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import gradio as gr
from .model_loader import Model
from PIL import Image
import cv2
import io
from huggingface_hub import snapshot_download
models_path = snapshot_download(repo_id="radames/UserControllableLT", repo_type="model")
# models fron pretrained/latent_transformer folder
models_files = {
"anime": "anime.pt",
"car": "car.pt",
"cat": "cat.pt",
"church": "church.pt",
"ffhq": "ffhq.pt",
}
models = {name: Model(models_path + "/" + path) for name, path in models_files.items()}
def cv_to_pil(img):
return Image.fromarray(cv2.cvtColor(img.astype("uint8"), cv2.COLOR_BGR2RGB))
def random_sample(model_name: str):
model = models[model_name]
img, latents = model.random_sample()
pil_img = cv_to_pil(img)
return pil_img, model_name, latents
def zoom(model_state, latents_state, dx=0, dy=0, dz=0, sxsy=[128, 128]):
model = models[model_state]
dx = dx
dy = dy
dz = dz
sx = sxsy[0]
sy = sxsy[1]
stop_points = []
img, latents_state = model.zoom(
latents_state, dz, sxsy=[sx, sy], stop_points=stop_points
) # dz, sxsy=[sx, sy], stop_points=stop_points)
pil_img = cv_to_pil(img)
return pil_img, latents_state
def translate(model_state, latents_state, dx=0, dy=0, dz=0, sxsy=[128, 128]):
model = models[model_state]
dx = dx
dy = dy
dz = dz
sx = sxsy[0]
sy = sxsy[1]
stop_points = []
zi = False
zo = False
img, latents_state = model.translate(
latents_state,
[dx, dy],
sxsy=[sx, sy],
stop_points=stop_points,
zoom_in=zi,
zoom_out=zo,
)
pil_img = cv_to_pil(img)
return pil_img, latents_state
def change_style(image: Image.Image, model_state, latents_state):
model = models[model_state]
img, latents_state = model.change_style(latents_state)
pil_img = cv_to_pil(img)
return pil_img, latents_state
def reset(model_state, latents_state):
model = models[model_state]
img, latents_state = model.reset(latents_state)
pil_img = cv_to_pil(img)
return pil_img, latents_state
def image_click(evt: gr.SelectData):
click_pos = evt.index
return click_pos
with gr.Blocks() as block:
model_state = gr.State(value="cat")
latents_state = gr.State({})
sxsy = gr.State([128, 128])
gr.Markdown("# UserControllableLT: User controllable latent transformer")
gr.Markdown("## Select model")
with gr.Row():
with gr.Column():
model_name = gr.Dropdown(
choices=list(models_files.keys()),
label="Select Pretrained Model",
value="cat",
)
with gr.Row():
button = gr.Button("Random sample")
reset_btn = gr.Button("Reset")
dx = gr.Slider(
minimum=-256, maximum=256, step_size=0.1, label="dx", value=0.0
)
dy = gr.Slider(
minimum=-256, maximum=256, step_size=0.1, label="dy", value=0.0
)
dz = gr.Slider(
minimum=-256, maximum=256, step_size=0.1, label="dz", value=0.0
)
with gr.Row():
change_style_bt = gr.Button("Change style")
with gr.Column():
image = gr.Image(type="pil", label="")
image.select(image_click, inputs=None, outputs=sxsy)
button.click(
random_sample, inputs=[model_name], outputs=[image, model_state, latents_state]
)
reset_btn.click(
reset,
inputs=[model_state, latents_state],
outputs=[image, latents_state],
)
change_style_bt.click(
change_style,
inputs=[image, model_state, latents_state],
outputs=[image, latents_state],
)
dx.change(
translate,
inputs=[model_state, latents_state, dx, dy, dz, sxsy],
outputs=[image, latents_state],
show_progress=False,
)
dy.change(
translate,
inputs=[model_state, latents_state, dx, dy, dz, sxsy],
outputs=[image, latents_state],
show_progress=False,
)
dz.change(
zoom,
inputs=[model_state, latents_state, dx, dy, dz, sxsy],
outputs=[image, latents_state],
show_progress=False,
)
block.queue()
block.launch()
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