add about dataset synthesis for LLLite doc

docs/train_lllite_README.md

@@ -26,7 +26,7 @@ Due to the limitations of the inference environment, only CrossAttention (attn1
 
 ### Preparing the dataset
 
-In addition to the normal dataset, please store the conditioning image in the directory specified by `conditioning_data_dir`. The conditioning image must have the same basename as the training image. The conditioning image will be automatically resized to the same size as the training image.
+In addition to the normal dataset, please store the conditioning image in the directory specified by `conditioning_data_dir`. The conditioning image must have the same basename as the training image. The conditioning image will be automatically resized to the same size as the training image. The conditioning image does not require a caption file.
 
 ```toml
 [[datasets.subsets]]
@@ -37,9 +37,9 @@ conditioning_data_dir = "path/to/conditioning/image/dir"
 
 At the moment, random_crop cannot be used.
 
-As a training data, it seems to be better to use the images generated by the original model as training images and the images processed from them as conditioning images. If you use images with a different style from the original model as training images, the model will have to learn not only the control but also the style. ControlNet-LLLite is not suitable for style learning because of its small capacity.
+For training data, it is easiest to use a synthetic dataset with the original model-generated images as training images and processed images as conditioning images (the quality of the dataset may be problematic). See below for specific methods of synthesizing datasets.
 
-If you use images other than the generated images as training images, please increase the dimension as described below.
+Note that if you use an image with a different art style than the original model as a training image, the model will have to learn not only the control but also the art style. ControlNet-LLLite has a small capacity, so it is not suitable for learning art styles. In such cases, increase the number of dimensions as described below.
 
 ### Training
 
@@ -57,6 +57,121 @@ If you want to generate images with a script, run `sdxl_gen_img.py`. You can spe
 
 Specify the conditioning image to be used for inference with `--guide_image_path`. Since preprocess is not performed, if it is Canny, specify an image processed with Canny (white line on black background). `--control_net_preps`, `--control_net_weights`, and `--control_net_ratios` are not supported.
 
+## How to synthesize a dataset
+
+### Generating training images
+
+Generate images with the base model for training. Please generate them with Web UI or ComfyUI etc. The image size should be the default size of the model (1024x1024, etc.). You can also use bucketing. In that case, please generate it at an arbitrary resolution.
+
+The captions and other settings when generating the images should be the same as when generating the images with the trained ControlNet-LLLite model.
+
+Save the generated images in an arbitrary directory. Specify this directory in the dataset configuration file.
+
+
+You can also generate them with `sdxl_gen_img.py` in this repository. For example, run as follows:
+
+```dos
+python sdxl_gen_img.py --ckpt path/to/model.safetensors --n_iter 1 --scale 10 --steps 36 --outdir path/to/output/dir --xformers --W 1024 --H 1024 --original_width 2048 --original_height 2048 --bf16 --sampler ddim --batch_size 4 --vae_batch_size 2 --images_per_prompt 512 --max_embeddings_multiples 1 --prompt "{portrait|digital art|anime screen cap|detailed illustration} of 1girl, {standing|sitting|walking|running|dancing} on {classroom|street|town|beach|indoors|outdoors}, {looking at viewer|looking away|looking at another}, {in|wearing} {shirt and skirt|school uniform|casual wear} { |, dynamic pose}, (solo), teen age, {0-1$$smile,|blush,|kind smile,|expression less,|happy,|sadness,} {0-1$$upper body,|full body,|cowboy shot,|face focus,} trending on pixiv, {0-2$$depth of fields,|8k wallpaper,|highly detailed,|pov,} {0-1$$summer, |winter, |spring, |autumn, } beautiful face { |, from below|, from above|, from side|, from behind|, from back} --n nsfw, bad face, lowres, low quality, worst quality, low effort, watermark, signature, ugly, poorly drawn"
+```
+
+This is a setting for VRAM 24GB. Adjust `--batch_size` and `--vae_batch_size` according to the VRAM size.
+
+The images are generated randomly using wildcards in `--prompt`. Adjust as necessary.
+
+### Processing images
+
+Use an external program to process the generated images. Save the processed images in an arbitrary directory. These will be the conditioning images.
+
+For example, you can use the following script to process the images with Canny.
+
+```python
+import glob
+import os
+import random
+import cv2
+import numpy as np
+
+IMAGES_DIR = "path/to/generated/images"
+CANNY_DIR = "path/to/canny/images"
+
+os.makedirs(CANNY_DIR, exist_ok=True)
+img_files = glob.glob(IMAGES_DIR + "/*.png")
+for img_file in img_files:
+    can_file = CANNY_DIR + "\\" + os.path.basename(img_file)
+    if os.path.exists(can_file):
+        print("Skip: " + img_file)
+        continue
+
+    print(img_file)
+
+    img = cv2.imread(img_file)
+
+    # random threshold
+    # while True:
+    #     threshold1 = random.randint(0, 127)
+    #     threshold2 = random.randint(128, 255)
+    #     if threshold2 - threshold1 > 80:
+    #         break
+
+    # fixed threshold
+    threshold1 = 100
+    threshold2 = 200
+
+    img = cv2.Canny(img, threshold1, threshold2)
+
+    cv2.imwrite(can_file, img)
+```
+
+### Creating caption files
+
+Create a caption file for each image with the same basename as the training image. It is fine to use the same caption as the one used when generating the image. 
+
+If you generated the images with `sdxl_gen_img.py`, you can use the following script to create the caption files (`*.txt`) from the metadata in the generated images.
+
+```python
+import glob
+import os
+from PIL import Image
+
+IMAGES_DIR = "path/to/generated/images"
+
+img_files = glob.glob(IMAGES_DIR + "/*.png")
+for img_file in img_files:
+    cap_file = img_file.replace(".png", ".txt")
+    if os.path.exists(cap_file):
+        print(f"Skip: {img_file}")
+        continue
+    print(img_file)
+
+    img = Image.open(img_file)
+    prompt = img.text["prompt"] if "prompt" in img.text else ""
+    if prompt == "":
+        print(f"Prompt not found in {img_file}")
+
+    with open(cap_file, "w") as f:
+        f.write(prompt + "\n")
+```
+
+### Creating a dataset configuration file
+
+You can use the command line arguments of `sdxl_train_control_net_lllite.py` to specify the conditioning image directory. However, if you want to use a `.toml` file, specify the conditioning image directory in `conditioning_data_dir`.
+
+```toml
+[general]
+flip_aug = false
+color_aug = false
+resolution = [1024,1024]
+
+[[datasets]]
+batch_size = 8
+enable_bucket = false
+
+    [[datasets.subsets]]
+    image_dir = "path/to/generated/image/dir"
+    caption_extension = ".txt"
+    conditioning_data_dir = "path/to/canny/image/dir"
+```
+
 ## Credit
 
 I would like to thank lllyasviel, the author of ControlNet, furusu, who provided me with advice on implementation and helped me solve problems, and ddPn08, who implemented the ControlNet dataset.