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--- |
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license: cc-by-nc-sa-4.0 |
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datasets: |
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- ChristophSchuhmann/improved_aesthetics_6.5plus |
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language: |
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- en |
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tags: |
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- controlnet |
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--- |
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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_). |
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* Based on my monologs at [github.com - Edge Drawing](https://github.com/lllyasviel/ControlNet/discussions/318) with detailed report and evaluations. |
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* For usage see the model page on [civitai.com - Model](https://civitai.com/models/149740). |
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* 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). |
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* For evaluation images see the corresponding .zip's at "files". |
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* To run your own evaluations you can use [this script at gitlab.com](https://gitlab.com/-/snippets/3602096). |
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**Edge Drawing Parameter Free** |
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_Clear and pristine! Wooow!_ |
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**Example** |
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sampler=UniPC steps=20 cfg=7.5 seed=0 batch=9 model: v1-5-pruned-emaonly.safetensors cherry-picked: 1/9 |
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prompt: _a detailed high-quality professional photo of swedish woman standing in front of a mirror, dark brown hair, white hat with purple feather_ |
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**Canndy Edge for comparison (default in Automatic1111)** |
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_Noise, artifacts and missing edges. Yuck! Ugh!_ |
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# Image dataset |
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* [laion2B-en aesthetics>=6.5 dataset](https://huggingface.co/datasets/ChristophSchuhmann/improved_aesthetics_6.5plus) |
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* `--min_image_size 512 --max_aspect_ratio 2 --resize_mode="center_crop" --image_size 512` |
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* resulting in 180k images |
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# Training |
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``` |
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accelerate launch train_controlnet.py ^ |
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--pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5" ^ |
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--output_dir="control-edgedrawing-[version]-fp16/" ^ |
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--dataset_name="mydataset" ^ |
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--mixed_precision="fp16" ^ |
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--resolution=512 ^ |
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--learning_rate=1e-5 ^ |
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--train_batch_size=1 ^ |
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--gradient_accumulation_steps=4 ^ |
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--gradient_checkpointing ^ |
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--use_8bit_adam ^ |
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--enable_xformers_memory_efficient_attention ^ |
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--set_grads_to_none ^ |
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--seed=0 |
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``` |
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# Evaluation |
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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: |
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* 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. |
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* canny edge-detector requires parameter tuning while edpf is parameter free. |
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* 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. |
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* 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) |
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* 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. |
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* 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". |
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# Versions |
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**Experiment 1 - 2023-09-19 - control-edgedrawing-default-drop50-fp16-checkpoint-40000** |
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Images converted with https://github.com/shaojunluo/EDLinePython (based on original (non-parameter free) edge drawing). Default settings are: |
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`smoothed=False` |
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``` |
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{ 'ksize' : 5 |
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, 'sigma' : 1.0 |
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, 'gradientThreshold': 36 |
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, 'anchorThreshold' : 8 |
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, 'scanIntervals' : 1 |
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} |
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``` |
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additional arguments: `--proportion_empty_prompts=0.5`. |
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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. |
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Update 2023-09-22: bug in algorithm produces too sparse images on default, see https://github.com/shaojunluo/EDLinePython/issues/4 |
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**Experiment 2 - 2023-09-20 - control-edgedrawing-default-noisy-drop0-fp16-checkpoint-40000** |
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Same as experiment 1 with `smoothed=True` and `--proportion_empty_prompts=0`. |
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Trained for 40000 steps with default settings => results are not good. conditioning images look too noisy. investigate algorithm. |
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**Experiment 3.0 - 2023-09-22 - control-edgedrawing-cv480edpf-drop0-fp16-checkpoint-45000** |
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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). |
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``` |
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ed = cv2.ximgproc.createEdgeDrawing() |
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params = cv2.ximgproc.EdgeDrawing.Params() |
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params.PFmode = True |
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ed.setParams(params) |
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edges = ed.detectEdges(image) |
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edge_map = ed.getEdgeImage(edges) |
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``` |
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45000 steps => looks good. released as **version 0.1 on civitai**. |
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resuming with left-right flipped images. |
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**Experiment 3.1 - 2023-09-24 - control-edgedrawing-cv480edpf-drop0-fp16-checkpoint-90000** |
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90000 steps (45000 steps on original, 45000 steps with left-right flipped images) => quality became better, might release as 0.2 on civitai. |
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**Experiment 3.2 - 2023-09-24 -control-edgedrawing-cv480edpf-drop0+50-fp16-checkpoint-118000** |
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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. |
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**Experiment 4.0 - 2023-09-25 - control-edgedrawing-cv480edpf-drop50-fp16-checkpoint-45000** |
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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. |
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**Experiment 4.1 - 2023-09-26 - control-edgedrawing-cv480edpf-drop50-fp16-checkpoint-90000** |
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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. |
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**Experiment 5.0 - 2023-09-28 - control-edgedrawing-cv480edpf-fastdup-fp16-checkpoint-45000** |
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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: |
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``` |
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180210 images in total |
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67854 duplicates |
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644 outliers |
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26 too dark |
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321 too bright |
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57 blurry |
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68621 unique removed (that's 38%!) |
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------ |
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111589 unique images (x2 left-right flip) |
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``` |
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restarted from 0 with left-right flipped images and `--mixed-precision="no"` to create a master release and convert to fp16 afterwards. |
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**Experiment 6.0 - 2023-10-02 - control-edgedrawing-cv480edpf-rect-fp16-checkpoint-45000|90000|135000** |
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see experiment 5.0. |
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* resized images with shortside to 512 which gives us rectangular images instead of 512x512 squares |
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* included images with aspect ratio > 2 |
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* center-cropped images to 512x(n)*64 | n=8..16 , which keeps them SD compatible |
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* sorted duplicates by `similarity` value from `laion2b-en-aesthetics65` to get the "best" `text` from all the duplicates according to laion |
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``` |
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183410 images in total |
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75686 duplicates |
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381 outliers |
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50 too dark |
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436 too bright |
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31 blurry |
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76288 unique removed (that's 42%!) |
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------ |
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107122 unique images (x2 left-right flip) |
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``` |
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1 epoch = 107122 * 2 / 4 = 53561 steps per epoch |
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restarted from 0 and `--mixed-precision="fp16"`. |
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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? |
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**Experiment 6.1 - control-edgedrawing-cv480edpf-rect-fp16-batch32-checkpoint-6696** |
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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**. |
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**Experiment 6.2 - control-edgedrawing-cv480edpf-rect-fp16-batch32-drop50-checkpoint-6696** |
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see experiment 6.1. restarted from 0 with `--proportion_empty_prompts=0.5`. |
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# Ideas |
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* experiment with higher gradient accumulation steps |
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* make conceptual captions for laion |
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* integrate edcolor |
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* try to fine-tune from canny |
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* image dataset with better captions (cc3m) |
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* remove images by semantic (use only photos, paintings etc. for edge detection) |
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* re-train with fp32 |
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# Question and answers |
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**Q: What's the point of another edge control net anyway?** |
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A: 🤷 |
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