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Merge pull request #15 from jhj0517/feature/image-restoration
Add image restoration with RealESRGAN
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,15 @@ | ||
| from modules.live_portrait.model_downloader import download_model | ||
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| MODELS_REALESRGAN_URL = { | ||
| "realesr-general-x4v3": "https://huggingface.co/jhj0517/realesr-general-x4v3/resolve/main/realesr-general-x4v3.pth", | ||
| "RealESRGAN_x2": "https://huggingface.co/ai-forever/Real-ESRGAN/resolve/main/RealESRGAN_x2.pth", | ||
| } | ||
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| MODELS_REALESRGAN_SCALABILITY = { | ||
| "realesr-general-x4v3": [1, 2, 4], | ||
| "RealESRGAN_x2": [2] | ||
| } | ||
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| def download_resrgan_model(file_path, url): | ||
| return download_model(file_path, url) |
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120
modules/image_restoration/real_esrgan/real_esrgan_inferencer.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,120 @@ | ||
| import os.path | ||
| import gradio as gr | ||
| import torch | ||
| import cv2 | ||
| from typing import Optional, Literal | ||
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| from modules.utils.paths import * | ||
| from modules.utils.image_helper import save_image | ||
| from .model_downloader import download_resrgan_model, MODELS_REALESRGAN_URL, MODELS_REALESRGAN_SCALABILITY | ||
| from .wrapper.rrdb_net import RRDBNet | ||
| from .wrapper.real_esrganer import RealESRGANer | ||
| from .wrapper.srvgg_net_compact import SRVGGNetCompact | ||
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| class RealESRGANInferencer: | ||
| def __init__(self, | ||
| model_dir: str = MODELS_REAL_ESRGAN_DIR, | ||
| output_dir: str = OUTPUTS_DIR): | ||
| self.model_dir = model_dir | ||
| self.output_dir = output_dir | ||
| self.device = self.get_device() | ||
| self.arc = None | ||
| self.model = None | ||
| self.face_enhancer = None | ||
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| self.available_models = list(MODELS_REALESRGAN_URL.keys()) | ||
| self.default_model = self.available_models[0] | ||
| self.model_config = { | ||
| "model_name": self.default_model, | ||
| "scale": 1, | ||
| "half_precision": True | ||
| } | ||
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| def load_model(self, | ||
| model_name: Optional[str] = None, | ||
| scale: Literal[1, 2, 4] = 1, | ||
| half_precision: bool = True, | ||
| progress: gr.Progress = gr.Progress()): | ||
| model_config = { | ||
| "model_name": model_name, | ||
| "scale": scale, | ||
| "half_precision": half_precision | ||
| } | ||
| if model_config == self.model_config and self.model is not None: | ||
| return | ||
| else: | ||
| self.model_config = model_config | ||
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| if model_name is None: | ||
| model_name = self.default_model | ||
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| model_path = os.path.join(self.model_dir, model_name) | ||
| if not model_name.endswith(".pth"): | ||
| model_path += ".pth" | ||
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| if not os.path.exists(model_path): | ||
| progress(0, f"Downloading RealESRGAN model to : {model_path}") | ||
| download_resrgan_model(model_path, MODELS_REALESRGAN_URL[model_name]) | ||
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| name, ext = os.path.splitext(model_name) | ||
| assert scale in MODELS_REALESRGAN_SCALABILITY[name] | ||
| if name == 'RealESRGAN_x2': # x4 RRDBNet model | ||
| arc = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4) | ||
| netscale = 4 | ||
| else: # x4 VGG-style model (S size) : "realesr-general-x4v3" | ||
| arc = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu') | ||
| netscale = 4 | ||
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| self.model = RealESRGANer( | ||
| scale=netscale, | ||
| model_path=model_path, | ||
| model=arc, | ||
| half=half_precision, | ||
| device=torch.device(self.get_device()) | ||
| ) | ||
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| def restore_image(self, | ||
| img_path: str, | ||
| model_name: Optional[str] = None, | ||
| scale: int = 1, | ||
| half_precision: Optional[bool] = None, | ||
| overwrite: bool = True): | ||
| model_config = { | ||
| "model_name": self.model_config["model_name"], | ||
| "scale": scale, | ||
| "half_precision": half_precision | ||
| } | ||
| half_precision = True if self.device == "cuda" else False | ||
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| if self.model is None or self.model_config != model_config: | ||
| self.load_model( | ||
| model_name=self.default_model if model_name is None else model_name, | ||
| scale=scale, | ||
| half_precision=half_precision | ||
| ) | ||
|
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| try: | ||
| with torch.autocast(device_type=self.device, enabled=(self.device == "cuda")): | ||
| output, img_mode = self.model.enhance(img_path, outscale=scale) | ||
| if img_mode == "RGB": | ||
| output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) | ||
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| if overwrite: | ||
| output_path = img_path | ||
| else: | ||
| output_path = get_auto_incremental_file_path(self.output_dir, extension="png") | ||
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| output_path = save_image(output, output_path=output_path) | ||
| return output_path | ||
| except Exception as e: | ||
| raise | ||
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| @staticmethod | ||
| def get_device(): | ||
| if torch.cuda.is_available(): | ||
| return "cuda" | ||
| elif torch.backends.mps.is_available(): | ||
| return "mps" | ||
| else: | ||
| return "cpu" |
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