content: import os
  import numpy as np
  import torch
  from contextlib import nullcontext
  from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
  from einops import rearrange
  from ldm.util import instantiate_from_config
  from ldm.models.diffusion.ddim import DDIMSampler
  from omegaconf import OmegaConf
  from PIL import Image
  from rich import print
  from transformers import CLIPImageProcessor
  from torch import autocast
  from torchvision import transforms
  
  
  def load_model_from_config(config, ckpt, device, verbose=False):
      print(f'Loading model from {ckpt}')
      pl_sd = torch.load(ckpt, map_location='cpu')
      if 'global_step' in pl_sd:
          print(f'Global Step: {pl_sd["global_step"]}')
      sd = pl_sd['state_dict']
      model = instantiate_from_config(config.model)
      m, u = model.load_state_dict(sd, strict=False)
      if len(m) > 0 and verbose:
          print('missing keys:')
          print(m)
      if len(u) > 0 and verbose:
          print('unexpected keys:')
          print(u)
  
      model.to(device)
      model.eval()
      return model
  
  
  def init_model(device, ckpt, half_precision=False):
      config = os.path.join(os.path.dirname(__file__), '../configs/sd-objaverse-finetune-c_concat-256.yaml')
      config = OmegaConf.load(config)
  
      # Instantiate all models beforehand for efficiency.
      models = dict()
      print('Instantiating LatentDiffusion...')
      if half_precision:
          models['turncam'] = torch.compile(load_model_from_config(config, ckpt, device=device)).half()
      else:
          models['turncam'] = torch.compile(load_model_from_config(config, ckpt, device=device))
      print('Instantiating StableDiffusionSafetyChecker...')
      models['nsfw'] = StableDiffusionSafetyChecker.from_pretrained(
          'CompVis/stable-diffusion-safety-checker').to(device)
      models['clip_fe'] = CLIPImageProcessor.from_pretrained(
          "openai/clip-vit-large-patch14")
      # We multiply all by some factor > 1 to make them less likely to be triggered.
      models['nsfw'].concept_embeds_weights *= 1.2
      models['nsfw'].special_care_embeds_weights *= 1.2
  
      return models
  
  @torch.no_grad()
  def sample_model_batch(model, sampler, input_im, xs, ys, n_samples=4, precision='autocast', ddim_eta=1.0, ddim_steps=75, scale=3.0, h=256, w=256):
      precision_scope = autocast if precision == 'autocast' else nullcontext
      with precision_scope("cuda"):
          with model.ema_scope():
              c = model.get_learned_conditioning(input_im).tile(n_samples, 1, 1)
              T = []
              for x, y in zip(xs, ys):
                  T.append([np.radians(x), np.sin(np.radians(y)), np.cos(np.radians(y)), 0])
              T = torch.tensor(np.array(T))[:, None, :].float().to(c.device)
              c = torch.cat([c, T], dim=-1)
              c = model.cc_projection(c)
              cond = {}
              cond['c_crossattn'] = [c]
              cond['c_concat'] = [model.encode_first_stage(input_im).mode().detach()
                                  .repeat(n_samples, 1, 1, 1)]
              if scale != 1.0:
                  uc = {}
                  uc['c_concat'] = [torch.zeros(n_samples, 4, h // 8, w // 8).to(c.device)]
                  uc['c_crossattn'] = [torch.zeros_like(c).to(c.device)]
              else:
                  uc = None
  
              shape = [4, h // 8, w // 8]
              samples_ddim, _ = sampler.sample(S=ddim_steps,
                                               conditioning=cond,
                                               batch_size=n_samples,
                                               shape=shape,
                                               verbose=False,
                                               unconditional_guidance_scale=scale,
                                               unconditional_conditioning=uc,
                                               eta=ddim_eta,
                                               x_T=None)
              # print(samples_ddim.shape)
              # samples_ddim = torch.nn.functional.interpolate(samples_ddim, 64, mode='nearest', antialias=False)
              x_samples_ddim = model.decode_first_stage(samples_ddim)
              ret_imgs = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0).cpu()
              del cond, c, x_samples_ddim, samples_ddim, uc, input_im
              torch.cuda.empty_cache()
              return ret_imgs
  
  @torch.no_grad()
  def predict_stage1_gradio(model, raw_im, save_path = "", adjust_set=[], device="cuda", ddim_steps=75, scale=3.0):
      # raw_im = raw_im.resize([256, 256], Image.LANCZOS)
      # input_im_init = preprocess_image(models, raw_im, preprocess=False)
      input_im_init = np.asarray(raw_im, dtype=np.float32) / 255.0
      input_im = transforms.ToTensor()(input_im_init).unsqueeze(0).to(device)
      input_im = input_im * 2 - 1
  
      # stage 1: 8
      delta_x_1_8 = [0] * 4 + [30] * 4 + [-30] * 4
      delta_y_1_8 = [0+90*(i%4) if i < 4 else 30+90*(i%4) for i in range(8)] + [30+90*(i%4) for i in range(4)]
  
      ret_imgs = []
      sampler = DDIMSampler(model)
      # sampler.to(device)
      if adjust_set != []:
          x_samples_ddims_8 = sample_model_batch(model, sampler, input_im, 
                                                 [delta_x_1_8[i] for i in adjust_set], [delta_y_1_8[i] for i in adjust_set], 
                                                 n_samples=len(adjust_set), ddim_steps=ddim_steps, scale=scale)
      else:
          x_samples_ddims_8 = sample_model_batch(model, sampler, input_im, delta_x_1_8, delta_y_1_8, n_samples=len(delta_x_1_8), ddim_steps=ddim_steps, scale=scale)
      sample_idx = 0
      for stage1_idx in range(len(delta_x_1_8)):
          if adjust_set != [] and stage1_idx not in adjust_set:
              continue
          x_sample = 255.0 * rearrange(x_samples_ddims_8[sample_idx].numpy(), 'c h w -> h w c')
          out_image = Image.fromarray(x_sample.astype(np.uint8))
          ret_imgs.append(out_image)
          if save_path:
              out_image.save(os.path.join(save_path, '%d.png'%(stage1_idx)))
          sample_idx += 1
      del x_samples_ddims_8
      del sampler
      torch.cuda.empty_cache()
      return ret_imgs
  
  def infer_stage_2(model, save_path_stage1, save_path_stage2, delta_x_2, delta_y_2, indices, device, ddim_steps=75, scale=3.0):
      for stage1_idx in indices:
          # save stage 1 image
          # x_sample = 255.0 * rearrange(x_samples_ddims[stage1_idx].cpu().numpy(), 'c h w -> h w c')
          # Image.fromarray(x_sample.astype(np.uint8)).save()
          stage1_image_path = os.path.join(save_path_stage1, '%d.png'%(stage1_idx))
  
          raw_im = Image.open(stage1_image_path)
          # input_im_init = preprocess_image(models, raw_im, preprocess=False)
          input_im_init = np.asarray(raw_im, dtype=np.float32) #/ 255.0
          input_im_init[input_im_init >= 253.0] = 255.0
          input_im_init = input_im_init / 255.0
          input_im = transforms.ToTensor()(input_im_init).unsqueeze(0).to(device)
          input_im = input_im * 2 - 1
          # infer stage 2
          sampler = DDIMSampler(model)
          # sampler.to(device)
          # stage2_in = x_samples_ddims[stage1_idx][None, ...].to(device) * 2 - 1
          x_samples_ddims_stage2 = sample_model_batch(model, sampler, input_im, delta_x_2, delta_y_2, n_samples=len(delta_x_2), ddim_steps=ddim_steps, scale=scale)
          for stage2_idx in range(len(delta_x_2)):
              x_sample_stage2 = 255.0 * rearrange(x_samples_ddims_stage2[stage2_idx].numpy(), 'c h w -> h w c')
              Image.fromarray(x_sample_stage2.astype(np.uint8)).save(os.path.join(save_path_stage2, '%d_%d.png'%(stage1_idx, stage2_idx)))
          del input_im
          del x_samples_ddims_stage2
          torch.cuda.empty_cache()
  
  def zero123_infer(model, input_dir_path, start_idx=0, end_idx=12, indices=None, device="cuda", ddim_steps=75, scale=3.0):
      # input_img_path = os.path.join(input_dir_path, "input_256.png")
      save_path_8 = os.path.join(input_dir_path, "stage1_8")
      save_path_8_2 = os.path.join(input_dir_path, "stage2_8")
      os.makedirs(save_path_8_2, exist_ok=True)
  
      # raw_im = Image.open(input_img_path)
      # # input_im_init = preprocess_image(models, raw_im, preprocess=False)
      # input_im_init = np.asarray(raw_im, dtype=np.float32) / 255.0
      # input_im = transforms.ToTensor()(input_im_init).unsqueeze(0).to(device)
      # input_im = input_im * 2 - 1
  
      # stage 2: 6*4 or 8*4
      delta_x_2 = [-10, 10, 0, 0]
      delta_y_2 = [0, 0, -10, 10]
      
      infer_stage_2(model, save_path_8, save_path_8_2, delta_x_2, delta_y_2, indices=indices if indices else list(range(start_idx,end_idx)), device=device, ddim_steps=ddim_steps, scale=scale)
  