kwargs['patch_bs'], 458 diff_bs=nerf_mesh_kwargs['diff_bs'], 459 render_bs=nerf_mesh_kwargs['render_bs'], 460 n_inverse_rays=nerf_mesh_kwargs['patch_size'] 2 nerf_mesh_kwargs['patch_bs_nerf'], 461 n_inverse_steps=nerf_mesh_kwargs['n_inverse_steps'], 462 init_inverse_steps=nerf_mesh_kwargs['init_inverse_steps'], 463 tet_init_inverse_steps=nerf_mesh_kwargs['tet_init_inverse_steps'], 464 default_prompt=nerf_mesh_kwargs['aux_prompt'], 465 default_neg_prompt=nerf_mesh_kwargs['aux_negative_prompt'], 466 alpha_soften=nerf_mesh_kwargs['alpha_soften'], 467 normal_reg_weight=lambda p: nerf_mesh_kwargs['normal_reg_weight'] (1 - p), 468 entropy_weight=lambda p: nerf_mesh_kwargs['start_entropy_weight'] + ( 469 nerf_mesh_kwargs['end_entropy_weight'] - nerf_mesh_kwargs['start_entropy_weight']) p, 470 bg_width=nerf_mesh_kwargs['entropy_d'], 471 mesh_normal_reg_weight=nerf_mesh_kwargs['mesh_smoothness'], 472 lr_schedule=lambda p: nerf_mesh_kwargs['start_lr'] + ( 473 nerf_mesh_kwargs['end_lr'] - nerf_mesh_kwargs['start_lr']) p, 474 tet_resolution=nerf_mesh_kwargs['tet_resolution'], 475 bake_texture=not superres_kwargs['do_superres'], 476 prog_bar=gr.Progress().tqdm, 477 out_dir=self.out_dir_3d, 478 save_interval=self.save_interval, 479 save_all_interval=self.save_all_interval, 480 mesh_reduction=128 / nerf_mesh_kwargs['tet_resolution'], 481 max_num_views=partial( 482 default_max_num_views, 483 start_num=nerf_mesh_kwargs['max_num_views'], 484 mid_num=nerf_mesh_kwargs['max_num_views'] // 2), 485 debug=self.debug, 486 **kwargs 487 ) 488 return out_mesh, ingp_states

File /usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py:115, in context_decorator..decorate_context(*args, kwargs) 112 @functools.wraps(func) 113 def decorate_context(*args, *kwargs): 114 with ctx_factory(): --> 115 return func(args, kwargs)

File ~/shares/SR004.nfs2/fominaav/3D/MVEdit/lib/pipelines/mvedit_3d_pipeline.py:1323, in MVEdit3DPipeline.call(self, prompt, negative_prompt, in_model, ingp_states, init_images, cond_images, extra_control_images, normals, nerf_code, density_grid, density_bitfield, camera_poses, intrinsics, intrinsics_size, use_reference, use_normal, cam_weights, keep_views, guidance_scale, num_inference_steps, denoising_strength, progress_to_dmtet, tet_resolution, patch_size, patch_bs, diff_bs, render_bs, n_inverse_rays, n_inverse_steps, init_inverse_steps, tet_init_inverse_steps, seg_padding, ip_adapter, tile_weight, depth_weight, blend_weight, lr_schedule, lr_multiplier, render_size_p, max_num_views, depth_p_weight, patch_rgb_weight, patch_normal_weight, entropy_weight, alpha_soften, normal_reg_weight, mesh_normal_reg_weight, ambient_light, mesh_reduction, mesh_simplify_texture_steps, dt_gamma_scale, testmode_dt_gamma_scale, bg_width, ablation_nodiff, debug, out_dir, save_interval, save_all_interval, default_prompt, default_neg_prompt, bake_texture, map_size, prog_bar) 1320 batchscheduler = [deepcopy(self.scheduler) for in range(num_cameras)] 1322 else: -> 1323 max_num_cameras = max(int(round(max_num_views(progress, progress_to_dmtet))), num_keep_views) 1324 if max_num_cameras < num_cameras: 1325 keep_ids = torch.arange(num_cameras, device=device)

UnboundLocalError: local variable 'num_keep_views' referenced before assignment

MY CODE

import os
import sys

sys.path.append(os.path.abspath(os.path.join(__file__, '../')))
if 'OMP_NUM_THREADS' not in os.environ:
    os.environ['OMP_NUM_THREADS'] = '16'

import shutil
import os.path as osp
import argparse
import torch
import gradio as gr
from functools import partial
from lib.core.mvedit_webui.shared_opts import send_to_click
from lib.core.mvedit_webui.tab_img_to_3d import create_interface_img_to_3d
from lib.core.mvedit_webui.tab_3d_to_3d import create_interface_3d_to_3d
from lib.core.mvedit_webui.tab_text_to_img_to_3d import create_interface_text_to_img_to_3d
from lib.core.mvedit_webui.tab_retexturing import create_interface_retexturing
from lib.core.mvedit_webui.tab_3d_to_video import create_interface_3d_to_video
from lib.core.mvedit_webui.tab_stablessdnerf_to_3d import create_interface_stablessdnerf_to_3d
from lib.apis.mvedit import MVEditRunner
from lib.version import __version__
from collections import OrderedDict
import random

DEBUG_SAVE_INTERVAL = {
    0: None,
    1: 4,
    2: 1}

torch.set_grad_enabled(False)
runner = MVEditRunner(
    device=torch.device('cuda'),
    local_files_only=False,
    unload_models=False,
    out_dir='viz',
    save_interval=DEBUG_SAVE_INTERVAL[0],
    save_all_interval=1 if DEBUG_SAVE_INTERVAL[0] == 2 else None,
    dtype=torch.float16,
    debug=False,
    no_safe=False
)

seed = random.randint(0, 2**31)

out_img = runner.run_text_to_img(seed, 512, 512, 'red car', '', 'DPMSolverMultistep', 32, 7, 
                                 'Lykon/dreamshaper-8', 'best quality, sharp focus, photorealistic, extremely detailed', 
                                 'worst quality, low quality, depth of field, blurry, out of focus, low-res, illustration, painting, drawing', {})

img_segm = runner.run_segmentation(out_img)
init_images = runner.run_zero123plus1_2(seed, img_segm)

nerf_mesh_args = OrderedDict([
    ('prompt', 'red car'),
    ('negative_prompt', ''),
    ('scheduler', 'DPMSolverMultistep'),
    ('steps', 24),
    ('denoising_strength', 0.5),
    ('random_init', False),
    ('cfg_scale', 7),
    ('checkpoint', 'runwayml/stable-diffusion-v1-5'),
    ('max_num_views', 32),
    ('aux_prompt', 'best quality, sharp focus, photorealistic, extremely detailed'),
    ('aux_negative_prompt', 'worst quality, low quality, depth of field, blurry, out of focus, low-res, '
                            'illustration, painting, drawing'),
    ('diff_bs', 4),
    ('patch_size', 128),
    ('patch_bs_nerf', 1),
    ('render_bs', 6),
    ('patch_bs', 8),
    ('alpha_soften', 0.02),
    ('normal_reg_weight', 4.0),
    ('start_entropy_weight', 0.0),
    ('end_entropy_weight', 4.0),
    ('entropy_d', 0.015),
    ('mesh_smoothness', 1.0),
    ('n_inverse_steps', 96),
    ('init_inverse_steps', 720),
    ('tet_init_inverse_steps', 120),
    ('start_lr', 0.01),
    ('end_lr', 0.005),
    ('tet_resolution', 128)])

superres_defaults = OrderedDict([
    ('do_superres', True),
    ('scheduler', 'DPMSolverSDEKarras'),
    ('steps', 24),
    ('denoising_strength', 0.4),
    ('random_init', False),
    ('cfg_scale', 7),
    ('checkpoint', 'runwayml/stable-diffusion-v1-5'),
    ('aux_prompt', 'best quality, sharp focus, photorealistic, extremely detailed'),
    ('aux_negative_prompt', 'worst quality, low quality, depth of field, blurry, out of focus, low-res, '
                            'illustration, painting, drawing'),
    ('patch_size', 512),
    ('patch_bs', 1),
    ('n_inverse_steps', 48),
    ('start_lr', 0.01),
    ('end_lr', 0.01)])

sr_args = list(superres_defaults.values())
nerf_mesh_args = list(nerf_mesh_args.values())
args = []
args.extend(nerf_mesh_args)
args.extend(sr_args)
args.extend(init_images)
args.extend({})

glb_path = runner.run_zero123plus1_2_to_mesh(seed, img_segm, *args)

can you please help me to run it correctly

[Lakonik]

Hi! MVEditRunner is designed to be used only within Gradio. For now, the only way to use MVEdit other than the WebUI is to call the APIs. Python scripts that directly call the diffusers pipelines (bypassing MVEditRunner and Gradio) will be added later.