|
--- |
|
license: cc-by-nc-sa-4.0 |
|
datasets: |
|
- ChristophSchuhmann/improved_aesthetics_6.5plus |
|
language: |
|
- en |
|
--- |
|
|
|
Controls image generation by edge maps generated with [Edge Drawing](https://github.com/CihanTopal/ED_Lib). Edge Drawing comes in different flavors: original (experiments 1-2), parameter-free (experiments 3+), color (not yet available). |
|
|
|
* Based on my monologs at [github.com - Edge Drawing](https://github.com/lllyasviel/ControlNet/discussions/318) |
|
* For usage see the model page on [civitai.com - Model](https://civitai.com/models/149740). |
|
* To generate edpf maps you can use the script [gitlab.com - edpf.py](https://gitlab.com/-/snippets/3601881). |
|
* For evaluation see the corresponding .zip with images in "files". |
|
* To run your own evaluations you can use the script [gitlab.com - inference.py](https://gitlab.com/-/snippets/3602096). |
|
|
|
**Edge Drawing Parameter Free** |
|
|
|
 |
|
|
|
**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)** |
|
|
|
 |
|
|
|
_notice all the missing edges, the noise and artifacts. 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, canny 1.1 model on even more, while edpf model so far is only trained on a 180k-360k. |
|
* canny edge-detector requires parameter tuning while edpf is parameter-free. |
|
* Do 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 edpf model? |
|
* When evaluating human images we need to be aware of Stable Diffusion's inherent limits, like disformed faces and hands. |
|
* When evaluating style we need to be aware of the bias from the image dataset (laion2b-en-aesthetics65), which might tend to generate "aesthetic" images, and not actually work "intrisically 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 => empty prompts were probably too excessive |
|
|
|
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 => conditioning images are too noisy |
|
|
|
**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 => This is **version 0.1 on civitai**. |
|
|
|
**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` => |
|
|
|
**Experiment 4.1 - control-edgedrawing-cv480edpf-drop50-fp16-checkpoint-45000** |
|
|
|
# Ideas |
|
|
|
* fine-tune off canny |
|
* cleanup image dataset (l65) |
|
* uncropped mod64 images |
|
* integrate edcolor |
|
* bigger image dataset (gcc) |
|
* cleanup image dataset (gcc) |
|
* re-train with fp32 |
|
|
|
# Question and answers |
|
|
|
**Q: What's the point of another edge control net anyway?** |
|
|
|
A: 🤷 |
|
|
