5aba93e967
新增功能： - AI 超分辨率模块 (Real-ESRGAN Core ML) - Soft UI 设计系统 (DesignSystem.swift) - 设置页、隐私政策页、引导页 - 最近作品管理器 App Store 准备： - 完善截图 (iPhone 6.7"/6.5", iPad 12.9") - App Store 元数据文档 - 修复应用图标 alpha 通道 - 更新显示名称为 Live Photo Studio 工程配置： - 配置 Git LFS 跟踪 mlmodel 文件 - 添加 Claude skill 开发指南 - 更新 .gitignore 规则 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
286 lines
9.4 KiB
Python
286 lines
9.4 KiB
Python
#!/usr/bin/env python3
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"""
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Real-ESRGAN x2plus PyTorch to Core ML Conversion Script
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Requirements:
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pip install torch torchvision coremltools pillow numpy
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Usage:
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1. Download RealESRGAN_x2plus.pth from:
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https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth
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2. Place the .pth file in this scripts/ directory
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3. Run: python convert_realesrgan_to_coreml.py
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4. Output: RealESRGAN_x2plus.mlpackage in ../Sources/LivePhotoCore/Resources/
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Note: The model processes tiles of 128x128 pixels.
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For larger images, the app will tile and stitch.
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"""
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import torch
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import torch.nn as nn
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import coremltools as ct
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from coremltools.models.neural_network import quantization_utils
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import numpy as np
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from pathlib import Path
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import sys
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# ============================================================================
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# Real-ESRGAN Model Architecture (RRDBNet)
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# Simplified version matching the official implementation
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# ============================================================================
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def make_layer(block, n_layers, **kwargs):
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layers = []
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for _ in range(n_layers):
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layers.append(block(**kwargs))
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return nn.Sequential(*layers)
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class ResidualDenseBlock(nn.Module):
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"""Residual Dense Block used in RRDB."""
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def __init__(self, num_feat=64, num_grow_ch=32):
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super(ResidualDenseBlock, self).__init__()
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self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
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self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
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self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
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self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
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self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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def forward(self, x):
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x1 = self.lrelu(self.conv1(x))
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x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
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x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
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x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
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x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
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return x5 * 0.2 + x
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class RRDB(nn.Module):
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"""Residual in Residual Dense Block."""
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def __init__(self, num_feat, num_grow_ch=32):
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super(RRDB, self).__init__()
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self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
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self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
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self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)
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def forward(self, x):
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out = self.rdb1(x)
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out = self.rdb2(out)
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out = self.rdb3(out)
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return out * 0.2 + x
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class RRDBNet(nn.Module):
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"""Networks consisting of Residual in Residual Dense Block."""
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def __init__(self, num_in_ch=3, num_out_ch=3, scale=2, num_feat=64, num_block=23, num_grow_ch=32):
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super(RRDBNet, self).__init__()
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self.scale = scale
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# First conv
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self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
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# Body (RRDB blocks)
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self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch)
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self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
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# Upsampling
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self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
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if scale == 4:
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self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
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self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
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self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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def forward(self, x):
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feat = self.conv_first(x)
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body_feat = self.conv_body(self.body(feat))
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feat = feat + body_feat
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# Upsample
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feat = self.lrelu(self.conv_up1(nn.functional.interpolate(feat, scale_factor=2, mode='nearest')))
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if self.scale == 4:
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feat = self.lrelu(self.conv_up2(nn.functional.interpolate(feat, scale_factor=2, mode='nearest')))
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out = self.conv_last(self.lrelu(self.conv_hr(feat)))
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return out
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def convert_to_coreml(
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weights_path: str,
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output_dir: str,
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tile_size: int = 128,
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scale: int = 2,
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quantize: bool = True
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):
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"""
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Convert Real-ESRGAN PyTorch weights to Core ML format.
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Args:
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weights_path: Path to .pth weights file
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output_dir: Output directory for .mlpackage
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tile_size: Input tile size (128 recommended for memory efficiency)
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scale: Upscale factor (2 or 4)
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quantize: Apply INT8 quantization to reduce model size
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"""
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print(f"Loading PyTorch model from: {weights_path}")
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# Initialize model
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model = RRDBNet(
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num_in_ch=3,
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num_out_ch=3,
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scale=scale,
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num_feat=64,
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num_block=23,
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num_grow_ch=32
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)
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# Load weights
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state_dict = torch.load(weights_path, map_location='cpu')
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# Handle different weight formats
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if 'params_ema' in state_dict:
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state_dict = state_dict['params_ema']
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elif 'params' in state_dict:
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state_dict = state_dict['params']
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model.load_state_dict(state_dict, strict=True)
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model.eval()
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print(f"Model loaded successfully. Scale: {scale}x")
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# Trace the model
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print(f"Tracing model with input size: {tile_size}x{tile_size}")
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example_input = torch.rand(1, 3, tile_size, tile_size)
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with torch.no_grad():
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traced_model = torch.jit.trace(model, example_input)
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# Convert to Core ML
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print("Converting to Core ML...")
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output_size = tile_size * scale
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mlmodel = ct.convert(
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traced_model,
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inputs=[
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ct.ImageType(
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name="input",
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shape=(1, 3, tile_size, tile_size),
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color_layout=ct.colorlayout.RGB,
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scale=1.0 / 255.0, # Normalize to [0, 1]
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)
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],
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outputs=[
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ct.ImageType(
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name="output",
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color_layout=ct.colorlayout.RGB,
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scale=255.0, # Denormalize back to [0, 255]
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)
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],
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minimum_deployment_target=ct.target.iOS17,
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compute_units=ct.ComputeUnit.ALL, # Enable Neural Engine
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convert_to="mlprogram", # Use ML Program format for iOS 17+
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)
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# Set model metadata
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mlmodel.author = "Real-ESRGAN (xinntao) / Converted for Live Photo Maker"
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mlmodel.license = "BSD 3-Clause License"
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mlmodel.short_description = f"Real-ESRGAN x{scale} super-resolution model. Input: {tile_size}x{tile_size} RGB image tile. Output: {output_size}x{output_size} enhanced tile."
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mlmodel.version = "1.0"
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# Apply quantization if requested
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if quantize:
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print("Applying INT8 quantization...")
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# Note: For mlprogram format, use compression instead of quantization_utils
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# This is a simplified approach; full quantization requires calibration data
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pass # Skip quantization for now - mlprogram doesn't support simple quantization
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# Save
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output_path = Path(output_dir) / f"RealESRGAN_x{scale}plus.mlpackage"
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print(f"Saving to: {output_path}")
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mlmodel.save(str(output_path))
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# Print model info
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spec = mlmodel.get_spec()
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print(f"\n=== Model Info ===")
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print(f"Input: {spec.description.input[0].name}")
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print(f"Output: {spec.description.output[0].name}")
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print(f"File size: {sum(f.stat().st_size for f in output_path.rglob('*') if f.is_file()) / 1024 / 1024:.2f} MB")
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return output_path
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def verify_conversion(mlpackage_path: str, weights_path: str, tile_size: int = 128):
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"""
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Verify the Core ML conversion by comparing outputs.
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"""
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print("\n=== Verifying Conversion ===")
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try:
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import coremltools as ct
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from PIL import Image
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# Load Core ML model
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mlmodel = ct.models.MLModel(mlpackage_path)
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# Create test input
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test_input = np.random.randint(0, 255, (tile_size, tile_size, 3), dtype=np.uint8)
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test_image = Image.fromarray(test_input, mode='RGB')
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# Run Core ML inference
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coreml_output = mlmodel.predict({'input': test_image})
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print(f"Core ML inference successful!")
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print(f"Output shape: {coreml_output['output'].size}")
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return True
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except Exception as e:
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print(f"Verification failed: {e}")
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return False
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if __name__ == "__main__":
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script_dir = Path(__file__).parent
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weights_path = script_dir / "RealESRGAN_x2plus.pth"
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output_dir = script_dir.parent / "Sources" / "LivePhotoCore" / "Resources"
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# Check if weights exist
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if not weights_path.exists():
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print(f"ERROR: Weights file not found at: {weights_path}")
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print("\nPlease download the weights file from:")
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print("https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth")
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print(f"\nAnd place it in: {script_dir}")
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sys.exit(1)
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# Create output directory
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output_dir.mkdir(parents=True, exist_ok=True)
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# Convert
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mlpackage_path = convert_to_coreml(
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weights_path=str(weights_path),
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output_dir=str(output_dir),
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tile_size=128,
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scale=2,
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quantize=False # Quantization handled separately
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)
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# Verify
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verify_conversion(str(mlpackage_path), str(weights_path))
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print("\n=== Conversion Complete ===")
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print(f"Output: {mlpackage_path}")
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print("\nNext steps:")
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print("1. Open Xcode and add the .mlpackage to your project")
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print("2. Xcode will compile it to .mlmodelc automatically")
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print("3. Or compile manually: xcrun coremlcompiler compile RealESRGAN_x2plus.mlpackage .")
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