However, I encountered a problem. When I use the model trained on COCO-Stuff and image size is 512*512, the generation quality seems poor.
layout = {
"bbox":
[
['metal', 0.04218750074505806, 0.25647059082984924, 0.10000000149011612, 0.5247058868408203],
['chair', 0.17940625548362732, 0.4312705993652344, 0.35014063119888306, 0.5062353014945984],
['sky-other', 0.606249988079071, 0.0, 0.734375, 0.09882353246212006],
['person', 0.0, 0.5493882298469543, 0.07332812249660492, 0.7298117876052856],
['pavement', 0.0, 0.5976470708847046, 0.9781249761581421, 1.0],
['building-other', 0.0, 0.0, 1.0, 0.7152941226959229],
['person', 0.8331093788146973, 0.5236706137657166, 0.913937509059906, 0.8113176226615906],
['chair', 0.422062486410141, 0.4221176505088806, 0.6030937433242798, 0.499505877494812],
['bus', 0.1626562476158142, 0.29044705629348755, 0.8476094007492065, 0.9376470446586609],
['person', 0.32343751192092896, 0.3623529374599457, 0.792187511920929, 0.5176470875740051],
['person', 0.9270156025886536, 0.49814116954803467, 0.9953437447547913, 0.8023764491081238],
['clothes', 0.15000000596046448, 0.567058801651001, 1.0, 1.0]
]
}
{
"dataset": "coco_stuff",
"num_bucket_per_side": [256, 256],
"width": 512,
"height": 512,
"prompt_template": "An image with {bbox}",
"cfg_scale": 4.5,
"num_inference_steps": 50,
"max_num_bbox": 18
}
I've tried different prompts and the results are very confusing.