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README.md

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+---
+license: openrail++
+base_model: runwayml/stable-diffusion-v1-5
+tags:
+  - stable-diffusion
+  - stable-diffusion-diffusers
+  - text-to-image
+  - diffusers
+  - lora
+  - lcm
+  - latent-consistency-model
+datasets:
+  - Mercity/laion-subset
+inference: true
+widget:
+  - text: "a futuristic cyberpunk city at night with neon lights and rain reflections"
+    parameters:
+      num_inference_steps: 6
+      guidance_scale: 1.0
+  - text: "a portrait of a cat wearing a detective hat, film noir style"
+    parameters:
+      num_inference_steps: 6
+      guidance_scale: 1.0
+  - text: "a majestic lion standing on a rock, overlooking the african savannah at sunset"
+    parameters:
+      num_inference_steps: 6
+      guidance_scale: 1.0
+---
+
+# LCM-LoRA SD1.5 - Checkpoint 800
+
+## Mid Training - Vibrant Style
+
+<div align="center">
+  <img src="https://huggingface.co/Mercity/lcm-lora-sd1.5-800/resolve/main/comparison_grid.png" alt="Checkpoint 800 Comparison Grid">
+</div>
+
+---
+
+## 📍 Part of Checkpoint Series
+
+This is **Checkpoint 800** in our LCM-LoRA training series. Each checkpoint has different characteristics:
+
+[Checkpoint 400](https://huggingface.co/Mercity/lcm-lora-sd1.5-400) • **Checkpoint 800** (current) • [Checkpoint 1200](https://huggingface.co/Mercity/lcm-lora-sd1.5-1200) • [Checkpoint 1600](https://huggingface.co/Mercity/lcm-lora-sd1.5-1600)
+
+---
+
+## Model Description
+
+This checkpoint represents training at **800 steps** in our LCM-LoRA progression for Stable Diffusion v1.5. 
+
+**Characteristics:**
+- Mid-training checkpoint with vibrant, artistic outputs. Strong visual impact with saturated colors and expressive style.
+- **Best for:** Artistic applications, vibrant aesthetic, expressive style
+- **Quality:** High visual impact, strong artistic direction, vivid colors
+
+**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](Mercity/lcm-lora-sd1.5-400) | Early training checkpoint showing foundational LCM capabilities. Provides decent...  |
+| **800** | [lcm-lora-sd1.5-800](Mercity/lcm-lora-sd1.5-800) | Mid-training checkpoint with vibrant, artistic outputs. Strong visual impact wit... **→ This checkpoint** |
+| 1200 | [lcm-lora-sd1.5-1200](Mercity/lcm-lora-sd1.5-1200) | Higher training with more refined outputs. Some prompts may show signs of overfi...  |
+| 1600 | [lcm-lora-sd1.5-1600](Mercity/lcm-lora-sd1.5-1600) | Final training checkpoint with mature, consistent outputs. Well-balanced and rel...  |
+
+---
+
+## Sample Outputs
+
+### Installation
+
+```bash
+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-sd1.5-800")
+
+# 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** | 800 |
+| **Base Model** | runwayml/stable-diffusion-v1-5 |
+| **Training Steps** | 800 |
+| **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:**
+1. Futuristic cyberpunk city with neon lights and rain reflections
+2. Portrait of a cat wearing a detective hat, film noir style
+3. Cozy coffee shop interior with warm lighting and plants
+4. Ancient Japanese temple in misty mountain landscape at sunrise
+5. Majestic lion on rock overlooking African savannah at sunset
+6. Magical forest with glowing blue mushrooms and fireflies
+7. Vintage red steam locomotive crossing stone viaduct over canyon
+
+<details>
+<summary>View individual samples</summary>
+
+All sample images for this checkpoint are available in the `samples/` directory.
+
+</details>
+
+---
+
+## 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
+
+---
+
+## Advanced Usage
+
+### Speed Optimization
+
+```python
+# Fuse LoRA for faster inference
+pipe.fuse_lora(lora_scale=1.0)
+
+# Use xformers for memory efficiency
+pipe.enable_xformers_memory_efficient_attention()
+
+# Compile model (PyTorch 2.0+)
+pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+```
+
+### Multiple LoRAs
+
+```python
+# Combine with other LoRAs
+pipe.load_lora_weights("other_style.safetensors", adapter_name="style")
+pipe.load_lora_weights("Mercity/lcm-lora-sd1.5-800", adapter_name="lcm")
+
+# Adjust weights
+pipe.set_adapters(["style", "lcm"], adapter_weights=[0.8, 1.0])
+```
+
+### Switch Between Checkpoints
+
+```python
+# Load different checkpoints from this series
+pipe.load_lora_weights("Mercity/lcm-lora-sd1.5-400")  
+pipe.load_lora_weights("Mercity/lcm-lora-sd1.5-800")  
+pipe.load_lora_weights("Mercity/lcm-lora-sd1.5-1200") 
+pipe.load_lora_weights("Mercity/lcm-lora-sd1.5-1600") 
+```
+
+---
+
+## 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
+
+1. **Always use `LCMScheduler`** - Required for LCM
+2. **Set `guidance_scale=1.0`** - CFG doesn't work with LCM
+3. **Use 4-8 steps** - Optimal range is 6 steps
+4. **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](https://huggingface.co/runwayml/stable-diffusion-v1-5)
+- **LCM Method**: [Latent Consistency Models](https://arxiv.org/abs/2310.04378)
+- **LoRA Method**: [Low-Rank Adaptation](https://arxiv.org/abs/2106.09685)
+- **Training Framework**: [Diffusers](https://github.com/huggingface/diffusers)
+
+---
+
+## More Information
+
+- **Other checkpoints in series**: [Checkpoint 400](https://huggingface.co/Mercity/lcm-lora-sd1.5-400) • **Checkpoint 800** (current) • [Checkpoint 1200](https://huggingface.co/Mercity/lcm-lora-sd1.5-1200) • [Checkpoint 1600](https://huggingface.co/Mercity/lcm-lora-sd1.5-1600)
+- **Discussions**: [Model discussions](https://huggingface.co/Mercity/lcm-lora-sd1.5-800/discussions)
+- **Report issues**: [Community tab](https://huggingface.co/Mercity/lcm-lora-sd1.5-800/discussions)