Files
SillyTavern-extras/live2d/tha3/app/app.py
2023-08-04 15:03:36 +09:00

534 lines
24 KiB
Python

import argparse
import os
import random
import requests
import sys
import threading
import time
import torch
import io
import torch.nn.functional as F
import wx
import numpy as np
from PIL import Image
from torchvision import transforms
from flask import Flask, Response
from flask_cors import CORS
from io import BytesIO
sys.path.append(os.getcwd())
from tha3.mocap.ifacialmocap_constants import *
from tha3.mocap.ifacialmocap_pose import create_default_ifacialmocap_pose
from tha3.mocap.ifacialmocap_pose_converter import IFacialMocapPoseConverter
from tha3.mocap.ifacialmocap_poser_converter_25 import create_ifacialmocap_pose_converter
from tha3.poser.modes.load_poser import load_poser
from tha3.poser.poser import Poser
from tha3.util import (
torch_linear_to_srgb, resize_PIL_image, extract_PIL_image_from_filelike,
extract_pytorch_image_from_PIL_image
)
from typing import Optional
# Global Variables
global_source_image = None
global_result_image = None
global_reload = None
is_talking_override = False
is_talking = False
global_timer_paused = False
# Flask setup
app = Flask(__name__)
CORS(app)
def unload():
global global_timer_paused
global_timer_paused = True
return "Animation Paused"
def start_talking():
global is_talking_override
is_talking_override = True
return "started"
def stop_talking():
global is_talking_override
is_talking_override = False
return "stopped"
def result_feed():
def generate():
while True:
if global_result_image is not None:
try:
rgb_image = global_result_image[:, :, [2, 1, 0]] # Swap B and R channels
pil_image = Image.fromarray(np.uint8(rgb_image)) # Convert to PIL Image
if global_result_image.shape[2] == 4: # Check if there is an alpha channel present
alpha_channel = global_result_image[:, :, 3] # Extract alpha channel
pil_image.putalpha(Image.fromarray(np.uint8(alpha_channel))) # Set alpha channel in the PIL Image
buffer = io.BytesIO() # Save as PNG with RGBA mode
pil_image.save(buffer, format='PNG')
image_bytes = buffer.getvalue()
except Exception as e:
print(f"Error when trying to write image: {e}")
yield (b'--frame\r\n' # Send the PNG image
b'Content-Type: image/png\r\n\r\n' + image_bytes + b'\r\n')
else:
time.sleep(0.1)
return Response(generate(), mimetype='multipart/x-mixed-replace; boundary=frame')
def live2d_load_file(stream):
global global_source_image
global global_reload
global global_timer_paused
global_timer_paused = False
try:
pil_image = Image.open(stream) # Load the image using PIL.Image.open
img_data = BytesIO() # Create a copy of the image data in memory using BytesIO
pil_image.save(img_data, format='PNG')
global_reload = Image.open(BytesIO(img_data.getvalue())) # Set the global_reload to the copy of the image data
except Image.UnidentifiedImageError:
print(f"Could not load image from file, loading blank")
full_path = os.path.join(os.getcwd(), "live2d\\tha3\\images\\inital.png")
MainFrame.load_image(None, full_path)
global_timer_paused = True
return 'OK'
def convert_linear_to_srgb(image: torch.Tensor) -> torch.Tensor:
rgb_image = torch_linear_to_srgb(image[0:3, :, :])
return torch.cat([rgb_image, image[3:4, :, :]], dim=0)
def launch_gui(device, model):
global initAMI
initAMI = True
parser = argparse.ArgumentParser(description='uWu Waifu')
# Add other parser arguments here
args, unknown = parser.parse_known_args()
try:
poser = load_poser(model, device)
pose_converter = create_ifacialmocap_pose_converter()
app = wx.App(redirect=False)
main_frame = MainFrame(poser, pose_converter, device)
main_frame.SetSize((750, 600))
#Lload default image (you can pass args.char if required)
full_path = os.path.join(os.getcwd(), "live2d\\tha3\\images\\inital.png")
main_frame.load_image(None, full_path)
#main_frame.Show(True)
main_frame.capture_timer.Start(100)
main_frame.animation_timer.Start(100)
wx.DisableAsserts() #prevent popup about debug alert closed from other threads
app.MainLoop()
except RuntimeError as e:
print(e)
sys.exit()
class MainFrame(wx.Frame):
def __init__(self, poser: Poser, pose_converter: IFacialMocapPoseConverter, device: torch.device):
super().__init__(None, wx.ID_ANY, "uWu Waifu")
self.pose_converter = pose_converter
self.poser = poser
self.device = device
self.image_load_counter = 0
self.custom_background_image = None # Add this line
self.sliders = {}
self.ifacialmocap_pose = create_default_ifacialmocap_pose()
self.source_image_bitmap = wx.Bitmap(self.poser.get_image_size(), self.poser.get_image_size())
self.result_image_bitmap = wx.Bitmap(self.poser.get_image_size(), self.poser.get_image_size())
self.wx_source_image = None
self.torch_source_image = None
self.last_pose = None
self.last_update_time = None
self.create_ui()
self.create_timers()
self.Bind(wx.EVT_CLOSE, self.on_close)
self.update_source_image_bitmap()
self.update_result_image_bitmap()
def create_timers(self):
self.capture_timer = wx.Timer(self, wx.ID_ANY)
self.Bind(wx.EVT_TIMER, self.update_capture_panel, id=self.capture_timer.GetId())
self.animation_timer = wx.Timer(self, wx.ID_ANY)
self.Bind(wx.EVT_TIMER, self.update_result_image_bitmap, id=self.animation_timer.GetId())
def on_close(self, event: wx.Event):
# Stop the timers
self.animation_timer.Stop()
self.capture_timer.Stop()
# Destroy the windows
self.Destroy()
event.Skip()
sys.exit(0)
def random_generate_value(self, min, max, origin_value):
random_value = random.choice(list(range(min, max, 1))) / 2500.0
randomized = origin_value + random_value
if randomized > 1.0:
randomized = 1.0
if randomized < 0:
randomized = 0
return randomized
def animationTalking(self):
global is_talking
current_pose = self.ifacialmocap_pose
# NOTE: randomize mouth
for blendshape_name in BLENDSHAPE_NAMES:
if "jawOpen" in blendshape_name:
if is_talking or is_talking_override:
current_pose[blendshape_name] = self.random_generate_value(-5000, 5000, abs(1 - current_pose[blendshape_name]))
else:
current_pose[blendshape_name] = 0
return current_pose
def animationHeadMove(self):
current_pose = self.ifacialmocap_pose
for key in [HEAD_BONE_Y]: #can add more to this list if needed
current_pose[key] = self.random_generate_value(-20, 20, current_pose[key])
return current_pose
def animationBlink(self):
current_pose = self.ifacialmocap_pose
if random.random() <= 0.03:
current_pose["eyeBlinkRight"] = 1
current_pose["eyeBlinkLeft"] = 1
else:
current_pose["eyeBlinkRight"] = 0
current_pose["eyeBlinkLeft"] = 0
return current_pose
def get_emotion_values(self, emotion): # Place to define emotion presets
emotions = {
'Happy': {'eyeLookInLeft': 0.0, 'eyeLookOutLeft': 0.0, 'eyeLookDownLeft': 0.0, 'eyeLookUpLeft': 1.0, 'eyeBlinkLeft': 0, 'eyeSquintLeft': 0.0, 'eyeWideLeft': 0.0, 'eyeLookInRight': 0.0, 'eyeLookOutRight': 0.0, 'eyeLookDownRight': 0.0, 'eyeLookUpRight': 0.0, 'eyeBlinkRight': 0, 'eyeSquintRight': 0.0, 'eyeWideRight': 0.0, 'browDownLeft': 0.0, 'browOuterUpLeft': 0.0, 'browDownRight': 0.0, 'browOuterUpRight': 0.0, 'browInnerUp': 0.0, 'noseSneerLeft': 0.0, 'noseSneerRight': 0.0, 'cheekSquintLeft': 0.0, 'cheekSquintRight': 0.0, 'cheekPuff': 0.0, 'mouthLeft': 0.0, 'mouthDimpleLeft': 0.0, 'mouthFrownLeft': 0.0, 'mouthLowerDownLeft': 0.0, 'mouthPressLeft': 0.0, 'mouthSmileLeft': 0.0, 'mouthStretchLeft': 0.0, 'mouthUpperUpLeft': 0.0, 'mouthRight': 0.0, 'mouthDimpleRight': 0.0, 'mouthFrownRight': 0.0, 'mouthLowerDownRight': 0.0, 'mouthPressRight': 0.0, 'mouthSmileRight': 0.0, 'mouthStretchRight': 0.0, 'mouthUpperUpRight': 0.0, 'mouthClose': 0.0, 'mouthFunnel': 0.0, 'mouthPucker': 0.0, 'mouthRollLower': 0.0, 'mouthRollUpper': 0.0, 'mouthShrugLower': 0.0, 'mouthShrugUpper': 0.0, 'jawLeft': 0.0, 'jawRight': 0.0, 'jawForward': 0.0, 'jawOpen': 0, 'tongueOut': 0.0, 'headBoneX': 0.0, 'headBoneY': 0.0, 'headBoneZ': 0.0, 'headBoneQuat': [0.0, 0.0, 0.0, 1.0], 'leftEyeBoneX': 0.0, 'leftEyeBoneY': 0.0, 'leftEyeBoneZ': 0.0, 'leftEyeBoneQuat': [0.0, 0.0, 0.0, 1.0], 'rightEyeBoneX': 0.0, 'rightEyeBoneY': 0.0, 'rightEyeBoneZ': 0.0, 'rightEyeBoneQuat': [0.0, 0.0, 0.0, 1.0]},
'Sad': {'eyeLookInLeft': 0.0, 'eyeLookOutLeft': 0.0, 'eyeLookDownLeft': 1.0, 'eyeLookUpLeft': 0.0, 'eyeBlinkLeft': 0, 'eyeSquintLeft': 0.0, 'eyeWideLeft': 0.0, 'eyeLookInRight': 0.0, 'eyeLookOutRight': 0.0, 'eyeLookDownRight': 0.0, 'eyeLookUpRight': 0.0, 'eyeBlinkRight': 0, 'eyeSquintRight': 0.0, 'eyeWideRight': 0.0, 'browDownLeft': 0.0, 'browOuterUpLeft': 0.0, 'browDownRight': 0.0, 'browOuterUpRight': 0.0, 'browInnerUp': 0.0, 'noseSneerLeft': 0.0, 'noseSneerRight': 0.0, 'cheekSquintLeft': 0.0, 'cheekSquintRight': 0.0, 'cheekPuff': 0.0, 'mouthLeft': 0.0, 'mouthDimpleLeft': 0.0, 'mouthFrownLeft': 0.0, 'mouthLowerDownLeft': 0.0, 'mouthPressLeft': 0.0, 'mouthSmileLeft': 0.0, 'mouthStretchLeft': 0.0, 'mouthUpperUpLeft': 0.0, 'mouthRight': 0.0, 'mouthDimpleRight': 0.0, 'mouthFrownRight': 0.0, 'mouthLowerDownRight': 0.0, 'mouthPressRight': 0.0, 'mouthSmileRight': 0.0, 'mouthStretchRight': 0.0, 'mouthUpperUpRight': 0.0, 'mouthClose': 0.0, 'mouthFunnel': 0.0, 'mouthPucker': 0.0, 'mouthRollLower': 0.0, 'mouthRollUpper': 0.0, 'mouthShrugLower': 0.0, 'mouthShrugUpper': 0.0, 'jawLeft': 0.0, 'jawRight': 0.0, 'jawForward': 0.0, 'jawOpen': 0, 'tongueOut': 0.0, 'headBoneX': 0.0, 'headBoneY': 0.0, 'headBoneZ': 0.0, 'headBoneQuat': [0.0, 0.0, 0.0, 1.0], 'leftEyeBoneX': 0.0, 'leftEyeBoneY': 0.0, 'leftEyeBoneZ': 0.0, 'leftEyeBoneQuat': [0.0, 0.0, 0.0, 1.0], 'rightEyeBoneX': 0.0, 'rightEyeBoneY': 0.0, 'rightEyeBoneZ': 0.0, 'rightEyeBoneQuat': [0.0, 0.0, 0.0, 1.0]},
'Angry': {'eyeLookInLeft': 1.0, 'eyeLookOutLeft': 1.0, 'eyeLookDownLeft': 1.0, 'eyeLookUpLeft': 1.0, 'eyeBlinkLeft': 0, 'eyeSquintLeft': 0.0, 'eyeWideLeft': 0.0, 'eyeLookInRight': 0.0, 'eyeLookOutRight': 0.0, 'eyeLookDownRight': 0.0, 'eyeLookUpRight': 0.0, 'eyeBlinkRight': 0, 'eyeSquintRight': 0.0, 'eyeWideRight': 0.0, 'browDownLeft': 0.0, 'browOuterUpLeft': 0.0, 'browDownRight': 0.0, 'browOuterUpRight': 0.0, 'browInnerUp': 0.0, 'noseSneerLeft': 0.0, 'noseSneerRight': 0.0, 'cheekSquintLeft': 0.0, 'cheekSquintRight': 0.0, 'cheekPuff': 0.0, 'mouthLeft': 0.0, 'mouthDimpleLeft': 0.0, 'mouthFrownLeft': 0.0, 'mouthLowerDownLeft': 0.0, 'mouthPressLeft': 0.0, 'mouthSmileLeft': 0.0, 'mouthStretchLeft': 0.0, 'mouthUpperUpLeft': 0.0, 'mouthRight': 0.0, 'mouthDimpleRight': 0.0, 'mouthFrownRight': 0.0, 'mouthLowerDownRight': 0.0, 'mouthPressRight': 0.0, 'mouthSmileRight': 0.0, 'mouthStretchRight': 0.0, 'mouthUpperUpRight': 0.0, 'mouthClose': 0.0, 'mouthFunnel': 0.0, 'mouthPucker': 0.0, 'mouthRollLower': 0.0, 'mouthRollUpper': 0.0, 'mouthShrugLower': 0.0, 'mouthShrugUpper': 0.0, 'jawLeft': 0.0, 'jawRight': 0.0, 'jawForward': 0.0, 'jawOpen': 0, 'tongueOut': 0.0, 'headBoneX': 0.0, 'headBoneY': 0.0, 'headBoneZ': 0.0, 'headBoneQuat': [0.0, 0.0, 0.0, 1.0], 'leftEyeBoneX': 0.0, 'leftEyeBoneY': 0.0, 'leftEyeBoneZ': 0.0, 'leftEyeBoneQuat': [0.0, 0.0, 0.0, 1.0], 'rightEyeBoneX': 0.0, 'rightEyeBoneY': 0.0, 'rightEyeBoneZ': 0.0, 'rightEyeBoneQuat': [0.0, 0.0, 0.0, 1.0]},
}
return emotions.get(emotion, {})
def animationMain(self):
self.ifacialmocap_pose = self.animationBlink()
self.ifacialmocap_pose = self.animationHeadMove()
self.ifacialmocap_pose = self.animationTalking()
return self.ifacialmocap_pose
def on_erase_background(self, event: wx.Event):
pass
def create_animation_panel(self, parent):
self.animation_panel = wx.Panel(parent, style=wx.RAISED_BORDER)
self.animation_panel_sizer = wx.BoxSizer(wx.HORIZONTAL)
self.animation_panel.SetSizer(self.animation_panel_sizer)
self.animation_panel.SetAutoLayout(1)
image_size = self.poser.get_image_size()
# Left Column (Image)
self.animation_left_panel = wx.Panel(self.animation_panel, style=wx.SIMPLE_BORDER)
self.animation_left_panel_sizer = wx.BoxSizer(wx.VERTICAL)
self.animation_left_panel.SetSizer(self.animation_left_panel_sizer)
self.animation_left_panel.SetAutoLayout(1)
self.animation_panel_sizer.Add(self.animation_left_panel, 1, wx.EXPAND)
self.result_image_panel = wx.Panel(self.animation_left_panel, size=(image_size, image_size),
style=wx.SIMPLE_BORDER)
self.result_image_panel.Bind(wx.EVT_PAINT, self.paint_result_image_panel)
self.result_image_panel.Bind(wx.EVT_ERASE_BACKGROUND, self.on_erase_background)
self.result_image_panel.Bind(wx.EVT_LEFT_DOWN, self.load_image)
self.animation_left_panel_sizer.Add(self.result_image_panel, 1, wx.EXPAND)
separator = wx.StaticLine(self.animation_left_panel, -1, size=(256, 1))
self.animation_left_panel_sizer.Add(separator, 0, wx.EXPAND)
self.animation_left_panel_sizer.Fit(self.animation_left_panel)
# Right Column (Sliders)
self.animation_right_panel = wx.Panel(self.animation_panel, style=wx.SIMPLE_BORDER)
self.animation_right_panel_sizer = wx.BoxSizer(wx.VERTICAL)
self.animation_right_panel.SetSizer(self.animation_right_panel_sizer)
self.animation_right_panel.SetAutoLayout(1)
self.animation_panel_sizer.Add(self.animation_right_panel, 1, wx.EXPAND)
separator = wx.StaticLine(self.animation_right_panel, -1, size=(256, 5))
self.animation_right_panel_sizer.Add(separator, 0, wx.EXPAND)
background_text = wx.StaticText(self.animation_right_panel, label="--- Background ---", style=wx.ALIGN_CENTER)
self.animation_right_panel_sizer.Add(background_text, 0, wx.EXPAND)
self.output_background_choice = wx.Choice(
self.animation_right_panel,
choices=[
"TRANSPARENT",
"GREEN",
"BLUE",
"BLACK",
"WHITE",
"LOADED",
"CUSTOM"
]
)
self.output_background_choice.SetSelection(0)
self.animation_right_panel_sizer.Add(self.output_background_choice, 0, wx.EXPAND)
#self.pose_converter.init_pose_converter_panel(self.animation_panel) # this changes sliders to breathing on
#sliders go here
blendshape_groups = {
'Eyes': ['eyeLookOutLeft', 'eyeLookOutRight', 'eyeLookDownLeft', 'eyeLookUpLeft', 'eyeWideLeft', 'eyeWideRight'],
'Mouth': ['mouthFrownLeft'],
'Cheek': ['cheekSquintLeft', 'cheekSquintRight', 'cheekPuff'],
'Brow': ['browDownLeft', 'browOuterUpLeft', 'browDownRight', 'browOuterUpRight', 'browInnerUp'],
'Eyelash': ['mouthSmileLeft'],
'Nose': ['noseSneerLeft', 'noseSneerRight'],
'Misc': ['tongueOut']
}
for group_name, variables in blendshape_groups.items():
collapsible_pane = wx.CollapsiblePane(self.animation_right_panel, label=group_name, style=wx.CP_DEFAULT_STYLE | wx.CP_NO_TLW_RESIZE)
collapsible_pane.Bind(wx.EVT_COLLAPSIBLEPANE_CHANGED, self.on_pane_changed)
self.animation_right_panel_sizer.Add(collapsible_pane, 0, wx.EXPAND)
pane_sizer = wx.BoxSizer(wx.VERTICAL)
collapsible_pane.GetPane().SetSizer(pane_sizer)
for variable in variables:
variable_label = wx.StaticText(collapsible_pane.GetPane(), label=variable)
# Multiply min and max values by 100 for the slider
slider = wx.Slider(
collapsible_pane.GetPane(),
value=0,
minValue=0,
maxValue=100,
size=(150, -1), # Set the width to 150 and height to default
style=wx.SL_HORIZONTAL | wx.SL_LABELS
)
slider.SetName(variable)
slider.Bind(wx.EVT_SLIDER, self.on_slider_change)
self.sliders[slider.GetId()] = slider
pane_sizer.Add(variable_label, 0, wx.ALIGN_CENTER | wx.ALL, 5)
pane_sizer.Add(slider, 0, wx.EXPAND)
self.animation_right_panel_sizer.Fit(self.animation_right_panel)
self.animation_panel_sizer.Fit(self.animation_panel)
def on_pane_changed(self, event):
# Update the layout when a collapsible pane is expanded or collapsed
self.animation_right_panel.Layout()
def on_slider_change(self, event):
slider = event.GetEventObject()
value = slider.GetValue() / 100.0 # Divide by 100 to get the actual float value
#print(value)
slider_name = slider.GetName()
self.ifacialmocap_pose[slider_name] = value
def create_ui(self):
#MAke the UI Elements
self.main_sizer = wx.BoxSizer(wx.VERTICAL)
self.SetSizer(self.main_sizer)
self.SetAutoLayout(1)
self.capture_pose_lock = threading.Lock()
#Main panel with JPS
self.create_animation_panel(self)
self.main_sizer.Add(self.animation_panel, wx.SizerFlags(0).Expand().Border(wx.ALL, 5))
def update_capture_panel(self, event: wx.Event):
data = self.ifacialmocap_pose
for rotation_name in ROTATION_NAMES:
value = data[rotation_name]
@staticmethod
def convert_to_100(x):
return int(max(0.0, min(1.0, x)) * 100)
def paint_source_image_panel(self, event: wx.Event):
wx.BufferedPaintDC(self.source_image_panel, self.source_image_bitmap)
def update_source_image_bitmap(self):
dc = wx.MemoryDC()
dc.SelectObject(self.source_image_bitmap)
if self.wx_source_image is None:
self.draw_nothing_yet_string(dc)
else:
dc.Clear()
dc.DrawBitmap(self.wx_source_image, 0, 0, True)
del dc
def draw_nothing_yet_string(self, dc):
dc.Clear()
font = wx.Font(wx.FontInfo(14).Family(wx.FONTFAMILY_SWISS))
dc.SetFont(font)
w, h = dc.GetTextExtent("Nothing yet!")
dc.DrawText("Nothing yet!", (self.poser.get_image_size() - w) // 2, (self.poser.get_image_size() - h) // 2)
def paint_result_image_panel(self, event: wx.Event):
wx.BufferedPaintDC(self.result_image_panel, self.result_image_bitmap)
def update_result_image_bitmap(self, event: Optional[wx.Event] = None):
global global_timer_paused
global initAMI
global global_result_image
global global_reload
if global_timer_paused:
return
try:
if global_reload is not None:
MainFrame.load_image(self, event=None, file_path=None) # call load_image function here
return
ifacialmocap_pose = self.animationMain() #GET ANIMATION CHANGES
current_pose = self.pose_converter.convert(ifacialmocap_pose)
if self.last_pose is not None and self.last_pose == current_pose:
return
self.last_pose = current_pose
if self.torch_source_image is None:
dc = wx.MemoryDC()
dc.SelectObject(self.result_image_bitmap)
self.draw_nothing_yet_string(dc)
del dc
return
pose = torch.tensor(current_pose, device=self.device, dtype=self.poser.get_dtype())
with torch.no_grad():
output_image = self.poser.pose(self.torch_source_image, pose)[0].float()
output_image = convert_linear_to_srgb((output_image + 1.0) / 2.0)
c, h, w = output_image.shape
output_image = (255.0 * torch.transpose(output_image.reshape(c, h * w), 0, 1)).reshape(h, w, c).byte()
numpy_image = output_image.detach().cpu().numpy()
wx_image = wx.ImageFromBuffer(numpy_image.shape[0],
numpy_image.shape[1],
numpy_image[:, :, 0:3].tobytes(),
numpy_image[:, :, 3].tobytes())
wx_bitmap = wx_image.ConvertToBitmap()
dc = wx.MemoryDC()
dc.SelectObject(self.result_image_bitmap)
dc.Clear()
dc.DrawBitmap(wx_bitmap,
(self.poser.get_image_size() - numpy_image.shape[0]) // 2,
(self.poser.get_image_size() - numpy_image.shape[1]) // 2, True)
numpy_image_bgra = numpy_image[:, :, [2, 1, 0, 3]] # Convert color channels from RGB to BGR and keep alpha channel
global_result_image = numpy_image_bgra
del dc
if(initAMI == True): #If the models are just now initalized stop animation to save
global_timer_paused = True
initAMI = False
self.Refresh()
except KeyboardInterrupt:
print("Update process was interrupted by the user.")
wx.Exit()
def resize_image(image, size=(512, 512)):
image.thumbnail(size, Image.LANCZOS) # Step 1: Resize the image to maintain the aspect ratio with the larger dimension being 512 pixels
new_image = Image.new("RGBA", size) # Step 2: Create a new image of size 512x512 with transparency
new_image.paste(image, ((size[0] - image.size[0]) // 2,
(size[1] - image.size[1]) // 2)) # Step 3: Paste the resized image into the new image, centered
return new_image
def load_image(self, event: wx.Event, file_path=None):
global global_source_image # Declare global_source_image as a global variable
global global_reload
if global_reload is not None:
file_path = "global_reload"
try:
if file_path == "global_reload":
pil_image = global_reload
else:
pil_image = resize_PIL_image(
extract_PIL_image_from_filelike(file_path),
(self.poser.get_image_size(), self.poser.get_image_size()))
w, h = pil_image.size
if pil_image.size != (512, 512):
print("Resizing Char Card to work")
pil_image = MainFrame.resize_image(pil_image)
w, h = pil_image.size
if pil_image.mode != 'RGBA':
self.source_image_string = "Image must have alpha channel!"
self.wx_source_image = None
self.torch_source_image = None
else:
self.wx_source_image = wx.Bitmap.FromBufferRGBA(w, h, pil_image.convert("RGBA").tobytes())
self.torch_source_image = extract_pytorch_image_from_PIL_image(pil_image) \
.to(self.device).to(self.poser.get_dtype())
global_source_image = self.torch_source_image # Set global_source_image as a global variable
self.update_source_image_bitmap()
except Exception as error:
print("Error: ", error)
global_reload = None #reset the globe load
self.Refresh()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='uWu Waifu')
parser.add_argument(
'--model',
type=str,
required=False,
default='separable_float',
choices=['standard_float', 'separable_float', 'standard_half', 'separable_half'],
help='The model to use.'
)
parser.add_argument('--char', type=str, required=False, help='The path to the character image.')
parser.add_argument(
'--device',
type=str,
required=False,
default='cuda',
choices=['cpu', 'cuda'],
help='The device to use for PyTorch ("cuda" for GPU, "cpu" for CPU).'
)
args = parser.parse_args()
launch_gui(device=args.device, model=args.model)