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my_gradio.py
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my_gradio.py
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import os
import os.path as osp
import time
from argparse import ArgumentParser
from functools import partial
import cv2
import dnnlib
import gradio as gr
import numpy as np
import torch
import torchvision.transforms.functional as F
from gradio_utils import (ImageMask, draw_mask_on_image, draw_points_on_image,
get_latest_points_pair, get_valid_mask,
on_change_single_global_state)
# from models import get_stn
# from models.spatial_transformers.spatial_transformer import (
# ComposedSTN, SpatialTransformer, unravel_index)
from PIL import Image
from torchvision.utils import make_grid, save_image
from utils.download import download_model
from utils.vis_tools.helpers import load_pil, normalize, pil_to_tensor
from viz.renderer import Renderer, add_watermark_np
def reverse_point_pairs(points):
new_points = []
for p in points:
new_points.append([p[1], p[0]])
return new_points
def clear_state(global_state, target=None):
"""Clear target history state from global_state
If target is not defined, points and mask will be both removed.
1. set global_state['points'] as empty dict
2. set global_state['mask'] as full-one mask.
"""
if target is None:
target = ['point', 'mask']
if not isinstance(target, list):
target = [target]
if 'point' in target:
global_state['points'] = dict()
print('Clear Points State!')
if 'mask' in target:
image_raw = global_state["images"]["image_raw"]
global_state['mask'] = np.ones((image_raw.size[1], image_raw.size[0]),
dtype=np.uint8)
print('Clear mask State!')
global_state['params']['alpha'] = 1
global_state['params']['alpha*'] = 1
return global_state
def init_images(global_state):
"""This function is called only ones with Gradio App is started.
0. pre-process global_state, unpack value from global_state of need
1. Re-init renderer
2. run `renderer._render_drag_impl` with `is_drag=False` to generate
new image
3. Assign images to global state and re-generate mask
"""
# new
if isinstance(global_state, gr.State):
state = global_state.value
else:
state = global_state
state['renderer'].init_network(
state['generator_params'], # res
valid_checkpoints_dict[state['pretrained_weight']], # pkl
state['params']['seed'], # w0_seed,
None, # w_load
state['params']['latent_space'] == 'w+', # w_plus
'const',
state['params']['trunc_psi'], # trunc_psi,
state['params']['trunc_cutoff'], # trunc_cutoff,
None, # input_transform
state['params']['lr'] # lr,
)
state['params']['alpha'] = 1
state['renderer']._render_drag_impl(state['generator_params'],
is_drag=False,
to_pil=True)
init_image = state['generator_params'].image
state['images']['image_orig'] = init_image
state['images']['image_raw'] = init_image
state['images']['image_show'] = Image.fromarray(
add_watermark_np(np.array(init_image)))
state['mask'] = np.ones((init_image.size[1], init_image.size[0]),
dtype=np.uint8)
state['images']['imageB_show'] = state['renderer']._apply_delta(state['generator_params'], #res
None, #new_ws
state['params']['seed']+1,
'const', #noise_mode
state['params']['trunc_psi'], # trunc_psi,
state['params']['trunc_cutoff'], # trunc_cutoff,
to_pil=True)
state['images']['imageC_show'] = state['renderer']._apply_delta(state['generator_params'], #res
None, #new_ws
state['params']['seed']+2,
'const', #noise_mode
state['params']['trunc_psi'], # trunc_psi,
state['params']['trunc_cutoff'], # trunc_cutoff,
to_pil=True)
state['images']['imageD_show'] = state['renderer']._apply_delta(state['generator_params'], #res
None, #new_ws
state['params']['seed']+3,
'const', #noise_mode
state['params']['trunc_psi'], # trunc_psi,
state['params']['trunc_cutoff'], # trunc_cutoff,
to_pil=True)
state['images']['imageE_show'] = state['renderer']._apply_delta(state['generator_params'], #res
None, #new_ws
state['params']['seed']+4,
'const', #noise_mode
state['params']['trunc_psi'], # trunc_psi,
state['params']['trunc_cutoff'], # trunc_cutoff,
to_pil=True)
state['images']['imageF_show'] = state['renderer']._apply_delta(state['generator_params'], #res
None, #new_ws
state['params']['seed']+5,
'const', #noise_mode
state['params']['trunc_psi'], # trunc_psi,
state['params']['trunc_cutoff'], # trunc_cutoff,
to_pil=True)
state['generator_params'].delta_ws = torch.zeros(state['generator_params'].w.shape)
state['generator_params'].delta_w_star = torch.zeros(state['generator_params'].w.shape)
return global_state
def update_image_draw(image, points, mask, show_mask, global_state=None):
image_draw = draw_points_on_image(image, points)
if show_mask and mask is not None and not (mask == 0).all() and not (
mask == 1).all():
image_draw = draw_mask_on_image(image_draw, mask)
image_draw = Image.fromarray(add_watermark_np(np.array(image_draw)))
if global_state is not None:
global_state['images']['image_show'] = image_draw
return image_draw
def preprocess_mask_info(global_state, image):
"""Function to handle mask information.
1. last_mask is None: Do not need to change mask, return mask
2. last_mask is not None:
2.1 global_state is remove_mask:
2.2 global_state is add_mask:
"""
if isinstance(image, dict):
last_mask = get_valid_mask(image['mask'])
else:
last_mask = None
mask = global_state['mask']
# mask in global state is a placeholder with all 1.
if (mask == 1).all():
mask = last_mask
# last_mask = global_state['last_mask']
editing_mode = global_state['editing_state']
if last_mask is None:
return global_state
if editing_mode == 'remove_mask':
updated_mask = np.clip(mask - last_mask, 0, 1)
print(f'Last editing_state is {editing_mode}, do remove.')
elif editing_mode == 'add_mask':
updated_mask = np.clip(mask + last_mask, 0, 1)
print(f'Last editing_state is {editing_mode}, do add.')
else:
updated_mask = mask
print(f'Last editing_state is {editing_mode}, '
'do nothing to mask.')
global_state['mask'] = updated_mask
# global_state['last_mask'] = None # clear buffer
return global_state
with gr.Blocks() as demo:
'''
LOAD GANGEALING
'''
# resolution = 512 # resolution the input image will be resized to (this can be any power of 2)
# ckpt = download_model('cat') # download model weights
# flow_size = 128
# stn = get_stn(['similarity', 'flow'], flow_size=flow_size, supersize=resolution).to('cuda') # instantiate STN
# stn.load_state_dict(ckpt['t_ema'])
### LOAD GANGEALING END
cache_dir = './checkpoints'#args.cache_dir
device = 'cuda'
valid_checkpoints_dict = {
f.split('/')[-1].split('.')[0]: osp.join(cache_dir, f)
for f in os.listdir(cache_dir)
if (f.endswith('pkl') and osp.exists(osp.join(cache_dir, f)))
}
print(f'File under cache_dir ({cache_dir}):')
print(os.listdir(cache_dir))
print('Valid checkpoint file:')
print(valid_checkpoints_dict)
# init_pkl = 'stylegan2_lions_512_pytorch'
init_pkl = 'stylegan2-afhqcat-512x512'
# renderer = Renderer()
global_state = gr.State({
"images": {
# image_orig: the original image, change with seed/model is changed
# image_raw: image with mask and points, change durning optimization
# image_show: image showed on screen
},
"temporal_params": {
# stop
},
'mask': None, # mask for visualization, 1 for editing and 0 for unchange
'last_mask': None, # last edited mask
'show_mask': True, # add button
"generator_params": dnnlib.EasyDict(),
"params": {
"seed": 0,
"motion_lambda": 20,
"r1_in_pixels": 3,
"r2_in_pixels": 12,
"magnitude_direction_in_pixels": 1.0,
"latent_space": "w+",
"trunc_psi": 0.7,
"trunc_cutoff": None,
"lr": 0.001,
},
"device": 'cuda',
"draw_interval": 10,
"renderer": Renderer(disable_timing=True),
# "renderer2": Renderer(disable_timing=Trdefue),
# "stn": stn,
"points": {},
"curr_point": None,
"curr_type_point": "start",
'editing_state': 'add_points',
'pretrained_weight': init_pkl
})
# init image
global_state = init_images(global_state)
with gr.Row():
with gr.Row():
# Left --> tools
with gr.Column(scale=3):
# Pickle
with gr.Row():
with gr.Column(scale=1, min_width=10):
gr.Markdown(value='Pickle', show_label=False)
with gr.Column(scale=4, min_width=10):
form_pretrained_dropdown = gr.Dropdown(
choices=list(valid_checkpoints_dict.keys()),
label="Pretrained Model",
value=init_pkl,
)
# Latent
with gr.Row():
with gr.Column(scale=1, min_width=10):
gr.Markdown(value='Latent', show_label=False)
with gr.Column(scale=4, min_width=10):
form_seed_number = gr.Number(
value=global_state.value['params']['seed'],
interactive=True,
label="Seed",
)
form_lr_number = gr.Number(
value=global_state.value["params"]["lr"],
interactive=True,
label="Step Size")
with gr.Row():
with gr.Column(scale=2, min_width=10):
form_reset_image = gr.Button("Reset Image")
with gr.Column(scale=3, min_width=10):
form_latent_space = gr.Radio(
['w', 'w+'],
value=global_state.value['params']
['latent_space'],
interactive=True,
label='Latent space to optimize',
show_label=False,
)
# Drag
with gr.Row():
with gr.Column(scale=1, min_width=10):
gr.Markdown(value='Drag', show_label=False)
with gr.Column(scale=4, min_width=10):
with gr.Row():
with gr.Column(scale=1, min_width=10):
enable_add_points = gr.Button('Add Points')
with gr.Column(scale=1, min_width=10):
undo_points = gr.Button('Reset Points')
with gr.Row():
with gr.Column(scale=1, min_width=5):
form_start_btn = gr.Button("Start")
with gr.Column(scale=1, min_width=5):
form_stop_btn = gr.Button("Stop")
with gr.Column(scale=1, min_width=5):
form_save_btn = gr.Button("Save")
form_steps_number = gr.Number(value=0,
label="Steps",
interactive=False)
# Mask
with gr.Row():
with gr.Column(scale=1, min_width=10):
gr.Markdown(value='Mask', show_label=False)
with gr.Column(scale=4, min_width=10):
enable_add_mask = gr.Button('Edit Flexible Area')
with gr.Row():
with gr.Column(scale=1, min_width=10):
form_reset_mask_btn = gr.Button("Reset mask")
with gr.Column(scale=1, min_width=10):
show_mask = gr.Checkbox(
label='Show Mask',
value=global_state.value['show_mask'],
show_label=False)
with gr.Row():
form_lambda_number = gr.Number(
value=global_state.value["params"]
["motion_lambda"],
interactive=True,
label="Lambda",
)
form_draw_interval_number = gr.Number(
value=global_state.value["draw_interval"],
label="Draw Interval (steps)",
interactive=True,
visible=False)
# with gr.Row():
# with gr.Column(scale=1, min_width=10):
# gr.Markdown(value='Adjust Alpha', show_label=False)
# with gr.Column(scale=4, min_width=10):
# form_alpha_number = gr.Slider(minimum=0,
# maximum=2, step=0.1,
# value=1,
# label='Alpha',
# interactive=True)
with gr.Row():
with gr.Column(scale=1, min_width=10):
gr.Markdown(value='Calibrate', show_label=False)
# with gr.Column(scale=1, min_width=10):
# # form_optimize_delta_w = gr.Button('Optimize')
# form_update_delta_w = gr.Button('Update')
with gr.Column(scale=1, min_width=10):
form_optimize_delta_w = gr.Button('Optimize')
# form_optimize_delta_w = gr.Button('Update')
with gr.Column(scale=1, min_width=10):
form_apply_to_all = gr.Button('Apply')
with gr.Row():
with gr.Column(scale=1, min_width=10):
gr.Markdown(value='Alpha*', show_label=False)
with gr.Column(scale=4, min_width=10):
form_alpha_number_star = gr.Slider(minimum=-50,
maximum=50, step=0.1,
value=1,
label='Alpha*',
interactive=True)
# Right --> Image
with gr.Column(scale=4):
form_image = ImageMask(
value=global_state.value['images']['image_show'],
brush_radius=20).style(
width=512,
height=512) # NOTE: hard image size code here.
# Right --> Image
with gr.Column(scale=4):
second_image = ImageMask(
value= global_state.value['images']['imageB_show'],
brush_radius=20).style(
width=512,
height=512) # NOTE: hard image size code here.
with gr.Row():
with gr.Column(scale=1):
third_image = ImageMask(
value= global_state.value['images']['imageC_show'],
brush_radius=20).style(
width=512,
height=512)
with gr.Column(scale=1):
forth_image = ImageMask(
value= global_state.value['images']['imageD_show'],
brush_radius=20).style(
width=512,
height=512)
with gr.Column(scale=1):
fifth_image = ImageMask(
value= global_state.value['images']['imageE_show'],
brush_radius=20).style(
width=512,
height=512)
with gr.Column(scale=1):
sixth_image = ImageMask(
value= global_state.value['images']['imageF_show'],
brush_radius=20).style(
width=512,
height=512)
gr.Markdown("""
## Quick Start
1. Select desired `Pretrained Model` and adjust `Seed` to generate an
initial image.
2. Click on image to add control points.
3. Click `Start` and enjoy it!
## Advance Usage
1. Change `Step Size` to adjust learning rate in drag optimization.
2. Select `w` or `w+` to change latent space to optimize:
* Optimize on `w` space may cause greater influence to the image.
* Optimize on `w+` space may work slower than `w`, but usually achieve
better results.
* Note that changing the latent space will reset the image, points and
mask (this has the same effect as `Reset Image` button).
3. Click `Edit Flexible Area` to create a mask and constrain the
unmasked region to remain unchanged.
""")
gr.HTML("""
<style>
.container {
position: absolute;
height: 50px;
text-align: center;
line-height: 50px;
width: 100%;
}
</style>
<div class="container">
Gradio demo supported by
<img src="https://avatars.githubusercontent.com/u/10245193?s=200&v=4" height="20" width="20" style="display:inline;">
<a href="https://github.com/open-mmlab/mmagic">OpenMMLab MMagic</a>
</div>
""")
# Network & latents tab listeners
def on_change_pretrained_dropdown(pretrained_value, global_state):
"""Function to handle model change.
1. Set pretrained value to global_state
2. Re-init images and clear all states
"""
global_state['pretrained_weight'] = pretrained_value
init_images(global_state)
clear_state(global_state)
return global_state, global_state["images"]['image_show'], global_state["images"]['imageB_show'], global_state["images"]['imageC_show'], global_state["images"]['imageD_show'], global_state["images"]['imageE_show'], global_state["images"]['imageF_show'], global_state['params']['alpha*']
form_pretrained_dropdown.change(
on_change_pretrained_dropdown,
inputs=[form_pretrained_dropdown, global_state],
outputs=[global_state, form_image, second_image, third_image, forth_image, fifth_image, sixth_image, form_alpha_number_star],
)
def on_click_reset_image(global_state):
"""Reset image to the original one and clear all states
1. Re-init images
2. Clear all states
"""
init_images(global_state)
clear_state(global_state)
return global_state, global_state['images']['image_show'], global_state['images']['imageB_show'], global_state['images']['imageC_show'], global_state['images']['imageD_show'], global_state['images']['imageE_show'], global_state['images']['imageF_show']
form_reset_image.click(
on_click_reset_image,
inputs=[global_state],
outputs=[global_state, form_image, second_image, third_image, forth_image, fifth_image, sixth_image],
)
# Update parameters
def on_change_update_image_seed(seed, global_state):
"""Function to handle generation seed change.
1. Set seed to global_state
2. Re-init images and clear all states
"""
global_state["params"]["seed"] = int(seed)
init_images(global_state)
clear_state(global_state)
return global_state, global_state['images']['image_show'], global_state['images']['imageB_show']
form_seed_number.change(
on_change_update_image_seed,
inputs=[form_seed_number, global_state],
outputs=[global_state, form_image, second_image],
)
def on_click_latent_space(latent_space, global_state):
"""Function to reset latent space to optimize.
NOTE: this function we reset the image and all controls
1. Set latent-space to global_state
2. Re-init images and clear all state
"""
global_state['params']['latent_space'] = latent_space
init_images(global_state)
clear_state(global_state)
return global_state, global_state['images']['image_show'], global_state['images']['imageB_show'], global_state['images']['imageC_show'], global_state['images']['imageD_show'], global_state['images']['imageE_show'], global_state['images']['imageF_show']
form_latent_space.change(on_click_latent_space,
inputs=[form_latent_space, global_state],
outputs=[global_state, form_image, second_image, third_image, forth_image, fifth_image, sixth_image])
# ==== Params
form_lambda_number.change(
partial(on_change_single_global_state, ["params", "motion_lambda"]),
inputs=[form_lambda_number, global_state],
outputs=[global_state],
)
def on_change_lr(lr, global_state):
if lr == 0:
print('lr is 0, do nothing.')
return global_state
else:
global_state["params"]["lr"] = lr
renderer = global_state['renderer']
renderer.update_lr(lr)
# renderer2 = global_state['renderer2']
# renderer2.update_lr(lr)
print('New optimizer: ')
print(renderer.w_optim)
return global_state
form_lr_number.change(
on_change_lr,
inputs=[form_lr_number, global_state],
outputs=[global_state],
)
def on_click_start(global_state, image):
p_in_pixels = []
t_in_pixels = []
valid_points = []
# handle of start drag in mask editing mode
global_state = preprocess_mask_info(global_state, image)
# Prepare the points for the inference
if len(global_state["points"]) == 0:
# yield on_click_start_wo_points(global_state, image)
image_raw = global_state['images']['image_raw']
update_image_draw(
image_raw,
global_state['points'],
global_state['mask'],
global_state['show_mask'],
global_state,
)
yield (
global_state,
0,
global_state['images']['image_show'],
# gr.File.update(visible=False),
gr.Button.update(interactive=True),
gr.Button.update(interactive=True),
gr.Button.update(interactive=True),
gr.Button.update(interactive=True),
gr.Button.update(interactive=True),
# latent space
gr.Radio.update(interactive=True),
gr.Button.update(interactive=True),
# NOTE: disable stop button
gr.Button.update(interactive=False),
# update other comps
gr.Dropdown.update(interactive=True),
gr.Number.update(interactive=True),
gr.Number.update(interactive=True),
gr.Button.update(interactive=True),
gr.Button.update(interactive=True),
gr.Checkbox.update(interactive=True),
# gr.Number.update(interactive=True),
gr.Number.update(interactive=True),
)
else:
# Transform the points into torch tensors
for key_point, point in global_state["points"].items():
try:
p_start = point.get("start_temp", point["start"])
p_end = point["target"]
if p_start is None or p_end is None:
continue
except KeyError:
continue
p_in_pixels.append(p_start)
t_in_pixels.append(p_end)
valid_points.append(key_point)
mask = torch.tensor(global_state['mask']).float()
drag_mask = 1 - mask
renderer: Renderer = global_state["renderer"]
# renderer2: Renderer = global_state["renderer2"]
global_state['temporal_params']['stop'] = False
global_state['editing_state'] = 'running'
# reverse points order
p_to_opt = reverse_point_pairs(p_in_pixels)
t_to_opt = reverse_point_pairs(t_in_pixels)
print('Running with:')
print(f' Source: {p_in_pixels}')
print(f' Target: {t_in_pixels}')
step_idx = 0
while True:
if global_state["temporal_params"]["stop"]:
break
# do drage here!
start_time = time.time()
renderer._render_drag_impl(
global_state['generator_params'],
p_to_opt, # point
t_to_opt, # target
drag_mask, # mask,
global_state['params']['motion_lambda'], # lambda_mask
reg=0,
feature_idx=5, # NOTE: do not support change for now
r1=global_state['params']['r1_in_pixels'], # r1
r2=global_state['params']['r2_in_pixels'], # r2
# random_seed = 0,
# noise_mode = 'const',
trunc_psi=global_state['params']['trunc_psi'],
# force_fp32 = False,
# layer_name = None,
# sel_channels = 3,
# base_channel = 0,
# img_scale_db = 0,
# img_normalize = False,
# untransform = False,
is_drag=True,
to_pil=True)
# print(f'Drag time: {time.time() - start_time}')
# print(f'Apply delta time: {time.time() - start_time}')
start_time = time.time()
if step_idx % global_state['draw_interval'] == 0:
# print('Current Source:')
for key_point, p_i, t_i in zip(valid_points, p_to_opt,
t_to_opt):
global_state["points"][key_point]["start_temp"] = [
p_i[1],
p_i[0],
]
global_state["points"][key_point]["target"] = [
t_i[1],
t_i[0],
]
start_temp = global_state["points"][key_point][
"start_temp"]
print(f' {start_temp}')
image_result = global_state['generator_params']['image']
image_draw = update_image_draw(
image_result,
global_state['points'],
global_state['mask'],
global_state['show_mask'],
global_state,
)
global_state['images']['image_raw'] = image_result
global_state['images']['imageB_show'] = renderer._apply_delta(global_state['generator_params'], #res
global_state['generator_params'].delta_ws, #new_ws
global_state['params']['seed']+1,
'const', #noise_mode
global_state['params']['trunc_psi'], # trunc_psi,
global_state['params']['trunc_cutoff'], # trunc_cutoff,
to_pil=True)
global_state['images']['imageC_show'] = renderer._apply_delta(global_state['generator_params'], #res
global_state['generator_params'].delta_ws, #new_ws
global_state['params']['seed']+2,
'const', #noise_mode
global_state['params']['trunc_psi'], # trunc_psi,
global_state['params']['trunc_cutoff'], # trunc_cutoff,
to_pil=True)
global_state['images']['imageD_show'] = renderer._apply_delta(global_state['generator_params'], #res
global_state['generator_params'].delta_ws, #new_ws
global_state['params']['seed']+3,
'const', #noise_mode
global_state['params']['trunc_psi'], # trunc_psi,
global_state['params']['trunc_cutoff'], # trunc_cutoff,
to_pil=True)
global_state['images']['imageE_show'] = renderer._apply_delta(global_state['generator_params'], #res
global_state['generator_params'].delta_ws, #new_ws
global_state['params']['seed']+4,
'const', #noise_mode
global_state['params']['trunc_psi'], # trunc_psi,
global_state['params']['trunc_cutoff'], # trunc_cutoff,
to_pil=True)
global_state['images']['imageF_show'] = renderer._apply_delta(global_state['generator_params'], #res
global_state['generator_params'].delta_ws, #new_ws
global_state['params']['seed']+5,
'const', #noise_mode
global_state['params']['trunc_psi'], # trunc_psi,
global_state['params']['trunc_cutoff'], # trunc_cutoff,
to_pil=True)
# print('Apply delta time: ', time.time() - start_time)
# save_location = './pilot-study/{}/{}'.format(global_state['pretrained_weight'], global_state['params']['seed'])
# os.makedirs(save_location, exist_ok=True)
# torch.save(global_state['generator_params'].delta_ws, os.path.join(save_location, f'{step_idx}.pt'))
# global_state['generator_params'].delta_ws
# with open(os.path.join(save_location, 'distance.txt'), 'a+') as the_file:
# the_file.write('{}: {}\n'.format(step_idx, global_state['generator_params'].distance_in_pixel.item()))
yield (
global_state,
step_idx,
global_state['images']['image_show'],
global_state['images']['imageB_show'],
global_state['images']['imageC_show'],
global_state['images']['imageD_show'],
global_state['images']['imageE_show'],
global_state['images']['imageF_show'],
# gr.File.update(visible=False),
gr.Button.update(interactive=False),
gr.Button.update(interactive=False),
gr.Button.update(interactive=False),
gr.Button.update(interactive=False),
gr.Button.update(interactive=False),
# latent space
gr.Radio.update(interactive=False),
gr.Button.update(interactive=False),
# enable stop button in loop
gr.Button.update(interactive=True),
# update other comps
gr.Dropdown.update(interactive=False),
gr.Number.update(interactive=False),
gr.Number.update(interactive=False),
gr.Button.update(interactive=False),
gr.Button.update(interactive=False),
gr.Checkbox.update(interactive=False),
# gr.Number.update(interactive=False),
gr.Number.update(interactive=False),
)
# print('Step time: ', time.time() - start_time)
# increate step
step_idx += 1
image_result = global_state['generator_params']['image']
global_state['images']['image_raw'] = image_result
image_draw = update_image_draw(image_result,
global_state['points'],
global_state['mask'],
global_state['show_mask'],
global_state)
# fp = NamedTemporaryFile(suffix=".png", delete=False)
# image_result.save(fp, "PNG")
global_state['editing_state'] = 'add_points'
yield (
global_state,
0, # reset step to 0 after stop.
global_state['images']['image_show'],
global_state['images']['imageB_show'],
global_state['images']['imageC_show'],
global_state['images']['imageD_show'],
global_state['images']['imageE_show'],
global_state['images']['imageF_show'],
# gr.File.update(visible=True, value=fp.name),
gr.Button.update(interactive=True),
gr.Button.update(interactive=True),
gr.Button.update(interactive=True),
gr.Button.update(interactive=True),
gr.Button.update(interactive=True),
# latent space
gr.Radio.update(interactive=True),
gr.Button.update(interactive=True),
# NOTE: disable stop button with loop finish
gr.Button.update(interactive=False),
# update other comps
gr.Dropdown.update(interactive=True),
gr.Number.update(interactive=True),
gr.Number.update(interactive=True),
gr.Checkbox.update(interactive=True),
gr.Number.update(interactive=True),
)
form_start_btn.click(
on_click_start,
inputs=[global_state, form_image],
outputs=[
global_state,
form_steps_number,
form_image,
second_image,
third_image,
forth_image,
fifth_image,
sixth_image,
# form_download_result_file,
# >>> buttons
form_reset_image,
enable_add_points,
enable_add_mask,
undo_points,
form_reset_mask_btn,
form_latent_space,
form_start_btn,
form_stop_btn,
# <<< buttonm
# >>> inputs comps
form_pretrained_dropdown,
form_seed_number,
form_lr_number,
show_mask,
form_lambda_number,
],
)
def on_click_alpha_star(alpha, global_state):
global_state['params']['alpha*'] = alpha
print(alpha)
renderer: Renderer = global_state["renderer"]
global_state['images']['image_show'] = renderer._apply_delta_2(global_state['generator_params'], #res
global_state['generator_params'].delta_w_star*alpha, #new_ws
global_state['params']['seed'],
# alpha,
'const', #noise_mode
global_state['params']['trunc_psi'], # trunc_psi,
global_state['params']['trunc_cutoff'], # trunc_cutoff,
to_pil=True
)
global_state['images']['imageB_show'] = renderer._apply_delta_2(global_state['generator_params'], #res
global_state['generator_params'].delta_w_star*alpha, #new_ws
global_state['params']['seed']+1,
# alpha,
'const', #noise_mode
global_state['params']['trunc_psi'], # trunc_psi,
global_state['params']['trunc_cutoff'], # trunc_cutoff,
to_pil=True
)
global_state['images']['imageC_show'] = renderer._apply_delta_2(global_state['generator_params'], #res
global_state['generator_params'].delta_w_star*alpha, #new_ws
global_state['params']['seed']+2,
# alpha,
'const', #noise_mode
global_state['params']['trunc_psi'], # trunc_psi,
global_state['params']['trunc_cutoff'], # trunc_cutoff,
to_pil=True
)
global_state['images']['imageD_show'] = renderer._apply_delta_2(global_state['generator_params'], #res
global_state['generator_params'].delta_w_star*alpha, #new_ws
global_state['params']['seed']+3,
# alpha,
'const', #noise_mode
global_state['params']['trunc_psi'], # trunc_psi,
global_state['params']['trunc_cutoff'], # trunc_cutoff,
to_pil=True
)
global_state['images']['imageE_show'] = renderer._apply_delta_2(global_state['generator_params'], #res
global_state['generator_params'].delta_w_star*alpha, #new_ws
global_state['params']['seed']+4,
# alpha,
'const', #noise_mode
global_state['params']['trunc_psi'], # trunc_psi,
global_state['params']['trunc_cutoff'], # trunc_cutoff,
to_pil=True
)
global_state['images']['imageF_show'] = renderer._apply_delta_2(global_state['generator_params'], #res
global_state['generator_params'].delta_w_star*alpha, #new_ws
global_state['params']['seed']+5,
# alpha,
'const', #noise_mode
global_state['params']['trunc_psi'], # trunc_psi,
global_state['params']['trunc_cutoff'], # trunc_cutoff,
to_pil=True
)
yield (
global_state,
0, # reset step to 0 after stop.
global_state['images']['image_show'],
global_state['images']['imageB_show'],
global_state['images']['imageC_show'],
global_state['images']['imageD_show'],
global_state['images']['imageE_show'],
global_state['images']['imageF_show'],
global_state['params']['alpha*'],
# gr.File.update(visible=True, value=fp.name),
gr.Button.update(interactive=True),
gr.Button.update(interactive=True),
gr.Button.update(interactive=True),
gr.Button.update(interactive=True),
gr.Button.update(interactive=True),
# latent space
gr.Radio.update(interactive=True),
gr.Button.update(interactive=True),
# NOTE: disable stop button with loop finish
gr.Button.update(interactive=False),
# update other comps
gr.Dropdown.update(interactive=True),
gr.Number.update(interactive=True),
gr.Number.update(interactive=True),
gr.Checkbox.update(interactive=True),
gr.Number.update(interactive=True),
)
form_alpha_number_star.change(
on_click_alpha_star,
inputs=[form_alpha_number_star, global_state],
outputs=[
global_state,
form_steps_number,
form_image,
second_image,
third_image,
forth_image,
fifth_image,
sixth_image,
# form_download_result_file,
# >>> buttons
form_alpha_number_star,
form_reset_image,
enable_add_points,
enable_add_mask,
undo_points,
form_reset_mask_btn,
form_latent_space,
form_start_btn,
form_stop_btn,
# <<< buttonm
# >>> inputs comps
form_pretrained_dropdown,
form_seed_number,
form_lr_number,
show_mask,
form_lambda_number,
],
)
def optimize_delta_w(global_state):
renderer: Renderer = global_state["renderer"]
# delta_w_star = renderer.optimize_delta_w_2(global_state['generator_params'], #res
delta_w_star = renderer.optimize_delta_w_batch(global_state['generator_params'], #res
global_state['generator_params'].delta_ws, #new_ws
global_state['params']['seed'],
# global_state['params']['alpha'],
'const', #noise_mode
global_state['params']['trunc_psi'], # trunc_psi,
global_state['params']['trunc_cutoff'], # trunc_cutoff,
to_pil=True,
log_tb=False)
# delta_w_star = renderer.update_delta_w(global_state['generator_params'], #res
# global_state['generator_params'].delta_ws, #new_ws
# global_state['params']['seed'],
# global_state['params']['alpha'],
# 'const', #noise_mode
# global_state['params']['trunc_psi'], # trunc_psi,
# global_state['params']['trunc_cutoff'], # trunc_cutoff,
# to_pil=True)
yield (
global_state,
0, # reset step to 0 after stop.
# global_state['images']['image_show'],
# global_state['images']['imageB_show'],
# global_state['images']['imageC_show'],
# global_state['images']['imageD_show'],
# global_state['images']['imageE_show'],
# global_state['images']['imageF_show'],
# gr.File.update(visible=True, value=fp.name),
gr.Button.update(interactive=True),
gr.Button.update(interactive=True),
gr.Button.update(interactive=True),
gr.Button.update(interactive=True),
gr.Button.update(interactive=True),
# latent space
gr.Radio.update(interactive=True),
gr.Button.update(interactive=True),
# NOTE: disable stop button with loop finish
gr.Button.update(interactive=False),