---
title: UltraPixel Multi-Stage (Community Fixed)
emoji: 🎨
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
license: apache-2.0
---

# 🎨 UltraPixel Multi-Stage Generator (Community Fixed)

A **properly working** UltraPixel-style high-resolution image generator that actually respects your parameter inputs.

## What's Different From Original UltraPixel Spaces?

The original public UltraPixel spaces have a critical flaw - they **hardcode CFG and timesteps inside the generation function**, making the UI sliders meaningless:

```python
# Original broken code:
extras.sampling_configs['cfg'] = 4          # ← Always uses 4!
extras.sampling_configs['timesteps'] = 20   # ← Ignores your slider!
```

### This Space Fixes That ✅

- **Real CFG Control**: Your slider values are actually passed to the model
- **Real Steps Control**: Set your own timesteps (10-100) per stage
- **Memory Optimized**: Won't OOM on ZeroGPU (max 3072×3072 with tiling)
- **No Login Required**: Public access for easy testing

## Features

- 🎯 **3-Stage Pipeline**: Stable Cascade architecture (Stage C → B → A)
- 🔧 **Independent Controls**: Separate CFG/steps for each stage
- 💾 **Memory Safe**: Aggressive cleanup between stages, forced tiling
- ⏱️ **120s Per Stage**: Each stage gets fresh GPU allocation
- 🔓 **Public Access**: No authentication needed

## How to Use

### Standard Workflow (3-4 minutes total)

1. **Stage C - Generate Initial Latent** (~30-60s)
   - Enter your prompt
   - Set CFG (recommended: 7.5) and Steps (recommended: 30)
   - Click "Generate Stage C"
   - Wait for completion

2. **Wait for GPU availability** (if needed during high traffic)

3. **Stage B - Upscale Latent** (~30-50s)
   - Adjust CFG (recommended: 5.0) and Steps (recommended: 15)
   - Click "Generate Stage B"
   - Uses the latent from Stage C automatically

4. **Wait again if needed**

5. **Stage A - Final Decode** (~60-90s)
   - Keep "Use Tiling" checked (prevents OOM)
   - Click "Generate Final Image"
   - Download your high-res result!

### Optimal Settings 💡

- **Stage C**: CFG 7-8, Steps 30-40
- **Stage B**: CFG 4-6, Steps 15-20
- **Stage A**: Always use tiling
- **Resolution Limits**: Max 3072×3072 for stability (1536×1536 per stage C/B)
- **For Training Data**: Generate at 3072px, then downscale to 1024px for optimal quality

## Technical Details

### Memory Management

Each stage runs in isolated `@spaces.GPU(duration=120)` calls:
- Models loaded only when needed
- Aggressive `torch.cuda.empty_cache()` after each stage
- Latents stored in-memory (automatically cleaned after 1 hour)
- VAE tiling enabled for Stage A decode

### Resolution Scaling

- **Stage C Input**: 512-1536px (base resolution)
- **Stage B Output**: 2× Stage C (1024-3072px)
- **Stage A Output**: Full decode to target resolution
- **Memory Usage**: ~20-30GB peak per stage (safe for ZeroGPU)

## Why This Matters

Many public AI spaces claim to offer "full control" but secretly override your parameters. This leads to:
- ❌ Inconsistent results despite changing settings
- ❌ Users wasting time tweaking sliders that do nothing
- ❌ Frustration when trying to reproduce outputs

This space guarantees that **your inputs = actual model parameters**.

## Deployment Notes

Built specifically for:
- ZeroGPU compatibility (120s duration per stage)
- Public/unlogged access
- High-resolution output (up to 3072×3072 stable)
- Proper parameter control

## Credits

- **Stable Cascade**: Stability AI
- **Original UltraPixel Concept**: Various community implementations
- **This Implementation**: Community-fixed version with proper parameter control

## License

Apache 2.0