min-dalle-test/min_dalle/min_dalle_torch.py

106 lines
4.0 KiB
Python

import os
from PIL import Image
from typing import Dict
from torch import LongTensor
import torch
torch.set_grad_enabled(False)
torch.set_num_threads(os.cpu_count())
from .min_dalle_base import MinDalleBase
from .models.dalle_bart_encoder_torch import DalleBartEncoderTorch
from .models.dalle_bart_decoder_torch import DalleBartDecoderTorch
from .models.vqgan_detokenizer import VQGanDetokenizer
class MinDalleTorch(MinDalleBase):
def __init__(
self,
is_mega: bool,
is_reusable: bool = True,
token_count: int = 256
):
print("initializing MinDalleTorch")
super().__init__(is_mega)
self.is_reusable = is_reusable
self.token_count = token_count
self.encoder_params_path = os.path.join(self.model_path, 'encoder.pt')
self.decoder_params_path = os.path.join(self.model_path, 'decoder.pt')
self.detoker_params_path = os.path.join('pretrained', 'vqgan', 'detoker.pt')
if is_reusable:
self.init_encoder()
self.init_decoder()
self.init_detokenizer()
def init_encoder(self):
print("initializing DalleBartEncoderTorch")
self.encoder = DalleBartEncoderTorch(
attention_head_count = 32 if self.is_mega else 16,
embed_count = 2048 if self.is_mega else 1024,
glu_embed_count = 4096 if self.is_mega else 2730,
text_token_count = 64,
text_vocab_count = 50272 if self.is_mega else 50264,
layer_count = 24 if self.is_mega else 12
)
params = torch.load(self.encoder_params_path)
self.encoder.load_state_dict(params, strict=False)
del params
if torch.cuda.is_available(): self.encoder = self.encoder.cuda()
def init_decoder(self):
print("initializing DalleBartDecoderTorch")
self.decoder = DalleBartDecoderTorch(
sample_token_count = self.token_count,
image_token_count = 256,
image_vocab_count = 16415 if self.is_mega else 16384,
attention_head_count = 32 if self.is_mega else 16,
embed_count = 2048 if self.is_mega else 1024,
glu_embed_count = 4096 if self.is_mega else 2730,
layer_count = 24 if self.is_mega else 12,
start_token = 16415 if self.is_mega else 16384,
batch_count = 2
)
params = torch.load(self.decoder_params_path)
self.decoder.load_state_dict(params, strict=False)
del params
if torch.cuda.is_available(): self.decoder = self.decoder.cuda()
def init_detokenizer(self):
print("initializing VQGanDetokenizer")
self.detokenizer = VQGanDetokenizer()
params = torch.load(self.detoker_params_path)
self.detokenizer.load_state_dict(params)
del params
if torch.cuda.is_available(): self.detokenizer = self.detokenizer.cuda()
def generate_image_tokens(self, text: str, seed: int) -> LongTensor:
text_tokens = self.tokenize_text(text)
text_tokens = torch.tensor(text_tokens).to(torch.long)
if torch.cuda.is_available(): text_tokens = text_tokens.cuda()
if not self.is_reusable: self.init_encoder()
print("encoding text tokens")
encoder_state = self.encoder.forward(text_tokens)
if not self.is_reusable: del self.encoder
if not self.is_reusable: self.init_decoder()
print("sampling image tokens")
torch.manual_seed(seed)
image_tokens = self.decoder.forward(text_tokens, encoder_state)
if not self.is_reusable: del self.decoder
return image_tokens
def generate_image(self, text: str, seed: int) -> Image.Image:
image_tokens = self.generate_image_tokens(text, seed)
if not self.is_reusable: self.init_detokenizer()
print("detokenizing image")
image = self.detokenizer.forward(image_tokens).to(torch.uint8)
if not self.is_reusable: del self.detokenizer
image = Image.fromarray(image.to('cpu').detach().numpy())
return image