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https://github.com/invoke-ai/InvokeAI
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6c6e534c1a
- Fixed codeformer module so that the facexlib files are downloaded into their pre-stored location in models/gfpgan/weights (shared with the GFPGAN module)
109 lines
4.4 KiB
Python
109 lines
4.4 KiB
Python
import os
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import torch
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import numpy as np
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import warnings
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import sys
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from ldm.invoke.globals import Globals
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pretrained_model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
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class CodeFormerRestoration():
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def __init__(self,
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codeformer_dir='models/codeformer',
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codeformer_model_path='codeformer.pth') -> None:
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if not os.path.isabs(codeformer_dir):
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codeformer_dir = os.path.join(Globals.root, codeformer_dir)
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self.model_path = os.path.join(codeformer_dir, codeformer_model_path)
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self.codeformer_model_exists = os.path.isfile(self.model_path)
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if not self.codeformer_model_exists:
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print('## NOT FOUND: CodeFormer model not found at ' + self.model_path)
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sys.path.append(os.path.abspath(codeformer_dir))
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def process(self, image, strength, device, seed=None, fidelity=0.75):
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if seed is not None:
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print(f'>> CodeFormer - Restoring Faces for image seed:{seed}')
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with warnings.catch_warnings():
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warnings.filterwarnings('ignore', category=DeprecationWarning)
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warnings.filterwarnings('ignore', category=UserWarning)
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from basicsr.utils.download_util import load_file_from_url
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from basicsr.utils import img2tensor, tensor2img
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from facexlib.utils.face_restoration_helper import FaceRestoreHelper
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from ldm.invoke.restoration.codeformer_arch import CodeFormer
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from torchvision.transforms.functional import normalize
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from PIL import Image
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cf_class = CodeFormer
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cf = cf_class(
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dim_embd=512,
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codebook_size=1024,
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n_head=8,
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n_layers=9,
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connect_list=['32', '64', '128', '256']
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).to(device)
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# note that this file should already be downloaded and cached at
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# this point
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checkpoint_path = load_file_from_url(url=pretrained_model_url,
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model_dir=os.path.abspath(os.path.dirname(self.model_path)),
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progress=True
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)
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checkpoint = torch.load(checkpoint_path)['params_ema']
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cf.load_state_dict(checkpoint)
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cf.eval()
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image = image.convert('RGB')
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# Codeformer expects a BGR np array; make array and flip channels
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bgr_image_array = np.array(image, dtype=np.uint8)[...,::-1]
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face_helper = FaceRestoreHelper(
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upscale_factor=1,
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use_parse=True,
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device=device,
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model_rootpath=os.path.join(Globals.root,'models','gfpgan','weights'),
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)
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face_helper.clean_all()
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face_helper.read_image(bgr_image_array)
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face_helper.get_face_landmarks_5(resize=640, eye_dist_threshold=5)
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face_helper.align_warp_face()
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for idx, cropped_face in enumerate(face_helper.cropped_faces):
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cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
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normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
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cropped_face_t = cropped_face_t.unsqueeze(0).to(device)
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try:
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with torch.no_grad():
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output = cf(cropped_face_t, w=fidelity, adain=True)[0]
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restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1))
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del output
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torch.cuda.empty_cache()
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except RuntimeError as error:
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print(f'\tFailed inference for CodeFormer: {error}.')
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restored_face = cropped_face
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restored_face = restored_face.astype('uint8')
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face_helper.add_restored_face(restored_face)
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face_helper.get_inverse_affine(None)
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restored_img = face_helper.paste_faces_to_input_image()
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# Flip the channels back to RGB
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res = Image.fromarray(restored_img[...,::-1])
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if strength < 1.0:
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# Resize the image to the new image if the sizes have changed
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if restored_img.size != image.size:
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image = image.resize(res.size)
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res = Image.blend(image, res, strength)
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cf = None
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return res
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