| from transformers import CLIPTokenizer, CLIPTextModel |
| import torch |
| import os |
|
|
| root = '/mnt/data/lipeng/' |
| pretrained_model_name_or_path = 'stabilityai/stable-diffusion-2-1-unclip' |
|
|
|
|
| weight_dtype = torch.float16 |
| device = torch.device("cuda:0") |
| tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="tokenizer") |
| text_encoder = CLIPTextModel.from_pretrained(pretrained_model_name_or_path, subfolder='text_encoder') |
| text_encoder = text_encoder.to(device, dtype=weight_dtype) |
|
|
| def generate_mv_embeds(): |
| path = './fixed_prompt_embeds_7view' |
| os.makedirs(path, exist_ok=True) |
| views = ["front", "front_right", "right", "back", "left", "front_left", 'face'] |
| clr_prompt = [f"a rendering image of 3D human, {view} view, color map." for view in views] |
| normal_prompt = [f"a rendering image of 3D human, {view} view, normal map." for view in views] |
|
|
|
|
| for id, text_prompt in enumerate([clr_prompt, normal_prompt]): |
| print(text_prompt) |
| text_inputs = tokenizer(text_prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt").to(device) |
| text_input_ids = text_inputs.input_ids |
| untruncated_ids = tokenizer(text_prompt, padding="longest", return_tensors="pt").input_ids |
| if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( |
| text_input_ids, untruncated_ids): |
| removed_text = tokenizer.batch_decode( |
| untruncated_ids[:, tokenizer.model_max_length - 1 : -1] |
| ) |
| if hasattr(text_encoder.config, "use_attention_mask") and text_encoder.config.use_attention_mask: |
| attention_mask = text_inputs.attention_mask.to(device) |
| else: |
| attention_mask = None |
| prompt_embeds = text_encoder(text_input_ids.to(device), attention_mask=attention_mask,) |
| prompt_embeds = prompt_embeds[0].detach().cpu() |
| print(prompt_embeds.shape) |
|
|
|
|
| |
| if id == 0: |
| torch.save(prompt_embeds, f'./{path}/clr_embeds.pt') |
| else: |
| torch.save(prompt_embeds, f'./{path}/normal_embeds.pt') |
| print('done') |
| |
|
|
|
|
| |
| generate_mv_embeds() |
|
|
