Duplicate from wangkanai/flux-upscale

Co-authored-by: wangkanai <wangkanai@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+library_name: realesrgan
+pipeline_tag: image-to-image
+tags:
+  - image-upscaling
+  - super-resolution
+  - realesrgan
+  - esrgan
+  - post-processing
+  - image-enhancement
+---
+
+<!-- README Version: v1.3 -->
+
+# FLUX Upscale Models Collection v1.3
+
+This repository contains Real-ESRGAN upscale models for post-processing and enhancing generated images. These models can upscale images by 2x or 4x while adding fine details and improving sharpness.
+
+## Model Description
+
+Real-ESRGAN (Real Enhanced Super-Resolution Generative Adversarial Networks) models for high-quality image upscaling. These models are commonly used as post-processing steps for AI-generated images to increase resolution and enhance details.
+
+**Key Capabilities**:
+- 2x and 4x image upscaling
+- Detail enhancement and sharpening
+- Noise reduction and artifact removal
+- Optimized for AI-generated images
+- CPU and GPU compatible
+
+## Repository Contents
+
+**Total Size**: ~192MB
+
+### Upscale Models (upscale_models/)
+- `4x-UltraSharp.pth` - 64MB - 4x upscaling with ultra-sharp detail enhancement
+- `RealESRGAN-x2plus.pth` - 64MB - 2x upscaling model
+- `RealESRGAN-x4plus.pth` - 64MB - 4x upscaling model
+
+## Hardware Requirements
+
+- **VRAM**: 4GB+ recommended for GPU inference
+- **Disk Space**: 192MB
+- **Memory**: 8GB+ system RAM recommended
+- **Compatible with**: CPU or GPU inference (CUDA, ROCm, or CPU)
+
+## Usage Examples
+
+### Basic Usage with Real-ESRGAN
+
+```python
+from basicsr.archs.rrdbnet_arch import RRDBNet
+from realesrgan import RealESRGANer
+import cv2
+
+# Load the upscaler model
+model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32)
+
+upsampler = RealESRGANer(
+    scale=4,
+    model_path="E:\\huggingface\\flux-upscale\\upscale_models\\4x-UltraSharp.pth",
+    model=model,
+    tile=0,
+    tile_pad=10,
+    pre_pad=0,
+    half=True  # Use FP16 for faster inference on GPU
+)
+
+# Load and upscale an image
+img = cv2.imread("input.png", cv2.IMREAD_COLOR)
+output, _ = upsampler.enhance(img, outscale=4)
+cv2.imwrite("output_upscaled.png", output)
+```
+
+### Using with FLUX Pipeline
+
+```python
+from diffusers import FluxPipeline
+from realesrgan import RealESRGANer
+from basicsr.archs.rrdbnet_arch import RRDBNet
+import torch
+import numpy as np
+
+# Generate image with FLUX
+pipe = FluxPipeline.from_pretrained(
+    "E:\\huggingface\\flux-dev-fp16",
+    torch_dtype=torch.float16
+)
+pipe.to("cuda")
+
+image = pipe(
+    prompt="a beautiful landscape with mountains",
+    num_inference_steps=30
+).images[0]
+
+# Convert PIL to numpy/cv2 format
+img_array = np.array(image)
+
+# Initialize upscaler
+model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32)
+upsampler = RealESRGANer(
+    scale=4,
+    model_path="E:\\huggingface\\flux-upscale\\upscale_models\\4x-UltraSharp.pth",
+    model=model,
+    half=True
+)
+
+# Upscale the generated image
+upscaled, _ = upsampler.enhance(img_array, outscale=4)
+
+# Save result
+import cv2
+cv2.imwrite("flux_upscaled_4x.png", upscaled)
+```
+
+### Tiled Processing for Large Images
+
+```python
+from basicsr.archs.rrdbnet_arch import RRDBNet
+from realesrgan import RealESRGANer
+import cv2
+
+# Configure for large images with tiling
+model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32)
+
+upsampler = RealESRGANer(
+    scale=4,
+    model_path="E:\\huggingface\\flux-upscale\\upscale_models\\RealESRGAN_x4plus.pth",
+    model=model,
+    tile=512,      # Process in 512x512 tiles
+    tile_pad=10,   # Padding to avoid seams
+    pre_pad=0,
+    half=True
+)
+
+# Process large image
+img = cv2.imread("large_image.png", cv2.IMREAD_COLOR)
+output, _ = upsampler.enhance(img, outscale=4)
+cv2.imwrite("large_upscaled.png", output)
+```
+
+## Model Comparison
+
+| Model | Scale | Best For | File Size | Speed |
+|-------|-------|----------|-----------|-------|
+| 4x-UltraSharp | 4x | Sharp details, AI-generated images | 64MB | Moderate |
+| RealESRGAN_x2plus | 2x | Moderate upscaling, faster processing | 64MB | Fast |
+| RealESRGAN_x4plus | 4x | General purpose 4x upscaling | 64MB | Moderate |
+
+**Model Selection Guide**:
+- **4x-UltraSharp**: Best for AI-generated images needing maximum sharpness
+- **RealESRGAN_x2plus**: Quick 2x upscaling with balanced quality
+- **RealESRGAN_x4plus**: General-purpose 4x upscaling for various image types
+
+## Model Specifications
+
+- **Architecture**: RRDB (Residual in Residual Dense Block)
+- **Input Channels**: 3 (RGB)
+- **Output Channels**: 3 (RGB)
+- **Feature Dimensions**: 64
+- **Network Blocks**: 23 (standard configuration)
+- **Growth Channels**: 32
+- **Format**: PyTorch `.pth` files
+- **Precision**: FP32 (supports FP16 inference)
+
+## Performance Tips
+
+- **GPU Acceleration**: Use `half=True` for FP16 inference on compatible GPUs (approximately 2x faster)
+- **Tiling for VRAM**: Enable tiling with `tile=512` to reduce VRAM usage for large images
+- **Tile Padding**: Use `tile_pad=10` to minimize visible seams between tiles
+- **Batch Processing**: Process multiple images sequentially to amortize model loading time
+- **CPU Fallback**: Models work on CPU but will be significantly slower (~10-20x)
+- **Optimal Scale**: Use 2x for faster processing, 4x for maximum detail enhancement
+- **Input Quality**: Better input images produce better upscaling results
+- **File Formats**: Use lossless formats (PNG) for best quality preservation
+
+## Use Cases
+
+- Post-processing AI-generated images from FLUX.1, Stable Diffusion, etc.
+- Enhancing FLUX.1-dev outputs for high-resolution prints
+- Increasing resolution of generated artwork for commercial use
+- Adding fine details to synthetic images
+- Print preparation for generated images (posters, canvas prints)
+- Upscaling video frames for AI video generation pipelines
+- Restoring and enhancing low-resolution generated content
+
+## Installation
+
+```bash
+pip install realesrgan basicsr
+```
+
+**Dependencies**:
+- Python 3.8+
+- PyTorch 1.7+
+- basicsr
+- realesrgan
+- opencv-python
+- numpy
+
+## License
+
+These models are released under the Apache 2.0 license.
+
+## Citation
+
+```bibtex
+@InProceedings{wang2021realesrgan,
+    author    = {Xintao Wang and Liangbin Xie and Chao Dong and Ying Shan},
+    title     = {Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data},
+    booktitle = {International Conference on Computer Vision Workshops (ICCVW)},
+    year      = {2021}
+}
+```
+
+## Links and Resources
+
+- **Real-ESRGAN Paper**: [arXiv:2107.10833](https://arxiv.org/abs/2107.10833)
+- **Official Repository**: [xinntao/Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN)
+- **BasicSR Library**: [xinntao/BasicSR](https://github.com/xinntao/BasicSR)
+- **Hugging Face**: [Real-ESRGAN Models](https://huggingface.co/models?other=real-esrgan)
+- **Model Downloads**: Available through official Real-ESRGAN releases
+
+## Model Card Contact
+
+For questions about Real-ESRGAN models, refer to the official Real-ESRGAN repository and documentation at https://github.com/xinntao/Real-ESRGAN

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