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import os
import gradio as gr
import legacy
import dnnlib
import numpy as np
import torch
import find_direction
import generator
import psp_wrapper
psp_encoder_path = "./pretrained/e4e_ffhq_encode.pt"
landmarks_path = "./pretrained/shape_predictor_68_face_landmarks.dat"
e4e_embedder = psp_wrapper.psp_encoder(psp_encoder_path, landmarks_path)
G_ffhq_path = "./pretrained/ffhq.pkl"
G_metfaces_path = "./pretrained/metfaces.pkl"
direction_folder = "./assets/directions/"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
with dnnlib.util.open_url(G_ffhq_path) as f:
G_ffhq = legacy.load_network_pkl(f)['G_ema'].to(device)
with dnnlib.util.open_url(G_metfaces_path) as f:
G_metfaces = legacy.load_network_pkl(f)['G_ema'].to(device)
G_dict = {"FFHQ": G_ffhq, "MetFaces": G_metfaces}
DESCRIPTION = '''# <a href="https://github.com/catlab-team/stylemc"> StyleMC:</a> Multi-Channel Based Fast Text-Guided Image Generation and Manipulation
'''
FOOTER = 'This space is built by <a href = "https://github.com/catlab-team">Catlab Team</a>.'
direction_map = {}
direction_list = []
directions = [f for f in os.listdir(direction_folder) if f.endswith(".npz")]
for d in directions:
with np.load(direction_folder + d) as data:
dir_name = d.split(".npz")[0]
direction_list.append(dir_name)
direction_map[dir_name] = {"direction": data["s"], "stylegan_type": "FFHQ"}
def add_direction(prompt, stylegan_type, id_loss_w):
new_dir_name = prompt+" "+stylegan_type+" w_id_loss"+str(id_loss_w)
if (prompt != None) and (new_dir_name not in direction_list):
print("adding direction with id:", new_dir_name)
direction = find_direction.find_direction(G_dict[stylegan_type], prompt)
print(f"new direction calculated with {stylegan_type} and id loss weight = {id_loss_w}")
direction_list.append(new_dir_name)
direction_map[new_dir_name] = {"direction":direction, "stylegan_type":stylegan_type}
return gr.Radio.update(choices=direction_list, value=None, visible=True)
def generate_output_image(image_path, direction_id, change_power):
direction = direction_map[direction_id]["direction"]
G=G_dict["FFHQ"]
w = e4e_embedder.get_w(image_path)
s = generator.w_to_s(GIn=G, wsIn=w)
output_image = generator.generate_from_style(
GIn=G,
styles=s,
styles_direction=direction,
change_power=change_power,
outdir='.'
)
return output_image
with gr.Blocks(css="style.css") as demo:
gr.Markdown(DESCRIPTION)
with gr.Box():
gr.Markdown('''### Step 1) Finding a global manipulation direction <br />
- Please enter the target **text prompt** and **identity loss weight** to find global manipulation direction.''')
with gr.Row():
with gr.Column():
style_gan_type = gr.Radio(["FFHQ", "MetFaces"], value = "FFHQ", label="StyleGAN Type", interactive=True)
with gr.Column():
identity_loss_weight = gr.Slider(
0.1, 10, value=0.5, step=0.1,label="Identity Loss Weight",interactive=True
)
with gr.Row():
with gr.Column():
with gr.Row():
text = gr.Textbox(
label="Enter your text prompt",
show_label=False,
max_lines=1,
placeholder="Enter your text prompt"
).style(container=False)
find_direction_btn = gr.Button("Find Direction").style(full_width=False)
with gr.Box():
gr.Markdown('''### Step 2) Text-guided manipulation <br />
- Please upload an image. <br />
- You can select any of the previously found **directions** and set the **manipulation strength** to manipulate the image.''')
with gr.Row():
direction_radio = gr.Dropdown(direction_list, value="photo_of_a_face_with_beard", label="List of Directions")
with gr.Row():
manipulation_strength = gr.Slider(
0.1, 25, value=10, step=0.1, label="Manipulation Strength",interactive=True
)
with gr.Row():
with gr.Column():
with gr.Row():
input_image = gr.Image(label="Input Image", type="filepath")
with gr.Row():
generate_btn = gr.Button("Generate")
with gr.Column():
with gr.Row():
generated_image = gr.Image(label="Generated Image",type="pil",interactive=False)
find_direction_btn.click(add_direction, inputs=[text, style_gan_type, identity_loss_weight], outputs=direction_radio)
generate_btn.click(generate_output_image, inputs=[input_image, direction_radio,manipulation_strength], outputs=generated_image)
demo.launch(debug=True)
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