README.md

---
license: cc-by-nc-sa-4.0
datasets:
- ChristophSchuhmann/improved_aesthetics_6.5plus
language:
- en
tags:
- controlnet
---

Controls image generation by edge maps generated with [Edge Drawing](https://github.com/CihanTopal/ED_Lib). Note that Edge Drawing comes in different flavors: original (_ed_), parameter free (_edpf_), color (_edcolor_).

* Based on my monologs at [github.com - Edge Drawing](https://github.com/lllyasviel/ControlNet/discussions/318) with detailed report and evaluations.
* For usage see the model page on [civitai.com - Model](https://civitai.com/models/149740).
* To generate edpf maps you can use [this space](https://huggingface.co/spaces/GeroldMeisinger/edpf) or [this script at gitlab.com](https://gitlab.com/-/snippets/3601881).
* For evaluation images see the corresponding .zip's at "files".
* To run your own evaluations you can use [this script at gitlab.com](https://gitlab.com/-/snippets/3602096).

**Edge Drawing Parameter Free**

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c0ec65a2ec8cb2f589233a/jmdCGeMJx4dKFGo44cuEq.png)

_Clear and pristine! Wooow!_

**Example**

sampler=UniPC steps=20 cfg=7.5 seed=0 batch=9 model: v1-5-pruned-emaonly.safetensors cherry-picked: 1/9

prompt: _a detailed high-quality professional photo of swedish woman standing in front of a mirror, dark brown hair, white hat with purple feather_

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c0ec65a2ec8cb2f589233a/2PSWsmzLdHeVG-i67S7jF.png)

**Canndy Edge for comparison (default in Automatic1111)**

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c0ec65a2ec8cb2f589233a/JZTpa-HZfw0NUYnxZ52Iu.png)

_Noise, artifacts and missing edges. Yuck! Ugh!_

# Image dataset

* [laion2B-en aesthetics>=6.5 dataset](https://huggingface.co/datasets/ChristophSchuhmann/improved_aesthetics_6.5plus)
* `--min_image_size 512 --max_aspect_ratio 2 --resize_mode="center_crop" --image_size 512`
* resulting in 180k images

# Training

```
accelerate launch train_controlnet.py ^
  --pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5" ^
  --output_dir="control-edgedrawing-[version]-fp16/" ^
  --dataset_name="mydataset" ^
  --mixed_precision="fp16" ^
  --resolution=512 ^
  --learning_rate=1e-5 ^
  --train_batch_size=1 ^
  --gradient_accumulation_steps=4 ^
  --gradient_checkpointing ^
  --use_8bit_adam ^
  --enable_xformers_memory_efficient_attention ^
  --set_grads_to_none ^
  --seed=0
```

# Evaluation

To evaluate the model it makes sense to compare it with the original Canny model. Original evaluations and comparisons are available at [ControlNet 1.0 repo](https://github.com/lllyasviel/ControlNet), [ControlNet 1.1 repo](https://github.com/lllyasviel/ControlNet-v1-1-nightly), [ControlNet paper v1](https://arxiv.org/abs/2302.05543v1), [ControlNet paper v2](https://arxiv.org/abs/2302.05543) and [Diffusers implementation](https://huggingface.co/takuma104/controlnet_dev/tree/main). Some points we have to keep in mind when comparing canny with edpf in order not to compare apples with oranges:
* canny 1.0 model was trained on 3M images with fp32, canny 1.1 model on even more, while edpf model so far is only trained on a 180k-360k with fp16.
* canny edge-detector requires parameter tuning while edpf is parameter free.
* Should we manually fine-tune canny to find the perfect input image or do we leave it at default? We could argue that "no fine-tuning required" is the usp of edpf and we want to compare in the default setting, whereas canny fine-tuning is subjective.
* Would the canny model actually benefit from a edpf pre-processor and we might not even require a specialized edpf model? (2023-09-25: see `eval_canny_edpf.zip` but it seems as if it doesn't work and the edpf model may be justified)
* When evaluating human images we need to be aware of Stable Diffusion's inherent limits, like disformed faces and hands, and don't attribute them to the control net.
* When evaluating style we need to be aware of the bias from the image dataset (`laion2b-en-aesthetics65`), which might tend to generating "aesthetic" images, and not actually work "intrisicly better".

# Versions

**Experiment 1 - 2023-09-19 - control-edgedrawing-default-drop50-fp16-checkpoint-40000**

Images converted with https://github.com/shaojunluo/EDLinePython (based on original (non-parameter free) edge drawing). Default settings are:

`smoothed=False`

```
{ 'ksize'            :  5
, 'sigma'            :  1.0
, 'gradientThreshold': 36
, 'anchorThreshold'  :  8
, 'scanIntervals'    :  1
}
```

additional arguments: `--proportion_empty_prompts=0.5`.

Trained for 40000 steps with default settings => results are not good. empty prompts were probably too excessive. retry with no drops and different algorithm parameters.

Update 2023-09-22: bug in algorithm produces too sparse images on default, see https://github.com/shaojunluo/EDLinePython/issues/4

**Experiment 2 - 2023-09-20 - control-edgedrawing-default-noisy-drop0-fp16-checkpoint-40000**

Same as experiment 1 with `smoothed=True` and `--proportion_empty_prompts=0`.

Trained for 40000 steps with default settings => results are not good. conditioning images look too noisy. investigate algorithm.

**Experiment 3.0 - 2023-09-22 - control-edgedrawing-cv480edpf-drop0-fp16-checkpoint-45000**

Conditioning images generated with [edpf.py](https://gitlab.com/-/snippets/3601881) using [opencv-contrib-python::ximgproc::EdgeDrawing](https://docs.opencv.org/4.8.0/d1/d1c/classcv_1_1ximgproc_1_1EdgeDrawing.html).

```
ed     = cv2.ximgproc.createEdgeDrawing()
params = cv2.ximgproc.EdgeDrawing.Params()
params.PFmode = True
ed.setParams(params)
edges    = ed.detectEdges(image)
edge_map = ed.getEdgeImage(edges)
``` 

45000 steps => looks good. released as **version 0.1 on civitai**.

resuming with left-right flipped images.

**Experiment 3.1 - 2023-09-24 - control-edgedrawing-cv480edpf-drop0-fp16-checkpoint-90000**

90000 steps (45000 steps on original, 45000 steps with left-right flipped images) => quality became better, might release as 0.2 on civitai.

**Experiment 3.2 - 2023-09-24 -control-edgedrawing-cv480edpf-drop0+50-fp16-checkpoint-118000**

resumed with epoch 2 from 90000 using `--proportion_empty_prompts=0.5` => results became worse, CN didn't pick up on no-prompts (I also tried intermediate checkpoint-104000). restarting with 50% drop.

**Experiment 4.0 - 2023-09-25 - control-edgedrawing-cv480edpf-drop50-fp16-checkpoint-45000**

see experiment 3.0. restarted from 0 with `--proportion_empty_prompts=0.5` => results are not good, 50% is probably too much for 45k steps. guessmode still doesn't work and tends to produces humans. resuming until 90k with right-left flipped in the hope it will get better with more images.

**Experiment 4.1 - 2023-09-26 - control-edgedrawing-cv480edpf-drop50-fp16-checkpoint-90000**

resumed from 45000 steps with left-right flipped images until 90000 steps => results are still not good, 50% is probably also too much for 90k steps. guessmode still doesn't work and tends to produces humans. aborting.

**Experiment 5.0 - 2023-09-28 - control-edgedrawing-cv480edpf-fastdup-fp16-checkpoint-45000**

see experiment 3. cleaned original images following the [fastdup introduction](https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/cleaning-image-dataset.ipynb) resulting in:
```
180210 images in total
 67854 duplicates
   644 outliers
    26 too dark
   321 too bright
    57 blurry
 68621 unique removed (that's 38%!)
------
111589 unique images (x2 left-right flip)
```

restarted from 0 with left-right flipped images and `--mixed-precision="no"` to create a master release and convert to fp16 afterwards.

**Experiment 6.0 - 2023-10-02 - control-edgedrawing-cv480edpf-rect-fp16-checkpoint-45000|90000|135000**

see experiment 5.0.
* resized images with shortside to 512 which gives us rectangular images instead of 512x512 squares
* included images with aspect ratio > 2
* center-cropped images to 512x(n)*64 | n=8..16 , which keeps them SD compatible
* sorted duplicates by `similarity` value from `laion2b-en-aesthetics65` to get the "best" `text` from all the duplicates according to laion

```
183410 images in total
 75686 duplicates
   381 outliers
    50 too dark
   436 too bright
    31 blurry
 76288 unique removed (that's 42%!)
------
107122 unique images (x2 left-right flip)
```

1 epoch = 107122 * 2 / 4 = 53561 steps per epoch

restarted from 0 and `--mixed-precision="fp16"`.

TODO: Why did I end up with less images after I added more images? fastdup suddenly finds even more duplicates. Is fastdup default threshold=0.9 too aggressive?

**Experiment 6.1 - control-edgedrawing-cv480edpf-rect-fp16-batch32-checkpoint-6696**

see experiment 6.0. restarted from 0 with `--train_batch_size=2 --gradient_accumulation_steps=16`. 1 epoch = 107122 * 2 / 32 = 6696 steps per epoch => released as **version 0.2 on civitai**.

**Experiment 6.2 - control-edgedrawing-cv480edpf-rect-fp16-batch32-drop50-checkpoint-6696**

see experiment 6.1. restarted from 0 with `--proportion_empty_prompts=0.5`.

# Ideas

* experiment with higher gradient accumulation steps
* make conceptual captions for laion
* integrate edcolor
* try to fine-tune from canny
* image dataset with better captions (cc3m)
* remove images by semantic (use only photos, paintings etc. for edge detection)
* re-train with fp32

# Question and answers

**Q: What's the point of another edge control net anyway?**

A: 🤷