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# JAX implementation of VQGAN from taming-transformers https://github.com/CompVis/taming-transformers
from functools import partial
from typing import Tuple, Optional
import math
import jax
import jax.numpy as jnp
import numpy as np
import flax.linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers.modeling_flax_utils import FlaxPreTrainedModel
from transformers import PretrainedConfig
### VQGAN CONFIGURATION ###
# https://github.com/patil-suraj/vqgan-jax/blob/main/configuration_vqgan.py
#######################
class VQGANConfig(PretrainedConfig):
def __init__(
self,
ch: int = 128,
out_ch: int = 3,
in_channels: int = 3,
num_res_blocks: int = 2,
resolution: int = 256,
z_channels: int = 256,
ch_mult: Tuple = (1, 1, 2, 2, 4),
attn_resolutions: Tuple[int] = (16,),
n_embed: int = 1024,
embed_dim: int = 256,
dropout: float = 0.0,
double_z: bool = False,
resamp_with_conv: bool = True,
give_pre_end: bool = False,
**kwargs,
):
super().__init__(**kwargs)
self.ch = ch
self.out_ch = out_ch
self.in_channels = in_channels
self.num_res_blocks = num_res_blocks
self.resolution = resolution
self.z_channels = z_channels
self.ch_mult = list(ch_mult)
self.attn_resolutions = list(attn_resolutions)
self.n_embed = n_embed
self.embed_dim = embed_dim
self.dropout = dropout
self.double_z = double_z
self.resamp_with_conv = resamp_with_conv
self.give_pre_end = give_pre_end
self.num_resolutions = len(ch_mult)
### VQGAN MODEL ###
# https://github.com/patil-suraj/vqgan-jax/blob/main/modeling_flax_vqgan.py
#######################
class Upsample(nn.Module):
in_channels: int
with_conv: bool
dtype: jnp.dtype = jnp.float32
def setup(self):
if self.with_conv:
self.conv = nn.Conv(
features=self.in_channels,
kernel_size=(3, 3),
strides=(1, 1),
padding=((1, 1), (1, 1)),
dtype=self.dtype,
)
def __call__(self, hidden_states):
batch, height, width, channels = hidden_states.shape
hidden_states = jax.image.resize(
hidden_states,
shape=(batch, height * 2, width * 2, channels),
method="nearest",
)
if self.with_conv:
hidden_states = self.conv(hidden_states)
return hidden_states
class Downsample(nn.Module):
in_channels: int
with_conv: bool
dtype: jnp.dtype = jnp.float32
def setup(self):
if self.with_conv:
self.conv = nn.Conv(
features=self.in_channels,
kernel_size=(3, 3),
strides=(2, 2),
padding="VALID",
dtype=self.dtype,
)
def __call__(self, hidden_states):
if self.with_conv:
pad = ((0, 0), (0, 1), (0, 1), (0, 0)) # pad height and width dim
hidden_states = jnp.pad(hidden_states, pad_width=pad)
hidden_states = self.conv(hidden_states)
else:
hidden_states = nn.avg_pool(hidden_states, window_shape=(2, 2), strides=(2, 2), padding="VALID")
return hidden_states
class ResnetBlock(nn.Module):
in_channels: int
out_channels: Optional[int] = None
use_conv_shortcut: bool = False
temb_channels: int = 512
dropout_prob: float = 0.0
dtype: jnp.dtype = jnp.float32
def setup(self):
self.out_channels_ = self.in_channels if self.out_channels is None else self.out_channels
self.norm1 = nn.GroupNorm(num_groups=32, epsilon=1e-6)
self.conv1 = nn.Conv(
features=self.out_channels_,
kernel_size=(3, 3),
strides=(1, 1),
padding=((1, 1), (1, 1)),
dtype=self.dtype,
)
if self.temb_channels:
self.temb_proj = nn.Dense(features=self.out_channels_, dtype=self.dtype)
self.norm2 = nn.GroupNorm(num_groups=32, epsilon=1e-6)
self.dropout = nn.Dropout(rate=self.dropout_prob)
self.conv2 = nn.Conv(
features=self.out_channels_,
kernel_size=(3, 3),
strides=(1, 1),
padding=((1, 1), (1, 1)),
dtype=self.dtype,
)
if self.in_channels != self.out_channels_:
if self.use_conv_shortcut:
self.conv_shortcut = nn.Conv(
features=self.out_channels_,
kernel_size=(3, 3),
strides=(1, 1),
padding=((1, 1), (1, 1)),
dtype=self.dtype,
)
else:
self.nin_shortcut = nn.Conv(
features=self.out_channels_,
kernel_size=(1, 1),
strides=(1, 1),
padding="VALID",
dtype=self.dtype,
)
def __call__(self, hidden_states, temb=None, deterministic: bool = True):
residual = hidden_states
hidden_states = self.norm1(hidden_states)
hidden_states = nn.swish(hidden_states)
hidden_states = self.conv1(hidden_states)
if temb is not None:
hidden_states = hidden_states + self.temb_proj(nn.swish(temb))[:, :, None, None] # TODO: check shapes
hidden_states = self.norm2(hidden_states)
hidden_states = nn.swish(hidden_states)
hidden_states = self.dropout(hidden_states, deterministic)
hidden_states = self.conv2(hidden_states)
if self.in_channels != self.out_channels_:
if self.use_conv_shortcut:
residual = self.conv_shortcut(residual)
else:
residual = self.nin_shortcut(residual)
return hidden_states + residual
class AttnBlock(nn.Module):
in_channels: int
dtype: jnp.dtype = jnp.float32
def setup(self):
conv = partial(
nn.Conv, self.in_channels, kernel_size=(1, 1), strides=(1, 1), padding="VALID", dtype=self.dtype
)
self.norm = nn.GroupNorm(num_groups=32, epsilon=1e-6)
self.q, self.k, self.v = conv(), conv(), conv()
self.proj_out = conv()
def __call__(self, hidden_states):
residual = hidden_states
hidden_states = self.norm(hidden_states)
query = self.q(hidden_states)
key = self.k(hidden_states)
value = self.v(hidden_states)
# compute attentions
batch, height, width, channels = query.shape
query = query.reshape((batch, height * width, channels))
key = key.reshape((batch, height * width, channels))
attn_weights = jnp.einsum("...qc,...kc->...qk", query, key)
attn_weights = attn_weights * (int(channels) ** -0.5)
attn_weights = nn.softmax(attn_weights, axis=2)
## attend to values
value = value.reshape((batch, height * width, channels))
hidden_states = jnp.einsum("...kc,...qk->...qc", value, attn_weights)
hidden_states = hidden_states.reshape((batch, height, width, channels))
hidden_states = self.proj_out(hidden_states)
hidden_states = hidden_states + residual
return hidden_states
class UpsamplingBlock(nn.Module):
config: VQGANConfig
curr_res: int
block_idx: int
dtype: jnp.dtype = jnp.float32
def setup(self):
if self.block_idx == self.config.num_resolutions - 1:
block_in = self.config.ch * self.config.ch_mult[-1]
else:
block_in = self.config.ch * self.config.ch_mult[self.block_idx + 1]
block_out = self.config.ch * self.config.ch_mult[self.block_idx]
self.temb_ch = 0
res_blocks = []
attn_blocks = []
for _ in range(self.config.num_res_blocks + 1):
res_blocks.append(
ResnetBlock(
block_in, block_out, temb_channels=self.temb_ch, dropout_prob=self.config.dropout, dtype=self.dtype
)
)
block_in = block_out
if self.curr_res in self.config.attn_resolutions:
attn_blocks.append(AttnBlock(block_in, dtype=self.dtype))
self.block = res_blocks
self.attn = attn_blocks
self.upsample = None
if self.block_idx != 0:
self.upsample = Upsample(block_in, with_conv=self.config.resamp_with_conv, dtype=self.dtype)
def __call__(self, hidden_states, temb=None, deterministic: bool = True):
for res_block in self.block:
hidden_states = res_block(hidden_states, temb, deterministic=deterministic)
for attn_block in self.attn:
hidden_states = attn_block(hidden_states)
if self.upsample is not None:
hidden_states = self.upsample(hidden_states)
return hidden_states
class DownsamplingBlock(nn.Module):
config: VQGANConfig
curr_res: int
block_idx: int
dtype: jnp.dtype = jnp.float32
def setup(self):
in_ch_mult = (1,) + tuple(self.config.ch_mult)
block_in = self.config.ch * in_ch_mult[self.block_idx]
block_out = self.config.ch * self.config.ch_mult[self.block_idx]
self.temb_ch = 0
res_blocks = []
attn_blocks = []
for _ in range(self.config.num_res_blocks):
res_blocks.append(
ResnetBlock(
in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout_prob=self.config.dropout, dtype=self.dtype
)
)
block_in = block_out
if self.curr_res in self.config.attn_resolutions:
attn_blocks.append(AttnBlock(block_in, dtype=self.dtype))
self.block = res_blocks
self.attn = attn_blocks
self.downsample = None
if self.block_idx != self.config.num_resolutions - 1:
self.downsample = Downsample(in_channels=block_in, with_conv=self.config.resamp_with_conv, dtype=self.dtype)
def __call__(self, hidden_states, temb=None, deterministic: bool = True):
for res_block in self.block:
hidden_states = res_block(hidden_states, temb, deterministic=deterministic)
for attn_block in self.attn:
hidden_states = attn_block(hidden_states)
if self.downsample is not None:
hidden_states = self.downsample(hidden_states)
return hidden_states
class MidBlock(nn.Module):
in_channels: int
temb_channels: int
dropout: float
dtype: jnp.dtype = jnp.float32
def setup(self):
self.block_1 = ResnetBlock(
in_channels=self.in_channels,
out_channels=self.in_channels,
temb_channels=self.temb_channels,
dropout_prob=self.dropout,
dtype=self.dtype,
)
self.attn_1 = AttnBlock(in_channels=self.in_channels, dtype=self.dtype)
self.block_2 = ResnetBlock(
in_channels=self.in_channels,
out_channels=self.in_channels,
temb_channels=self.temb_channels,
dropout_prob=self.dropout,
dtype=self.dtype,
)
def __call__(self, hidden_states, temb=None, deterministic: bool = True):
hidden_states = self.block_1(hidden_states, temb, deterministic=deterministic)
hidden_states = self.attn_1(hidden_states)
hidden_states = self.block_2(hidden_states, temb, deterministic=deterministic)
return hidden_states
class Encoder(nn.Module):
config: VQGANConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.temb_ch = 0
# downsampling
self.conv_in = nn.Conv(
self.config.ch,
kernel_size=(3, 3),
strides=(1, 1),
padding=((1, 1), (1, 1)),
dtype=self.dtype,
)
curr_res = self.config.resolution
downsample_blocks = []
for i_level in range(self.config.num_resolutions):
downsample_blocks.append(DownsamplingBlock(self.config, curr_res, block_idx=i_level, dtype=self.dtype))
if i_level != self.config.num_resolutions - 1:
curr_res = curr_res // 2
self.down = downsample_blocks
# middle
mid_channels = self.config.ch * self.config.ch_mult[-1]
self.mid = MidBlock(mid_channels, self.temb_ch, self.config.dropout, dtype=self.dtype)
# end
self.norm_out = nn.GroupNorm(num_groups=32, epsilon=1e-6)
self.conv_out = nn.Conv(
2 * self.config.z_channels if self.config.double_z else self.config.z_channels,
kernel_size=(3, 3),
strides=(1, 1),
padding=((1, 1), (1, 1)),
dtype=self.dtype,
)
def __call__(self, pixel_values, deterministic: bool = True):
# timestep embedding
temb = None
# downsampling
hidden_states = self.conv_in(pixel_values)
for block in self.down:
hidden_states = block(hidden_states, temb, deterministic=deterministic)
# middle
hidden_states = self.mid(hidden_states, temb, deterministic=deterministic)
# end
hidden_states = self.norm_out(hidden_states)
hidden_states = nn.swish(hidden_states)
hidden_states = self.conv_out(hidden_states)
return hidden_states
class Decoder(nn.Module):
config: VQGANConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.temb_ch = 0
# compute in_ch_mult, block_in and curr_res at lowest res
block_in = self.config.ch * self.config.ch_mult[self.config.num_resolutions - 1]
curr_res = self.config.resolution // 2 ** (self.config.num_resolutions - 1)
self.z_shape = (1, self.config.z_channels, curr_res, curr_res)
print("Working with z of shape {} = {} dimensions.".format(self.z_shape, np.prod(self.z_shape)))
# z to block_in
self.conv_in = nn.Conv(
block_in,
kernel_size=(3, 3),
strides=(1, 1),
padding=((1, 1), (1, 1)),
dtype=self.dtype,
)
# middle
self.mid = MidBlock(block_in, self.temb_ch, self.config.dropout, dtype=self.dtype)
# upsampling
upsample_blocks = []
for i_level in reversed(range(self.config.num_resolutions)):
upsample_blocks.append(UpsamplingBlock(self.config, curr_res, block_idx=i_level, dtype=self.dtype))
if i_level != 0:
curr_res = curr_res * 2
self.up = list(reversed(upsample_blocks)) # reverse to get consistent order
# end
self.norm_out = nn.GroupNorm(num_groups=32, epsilon=1e-6)
self.conv_out = nn.Conv(
self.config.out_ch,
kernel_size=(3, 3),
strides=(1, 1),
padding=((1, 1), (1, 1)),
dtype=self.dtype,
)
def __call__(self, hidden_states, deterministic: bool = True):
# timestep embedding
temb = None
# z to block_in
hidden_states = self.conv_in(hidden_states)
# middle
hidden_states = self.mid(hidden_states, temb, deterministic=deterministic)
# upsampling
for block in reversed(self.up):
hidden_states = block(hidden_states, temb, deterministic=deterministic)
# end
if self.config.give_pre_end:
return hidden_states
hidden_states = self.norm_out(hidden_states)
hidden_states = nn.swish(hidden_states)
hidden_states = self.conv_out(hidden_states)
return hidden_states
class VectorQuantizer(nn.Module):
"""
see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py
____________________________________________
Discretization bottleneck part of the VQ-VAE.
Inputs:
- n_e : number of embeddings
- e_dim : dimension of embedding
- beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
_____________________________________________
"""
config: VQGANConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.embedding = nn.Embed(self.config.n_embed, self.config.embed_dim, dtype=self.dtype) # TODO: init
def __call__(self, hidden_states):
"""
Inputs the output of the encoder network z and maps it to a discrete
one-hot vector that is the index of the closest embedding vector e_j
z (continuous) -> z_q (discrete)
z.shape = (batch, channel, height, width)
quantization pipeline:
1. get encoder input (B,C,H,W)
2. flatten input to (B*H*W,C)
"""
# flatten
hidden_states_flattended = hidden_states.reshape((-1, self.config.embed_dim))
# dummy op to init the weights, so we can access them below
self.embedding(jnp.ones((1, 1), dtype="i4"))
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
emb_weights = self.variables["params"]["embedding"]["embedding"]
distance = (
jnp.sum(hidden_states_flattended ** 2, axis=1, keepdims=True)
+ jnp.sum(emb_weights ** 2, axis=1)
- 2 * jnp.dot(hidden_states_flattended, emb_weights.T)
)
# get quantized latent vectors
min_encoding_indices = jnp.argmin(distance, axis=1)
z_q = self.embedding(min_encoding_indices).reshape(hidden_states.shape)
# reshape to (batch, num_tokens)
min_encoding_indices = min_encoding_indices.reshape(hidden_states.shape[0], -1)
# compute the codebook_loss (q_loss) outside the model
# here we return the embeddings and indices
return z_q, min_encoding_indices
def get_codebook_entry(self, indices, shape=None):
# indices are expected to be of shape (batch, num_tokens)
# get quantized latent vectors
batch, num_tokens = indices.shape
z_q = self.embedding(indices)
z_q = z_q.reshape(batch, int(math.sqrt(num_tokens)), int(math.sqrt(num_tokens)), -1)
return z_q
class VQModule(nn.Module):
config: VQGANConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.encoder = Encoder(self.config, dtype=self.dtype)
self.decoder = Decoder(self.config, dtype=self.dtype)
self.quantize = VectorQuantizer(self.config, dtype=self.dtype)
self.quant_conv = nn.Conv(
self.config.embed_dim,
kernel_size=(1, 1),
strides=(1, 1),
padding="VALID",
dtype=self.dtype,
)
self.post_quant_conv = nn.Conv(
self.config.z_channels,
kernel_size=(1, 1),
strides=(1, 1),
padding="VALID",
dtype=self.dtype,
)
def encode(self, pixel_values, deterministic: bool = True):
hidden_states = self.encoder(pixel_values, deterministic=deterministic)
hidden_states = self.quant_conv(hidden_states)
quant_states, indices = self.quantize(hidden_states)
return quant_states, indices
def decode(self, hidden_states, deterministic: bool = True):
hidden_states = self.post_quant_conv(hidden_states)
hidden_states = self.decoder(hidden_states, deterministic=deterministic)
return hidden_states
def decode_code(self, code_b):
hidden_states = self.quantize.get_codebook_entry(code_b)
hidden_states = self.decode(hidden_states)
return hidden_states
def __call__(self, pixel_values, deterministic: bool = True):
quant_states, indices = self.encode(pixel_values, deterministic)
hidden_states = self.decode(quant_states, deterministic)
return hidden_states, indices
class VQGANPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface
for downloading and loading pretrained models.
"""
config_class = VQGANConfig
base_model_prefix = "model"
module_class: nn.Module = None
def __init__(
self,
config: VQGANConfig,
input_shape: Tuple = (1, 256, 256, 3),
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
**kwargs,
):
module = self.module_class(config=config, dtype=dtype, **kwargs)
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype)
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict:
# init input tensors
pixel_values = jnp.zeros(input_shape, dtype=jnp.float32)
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
return self.module.init(rngs, pixel_values)["params"]
def encode(self, pixel_values, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False):
# Handle any PRNG if needed
rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
return self.module.apply(
{"params": params or self.params}, jnp.array(pixel_values), not train, rngs=rngs, method=self.module.encode
)
def decode(self, hidden_states, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False):
# Handle any PRNG if needed
rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
return self.module.apply(
{"params": params or self.params},
jnp.array(hidden_states),
not train,
rngs=rngs,
method=self.module.decode,
)
def decode_code(self, indices, params: dict = None):
return self.module.apply(
{"params": params or self.params}, jnp.array(indices, dtype="i4"), method=self.module.decode_code
)
def __call__(
self,
pixel_values,
params: dict = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
):
# Handle any PRNG if needed
rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
return self.module.apply(
{"params": params or self.params},
jnp.array(pixel_values),
not train,
rngs=rngs,
)
class VQModel(VQGANPreTrainedModel):
module_class = VQModule
## Utility testing ###
from PIL import Image
def preprocess_vqgan(x):
x = 2.*x - 1.
return x
def custom_to_pil(x):
x = np.clip(x, -1., 1.)
x = (x + 1.)/2.
x = (255*x).astype(np.uint8)
x = Image.fromarray(x)
if not x.mode == "RGB":
x = x.convert("RGB")
return x
def preprocess(img:Image.Image, target_image_size=256,):
s = min((img.width, img.height))
if s < target_image_size:
raise ValueError(f'min dim for image {s} < {target_image_size}')
r = target_image_size / s
s = (round(r * img.size[1]), round(r * img.size[0]))
# img = Image.resize(img, s, interpolation=Image.LANCZOS)
img = img.resize(s, resample=Image.LANCZOS)
# Center crop
def crop_center(pil_img, crop_width, crop_height):
img_width, img_height = pil_img.size
return pil_img.crop(((img_width - crop_width) // 2,
(img_height - crop_height) // 2,
(img_width + crop_width) // 2,
(img_height + crop_height) // 2))
# img = TF.center_crop(img, output_size=2 * [target_image_size])
img = crop_center(img, target_image_size, target_image_size)
# img = torch.unsqueeze(T.ToTensor()(img), 0)
return np.asarray(img)[np.newaxis, :]/255.
if __name__ == "__main__":
image = "0810.png"
size=384
image = Image.open(image)
import ipdb; ipdb.set_trace()
image = preprocess(image, size)
model = VQModel.from_pretrained("valhalla/vqgan-imagenet-f16-1024")
quant_states, indices = model.encode(image)
rec = model.decode(quant_states)
custom_to_pil(preprocess_vqgan(image[0])).save("orig.png")
custom_to_pil(preprocess_vqgan(np.asarray(rec[0]))).save("rec.png")