README.md

---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- quantized
- fp8
- e4m3
- Text-to-Image
- ControlNet
- Diffusers
- Flux.1-dev
- image-generation
- Stable Diffusion
- quantization
- reduced-precision
base_model:
- Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0
base_model_relation: quantized
inference:
  parameters:
    torch_dtype: torch.float8_e4m3fn
---

# FLUX.1-dev-ControlNet-Union-Pro-2.0 (FP8 Quantized)

This repository contains an FP8 quantized version of the [Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0) model. **This is NOT a fine-tuned model** but a direct quantization of the original BFloat16 model to FP8 format for optimized inference performance. We provide an [online demo](https://huggingface.co/spaces/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0).

# Quantization Details
This model has been quantized from the original BFloat16 format to FP8 format using PyTorch's native FP8 support. Here are the specifics:

- **Quantization Technique**: Native FP8 quantization
- **Precision**: E4M3 format (4 bits for exponent, 3 bits for mantissa)
- **Library Used**: PyTorch's built-in FP8 support
- **Data Type**: `torch.float8_e4m3fn`
- **Original Model**: BFloat16 format (Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0)
- **Model Size Reduction**: ~50% smaller than the original model

The benefits of FP8 quantization include:
- **Reduced Memory Usage**: Approximately 50% smaller model size compared to BFloat16/FP16
- **Faster Inference**: Potential speed improvements, especially on hardware with FP8 support
- **Minimal Quality Loss**: Carefully calibrated quantization process to preserve output quality

**Important Note**: This is a direct quantization of [Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0) and preserves all the functionality of the original model. No fine-tuning or additional training has been performed.

# Keynotes
In comparison with [Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro),
- Remove mode embedding, has smaller model size.
- Improve on canny and pose, better control and aesthetics.
- Add support for soft edge. Remove support for tile.

# Model Cards
- This ControlNet consists of 6 double blocks and 0 single block. Mode embedding is removed.
- We train the model from scratch for 300k steps using a dataset of 20M high-quality general and human images. We train at 512x512 resolution in BFloat16, batch size = 128, learning rate = 2e-5, the guidance is uniformly sampled from [1, 7]. We set the text drop ratio to 0.20.
- This model supports multiple control modes, including canny, soft edge, depth, pose, gray. You can use it just as a normal ControlNet.
- This model can be jointly used with other ControlNets.

# Showcases

<table>
  <tr>
    <td><img src="./images/canny.png" alt="canny" style="height:100%"></td>
  </tr>
  <tr>
    <td><img src="./images/softedge.png" alt="softedge" style="height:100%"></td>
  </tr>
  <tr>
    <td><img src="./images/pose.png" alt="pose" style="height:100%"></td>
  </tr>
  <tr>
    <td><img src="./images/depth.png" alt="depth" style="height:100%"></td>
  </tr>
  <tr>
    <td><img src="./images/gray.png" alt="gray" style="height:100%"></td>
  </tr>
</table>
 
# Inference
```python
import torch
from diffusers.utils import load_image
from diffusers import FluxControlNetPipeline, FluxControlNetModel

base_model = 'black-forest-labs/FLUX.1-dev'
controlnet_model_union_fp8 = 'ABDALLALSWAITI/FLUX.1-dev-ControlNet-Union-Pro-2.0-fp8'

# Load using FP8 data type
controlnet = FluxControlNetModel.from_pretrained(controlnet_model_union_fp8, torch_dtype=torch.float8_e4m3fn)
pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16)
pipe.to("cuda")

# replace with other conds
control_image = load_image("./conds/canny.png")
width, height = control_image.size

prompt = "A young girl stands gracefully at the edge of a serene beach, her long, flowing hair gently tousled by the sea breeze. She wears a soft, pastel-colored dress that complements the tranquil blues and greens of the coastal scenery. The golden hues of the setting sun cast a warm glow on her face, highlighting her serene expression. The background features a vast, azure ocean with gentle waves lapping at the shore, surrounded by distant cliffs and a clear, cloudless sky. The composition emphasizes the girl's serene presence amidst the natural beauty, with a balanced blend of warm and cool tones."

image = pipe(
    prompt, 
    control_image=control_image,
    width=width,
    height=height,
    controlnet_conditioning_scale=0.7,
    control_guidance_end=0.8,
    num_inference_steps=30, 
    guidance_scale=3.5,
    generator=torch.Generator(device="cuda").manual_seed(42),
).images[0]
```

# Multi-Inference
```python
import torch
from diffusers.utils import load_image

# use local files for this moment
from pipeline_flux_controlnet import FluxControlNetPipeline
from controlnet_flux import FluxControlNetModel

base_model = 'black-forest-labs/FLUX.1-dev'
controlnet_model_union_fp8 = 'ABDALLALSWAITI/FLUX.1-dev-ControlNet-Union-Pro-2.0-fp8'

# Load using FP8 data type
controlnet = FluxControlNetModel.from_pretrained(controlnet_model_union_fp8, torch_dtype=torch.float8_e4m3fn)
pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=[controlnet], torch_dtype=torch.bfloat16) # use [] to enable multi-CNs
pipe.to("cuda")

# replace with other conds
control_image = load_image("./conds/canny.png")
width, height = control_image.size

prompt = "A young girl stands gracefully at the edge of a serene beach, her long, flowing hair gently tousled by the sea breeze. She wears a soft, pastel-colored dress that complements the tranquil blues and greens of the coastal scenery. The golden hues of the setting sun cast a warm glow on her face, highlighting her serene expression. The background features a vast, azure ocean with gentle waves lapping at the shore, surrounded by distant cliffs and a clear, cloudless sky. The composition emphasizes the girl's serene presence amidst the natural beauty, with a balanced blend of warm and cool tones."

image = pipe(
    prompt, 
    control_image=[control_image, control_image], # try with different conds such as canny&depth, pose&depth
    width=width,
    height=height,
    controlnet_conditioning_scale=[0.35, 0.35],
    control_guidance_end=[0.8, 0.8],
    num_inference_steps=30, 
    guidance_scale=3.5,
    generator=torch.Generator(device="cuda").manual_seed(42),
).images[0]
```

# Recommended Parameters
You can adjust controlnet_conditioning_scale and control_guidance_end for stronger control and better detail preservation. For better stability, we highly suggest to use detailed prompt, for some cases, multi-conditions help.
- Canny: use cv2.Canny, controlnet_conditioning_scale=0.7, control_guidance_end=0.8.
- Soft Edge: use [AnylineDetector](https://github.com/huggingface/controlnet_aux), controlnet_conditioning_scale=0.7, control_guidance_end=0.8.
- Depth: use [depth-anything](https://github.com/DepthAnything/Depth-Anything-V2), controlnet_conditioning_scale=0.8, control_guidance_end=0.8.
- Pose: use [DWPose](https://github.com/IDEA-Research/DWPose/tree/onnx), controlnet_conditioning_scale=0.9, control_guidance_end=0.65.
- Gray: use cv2.cvtColor, controlnet_conditioning_scale=0.9, control_guidance_end=0.8.

# Using FP8 Model
This repository includes the FP8 quantized version of the model. To use it, you'll need PyTorch with FP8 support:

```python
import torch
from diffusers.utils import load_image
from diffusers import FluxControlNetPipeline, FluxControlNetModel

base_model = 'black-forest-labs/FLUX.1-dev'
controlnet_model_union_fp8 = 'ABDALLALSWAITI/FLUX.1-dev-ControlNet-Union-Pro-2.0-fp8'

# Load using FP8 data type
controlnet = FluxControlNetModel.from_pretrained(controlnet_model_union_fp8, torch_dtype=torch.float8_e4m3fn)
pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16)
pipe.to("cuda")

# The rest of the code is the same as with the original model
```

See `fp8_inference_example.py` for a complete example.


# Resources
- [InstantX/FLUX.1-dev-IP-Adapter](https://huggingface.co/InstantX/FLUX.1-dev-IP-Adapter)
- [InstantX/FLUX.1-dev-Controlnet-Canny](https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Canny)
- [Shakker-Labs/FLUX.1-dev-ControlNet-Depth](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Depth)
- [Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro)

# Acknowledgements
This model is developed by [Shakker Labs](https://huggingface.co/Shakker-Labs). The original idea is inspired by [xinsir/controlnet-union-sdxl-1.0](https://huggingface.co/xinsir/controlnet-union-sdxl-1.0). All copyright reserved.