Higher training with more refined outputs. Some prompts may show signs of overfitting (e.g., occasional missing details).
Best for: Balanced colors, natural tones, specific use cases
Quality: High technical quality but may miss some prompt details

Key Features:

⚡ 10x Faster: Generate images in 4-6 steps vs 50 steps
🎯 LoRA Adapter: Only ~100MB, works with any SD1.5 model
🔧 Easy Integration: Drop-in replacement using diffusers
📊 Proven Quality: See comparison grid above

Checkpoint Comparison

This checkpoint is part of a training series. Compare with other checkpoints:

Steps	Model	Characteristics
400	lcm-lora-sd1.5-400	Early training checkpoint showing foundational LCM capabilities. Provides decent...
800	lcm-lora-sd1.5-800	Mid-training checkpoint with vibrant, artistic outputs. Strong visual impact wit...
1200	lcm-lora-sd1.5-1200	Higher training with more refined outputs. Some prompts may show signs of overfi... → This checkpoint
1600	lcm-lora-sd1.5-1600	Final training checkpoint with mature, consistent outputs. Well-balanced and rel...

Performance Metrics Across Series

Compare training progression and characteristics:

Steps	Model Link	Style	Speed (RTX 3090)	Best For
400	lcm-lora-sd1.5-400	Soft, baseline	2-3s @ 6 steps	Fast experimentation, understanding earl...
800	lcm-lora-sd1.5-800	Vibrant, saturated	2-3s @ 6 steps	Artistic applications, vibrant aesthetic...
1200	lcm-lora-sd1.5-1200	Balanced, natural	2-3s @ 6 steps	Balanced colors, natural tones, specific... ← Current
1600	lcm-lora-sd1.5-1600	Mature, consistent	2-3s @ 6 steps	Most training, consistent results, produ...

Visual Comparison Across All Checkpoints

See how outputs evolve across the training series. Each grid shows: Baseline SD1.5 (50 steps) vs LCM-LoRA at 2, 4, and 6 steps.

Checkpoint 400

Checkpoint 800

Checkpoint 1200 (This Checkpoint)

Checkpoint 1600

Sample Outputs

Installation

pip install --upgrade diffusers transformers accelerate

Basic Usage

python

import torch
from diffusers import StableDiffusionPipeline, LCMScheduler

# Load base SD1.5 model
pipe = StableDiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    torch_dtype=torch.float16
)
pipe.to("cuda")

# Load this LCM-LoRA checkpoint
pipe.load_lora_weights("Mercity/lcm-lora-sd1.5-1200")

# IMPORTANT: Use LCM scheduler
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

# Generate with just 4-6 steps!
prompt = "a portrait of a cat wearing a detective hat, film noir style"
image = pipe(
    prompt=prompt,
    num_inference_steps=6,
    guidance_scale=1.0
).images[0]

image.save("output.png")

Recommended Settings

python

num_inference_steps = 6        # Optimal for this checkpoint
guidance_scale = 1.0            # Required for LCM

Training Details

Parameter	Value
Checkpoint	1200
Base Model	runwayml/stable-diffusion-v1-5
Training Steps	1200
Dataset	Mercity/laion-subset
LoRA Rank	96
LoRA Alpha	96
Resolution	512×512
Batch Size	64
Learning Rate	1e-4
Optimizer	AdamW

Sample Outputs

The comparison grid above shows outputs from this checkpoint at 2, 4, and 6 inference steps, compared to standard SD1.5 at 50 steps.

Prompts included:

Futuristic cyberpunk city with neon lights and rain reflections
Portrait of a cat wearing a detective hat, film noir style
Cozy coffee shop interior with warm lighting and plants
Ancient Japanese temple in misty mountain landscape at sunrise
Majestic lion on rock overlooking African savannah at sunset
Magical forest with glowing blue mushrooms and fireflies
Vintage red steam locomotive crossing stone viaduct over canyon

View individual samples

All sample images for this checkpoint are available in the samples/ directory.

Out-of-Distribution (OOD) Validation Images

To test generalization beyond the training distribution, we generated images for 5 OOD prompts that are deliberately different from training prompts:

🐠 Underwater Scene
- "underwater coral reef with colorful fish and sea anemones, crystal clear water, natural sunlight filtering through"
- Tests: Water effects, marine life, underwater lighting (not in training)
🚀 Space/Astronomy
- "astronaut floating in space with earth in background, stars and galaxies, cinematic lighting, 4k"
- Tests: Zero gravity, cosmic environment, space rendering (not in training)
🍰 Food Photography
- "gourmet chocolate cake with berries on elegant plate, professional food photography, soft studio lighting"
- Tests: Food textures, studio lighting, product photography (not in training)
👴 Human Portrait
- "close-up portrait of elderly man with weathered face and kind eyes, dramatic side lighting, black and white"
- Tests: Human facial features, skin texture, B&W conversion (training had cat portrait, not human closeup)
🎨 Abstract Art
- "abstract watercolor painting with flowing colors, pink and blue gradient, artistic ethereal style"
- Tests: Non-representational art, color blending (training was all representational)

Why OOD Validation? These prompts test whether the model truly learned general concepts rather than just memorizing training prompts. Good OOD performance indicates robust generalization.

All validation images can be found in the validation/ directory. See validation/prompts.txt for the complete list of prompts used.

Performance

Speed Comparison

Method	Steps	Time (A100)	Time (RTX 3090)
SD1.5 Default	50	~15s	~25s
SD1.5 Fast	25	~8s	~13s
LCM-LoRA (this)	6	~2s	~3s
LCM-LoRA (this)	4	~1.5s	~2s

Quality Progression

2 steps: Fast, captures main composition
4 steps: Good balance, suitable for most cases
6 steps: Best quality (recommended)
8 steps: Slightly better, diminishing returns

Series Information

Training Progression

This checkpoint is part of a training series showing LCM-LoRA evolution:

Training Steps:  400 ──→ 800 ──→ 1200 ──→ 1600
                  ↓       ↓        ↓        ↓
Quality:      Baseline  Peak   Refined   Mature
Style:        Soft    Vibrant  Balanced  Stable

Download All Checkpoints

bash

# Download all checkpoints for comparison
huggingface-cli download Mercity/lcm-lora-sd1.5-400
huggingface-cli download Mercity/lcm-lora-sd1.5-800
huggingface-cli download Mercity/lcm-lora-sd1.5-1200
huggingface-cli download Mercity/lcm-lora-sd1.5-1600

Usage Tips

For Best Results

Always use LCMScheduler - Required for LCM
Set guidance_scale=1.0 - CFG doesn't work with LCM
Use 4-8 steps - Optimal range is 6 steps
Same prompts as SD1.5 - No special prompting needed

Checkpoint Selection

Testing/comparison? Try different checkpoints to find your preference
Different characteristics: Each checkpoint has unique qualities
Training progression: See how the model evolves with more training

Limitations

Trained on 512×512 resolution (best results at this size)
Requires LCMScheduler - other schedulers won't work
guidance_scale must be 1.0 (CFG incompatible with LCM)
Each checkpoint has slightly different characteristics

Citation

If you use this model in your research, please cite:

bibtex

@article{luo2023latent,
  title={Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference},
  author={Luo, Simian and Tan, Yiqin and Huang, Longbo and Li, Jian and Zhao, Hang},
  journal={arXiv preprint arXiv:2310.04378},
  year={2023}
}

@article{hu2021lora,
  title={LoRA: Low-Rank Adaptation of Large Language Models},
  author={Hu, Edward J and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Wang, Lu and Chen, Weizhu},
  journal={arXiv preprint arXiv:2106.09685},
  year={2021}
}

License

This model is released under the same license as Stable Diffusion v1.5:

CreativeML Open RAIL-M License
Commercial use allowed with restrictions
See: https://huggingface.co/spaces/CompVis/stable-diffusion-license

Acknowledgments

Base Model: Stable Diffusion v1.5
LCM Method: Latent Consistency Models
LoRA Method: Low-Rank Adaptation
Training Framework: Diffusers

More Information

Other checkpoints in series: Checkpoint 400 • Checkpoint 800 • Checkpoint 1200 (current) • Checkpoint 1600
Discussions: Model discussions
Report issues: Community tab