app.py

import streamlit as st
from PIL import Image
import jax
import jax.numpy as jnp  # JAX NumPy
import numpy as np
from flax import linen as nn  # Linen API
from huggingface_hub import HfFileSystem
from flax.serialization import msgpack_restore, from_state_dict
import time
from local_response_norm import LocalResponseNorm

LATENT_DIM = 100

class Generator(nn.Module):
  @nn.compact
  def __call__(self, latent, training=True):
    x = nn.Dense(features=32)(latent)
    # x = nn.BatchNorm(not training)(x)
    x = nn.relu(x)
    x = nn.Dense(features=2*2*256)(x)
    x = nn.BatchNorm(not training)(x)
    x = nn.relu(x)
    x = nn.Dropout(0.5, deterministic=not training)(x)
    x = x.reshape((x.shape[0], 2, 2, -1))
    x4o = nn.ConvTranspose(features=3, kernel_size=(2, 2), strides=(2, 2))(x)
    x4 = nn.ConvTranspose(features=128, kernel_size=(2, 2), strides=(2, 2))(x)
    x4 = LocalResponseNorm()(x4)
    # x4 = nn.BatchNorm(not training)(x4)
    x8 = nn.relu(x4)
    # x8 = nn.Dropout(0.5, deterministic=not training)(x8)
    x8o = nn.ConvTranspose(features=3, kernel_size=(2, 2), strides=(2, 2))(x8)
    x8 = nn.ConvTranspose(features=64, kernel_size=(2, 2), strides=(2, 2))(x8)
    x8 = LocalResponseNorm()(x8)
    # x8 = nn.BatchNorm(not training)(x8)
    x16 = nn.relu(x8)
    # x16 = nn.Dropout(0.5, deterministic=not training)(x16)
    x16o = nn.ConvTranspose(features=3, kernel_size=(2, 2), strides=(2, 2))(x16)
    x16 = nn.ConvTranspose(features=32, kernel_size=(2, 2), strides=(2, 2))(x16)
    x16 = LocalResponseNorm()(x16)
    # x16 = nn.BatchNorm(not training)(x16)
    x32 = nn.relu(x16)
    # x32 = nn.Dropout(0.5, deterministic=not training)(x32)
    x32o = nn.ConvTranspose(features=3, kernel_size=(2, 2), strides=(2, 2))(x32)
    return (nn.tanh(x32o), nn.tanh(x16o), nn.tanh(x8o), nn.tanh(x4o))

generator = Generator()
variables = generator.init(jax.random.PRNGKey(0), jnp.zeros([1, LATENT_DIM]), training=False)

fs = HfFileSystem()
with fs.open("PrakhAI/AIPlane/g_checkpoint.msgpack", "rb") as f:
  g_state = from_state_dict(variables, msgpack_restore(f.read()))

def sample_latent(key):
  return jax.random.normal(key, shape=(1, LATENT_DIM))

if st.button('Generate Plane'):
  latents = sample_latent(jax.random.PRNGKey(int(1_000_000 * time.time())))
  (g_out32, g_out16, g_out8, g_out4) = generator.apply({'params': g_state['params'], 'batch_stats': g_state['batch_stats']}, latents, training=False)
  img = ((np.array(g_out32[0])+1)*255./2.).astype(np.uint8)
  st.image(Image.fromarray(img))
  st.write("The model and its details are at https://huggingface.co/PrakhAI/AIPlane")