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import streamlit as st |
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from PIL import Image |
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import jax |
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import jax.numpy as jnp |
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import numpy as np |
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from flax import linen as nn |
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from huggingface_hub import HfFileSystem |
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from flax.serialization import msgpack_restore, from_state_dict |
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import time |
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from local_response_norm import LocalResponseNorm |
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from generator import Generator, LATENT_DIM |
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generator = Generator() |
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variables = generator.init(jax.random.PRNGKey(0), jnp.zeros([1, LATENT_DIM]), training=False) |
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fs = HfFileSystem() |
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with fs.open("PrakhAI/AIPlane2/g_checkpoint.msgpack", "rb") as f: |
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g_state = from_state_dict(variables, msgpack_restore(f.read())) |
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def sample_latent(key): |
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return jax.random.normal(key, shape=(1, LATENT_DIM)) |
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if st.button('Generate Plane'): |
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latents = sample_latent(jax.random.PRNGKey(int(1_000_000 * time.time()))) |
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(g_out128, _, _, _, _, _) = generator.apply({'params': g_state['params'], 'batch_stats': g_state['batch_stats']}, latents, training=False) |
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img = ((np.array(g_out32[0])+1)*255./2.).astype(np.uint8) |
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st.image(Image.fromarray(img)) |
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st.write("The model and its details are at https://huggingface.co/PrakhAI/AIPlane2") |