mirror of
https://github.com/deepseek-ai/DreamCraft3D
synced 2025-03-10 22:00:08 +00:00
98 lines
3.3 KiB
Python
98 lines
3.3 KiB
Python
import json
|
|
import os
|
|
from dataclasses import dataclass
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
from diffusers import IFPipeline
|
|
from transformers import T5EncoderModel, T5Tokenizer
|
|
|
|
import threestudio
|
|
from threestudio.models.prompt_processors.base import PromptProcessor, hash_prompt
|
|
from threestudio.utils.misc import cleanup
|
|
from threestudio.utils.typing import *
|
|
|
|
|
|
@threestudio.register("deep-floyd-prompt-processor")
|
|
class DeepFloydPromptProcessor(PromptProcessor):
|
|
@dataclass
|
|
class Config(PromptProcessor.Config):
|
|
pretrained_model_name_or_path: str = "DeepFloyd/IF-I-XL-v1.0"
|
|
|
|
cfg: Config
|
|
|
|
### these functions are unused, kept for debugging ###
|
|
def configure_text_encoder(self) -> None:
|
|
os.environ["TOKENIZERS_PARALLELISM"] = "false"
|
|
self.text_encoder = T5EncoderModel.from_pretrained(
|
|
self.cfg.pretrained_model_name_or_path,
|
|
subfolder="text_encoder",
|
|
load_in_8bit=True,
|
|
variant="8bit",
|
|
device_map="auto",
|
|
) # FIXME: behavior of auto device map in multi-GPU training
|
|
self.pipe = IFPipeline.from_pretrained(
|
|
self.cfg.pretrained_model_name_or_path,
|
|
text_encoder=self.text_encoder, # pass the previously instantiated 8bit text encoder
|
|
unet=None,
|
|
)
|
|
|
|
def destroy_text_encoder(self) -> None:
|
|
del self.text_encoder
|
|
del self.pipe
|
|
cleanup()
|
|
|
|
def get_text_embeddings(
|
|
self, prompt: Union[str, List[str]], negative_prompt: Union[str, List[str]]
|
|
) -> Tuple[Float[Tensor, "B 77 4096"], Float[Tensor, "B 77 4096"]]:
|
|
text_embeddings, uncond_text_embeddings = self.pipe.encode_prompt(
|
|
prompt=prompt, negative_prompt=negative_prompt, device=self.device
|
|
)
|
|
return text_embeddings, uncond_text_embeddings
|
|
|
|
###
|
|
|
|
@staticmethod
|
|
def spawn_func(pretrained_model_name_or_path, prompts, cache_dir, device):
|
|
max_length = 77
|
|
tokenizer = T5Tokenizer.from_pretrained(
|
|
pretrained_model_name_or_path,
|
|
subfolder="tokenizer",
|
|
local_files_only=True
|
|
)
|
|
text_encoder = T5EncoderModel.from_pretrained(
|
|
pretrained_model_name_or_path,
|
|
subfolder="text_encoder",
|
|
torch_dtype=torch.float16, # suppress warning
|
|
load_in_8bit=True,
|
|
variant="8bit",
|
|
device_map="auto",
|
|
local_files_only=True
|
|
)
|
|
with torch.no_grad():
|
|
text_inputs = tokenizer(
|
|
prompts,
|
|
padding="max_length",
|
|
max_length=max_length,
|
|
truncation=True,
|
|
add_special_tokens=True,
|
|
return_tensors="pt",
|
|
)
|
|
text_input_ids = text_inputs.input_ids
|
|
attention_mask = text_inputs.attention_mask
|
|
text_embeddings = text_encoder(
|
|
text_input_ids.to(text_encoder.device),
|
|
attention_mask=attention_mask.to(text_encoder.device),
|
|
)
|
|
text_embeddings = text_embeddings[0]
|
|
|
|
for prompt, embedding in zip(prompts, text_embeddings):
|
|
torch.save(
|
|
embedding,
|
|
os.path.join(
|
|
cache_dir,
|
|
f"{hash_prompt(pretrained_model_name_or_path, prompt)}.pt",
|
|
),
|
|
)
|
|
|
|
del text_encoder |