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import tensorflow as tf | |
tf.keras.mixed_precision.set_global_policy("mixed_float16") | |
import glob | |
import os | |
import keras_cv | |
import numpy as np | |
import PIL | |
import wandb | |
from tqdm import tqdm | |
def download_unet_params(run_id, run_name) -> str: | |
run = wandb.init(project="experimentation_images", name=run_name) | |
run_artifact_id = f"sayakpaul/dreambooth-keras/run_{run_id}_model:v0" | |
artifact = run.use_artifact(run_artifact_id, type="model") | |
artifact_dir = artifact.download() | |
unet_params_path = glob.glob(f"{artifact_dir}/*.h5")[0] | |
return run, unet_params_path | |
# Initialize the SD model. | |
img_height = img_width = 512 | |
sd_model = keras_cv.models.StableDiffusion( | |
img_width=img_width, img_height=img_height, jit_compile=True | |
) | |
# Download run data. | |
api = wandb.Api() | |
runs = api.runs("sayakpaul/dreambooth-keras") | |
# Initialize variables. | |
num_steps = [25, 50, 75, 100] | |
num_images_to_gen = 3 | |
caption = "A photo of sks dog in a bucket" | |
unconditional_guidance_scales = [7.5, 15, 30] | |
# Generate example results. | |
for run in tqdm(runs): | |
run_id = run.id | |
run_name = run.name | |
print(f"Generating images for {run_name}.") | |
new_run, unet_params_path = download_unet_params(run_id, run_name) | |
sd_model.diffusion_model.load_weights(unet_params_path) | |
os.makedirs(run_name, exist_ok=True) | |
for steps in num_steps: | |
for scale in unconditional_guidance_scales: | |
images = sd_model.text_to_image( | |
caption, | |
batch_size=num_images_to_gen, | |
num_steps=steps, | |
unconditional_guidance_scale=scale, | |
) | |
wandb.log( | |
{ | |
f"num_steps@{steps}-ugs@{scale}": [ | |
wandb.Image( | |
PIL.Image.fromarray(image), caption=f"{i}: {caption}" | |
) | |
for i, image in enumerate(images) | |
] | |
} | |
) | |
new_run.finish() |
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