File size: 2,581 Bytes
341a78a | 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 | # Copyright (c) Alibaba, Inc. and its affiliates.
import os
from facechain.inference import GenPortrait
import cv2
from facechain.utils import snapshot_download
from facechain.constants import neg_prompt, pos_prompt_with_cloth, pos_prompt_with_style, styles, base_models
def generate_pos_prompt(style_model, prompt_cloth):
if style_model in base_models[0]['style_list'][:-1] or style_model is None:
pos_prompt = pos_prompt_with_cloth.format(prompt_cloth)
else:
matched = list(filter(lambda style: style_model == style['name'], styles))
if len(matched) == 0:
raise ValueError(f'styles not found: {style_model}')
matched = matched[0]
pos_prompt = pos_prompt_with_style.format(matched['add_prompt_style'])
return pos_prompt
use_main_model = True
use_face_swap = True
use_post_process = True
use_stylization = False
use_depth_control = False
use_pose_model = False
pose_image = 'poses/man/pose1.png'
processed_dir = './processed'
num_generate = 5
base_model = 'ly261666/cv_portrait_model'
revision = 'v2.0'
multiplier_style = 0.25
multiplier_human = 0.85
base_model_sub_dir = 'film/film'
train_output_dir = './output'
output_dir = './generated'
style = styles[0]
model_id = style['model_id']
if model_id == None:
style_model_path = None
pos_prompt = generate_pos_prompt(style['name'], style['add_prompt_style'])
else:
if os.path.exists(model_id):
model_dir = model_id
else:
model_dir = snapshot_download(model_id, revision=style['revision'])
style_model_path = os.path.join(model_dir, style['bin_file'])
pos_prompt = generate_pos_prompt(style['name'], style['add_prompt_style']) # style has its own prompt
if not use_pose_model:
pose_model_path = None
use_depth_control = False
pose_image = None
else:
model_dir = snapshot_download('damo/face_chain_control_model', revision='v1.0.1')
pose_model_path = os.path.join(model_dir, 'model_controlnet/control_v11p_sd15_openpose')
gen_portrait = GenPortrait(pose_model_path, pose_image, use_depth_control, pos_prompt, neg_prompt, style_model_path,
multiplier_style, multiplier_human, use_main_model,
use_face_swap, use_post_process,
use_stylization)
outputs = gen_portrait(processed_dir, num_generate, base_model,
train_output_dir, base_model_sub_dir, revision)
os.makedirs(output_dir, exist_ok=True)
for i, out_tmp in enumerate(outputs):
cv2.imwrite(os.path.join(output_dir, f'{i}.png'), out_tmp)
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