min-dalle-test/min_dalle/min_dalle_flax.py

87 lines
3.2 KiB
Python

import jax
import numpy
from PIL import Image
import torch
from .min_dalle_base import MinDalleBase
from .models.dalle_bart_encoder_flax import DalleBartEncoderFlax
from .models.dalle_bart_decoder_flax import DalleBartDecoderFlax
from .models.vqgan_detokenizer import VQGanDetokenizer
from .load_params import load_dalle_bart_flax_params, load_vqgan_torch_params
class MinDalleFlax(MinDalleBase):
def __init__(self, is_mega: bool, is_reusable: bool = True):
super().__init__(is_mega)
self.is_reusable = is_reusable
print("initializing MinDalleFlax")
self.model_params = load_dalle_bart_flax_params(self.model_path)
if is_reusable:
self.init_encoder()
self.init_decoder()
self.init_detokenizer()
def init_encoder(self):
print("initializing DalleBartEncoderFlax")
self.encoder: DalleBartEncoderFlax = DalleBartEncoderFlax(
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
).bind({'params': self.model_params.pop('encoder')})
def init_decoder(self):
print("initializing DalleBartDecoderFlax")
self.decoder = DalleBartDecoderFlax(
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
)
def init_detokenizer(self):
print("initializing VQGanDetokenizer")
params = load_vqgan_torch_params('./pretrained/vqgan')
self.detokenizer = VQGanDetokenizer()
self.detokenizer.load_state_dict(params)
del params
def generate_image(self, text: str, seed: int) -> Image.Image:
text_tokens = self.tokenize_text(text)
if not self.is_reusable: self.init_encoder()
print("encoding text tokens")
encoder_state = self.encoder(text_tokens)
if not self.is_reusable: del self.encoder
if not self.is_reusable:
self.init_decoder()
params = self.model_params.pop('decoder')
else:
params = self.model_params['decoder']
print("sampling image tokens")
image_tokens = self.decoder.sample_image_tokens(
text_tokens,
encoder_state,
jax.random.PRNGKey(seed),
params
)
if not self.is_reusable: del self.decoder
image_tokens = torch.tensor(numpy.array(image_tokens))
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