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import gradio as gr
import torch
from diffusers.models import UNet2DModel
from huggingface_hub import hf_hub_download
from oadg.sampling import sample, make_conditional_paths_and_realization, initialize_empty_realizations_and_paths
from oadg.sampling import evaluate_entropy
image_size = 32
batch_size = 1
device = 'cpu'
path = hf_hub_download(repo_id="porestar/oadg_mnist_32", filename="model.pt")
model = UNet2DModel(
sample_size=32,
in_channels=2,
out_channels=2,
layers_per_block=2,
block_out_channels=(64, 64, 128, 128),
down_block_types=(
"DownBlock2D",
"DownBlock2D",
"AttnDownBlock2D",
"DownBlock2D",
),
up_block_types=(
"UpBlock2D",
"AttnUpBlock2D",
"UpBlock2D",
"UpBlock2D",
),
)
model.load_state_dict(torch.load(path, map_location=torch.device('cpu')))
model = model.to(device)
def sample_image(img):
if img is None:
idx_start, random_paths, realization = initialize_empty_realizations_and_paths(batch_size, image_size, image_size, device=device)
else:
img = (img > 0).astype(int)
idx_start, random_paths, realization = make_conditional_paths_and_realization(img, batch_size=batch_size, device=device)
img = sample(model, batch_size=batch_size, image_size=image_size,
realization=realization, idx_start=idx_start, random_paths=random_paths, device=device)
img = img.reshape(image_size, image_size) * 255
entropy = evaluate_entropy(model, batch_size=batch_size, image_size=image_size,
realization=realization, idx_start=idx_start, random_paths=random_paths, device=device)
entropy = (entropy.reshape(image_size, image_size) * 255).astype(int)
return entropy, img
img = gr.Image(image_mode="L", source="canvas", shape=(image_size, image_size), invert_colors=True, label="Drawing Canvas")
out_realization = gr.Image(image_mode="L", shape=(image_size, image_size), invert_colors=True, label="Sample Realization")
out_entropy = gr.Image(image_mode="L", shape=(image_size, image_size), invert_colors=True, label="Entropy of Drawn Data")
demo = gr.Interface(fn=sample_image, inputs=img, outputs=[out_entropy, out_realization],
title="Order Agnostic Autoregressive Diffusion MNIST Demo",
description="""Sample conditional or unconditional images by drawing into the canvas.
Outputs a random sampled realization and predicted entropy under the trained model for the conditioning data.""")
demo.launch()
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