Controls image generation by edge maps generated with Edge Drawing. Note that Edge Drawing comes in different flavors: original (ed), parameter free (edpf), color (edcolor).

Based on my monologs at github.com - Edge Drawing with detailed report and evaluations.
For usage see the model page on civitai.com - Model.
To generate edpf maps you can use this space or this script at gitlab.com.
For evaluation images see the corresponding .zip's at "files".
To run your own evaluations you can use this script at gitlab.com.

Edge Drawing Parameter Free

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

Canndy Edge for comparison (default in Automatic1111)

Noise, artifacts and missing edges. Yuck! Ugh!

Image dataset

laion2B-en aesthetics>=6.5 dataset
--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, ControlNet 1.1 repo, ControlNet paper v1, ControlNet paper v2 and Diffusers implementation. 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 using opencv-contrib-python::ximgproc::EdgeDrawing.

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 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: 🤷