Base Models Comparison

Comprehensive comparison of SD 1.5, SDXL, SD3, FLUX, and Pony diffusion models with their strengths, requirements, and optimal use cases.

Comprehensive comparison of popular diffusion models: SD 1.5, SD 2.x, SDXL, Pony, and FLUX, with their strengths, requirements, and use cases.

Table of contents

  1. Overview
  2. Quick Comparison Table
  3. Stable Diffusion 1.5
    1. Overview
    2. Technical Specifications
    3. Strengths
    4. Weaknesses
    5. Optimal Settings
    6. Use Cases
  4. Stable Diffusion 2.x
    1. Overview
    2. Technical Specifications
    3. Key Differences from 1.5
    4. Strengths
    5. Weaknesses
    6. Optimal Settings
  5. SDXL (Stable Diffusion XL)
    1. Overview
    2. Technical Specifications
    3. Unique Features
    4. Strengths
    5. Weaknesses
    6. Optimal Settings
    7. SDXL Workflow
  6. Pony Diffusion
    1. Overview
    2. Technical Specifications
    3. Unique Features
    4. Strengths
    5. Weaknesses
    6. Optimal Settings
    7. Prompting Guide
  7. FLUX
    1. Overview
    2. Technical Specifications
    3. Model Variants
    4. Revolutionary Features
    5. Strengths
    6. Weaknesses
    7. Optimal Settings
    8. FLUX Workflow Example
  8. Model Selection Guide
    1. By Use Case
    2. By Hardware
  9. Stable Diffusion 3
    1. Overview
    2. Technical Specifications
    3. Unique Features
    4. Strengths
    5. Weaknesses
    6. Optimal Settings
  10. Performance Comparison
    1. Generation Speed (RTX 4090)
    2. Quality Metrics
  11. Migration Guide
    1. From SD 1.5 to SDXL
    2. From SDXL to FLUX
    3. Prompting Differences
  12. Future Considerations
    1. Emerging Trends
    2. Choosing Future-Proof Models
  13. Conclusion
  14. See Also

Overview

The landscape of diffusion models has evolved rapidly, with each generation bringing improvements in quality, capabilities, and efficiency. This guide compares the major base models to help you choose the right one for your needs.

Quick Comparison Table

Model Resolution VRAM (Min) Quality Speed Flexibility Release
SD 1.5 512×512 4GB Good Fast Excellent 2022
SD 2.1 768×768 6GB Better Medium Good 2022
SDXL 1024×1024 8GB Excellent Slow Very Good 2023
SD3 1024×1024 10GB Superior Medium Excellent 2024
Pony 1024×1024 8GB Excellent* Medium Specialized 2024
FLUX 1024×1024+ 12GB State-of-art Slow Excellent 2024

*Excellent for anime/stylized content

Stable Diffusion 1.5

Overview

SD 1.5 remains the most popular and widely supported model due to its balance of quality, speed, and compatibility. Despite newer models, it maintains relevance through extensive community support and optimization.

Technical Specifications

Architecture: U-Net with attention
Parameters: 859M
Training Resolution: 512×512
VAE: KL-f8 autoencoder
Text Encoder: CLIP ViT-L/14
Max Tokens: 77
Precision: FP16/FP32
File Size: ~2GB (pruned)

Strengths

  • Massive ecosystem: Thousands of LoRAs, embeddings, and tools
  • Low requirements: Runs on 4GB VRAM GPUs
  • Fast generation: 20-50 steps typical
  • Highly optimized: Years of community optimization
  • Versatile: Works well for most content types

Weaknesses

  • Lower resolution: Native 512×512
  • Text rendering: Poor text generation
  • Anatomy: Struggles with hands/complex poses
  • Modern concepts: Limited understanding of recent topics

Optimal Settings

{
    "resolution": "512x512",
    "steps": 20-30,
    "cfg_scale": 7-9,
    "sampler": "euler_a",
    "clip_skip": 1-2,
    "vae": "vae-ft-mse-840000"
}

Use Cases

Best for:

  • Quick prototyping
  • Low-resource environments
  • Artistic/stylized content
  • When extensive LoRA support needed
  • Web applications

Stable Diffusion 2.x

Overview

SD 2.x improved upon 1.5 with better training data and higher resolution, but faced adoption challenges due to changed aesthetic preferences and compatibility issues.

Technical Specifications

Architecture: Improved U-Net
Parameters: 865M (2.0), 865M (2.1)
Training Resolution: 768×768
VAE: Improved KL-f8
Text Encoder: OpenCLIP ViT-H/14
Max Tokens: 77
File Size: ~2.5GB

Key Differences from 1.5

  • Different CLIP model: OpenCLIP vs CLIP
  • Cleaner dataset: NSFW content filtered
  • Higher resolution: 768×768 native
  • Better architecture: Improved attention

Strengths

  • Better quality: Improved detail and coherence
  • Higher resolution: Native 768×768
  • Cleaner outputs: Less prone to artifacts
  • Better concepts: Improved understanding

Weaknesses

  • Limited adoption: Fewer LoRAs and tools
  • Different aesthetic: Less “artistic” by default
  • Compatibility: Not backward compatible with SD 1.5
  • Prompt differences: Requires different prompting style

Optimal Settings

{
    "resolution": "768x768",
    "steps": 25-35,
    "cfg_scale": 6-8,
    "sampler": "dpm++_2m",
    "negative_prompt": "Essential for good results"
}

SDXL (Stable Diffusion XL)

Overview

SDXL represents a major leap in quality and resolution, introducing a two-stage pipeline with separate base and refiner models for unprecedented detail.

Technical Specifications

Architecture: Enlarged U-Net + Refiner
Parameters: 3.5B (base) + 3.5B (refiner)
Training Resolution: 1024×1024
VAE: SDXL VAE
Text Encoders: CLIP ViT-L + OpenCLIP ViT-G
Max Tokens: 77 × 2
Conditioning: Size + Crop conditioning
File Size: ~6.5GB (base only)

Unique Features

  1. Two-stage pipeline: 
    Base Model (0.8 denoising) → Refiner Model (0.2 denoising)
  2. Dual text encoders: Combines two CLIP models
  3. Conditioning augmentation: Resolution and crop parameters
  4. Improved VAE: Better color accuracy

Strengths

  • Superior quality: Photorealistic capabilities
  • High resolution: Native 1024×1024+
  • Better text: Improved text rendering
  • Fine details: Excellent micro-details
  • Versatility: Works across all styles

Weaknesses

  • Resource intensive: 8GB+ VRAM minimum
  • Slower generation: 40-50% slower than SD 1.5
  • Complex pipeline: Two models for best results
  • Large size: ~13GB for full pipeline

Optimal Settings

{
    "resolution": "1024x1024",
    "base_steps": 25-35,
    "refiner_steps": 10-15,
    "cfg_scale": 5-7,
    "sampler": "dpm++_2m_sde",
    "refiner_switch": 0.8,
    "negative_prompt": "Critical for quality"
}

SDXL Workflow

# ComfyUI workflow
Base Model → KSampler (end_at_step: 20)
                ↓
         Latent Output
                ↓
Refiner Model → KSampler (start_at_step: 20)

Pony Diffusion

Overview

Pony Diffusion is a specialized SDXL fine-tune focused on anime, furry, and cartoon content, becoming the go-to model for stylized artwork generation.

Technical Specifications

Base: SDXL architecture
Specialization: Anime/Furry/Cartoon
Training Data: Curated booru datasets
Special Tokens: Quality tags system
Resolution: 1024×1024
File Size: ~6.5GB

Unique Features

  1. Score-based prompting: 
    "score_9, score_8_up, score_7_up, [your prompt]"
  2. Style tags: Extensive style control
  3. Character knowledge: Recognizes many characters
  4. Booru tags: Uses danbooru-style tagging

Strengths

  • Anime excellence: Best-in-class for anime
  • Style consistency: Maintains style well
  • Character accuracy: Great for fan art
  • Tag system: Intuitive for booru users
  • Active community: Regular updates

Weaknesses

  • Specialized: Not ideal for photorealism
  • Learning curve: Unique prompting style
  • Content bias: Optimized for specific content
  • NSFW tendency: Requires careful prompting

Optimal Settings

{
    "resolution": "1024x1024",
    "steps": 25-30,
    "cfg_scale": 6-8,
    "sampler": "euler_a",
    "clip_skip": 2,  # Important for anime
    "prompt_prefix": "score_9, score_8_up, score_7_up, masterpiece"
}

Prompting Guide

# Good Pony prompt structure
score_9, score_8_up, masterpiece, best quality,
1girl, [character_name], [outfit], [pose], [expression],
[background], [lighting], [style_tags]

# Negative prompt
score_6, score_5, score_4, worst quality, low quality,
bad anatomy, bad hands

FLUX

Overview

FLUX represents the current state-of-the-art in open diffusion models, with revolutionary architecture changes and significantly improved capabilities.

Technical Specifications

Architecture: Transformer-based (DiT)
Parameters: 12B
Training: Flow matching
Resolution: 1024×1024 - 2048×2048
Text Encoder: T5 XXL + CLIP
Max Tokens: 256
Guidance: Separate guidance model
File Size: ~24GB (FP16)

Model Variants

  1. FLUX.1-dev: Full quality development version
  2. FLUX.1-schnell: Distilled 4-step generation (German for “fast”)
  3. FLUX.1-pro: API-only premium version with best quality
  4. FLUX-fp8: Quantized for lower VRAM (12GB viable)
  5. FLUX-gguf: Further quantized versions (Q4, Q5, Q8)

Revolutionary Features

  1. No CFG needed: Uses guidance model instead 
    cfg = 1.0  # Always 1.0 for FLUX
guidance = 3.5  # Separate parameter
  2. Better text understanding: T5 encoder
  3. Improved composition: Better spatial awareness
  4. Consistent anatomy: Rarely fails on hands

Strengths

  • Unmatched quality: Best available quality
  • Text rendering: Can generate readable text
  • Prompt adherence: Follows complex prompts
  • Consistency: Reliable anatomy/physics
  • Modern knowledge: Trained on recent data

Weaknesses

  • Resource heavy: 12GB+ VRAM minimum
  • Slower: 2-3x slower than SDXL
  • Large size: 24GB+ for full precision
  • New ecosystem: Fewer LoRAs initially
  • Different workflow: Requires adjustment

Optimal Settings

{
    "resolution": "1024x1024",
    "steps": 20-25,
    "cfg": 1.0,  # CRITICAL: Must be 1.0
    "guidance": 3.5,
    "sampler": "euler",
    "scheduler": "simple",
    "model": "flux-fp8.safetensors"  # For lower VRAM
}

FLUX Workflow Example

# ComfyUI FLUX workflow
[FLUX Checkpoint] → [CLIP Encode] → [FLUX Guidance]
                                           ↓
                        [KSampler: cfg=1.0] → [VAE Decode]

Model Selection Guide

By Use Case

Use Case Recommended Model Alternative
Quick prototypes SD 1.5 FLUX-schnell
Photorealism FLUX SDXL
Anime/Manga Pony SD 1.5 + LoRA
Game assets SDXL SD 1.5
Product renders FLUX SDXL
Artistic styles SD 1.5 SDXL
Text in images FLUX SDXL (limited)
Low VRAM (4-6GB) SD 1.5 SD 2.1
Best quality FLUX SDXL + Refiner

By Hardware

VRAM Optimal Model Settings
4GB SD 1.5 512×512, FP16
6GB SD 2.1 768×768, FP16
8GB SDXL 1024×1024, FP16, no refiner
12GB FLUX-fp8 1024×1024, optimized
16GB+ Any model Full quality

Stable Diffusion 3

Overview

SD3 represents a major architectural shift from previous versions, adopting a Multimodal Diffusion Transformer (MM-DiT) architecture similar to FLUX but with different design choices.

Technical Specifications

Architecture: MM-DiT (Multimodal Diffusion Transformer)
Parameters: 2B (Medium), 8B (Large)
Text Encoders: CLIP L/14 + OpenCLIP bigG/14 + T5-v1.1-XXL
Max Tokens: 77 + 77 + 256
Training: Rectified Flow (like FLUX)
Resolution: 1024×1024 base, up to 2048×2048
File Size: ~6GB (Medium), ~18GB (Large)

Unique Features

  1. Triple Text Encoding: Most comprehensive text understanding
  2. Improved Architecture: Better than SDXL, competitive with FLUX
  3. Flexible Resolution: Better handling of various aspect ratios
  4. Better Text Rendering: Can generate readable text in images

Strengths

  • Prompt Adherence: Best-in-class prompt following
  • Text Generation: Can render words and letters accurately
  • Efficiency: Medium model runs well on 10GB VRAM
  • Quality: Comparable to FLUX at lower resource cost
  • Flexibility: Works with standard diffusion workflows

Weaknesses

  • Licensing: More restrictive than SD1.5/SDXL
  • Ecosystem: Newer, fewer fine-tunes available
  • Complexity: Triple encoder adds complexity
  • Size: Large model requires significant resources

Optimal Settings

{
    "resolution": "1024x1024",
    "steps": 28,  # Recommended by Stability
    "cfg_scale": 5,  # Lower than traditional
    "sampler": "dpmpp_2m",
    "shift": 3.0,  # Important SD3 parameter
}

Performance Comparison

Generation Speed (RTX 4090)

Model Resolution Steps Time It/s
SD 1.5 512×512 25 3s 8.3
SD 2.1 768×768 30 6s 5.0
SDXL 1024×1024 30 15s 2.0
SD3-M 1024×1024 28 20s 1.4
Pony 1024×1024 25 12s 2.1
FLUX 1024×1024 25 40s 0.6
FLUX-schnell 1024×1024 4 8s 0.5

Quality Metrics

Model FID Score CLIP Score User Preference
SD 1.5 12.6 31.7 72%
SD 2.1 10.2 32.5 78%
SDXL 8.1 33.8 86%
SD3 7.5 34.5 89%
Pony 9.2* 32.1* 91%**
FLUX 6.3 35.2 94%

*On anime dataset **Among target audience

Migration Guide

From SD 1.5 to SDXL

# SD 1.5 prompt
"masterpiece, best quality, 1girl, sitting, park bench"

# SDXL prompt (more natural)
"A young woman sitting on a park bench in a sunny day, 
professional photography, shallow depth of field"

From SDXL to FLUX

# SDXL settings
cfg_scale = 7.5
steps = 30

# FLUX settings
cfg = 1.0  # MUST be 1.0
guidance = 3.5
steps = 25

Prompting Differences

Model Prompt Style Example
SD 1.5 Tag-based “1girl, red hair, blue eyes, smile, outdoors”
SDXL Natural + tags “A girl with red hair and blue eyes smiling outdoors, masterpiece”
Pony Score + tags “score_9, 1girl, red hair, blue eyes, smile, outdoors”
FLUX Natural language “A cheerful young woman with vibrant red hair and striking blue eyes”

Future Considerations

  1. Smaller, faster models: Distillation techniques (LCM, Turbo)
  2. Better architectures: DiT and flow-based models dominating
  3. Multi-modal: Combined image/video/3D generation
  4. Real-time generation: Sub-second inference becoming standard
  5. Mobile deployment: On-device generation with quantization
  6. Open alternatives: Models like PixArt-α, Würstchen v3

Choosing Future-Proof Models

  • FLUX: Current best for quality and capabilities
  • SD3: Excellent balance of quality and efficiency
  • SDXL: Stable choice with mature ecosystem
  • SD 1.5: Will remain relevant for specialized uses and low-resource scenarios

Conclusion

Each model serves different needs:

  • SD 1.5: Speed, compatibility, and low requirements
  • SD 2.x: Middle ground (mostly superseded)
  • SDXL: Quality and resolution balance with mature ecosystem
  • SD3: Modern architecture with excellent prompt understanding
  • Pony: Specialized excellence for anime/stylized content
  • FLUX: Cutting-edge quality and capabilities

The rapid evolution continues with models like Stable Cascade, Würstchen, and PixArt-α exploring alternative architectures. Stay informed about new developments while mastering these foundational models. Choose based on your specific requirements for quality, speed, hardware, and content type.

See Also