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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_8view'
os.makedirs(path, exist_ok=True)
views = ["front", "front_right", "right", "back_right", "back", " back_left", "left", "front_left"]
# views = ["front", "front_right", "right", "back", "left", "front_left"]
# views = ["front", "right", "back", "left"]
clr_prompt = [f"a rendering image of 3D models, {view} view, color map." for view in views]
normal_prompt = [f"a rendering image of 3D models, {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)
# print(prompt_embeds.dtype)
if id == 0:
torch.save(prompt_embeds, f'./{path}/clr_embeds.pt')
else:
torch.save(prompt_embeds, f'./{path}/normal_embeds.pt')
print('done')
def generate_img_embeds():
path = './fixed_prompt_embeds_persp2ortho'
os.makedirs(path, exist_ok=True)
text_prompt = ["a orthogonal renderining image of 3D models"]
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)
# print(prompt_embeds.dtype)
torch.save(prompt_embeds, f'./{path}/embeds.pt')
print('done')
generate_img_embeds() |