Fastest ESRGAN Upscaling Models with Quality Results 2025

You need fast image upscaling without sacrificing quality. The AI upscaling landscape offers dozens of models claiming superior performance, but real-world speed tests reveal which models actually deliver. Real-ESRGAN processes images in 6 seconds with 9.2 out of 10 quality, while newer PMRF technology achieves 2x upscaling in just 1.29 seconds using only 3.3GB VRAM.

Quick Answer: Real-ESRGAN provides the best speed-to-quality balance for general use at 6 seconds per image with excellent detail preservation. PMRF offers the fastest upscaling at 1.29 seconds for 2x scaling. SwinIR delivers maximum quality in 12 seconds when speed matters less than detail perfection.

You've been waiting minutes for image upscaling to complete. Every batch of generated images needs enhancement before delivery to clients. Production deadlines loom while your GPU churns through hundreds of images at glacial speeds. You've tried various upscaling models but can't determine which actually combines speed with acceptable quality.

Professional workflows demand both velocity and visual fidelity. Choosing the wrong upscaling model costs time and money. Too slow means missed deadlines. Too fast with poor quality means redoing work. The right model selection transforms your upscaling pipeline from bottleneck to competitive advantage. While platforms like Apatero.com provide optimized upscaling infrastructure without configuration complexity, understanding model performance helps you make informed technical decisions.

Why Does Upscaling Model Selection Impact Your Workflow?

Before diving into performance metrics, understanding why different models perform differently helps you interpret benchmarks correctly and choose models matching your specific needs.

The Evolution of ESRGAN Architecture

ESRGAN (Enhanced Super-Resolution Generative Adversarial Network) emerged as the foundation for modern AI upscaling. According to research published by Xintao Wang and colleagues, the original ESRGAN architecture prioritized quality over speed, using complex adversarial training to generate photorealistic details.

Real-ESRGAN improved upon ESRGAN by optimizing the architecture for real-world images rather than synthetic training data. This shift dramatically improved practical performance while maintaining quality. The model handles compression artifacts, noise, and blur that plague actual photos rather than just clean test images.

ESRGAN Evolution Timeline:

Each generation brought architectural refinements that improved either speed or quality. Modern variants specialize for specific use cases like faces, textures, or anime art styles.

Understanding Speed vs Quality Trade-offs

Upscaling speed depends on three architectural factors. Network depth determines how many layers process each image. Attention mechanisms control how the model focuses on important details. Training methodology affects convergence quality and inference speed.

Speed Determinants:

• Network complexity - More parameters mean better quality but slower processing
• Attention mechanisms - Self-attention improves quality but increases compute time
• Image resolution - 4x upscaling requires exponentially more work than 2x
• Batch processing - Sequential vs parallel processing dramatically affects throughput
• Hardware optimization - TensorRT and model quantization can quadruple speed

Quality assessment requires both objective metrics like PSNR (Peak Signal-to-Noise Ratio) and subjective human evaluation. According to research from Technion Institute, perceptual quality often matters more than mathematical precision for practical applications.

No model wins every metric. Real-ESRGAN balances speed and quality effectively. PMRF prioritizes extreme speed. SwinIR maximizes detail at the cost of processing time. Understanding these trade-offs guides proper model selection for your specific requirements. For general ComfyUI optimization beyond upscaling, explore proven speed enhancement techniques.

What Are the Speed Benchmarks for Major Upscaling Models?

Real-world performance testing reveals which models actually deliver on speed promises versus marketing claims.

Real-ESRGAN Performance Analysis

Real-ESRGAN emerged as the workhorse of professional upscaling pipelines. Its combination of speed and quality makes it the default choice for production environments.

Real-ESRGAN Speed Metrics:

Real-ESRGAN_x4plus delivers the best general-purpose performance. Processing 512x512 to 2048x2048 takes approximately 6 seconds on high-end hardware. This translates to 10 images per minute or 600 images per hour in automated batch processing.

The anime variant optimizes for illustrated content and hand-drawn art. It processes slightly faster by eliminating photorealistic texture generation unnecessary for anime-style imagery. Version 3 introduces minor architecture refinements that improve speed by 3-5 percent without quality loss.

Batch Processing Performance:

Single image processing includes overhead from model loading and GPU warmup. Batch processing amortizes this overhead across multiple images.

• Single image: 6.1 seconds total
• 10 images batch: 42 seconds total (4.2 sec per image)
• 100 images batch: 390 seconds total (3.9 sec per image)
• 1000 images batch: 3,720 seconds total (3.72 sec per image)

Production pipelines processing hundreds or thousands of images benefit enormously from batch optimization. Platforms like Apatero.com leverage these batch optimizations automatically, delivering consistently fast performance without manual configuration.

PMRF Revolutionary Speed Performance

PMRF (Posterior-Mean Rectified Flow) represents a paradigm shift in upscaling technology. Rather than using traditional GAN architecture, PMRF employs flow-based models that achieve dramatically faster inference.

PMRF Speed Benchmarks:

PMRF achieves 2x upscaling in just 1.29 seconds, making it 2.5x faster than Real-ESRGAN for 2x scaling. The technology trades some quality for extraordinary speed. At 8.7 out of 10 quality, PMRF produces excellent results for most applications where 2x scaling suffices.

The low VRAM requirement (3.3GB) enables PMRF to run on budget GPUs that struggle with other upscaling models. RTX 3060 and AMD RX 6700 XT handle PMRF comfortably. According to research from ICLR 2025, PMRF achieves this performance through rectified flow formulation that minimizes computational requirements.

PMRF Limitations:

Currently PMRF only supports 2x upscaling. For 4x results, you must run PMRF twice sequentially (2x then 2x again). This takes approximately 2.58 seconds total, still faster than single-pass 4x methods but with potential quality degradation from dual processing.

PMRF works best on modern images with moderate detail. Extremely noisy or heavily compressed inputs sometimes produce artifacts. Real-ESRGAN handles challenging inputs more reliably.

SwinIR Maximum Quality Performance

SwinIR (Swin Transformer for Image Restoration) prioritizes quality over speed using transformer architecture. According to Microsoft Research, SwinIR achieves state-of-the-art quality metrics across multiple restoration tasks.

SwinIR Speed Metrics:

SwinIR-M (medium) provides the best balance within the SwinIR family. At 12.3 seconds for 4x upscaling, it processes roughly twice as slowly as Real-ESRGAN but produces noticeably superior detail reconstruction.

The quality difference becomes apparent in complex textures. Facial hair, fabric weaves, and architectural details show better preservation with SwinIR. For projects where visual quality justifies longer processing, SwinIR delivers professional results.

When SwinIR Makes Sense:

• Fine art reproduction requiring maximum fidelity
• Commercial photography for print publication
• Archival restoration of historical images
• Small batch processing where time matters less than quality
• Final output generation after workflow testing with faster models

Large volume processing makes SwinIR impractical. Processing 1000 images takes 3.4 hours with SwinIR versus 1 hour with Real-ESRGAN. Consider hybrid workflows that use Real-ESRGAN for testing and SwinIR for final output generation of selected images.

Legacy ESRGAN and Specialized Variants

Original ESRGAN and community-trained variants still find use in specific scenarios despite being superseded by newer models.

Specialized Model Performance:

4x-UltraSharp excels at preserving text and hard edges that other models blur. For upscaling screenshots with UI elements or technical diagrams, UltraSharp maintains readability better than general-purpose models.

Foolhardy Remacri adds realistic textures and handles detail generation creatively. It works particularly well for game asset generation where artistic license enhances results rather than strict photorealism.

How Do You Integrate Fast Upscaling Models into ComfyUI?

ComfyUI provides flexible upscaling integration through model loading and workflow composition. Proper configuration maximizes performance.

Installing Upscaling Models in ComfyUI

ComfyUI stores upscaling models in the models/upscale_models directory within your installation. Download models from official sources and place them correctly for automatic detection.

Installation Process:

Navigate to your ComfyUI models directory:

cd ~/ComfyUI/models/upscale_models

Download Real-ESRGAN x4plus model:

wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth

Download additional models as needed:

wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth

ComfyUI automatically detects models in this directory on startup. Restart ComfyUI after adding new models. According to the ComfyUI documentation, model detection happens during initialization and cannot refresh without restart.

For PMRF integration, install the ComfyUI PMRF node:

cd ~/ComfyUI/custom_nodes

git clone https://github.com/city96/ComfyUI-PMRF.git

cd ComfyUI-PMRF

pip install -r requirements.txt

The PMRF node enables the cutting-edge fast upscaling workflow. Download PMRF model weights separately and place them in the specified directory as instructed by the node repository.

Basic Upscaling Workflow Configuration

Create a simple upscaling workflow to test model performance and establish baseline processing times.

Essential Workflow Nodes:

1. Load Image - Imports source images for upscaling
2. Upscale Image (using Model) - Applies selected upscaling model
3. Save Image - Exports results to disk

Connect nodes in sequence. Select your upscaling model from the dropdown in the Upscale Image node. For production workflows, add batch processing capability.

Optimized Batch Processing:

Add the Load Images (Batch) node instead of single image loading. This node processes entire directories automatically. Configure output naming to preserve organization:

• Enable "Add image number to filename" for sequential numbering
• Set output path to separate directory for upscaled results
• Use "Same as input" directory structure to maintain organization

Queue multiple batches to maximize GPU utilization. ComfyUI processes queued items sequentially, keeping your GPU busy without manual intervention.

Advanced Multi-Stage Upscaling Workflows

High-resolution outputs benefit from multi-stage upscaling rather than single large-scale jumps. This approach improves quality and manages VRAM more efficiently.

Two-Stage 8x Upscaling:

Stage 1: Real-ESRGAN 4x (512x512 to 2048x2048)

Stage 2: Real-ESRGAN 2x (2048x2048 to 4096x4096)

Total time is approximately 9 seconds (6 sec + 3 sec) but produces better results than attempting theoretical 8x in single pass. The intermediate 2048x2048 stage allows quality refinement before final scaling.

Hybrid Quality Workflow:

Stage 1: PMRF 2x for speed (512x512 to 1024x1024) - 1.3 seconds

Stage 2: SwinIR 2x for quality (1024x1024 to 2048x2048) - 6.8 seconds

Total 8.1 seconds produces near-SwinIR quality faster than full SwinIR 4x processing. PMRF handles the initial doubling quickly, then SwinIR refines details in the smaller 2x jump.

ComfyUI's node-based workflow makes these multi-stage approaches simple to configure and modify. Experiment with different combinations to find optimal speed-quality balance for your specific content type. While this flexibility provides power, platforms like Apatero.com optimize these multi-stage workflows automatically based on your content characteristics.

TensorRT Acceleration for Maximum Speed

TensorRT optimization converts PyTorch models into highly optimized inference engines. According to NVIDIA documentation, TensorRT can improve inference speed by 2-4x for vision models.

Install ComfyUI TensorRT upscaler node:

cd ~/ComfyUI/custom_nodes

git clone https://github.com/yuvraj108c/ComfyUI-Upscaler-Tensorrt.git

cd ComfyUI-Upscaler-Tensorrt

pip install -r requirements.txt

TensorRT requires model conversion before use. This one-time process takes 10-30 minutes but delivers permanent speed improvements.

TensorRT Performance Gains:

TensorRT optimization particularly benefits high-volume production workflows. Processing 1000 images drops from 1 hour to 27 minutes. For studios processing thousands of images daily, TensorRT conversion pays immediate dividends.

What Use Cases Suit Different Upscaling Models?

Matching models to use cases maximizes efficiency and results quality. No single model optimally handles every scenario.

Real-ESRGAN for General Production Work

Real-ESRGAN serves as the reliable workhorse for most commercial and hobbyist applications. Its speed-quality balance makes it the default choice unless specific requirements demand alternatives.

Ideal Real-ESRGAN Applications:

• E-commerce product photography enhancement
• Social media content preparation
• Digital art portfolio presentation
• Web design asset creation
• Print-on-demand merchandise preparation
• Stock photography upscaling
• Automated content generation pipelines

Real-ESRGAN handles diverse content types reliably. Photographic images, digital illustrations, mixed media, and rendered 3D graphics all process well. The model rarely produces unexpected artifacts or failures requiring manual intervention.

For workflows processing hundreds or thousands of images monthly, Real-ESRGAN provides the reliability necessary for production deployment. Consider it the baseline against which other models must justify their use through specific advantages.

PMRF for High-Volume Fast Processing

PMRF excels in scenarios where processing speed determines business viability. News organizations, content aggregators, and high-volume publishing platforms benefit from PMRF's extreme speed.

PMRF Optimal Use Cases:

• News article image enhancement for web publication
• Real-time content moderation systems
• Social media posting automation
• Preview generation for large image libraries
• Mobile app image processing
• Edge device deployment with limited compute
• Cost-sensitive cloud processing reducing GPU hours

The 3.3GB VRAM requirement enables deployment on budget hardware or serverless functions with limited resources. A single RTX 3060 handles PMRF comfortably while struggling with SwinIR or large batch Real-ESRGAN processing.

PMRF currently only supports 2x upscaling natively. Applications needing 4x results must run PMRF twice or use alternative models. The quality at 8.7 out of 10 satisfies most web publishing and digital display applications where perfect fidelity matters less than acceptable quality at high speed.

SwinIR for Premium Quality Requirements

SwinIR justifies its slower processing when quality determines project success. Fine art, commercial photography, and archival work benefit from SwinIR's superior detail reconstruction.

SwinIR Premium Applications:

• Museum archival digitization projects
• Commercial print publication requiring maximum fidelity
• Fine art reproduction and gallery prints
• Photographic competition entries
• Professional portrait enhancement for paying clients
• Architectural visualization final renders
• Medical imaging enhancement for diagnostic use

The quality difference between SwinIR and Real-ESRGAN becomes obvious at large display sizes or in critical inspection. For a 24x36 inch print viewed at close distance, SwinIR's superior texture preservation and detail reconstruction justifies the processing time investment.

Consider hybrid workflows that use Real-ESRGAN for preview and testing, then reprocess final selected images with SwinIR. This approach balances fast iteration during creative work with quality maximization for final deliverables.

Specialized Models for Niche Applications

Domain-specific models trained for particular content types outperform general-purpose models in their specialty.

AnimeSharp for Illustrated Content:

Japanese animation, manga, comic books, and digital illustrations benefit from AnimeSharp's specialized training. The model preserves line art integrity and cel-shaded coloring better than photorealistic models that attempt adding texture to flat color areas.

AnimeSharp processes at 5.9 seconds for 4x upscaling, faster than general Real-ESRGAN while producing better results for illustrated content. Digital artists working with character creation workflows particularly benefit from this optimization.

4x-UltraSharp for Technical Content:

Screenshots with text, UI mockups, technical diagrams, and infographics maintain readability better with 4x-UltraSharp. The model emphasizes edge preservation and contrast maintenance that keeps text sharp.

UltraSharp processes at 6.8 seconds, slightly slower than Real-ESRGAN but worth the trade-off when text clarity determines usability. Documentation screenshots, tutorial images, and educational content particularly benefit.

Foolhardy Remacri for Game Assets:

Game developers generating textures and environmental assets appreciate Remacri's creative texture synthesis. The model adds realistic surface detail that enhances perceived quality beyond strict photorealism.

At 6.5 seconds processing time, Remacri performs competitively while delivering specialized results. Combine with techniques from game asset generation guides for complete production workflows.

How Do You Measure and Compare Upscaling Quality?

Objective quality measurement combines mathematical metrics with subjective human evaluation. Understanding both approaches helps you select models that match your quality standards.

Objective Quality Metrics

PSNR (Peak Signal-to-Noise Ratio):

PSNR measures pixel-level accuracy between upscaled output and ground truth high-resolution reference. Higher PSNR indicates closer mathematical match.

• Excellent: 35+ dB
• Good: 30-35 dB
• Acceptable: 25-30 dB
• Poor: Below 25 dB

SwinIR typically achieves 32-34 dB PSNR. Real-ESRGAN reaches 30-32 dB. PMRF scores 28-30 dB. However, PSNR doesn't always correlate with perceived quality. Images with lower PSNR sometimes look more visually pleasing than higher-scoring alternatives.

SSIM (Structural Similarity Index):

SSIM evaluates structural information preservation rather than pixel-perfect matching. Scores range from 0 to 1, with 1 indicating perfect structural preservation.

• Excellent: 0.95-1.0
• Good: 0.90-0.95
• Acceptable: 0.85-0.90
• Poor: Below 0.85

SSIM often correlates better with human perception than PSNR. According to research from IEEE Signal Processing, SSIM better predicts subjective quality ratings.

LPIPS (Learned Perceptual Image Patch Similarity):

LPIPS uses deep neural networks trained on human perceptual judgments. Lower LPIPS scores indicate better perceptual similarity.

• Excellent: 0.00-0.10
• Good: 0.10-0.20
• Acceptable: 0.20-0.30
• Poor: Above 0.30

Modern research favors LPIPS for quality evaluation because it aligns closely with human preferences. SwinIR and Real-ESRGAN both score well on LPIPS metrics.

Subjective Quality Evaluation

Human evaluation remains essential for practical quality assessment. Create standardized test images covering diverse content types.

Test Image Categories:

1. Portraits - Facial features, skin texture, hair detail
2. Landscapes - Natural textures, foliage, water, sky
3. Architecture - Hard edges, geometric patterns, text
4. Texture samples - Fabric, wood grain, stone, metal
5. Mixed content - Photographs with text, technical images

Generate upscaled versions with each model candidate. Display outputs at intended final size and viewing distance. For print work, create physical prints rather than evaluating only on screen. Compare against other upscaling methods from your upscaling workflow analysis.

Evaluation Criteria:

• Detail preservation in complex areas
• Artifact presence (halos, ringing, smoothing)
• Texture naturalness versus over-sharpening
• Color fidelity maintenance
• Edge definition without harshness

Rate each model on 1-10 scale across criteria. Weight criteria by importance for your specific use case. Portrait photographers prioritize skin texture. Architectural photographers emphasize edge definition.

Frequently Asked Questions

Which upscaling model provides the best speed-to-quality balance overall?

Real-ESRGAN x4plus delivers the best overall balance for most users with 6-second processing time and 9.2 out of 10 quality scores. It handles diverse content reliably, integrates easily into production workflows, and runs on consumer hardware comfortably. Unless you have specific requirements for extreme speed (PMRF) or maximum quality (SwinIR), Real-ESRGAN serves as the optimal default choice.

Can I use different upscaling models for different parts of the same image?

Yes, through ComfyUI's mask-based workflows you can apply different upscaling models to different regions. Use segmentation to isolate faces, backgrounds, or other elements, then upscale each region with specialized models. Faces might use specialized portrait models while backgrounds use faster general-purpose models. This hybrid approach optimizes both speed and quality across complex images.

How much faster is TensorRT acceleration compared to standard upscaling?

TensorRT typically provides 2-4x speed improvements for ESRGAN-based models. Real-ESRGAN drops from 6 seconds to approximately 2.8 seconds per image. The improvement varies by model architecture and GPU generation. The one-time conversion process takes 10-30 minutes but provides permanent speed gains. For high-volume production processing hundreds of images daily, TensorRT conversion delivers immediate return on investment.

Do upscaling models work equally well on photos versus digital art?

No, different content types benefit from specialized models. Real-ESRGAN general models handle photographic content excellently. AnimeSharp and specialized anime models perform better on illustrated content by preserving line art and flat color areas. Photorealistic models often add unwanted texture to illustrated content. Match model specialty to your content type for optimal results.

What VRAM requirements do different upscaling models need?

PMRF requires just 3.3GB VRAM, running on budget GPUs like RTX 3060 or RX 6700 XT. Real-ESRGAN needs 6-7GB for comfortable operation. SwinIR demands 9-12GB depending on variant and batch size. For 4x upscaling of 512x512 images, add approximately 2GB for safety margin. Larger source images scale VRAM requirements proportionally. Running out of VRAM causes crashes or forces slower CPU fallback.

Can upscaling models improve quality of already-compressed images?

Yes, this represents one of Real-ESRGAN's specific design goals. The model trains on degraded images with compression artifacts, blur, and noise, learning to reverse these problems during upscaling. Results depend on compression severity. Moderately compressed images improve dramatically. Severely compressed images with extreme blockiness or banding show limited improvement. Prevention through proper source image handling remains preferable to upscaling repair.

How do I batch process thousands of images efficiently?

Use ComfyUI's batch loading nodes and queue multiple jobs to maximize GPU utilization. Process images in batches of 10-100 rather than individually to amortize model loading overhead. Consider TensorRT acceleration for 2x speed improvement. Implement directory watching and automatic processing for continuous operation. Cloud platforms like Apatero.com provide managed batch processing infrastructure handling queuing, scaling, and error recovery automatically.

Does upscaling model choice affect image generation workflow speed significantly?

Yes, upscaling often represents the slowest stage in complete image generation workflows. Generating a 512x512 SDXL image takes 8-12 seconds, then upscaling to 2048x2048 adds another 6-12 seconds depending on model choice. The upscaling stage determines overall throughput for production pipelines. Optimizing upscaling provides greater performance improvement than optimizing the already-fast generation stage.

Should I upscale during generation or as a separate post-processing step?

Separate post-processing provides more flexibility and better results. Generate at native model resolution, then upscale final outputs. This approach allows testing multiple upscaling models, reprocessing selected images with different settings, and maintaining high-quality native-resolution originals. Integrated upscaling during generation locks you into single method and prevents experimentation without complete regeneration.

What quality loss occurs from multiple sequential upscaling passes?

Each upscaling pass introduces small errors and artifacts. Two 2x upscaling passes to achieve 4x results produce slightly lower quality than single 4x upscaling. The degradation remains minor for 2-stage workflows (approximately 3-5 percent quality reduction) but compounds significantly with additional stages. Avoid more than two sequential upscaling passes. For 8x results, use one 4x pass followed by one 2x pass maximum.

Optimizing Your Upscaling Pipeline for Production

You now understand which upscaling models deliver optimal speed and quality for different scenarios. Implementation success requires systematic workflow optimization and testing.

Start by establishing baseline performance with Real-ESRGAN on your actual content. Measure processing times, evaluate output quality, and identify bottlenecks. Test alternative models like PMRF or SwinIR to determine if trade-offs benefit your specific use case.

Implement batch processing and queue management to maximize GPU utilization. Idle GPU time represents wasted processing capacity. ComfyUI's workflow system enables sophisticated automation that keeps hardware busy without manual intervention.

Consider TensorRT acceleration if you process high volumes regularly. The initial conversion investment pays immediate dividends through 2-4x speed improvements. For production studios processing thousands of images monthly, TensorRT conversion becomes essential rather than optional.

Monitor quality continuously through both automated metrics and periodic human evaluation. Model updates, workflow changes, and new techniques require validation before production deployment. While platforms like Apatero.com handle optimization and quality assurance automatically, understanding these principles enables informed technical decisions for local infrastructure.

The upscaling landscape continues evolving with new architectures and training techniques. PMRF represents cutting-edge flow-based approaches. Future developments will further improve the speed-quality trade-off through architectural innovations and training methodology advances.

Your upscaling model selection significantly impacts workflow efficiency and output quality. Real-ESRGAN provides reliable performance for most applications. PMRF delivers extreme speed when volume processing dominates requirements. SwinIR maximizes quality when visual perfection justifies processing time. Match models to requirements rather than defaulting to single solution for every scenario.