Refactor: use VQGAN model from github, remove local copy

app/app_gradio.py

@@ -19,7 +19,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 
-from dalle_mini.vqgan_jax.modeling_flax_vqgan import VQModel
+from vqgan_jax.modeling_flax_vqgan import VQModel
 from dalle_mini.model import CustomFlaxBartForConditionalGeneration
 
 import gradio as gr

dalle_mini/vqgan_jax/README.md

@@ -1,5 +0,0 @@
-## vqgan-jax
-
-Files copied from [patil-suraj/vqgan-jax](https://github.com/patil-suraj/vqgan-jax/tree/main/vqgan_jax)
-
-Required for VQGAN Jax model.

dalle_mini/vqgan_jax/configuration_vqgan.py

@@ -1,40 +0,0 @@
-from typing import Tuple
-
-from transformers import PretrainedConfig
-
-
-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: 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)

dalle_mini/vqgan_jax/modeling_flax_vqgan.py

@@ -1,609 +0,0 @@
-# JAX implementation of VQGAN from taming-transformers https://github.com/CompVis/taming-transformers
-
-from functools import partial
-from typing import Tuple
-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 .configuration_vqgan import VQGANConfig
-
-
-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(
-                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(
-                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: 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(
-            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(self.out_channels_, dtype=self.dtype)
-
-        self.norm2 = nn.GroupNorm(num_groups=32, epsilon=1e-6)
-        self.dropout = nn.Dropout(self.dropout_prob)
-        self.conv2 = nn.Conv(
-            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(
-                    self.out_channels_,
-                    kernel_size=(3, 3),
-                    strides=(1, 1),
-                    padding=((1, 1), (1, 1)),
-                    dtype=self.dtype,
-                )
-            else:
-                self.nin_shortcut = nn.Conv(
-                    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, 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(
-                    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.downsample = None
-        if self.block_idx != self.config.num_resolutions - 1:
-            self.downsample = Downsample(block_in, 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(
-            self.in_channels,
-            self.in_channels,
-            temb_channels=self.temb_channels,
-            dropout_prob=self.dropout,
-            dtype=self.dtype,
-        )
-        self.attn_1 = AttnBlock(self.in_channels, dtype=self.dtype)
-        self.block_2 = ResnetBlock(
-            self.in_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

dev/notebooks/demo/model-sweep.py

@@ -11,19 +11,15 @@ from flax.jax_utils import replicate, unreplicate
 from transformers.models.bart.modeling_flax_bart import *
 from transformers import BartTokenizer, FlaxBartForConditionalGeneration
 
-import io
-
-import requests
 from PIL import Image
 import numpy as np
 import matplotlib.pyplot as plt
 
-import torch
 import torchvision.transforms as T
 import torchvision.transforms.functional as TF
 from torchvision.transforms import InterpolationMode
 
-from dalle_mini.vqgan_jax.modeling_flax_vqgan import VQModel
+from vqgan_jax.modeling_flax_vqgan import VQModel
 
 # TODO: set those args in a config file
 OUTPUT_VOCAB_SIZE = 16384 + 1  # encoded image token space + 1 for bos

dev/notebooks/demo/tpu-demo.ipynb

@@ -51,14 +51,6 @@
     "jax.devices()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "d408065c",
-   "metadata": {},
-   "source": [
-    "`dalle_mini` is a local package that contains the VQGAN-JAX model by Suraj, and other utilities. You can also `cd` to the directory that contains your checkout of [`vqgan-jax`](https://github.com/patil-suraj/vqgan-jax.git)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -66,8 +58,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from dalle_mini.vqgan_jax.modeling_flax_vqgan import VQModel\n",
-    "#%cd /content/vqgan-jax"
+    "from vqgan_jax.modeling_flax_vqgan import VQModel"
    ]
   },
   {
@@ -447,7 +438,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,

dev/notebooks/encoding/vqgan-jax-encoding-with-captions.ipynb

@@ -50,14 +50,6 @@
     "## VQGAN-JAX model"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "bb408f6c",
-   "metadata": {},
-   "source": [
-    "`dalle_mini` is a local package that contains the VQGAN-JAX model and other utilities."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -65,7 +57,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from dalle_mini.vqgan_jax.modeling_flax_vqgan import VQModel"
+    "from vqgan_jax.modeling_flax_vqgan import VQModel"
    ]
   },
   {

dev/notebooks/encoding/vqgan-jax-encoding-yfcc100m.ipynb

@@ -52,14 +52,6 @@
     "## VQGAN-JAX model"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "bb408f6c",
-   "metadata": {},
-   "source": [
-    "`dalle_mini` is a local package that contains the VQGAN-JAX model and other utilities."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 93,
@@ -67,7 +59,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from dalle_mini.vqgan_jax.modeling_flax_vqgan import VQModel"
+    "from vqgan_jax.modeling_flax_vqgan import VQModel"
    ]
   },
   {
@@ -1111,9 +1103,13 @@
   }
  ],
  "metadata": {
+  "interpreter": {
+   "hash": "db471c52d602b4f5f40ecaf278e88ccfef85c29d0a1a07185b0d51fc7acf4e26"
+  },
   "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.9.0 64-bit ('Python39')"
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1125,12 +1121,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
-  },
-  "interpreter": {
-   "hash": "db471c52d602b4f5f40ecaf278e88ccfef85c29d0a1a07185b0d51fc7acf4e26"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

dev/predictions/wandb-examples.py

@@ -23,7 +23,7 @@ import torchvision.transforms as T
 import torchvision.transforms.functional as TF
 from torchvision.transforms import InterpolationMode
 
-from dalle_mini.vqgan_jax.modeling_flax_vqgan import VQModel
+from vqgan_jax.modeling_flax_vqgan import VQModel
 
 # TODO: set those args in a config file
 OUTPUT_VOCAB_SIZE = 16384 + 1  # encoded image token space + 1 for bos

dev/requirements.txt

@@ -0,0 +1,16 @@
+# Note: install with the following command:
+# pip install -r requirements.txt -f https://storage.googleapis.com/jax-releases/libtpu_releases.html
+# Otherwise it won't find the appropriate libtpu_nightly
+requests
+jax[tpu]>=0.2.16
+-e git+https://github.com/huggingface/transformers.git@master#egg=transformers
+-e git+https://github.com/huggingface/datasets.git@master#egg=datasets
+flax
+jupyter
+wandb
+nltk
+optax
+git+https://github.com/patil-suraj/vqgan-jax.git@610d842dd33c739325a944102ed33acc07692dd5
+
+# Inference
+ftfy