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---
title: FLUX Fast & Furious
emoji: πΌπ
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.35.0
app_file: app.py
pinned: false
license: openrail++
short_description: 'FLUX 8 Step Fast & High Quality Mode'
---
I'll create comprehensive documentation for this FLUX Fast & Furious image generation code in both English and Korean.
## English Documentation
### FLUX: Fast & Furious - Hyper-Speed Image Generation
This application implements an accelerated version of the FLUX.1-dev image generation model, optimized by ByteDance's AutoML team using their Hyper-SD technology to achieve high-quality image generation in just 8 steps instead of the typical 20-50 steps.
#### Key Features
1. **Hyper-Speed Generation**
- Utilizes Hyper-SD LoRA (Low-Rank Adaptation) technology from ByteDance
- Reduces inference steps from 20-50 to just 6-25 steps (default: 8)
- Maintains high image quality while dramatically reducing generation time
- Optimized for CUDA with TF32 precision enabled for maximum performance
2. **Neon-Themed User Interface**
- Custom cyberpunk-inspired design with glowing neon effects
- Animated hover effects and dynamic visual feedback
- Dark theme with blue, cyan, and magenta color accents
- Responsive layout optimized for both desktop and mobile devices
3. **User-Friendly Features**
- **Example Prompts**: Five pre-written creative prompts covering various genres:
- Cyberpunk cityscapes
- Fantasy fairy scenes
- Epic dragon imagery
- Sci-fi space stations
- Underwater ancient cities
- **Click-to-Use Examples**: Simply click any example to instantly populate the prompt field
- **Advanced Settings**: Collapsible panel for fine-tuning generation parameters
4. **Customizable Generation Parameters**
- **Image Dimensions**: Adjustable width and height (256-1152 pixels)
- **Inference Steps**: Control speed vs. quality trade-off (6-25 steps)
- **Guidance Scale**: Adjust prompt adherence (0.0-5.0)
- **Seed Control**: Reproducible results with manual seed input
#### Technical Implementation
The application leverages cutting-edge technologies:
- **FLUX.1-dev**: State-of-the-art diffusion model from Black Forest Labs
- **Hyper-SD LoRA**: ByteDance's acceleration technology achieving 5-10x speedup
- **BFloat16 Precision**: Reduced memory usage while maintaining quality
- **Gradio Spaces**: GPU-accelerated deployment with automatic resource management
- **Custom CSS**: Neon-themed styling with glow effects and animations
The generation pipeline:
1. Loads the base FLUX.1-dev model in bfloat16 precision
2. Applies Hyper-SD LoRA weights with 0.125 scaling factor
3. Fuses LoRA weights for optimal performance
4. Generates images using accelerated inference with custom parameters
5. Outputs high-quality 1024x1024 images (default) in seconds
#### Performance Optimization
- **GPU Acceleration**: Automatic CUDA optimization with @spaces.GPU decorator
- **Memory Efficiency**: BFloat16 precision reduces VRAM usage by 50%
- **Inference Mode**: Torch inference mode and autocast for maximum speed
- **TF32 Support**: Enabled for compatible GPUs for additional speedup
- **Cached Models**: Local model caching to reduce loading times
#### Use Cases
Perfect for:
- Rapid prototyping of visual concepts
- Creative exploration with instant feedback
- Production of high-quality images for various projects
- Testing different artistic styles and compositions
- Educational purposes to understand AI image generation
---
## νκΈ μ€λͺ
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### FLUX: Fast & Furious - μ΄κ³ μ μ΄λ―Έμ§ μμ±κΈ°
μ΄ μ ν리μΌμ΄μ
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#### μ£Όμ κΈ°λ₯
1. **μ΄κ³ μ μμ±**
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2. **λ€μ¨ ν
λ§ μ¬μ©μ μΈν°νμ΄μ€**
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- νλμ, μ²λ‘μ, λ§μ ν μμ μ
μΌνΈκ° μλ λ€ν¬ ν
λ§
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- **μμ ν둬ννΈ**: λ€μν μ₯λ₯΄λ₯Ό λ€λ£¨λ 5κ°μ μ°½μμ μΈ ν둬ννΈ μ 곡:
- μ¬μ΄λ²νν¬ λμ νκ²½
- ννμ§ μμ μ₯λ©΄
- μ
μ₯ν λλκ³€ μ΄λ―Έμ§
- SF μ°μ£Ό μ κ±°μ₯
- μμ€ κ³ λ λμ
- **ν΄λ¦νμ¬ μ¬μ©**: μμλ₯Ό ν΄λ¦νλ©΄ μ¦μ ν둬ννΈ νλμ μ
λ ₯
- **κ³ κΈ μ€μ **: μμ± λ§€κ°λ³μ λ―ΈμΈ μ‘°μ μ μν μ μ μ μλ ν¨λ
4. **λ§μΆ€ν μμ± λ§€κ°λ³μ**
- **μ΄λ―Έμ§ ν¬κΈ°**: μ‘°μ κ°λ₯ν λλΉμ λμ΄ (256-1152 ν½μ
)
- **μΆλ‘ λ¨κ³**: μλ λ νμ§ κ· ν μ‘°μ (6-25λ¨κ³)
- **κ°μ΄λμ€ μ€μΌμΌ**: ν둬ννΈ μ€μλ μ‘°μ (0.0-5.0)
- **μλ μ μ΄**: μλ μλ μ
λ ₯μΌλ‘ μ¬ν κ°λ₯ν κ²°κ³Ό
#### κΈ°μ μ ꡬν
μ ν리μΌμ΄μ
μ μ΅μ²¨λ¨ κΈ°μ μ νμ©ν©λλ€:
- **FLUX.1-dev**: Black Forest Labsμ μ΅μ νμ° λͺ¨λΈ
- **Hyper-SD LoRA**: 5-10λ°° μλ ν₯μμ λ¬μ±νλ ByteDanceμ κ°μ κΈ°μ
- **BFloat16 μ λ°λ**: νμ§μ μ μ§νλ©΄μ λ©λͺ¨λ¦¬ μ¬μ©λ κ°μ
- **Gradio Spaces**: μλ 리μμ€ κ΄λ¦¬κ° ν¬ν¨λ GPU κ°μ λ°°ν¬
- **컀μ€ν
CSS**: λ°κ΄ ν¨κ³Όμ μ λλ©μ΄μ
μ΄ μλ λ€μ¨ ν
λ§ μ€νμΌλ§
μμ± νμ΄νλΌμΈ:
1. bfloat16 μ λ°λλ‘ κΈ°λ³Έ FLUX.1-dev λͺ¨λΈ λ‘λ
2. 0.125 μ€μΌμΌλ§ ν©ν°λ‘ Hyper-SD LoRA κ°μ€μΉ μ μ©
3. μ΅μ μ±λ₯μ μν LoRA κ°μ€μΉ μ΅ν©
4. μ¬μ©μ μ μ λ§€κ°λ³μλ‘ κ°μνλ μΆλ‘ μ μ¬μ©νμ¬ μ΄λ―Έμ§ μμ±
5. λͺ μ΄ λ§μ κ³ νμ§ 1024x1024 μ΄λ―Έμ§(κΈ°λ³Έκ°) μΆλ ₯
#### μ±λ₯ μ΅μ ν
- **GPU κ°μ**: @spaces.GPU λ°μ½λ μ΄ν°λ‘ μλ CUDA μ΅μ ν
- **λ©λͺ¨λ¦¬ ν¨μ¨μ±**: BFloat16 μ λ°λλ‘ VRAM μ¬μ©λ 50% κ°μ
- **μΆλ‘ λͺ¨λ**: μ΅λ μλλ₯Ό μν Torch μΆλ‘ λͺ¨λμ μλ μΊμ€νΈ
- **TF32 μ§μ**: νΈν GPUμμ μΆκ° μλ ν₯μμ μν΄ νμ±ν
- **μΊμλ λͺ¨λΈ**: λ‘λ© μκ° λ¨μΆμ μν λ‘컬 λͺ¨λΈ μΊμ±
#### μ¬μ© μ¬λ‘
λ€μκ³Ό κ°μ μ©λμ μ ν©ν©λλ€:
- μκ°μ 컨μ
μ μ μν νλ‘ν νμ΄ν
- μ¦κ°μ μΈ νΌλλ°±μΌλ‘ μ°½μμ νμ
- λ€μν νλ‘μ νΈλ₯Ό μν κ³ νμ§ μ΄λ―Έμ§ μ μ
- λ€μν μμ μ μ€νμΌκ³Ό κ΅¬μ± ν
μ€νΈ
- AI μ΄λ―Έμ§ μμ± μ΄ν΄λ₯Ό μν κ΅μ‘ λͺ©μ |