Update README.md

README.md

@@ -10,6 +10,65 @@ The training captions are like `Yellow blob emoji with smiling face with smiling
 - Blob emoji face drives a red sport car along a curved road on a cliff overlooking the sea. The sea is dotted with whitecaps. The sky is blue, and cumulonimbus clouds float on the horizon. --w 1664 --h 928 --s 50 --d 12345678
 ![sample2](yellow_blob_2.png)
 
+### Dataset Creation Procedure
+
+The dataset was created following these steps:
+
+-   The SVG files from [C1710/blobmoji](https://github.com/C1710/blobmoji) (licensed under ASL 2.0) were used. Specifically, 118 different yellow blob emojis were selected from the SVG files.
+-   `cairosvg` was used to convert these SVGs into 512x512 pixel transparent PNGs.
+-   A script was then used to pad the images to 640x640 pixels and generate four versions of each image with different background colors: white, light gray, gray, and black. This resulted in a total of 472 images.
+-   The captions were generated based on the official Unicode names of the emojis. The prefix `Yellow blob emoji with ` and the suffix `. The background is <color>.` were added to each name.
+    -   For example: `Yellow blob emoji with smiling face with smiling eyes. The background is gray.`
+    -   Note: For some emojis (e.g., devil, zombie), the word `Yellow` was omitted from the prefix.
+
+### Dataset Definition
+
+```
+# general configurations
+[general]
+resolution = [640, 640]
+batch_size = 16
+enable_bucket = true
+bucket_no_upscale = false
+caption_extension = ".txt"
+
+[[datasets]]
+image_directory = "path/to/images_and_captions_dir"
+cache_directory = "path/to/cache_dir"
+```
+
+### Training Command
+
+```
+accelerate launch --num_cpu_threads_per_process 1 --mixed_precision bf16 --rdzv_backend=c10d \
+src/musubi_tuner/qwen_image_train_network.py  \
+--dit path/to/dit.safetensors --vae path/to/vae.safetensors \
+--text_encoder path/to/vlm.safetensors \
+--dataset_config path/to/blob_emoji_v1_640_bs16.toml \
+--output_dir path/to/output_dir \
+--learning_rate 2e-4 \
+--timestep_sampling shift --weighting_scheme none --discrete_flow_shift 2.0 \
+--max_train_epochs 16 --mixed_precision bf16 --seed 42 --gradient_checkpointing \
+--network_module=networks.lora_qwen_image \
+--network_dim=4 --network_args loraplus_lr_ratio=4 \
+--save_every_n_epochs=1  --max_data_loader_n_workers 2 \
+--persistent_data_loader_workers \
+--logging_dir ./logs --log_prefix qwenimage-blob4-2e4- \
+--output_name qwenimage-blob4-2e4 \
+--optimizer_type adamw8bit --flash_attn --split_attn \
+--log_with tensorboard \
+--sample_every_n_epochs 1 --sample_prompts path/to/prompts_qwen_blob_emoji.txt \
+--fp8_base --fp8_scaled
+```
+
+### Training Details
+
+-   Training was conducted on a Windows machine with a multi-GPU setup (2x RTX A6000).
+-   If you are not using a Windows environment or not performing multi-GPU training, please remove the `--rdzv_backend=c10d` argument.
+-   Please note that due to the 2-GPU setup, the effective batch size is 32. To achieve the same results with limited VRAM, increase the gradient accumulation steps. However, you should be able to train successfully with a lower batch size by adjusting the learning rate.
+-   The model was trained for 6 epochs (90 steps), which took approximately 1 hour with the Power Limit set to 60%.
+-   Finally, the weights from all 6 epochs were merged using the LoRA Post-Hoc EMA script from Musubi Tuner with `sigma_rel=0.2`.
+
 ## fp-1f-kisekae-1024-v4-2-PfPHEMA.safetensors
 
 Post-Hoc EMA (with Power function sigma_rel=0.2) version of the following LoRA. The usage is the same.