README.md

---
license: apache-2.0
library_name: diffusers
pipeline_tag: text-to-image
tags:
  - flux
  - text-to-image
  - image-generation
  - fp16
---

<!-- README Version: v1.4 -->

# FLUX.1-dev FP16

High-quality text-to-image generation model from Black Forest Labs. This repository contains the FLUX.1-dev model in FP16 precision for optimal quality and compatibility with modern GPUs.

## Model Description

FLUX.1-dev is a state-of-the-art text-to-image diffusion model designed for high-fidelity image generation. This FP16 version maintains full precision for maximum quality output, ideal for creative professionals and researchers requiring the highest image quality.

**Key Capabilities**:
- High-resolution text-to-image generation
- Advanced prompt understanding with T5-XXL text encoder
- Superior detail and coherence in generated images
- Wide range of artistic styles and subjects
- Multi-text encoder architecture (CLIP + T5)

## Repository Contents

```
flux-dev-fp16/
├── checkpoints/flux/
│   └── flux1-dev-fp16.safetensors          # 23 GB - Complete model checkpoint
├── clip/
│   └── t5xxl_fp16.safetensors              # 9.2 GB - T5-XXL text encoder
├── clip_vision/
│   └── clip_vision_h.safetensors           # CLIP vision encoder
├── diffusion_models/flux/
│   └── flux1-dev-fp16.safetensors          # 23 GB - Diffusion model
├── text_encoders/
│   ├── clip-vit-large.safetensors          # 1.6 GB - CLIP ViT-Large encoder
│   ├── clip_g.safetensors                  # 1.3 GB - CLIP-G encoder
│   ├── clip_l.safetensors                  # 235 MB - CLIP-L encoder
│   └── t5xxl_fp16.safetensors              # 9.2 GB - T5-XXL encoder
└── vae/flux/
    └── flux-vae-bf16.safetensors           # 160 MB - VAE decoder (BF16)

Total Size: ~72 GB
```

## Hardware Requirements

### Minimum Requirements
- **VRAM**: 24 GB (RTX 3090, RTX 4090, A5000, A6000)
- **RAM**: 32 GB system memory
- **Disk Space**: 80 GB free space
- **GPU**: NVIDIA GPU with Compute Capability 7.0+ (Volta or newer)

### Recommended Requirements
- **VRAM**: 32+ GB (RTX 6000 Ada, A6000, H100)
- **RAM**: 64 GB system memory
- **Disk Space**: 100+ GB for workspace and outputs
- **GPU**: NVIDIA RTX 4090 or professional GPUs

### Performance Notes
- FP16 precision provides best quality but highest VRAM usage
- Consider FP8 version if VRAM is limited (see `flux-dev-fp8` directory)
- Generation time: ~30-60 seconds per image at 1024x1024 (depending on GPU)

## Usage Examples

### Using with Diffusers Library

```python
import torch
from diffusers import FluxPipeline

# Load the pipeline with local model files
pipe = FluxPipeline.from_pretrained(
    "E:/huggingface/flux-dev-fp16",
    torch_dtype=torch.float16
)
pipe = pipe.to("cuda")

# Generate an image
prompt = "A majestic lion standing on a cliff at sunset, cinematic lighting, photorealistic"
image = pipe(
    prompt=prompt,
    num_inference_steps=50,
    guidance_scale=7.5,
    height=1024,
    width=1024
).images[0]

image.save("output.png")
```

### Using with ComfyUI

1. Copy model files to ComfyUI directories:
   - `checkpoints/flux/flux1-dev-fp16.safetensors` → `ComfyUI/models/checkpoints/`
   - `text_encoders/*.safetensors` → `ComfyUI/models/clip/`
   - `vae/flux/flux-vae-bf16.safetensors` → `ComfyUI/models/vae/`

2. In ComfyUI:
   - Load Checkpoint: Select `flux1-dev-fp16`
   - Text Encoder: Automatically loaded
   - VAE: Select `flux-vae-bf16`

### Using Individual Components

```python
from diffusers import AutoencoderKL
from transformers import T5EncoderModel, CLIPTextModel

# Load text encoders
t5_encoder = T5EncoderModel.from_pretrained(
    "E:/huggingface/flux-dev-fp16/text_encoders",
    torch_dtype=torch.float16,
    filename="t5xxl_fp16.safetensors"
)

clip_encoder = CLIPTextModel.from_pretrained(
    "E:/huggingface/flux-dev-fp16/text_encoders",
    torch_dtype=torch.float16,
    filename="clip_l.safetensors"
)

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/flux-dev-fp16/vae/flux",
    torch_dtype=torch.bfloat16,
    filename="flux-vae-bf16.safetensors"
)
```

## Model Specifications

**Architecture**:
- **Type**: Latent Diffusion Transformer
- **Parameters**: ~12B (diffusion model)
- **Text Encoders**:
  - T5-XXL: 4.7B parameters (FP16)
  - CLIP-G: 1.3B parameters
  - CLIP-L: 235M parameters
- **VAE**: BF16 precision (160M parameters)

**Precision**:
- **Diffusion Model**: FP16 (float16)
- **Text Encoders**: FP16 (float16)
- **VAE**: BF16 (bfloat16)

**Format**:
- `.safetensors` - Secure tensor format with fast loading

**Resolution Support**:
- Native: 1024x1024
- Range: 512x512 to 2048x2048
- Aspect ratios: Supports non-square resolutions

## Performance Tips

### Memory Optimization
```python
# Enable memory efficient attention
pipe.enable_attention_slicing()

# Enable VAE tiling for high resolutions
pipe.enable_vae_tiling()

# Use CPU offloading if VRAM limited (slower)
pipe.enable_sequential_cpu_offload()
```

### Speed Optimization
```python
# Use torch.compile for faster inference (PyTorch 2.0+)
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)

# Reduce inference steps (trade quality for speed)
image = pipe(prompt, num_inference_steps=25)  # Default is 50
```

### Quality Optimization
- Use 50-75 inference steps for best quality
- Guidance scale: 7-9 for balanced results
- Higher guidance (10-15) for stronger prompt adherence
- Consider prompt engineering for better results

## License

This model is released under the **Apache 2.0 License**.

**Usage Terms**:
- ✅ Commercial use allowed
- ✅ Modification and redistribution allowed
- ✅ Patent use allowed
- ⚠️ Requires attribution to Black Forest Labs

See the LICENSE file for full terms.

## Citation

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

```bibtex
@misc{flux-dev,
  title={FLUX.1-dev: High-Quality Text-to-Image Generation},
  author={Black Forest Labs},
  year={2024},
  howpublished={\url{https://blackforestlabs.ai/}}
}
```

## Related Resources

- **Official Website**: https://blackforestlabs.ai/
- **Model Card**: https://huggingface.co/black-forest-labs/FLUX.1-dev
- **Documentation**: https://huggingface.co/docs/diffusers/en/api/pipelines/flux
- **Community**: https://huggingface.co/black-forest-labs

## Version Information

- **Model Version**: FLUX.1-dev
- **Precision**: FP16
- **Release**: 2024
- **README Version**: v1.4

---

For FP8 precision version (lower VRAM usage), see `E:/huggingface/flux-dev-fp8/`