Spaces:
Running
Running
File size: 5,517 Bytes
bb1671a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 |
from os import getenv
from typing import Optional
import gradio as gr
import torch
from PIL import Image
from torchvision.transforms import v2 as T
from dreamsim import DreamsimBackbone, DreamsimEnsemble, DreamsimModel
_ = torch.set_grad_enabled(False)
torchdev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.set_float32_matmul_precision("high")
HF_TOKEN = getenv("HF_TOKEN", None)
MODEL_REPO = "neggles/dreamsim"
MODEL_VARIANTS: dict[str, str] = {
"Ensemble": "ensemble_vitb16",
"CLIP ViT-B/32": "clip_vitb32",
"OpenCLIP ViT-B/32": "open_clip_vitb32",
"DINO ViT-B/16": "dino_vitb16",
}
loaded_models: dict[str, Optional[DreamsimBackbone]] = {
"ensemble_vitb16": None,
"clip_vitb32": None,
"open_clip_vitb32": None,
"dino_vitb16": None,
}
def pil_ensure_rgb(image: Image.Image) -> Image.Image:
# convert to RGB/RGBA if not already (deals with palette images etc.)
if image.mode not in ["RGB", "RGBA"]:
image = image.convert("RGBA") if "transparency" in image.info else image.convert("RGB")
# convert RGBA to RGB with white background
if image.mode == "RGBA":
canvas = Image.new("RGBA", image.size, (255, 255, 255))
canvas.alpha_composite(image)
image = canvas.convert("RGB")
return image
def pil_pad_square(
image: Image.Image,
fill: tuple[int, int, int] = (255, 255, 255),
) -> Image.Image:
w, h = image.size
# get the largest dimension so we can pad to a square
px = max(image.size)
# pad to square with white background
canvas = Image.new("RGB", (px, px), fill)
canvas.paste(image, ((px - w) // 2, (px - h) // 2))
return canvas
def load_model(variant: str) -> DreamsimBackbone:
global loaded_models
if variant in MODEL_VARIANTS:
# resolve the repo branch for the model variant
variant = MODEL_VARIANTS[variant]
match variant:
case "ensemble_vitb16":
if loaded_models[variant] is None:
model: DreamsimEnsemble = DreamsimEnsemble.from_pretrained(
MODEL_REPO,
token=HF_TOKEN,
revision=variant,
)
model.do_resize = False
loaded_models[variant] = model
case "clip_vitb32" | "open_clip_vitb32" | "dino_vitb16":
if loaded_models[variant] is None:
model: DreamsimModel = DreamsimModel.from_pretrained(
MODEL_REPO,
token=HF_TOKEN,
revision=variant,
)
model.do_resize = False
loaded_models[variant] = model
case _:
raise ValueError(f"Unknown model variant: {variant}")
return loaded_models[variant]
def predict(
variant: str,
resize_to: Optional[int],
image_a: Image.Image,
image_b: Image.Image,
):
# Load model
model: DreamsimModel | DreamsimEnsemble = load_model(variant)
model = model.eval().to(torchdev)
# yeet alpha, make white background
image_a, image_b = pil_ensure_rgb(image_a), pil_ensure_rgb(image_b)
# pad to square
image_a, image_b = pil_pad_square(image_a), pil_pad_square(image_b)
# Resize images, if necessary
if resize_to is not None:
image_a.thumbnail((resize_to, resize_to), resample=Image.Resampling.BICUBIC)
image_b.thumbnail((resize_to, resize_to), resample=Image.Resampling.BICUBIC)
# Preprocess images
transforms = T.Compose([T.ToImage(), T.ToDtype(torch.float32, scale=True)])
batch = torch.stack([transforms(image_a).unsqueeze(0), transforms(image_b).unsqueeze(0)], dim=0)
loss = model(batch.to(model.device, model.dtype)).cpu().item()
score = 1.0 - loss
return score, variant
def main():
with gr.Blocks(title="DreamSIM Perceptual Similarity") as demo:
with gr.Row():
with gr.Column():
img_input = gr.Image(label="Input", type="pil", image_mode="RGB", scale=1)
with gr.Column():
img_target = gr.Image(label="Target", type="pil", image_mode="RGB", scale=1)
with gr.Row(equal_height=True):
with gr.Column():
variant = gr.Radio(
choices=list(MODEL_VARIANTS.keys()), label="Model Variant", value="Ensemble"
)
resize_to = gr.Dropdown(label="Resize To", choices=[224, 384, 512, None], value=224)
with gr.Column():
score = gr.Number(label="Similarity Score", precision=8, minimum=0, maximum=1)
variant_out = gr.Textbox(label="Variant", interactive=False)
with gr.Row():
clear = gr.ClearButton(
components=[img_input, img_target, score], variant="secondary", size="lg"
)
submit = gr.Button(value="Submit", variant="primary", size="lg")
submit.click(
predict,
inputs=[variant, resize_to, img_input, img_target],
outputs=[score, variant_out],
api_name=False,
)
examples = gr.Examples(
[
["examples/img_a_1.png", "examples/ref_1.png", "Ensemble", 224],
["examples/img_b_1.png", "examples/ref_1.png", "Ensemble", 224],
],
inputs=[img_input, img_target, variant, resize_to],
)
demo.queue(max_size=10)
demo.launch()
if __name__ == "__main__":
main()
|