Merge pull request #1003 from bmaltais/dev2

v21.7.8

Dockerfile

@@ -46,4 +46,4 @@ COPY --chown=appuser . .
 STOPSIGNAL SIGINT
 ENV LD_PRELOAD=libtcmalloc.so
 ENV PATH="$PATH:/home/appuser/.local/bin"
-CMD python3 "./kohya_gui.py" ${CLI_ARGS} --listen 0.0.0.0 --server_port 7860 --headless
+CMD python3 "./kohya_gui.py" ${CLI_ARGS} --listen 0.0.0.0 --server_port 7860

README.md

@@ -98,6 +98,14 @@ The following limitations apply:
 If you run on Linux, there is an alternative docker container port with less limitations. You can find the project [here](https://github.com/P2Enjoy/kohya_ss-docker).
 
 ### Linux and macOS
+#### Linux pre-requirements
+
+venv support need to be pre-installed. Can be done on ubuntu 22.04 with `apt install python3.10-venv`
+
+Make sure to use a version of python >= 3.10.6 and < 3.11.0
+
+#### Setup
+
 In the terminal, run
 
 ```bash
@@ -345,6 +353,26 @@ This will store a backup file with your current locally installed pip packages a
 
 ## Change History
 
+* 2023/06/14 (v21.7.8)
+- Add tkinter to dockerised version (thanks to @burdokow)
+- Add option to create caption files from folder names to the `group_images.py` tool.
+- Prodigy optimizer is supported in each training script. It is a member of D-Adaptation and is effective for DyLoRA training. [PR #585](https://github.com/kohya-ss/sd-scripts/pull/585) Please see the PR for details. Thanks to sdbds!
+  - Install the package with `pip install prodigyopt`. Then specify the option like `--optimizer_type="prodigy"`.
+- Arbitrary Dataset is supported in each training script (except XTI). You can use it by defining a Dataset class that returns images and captions.
+  - Prepare a Python script and define a class that inherits `train_util.MinimalDataset`. Then specify the option like `--dataset_class package.module.DatasetClass` in each training script.
+  - Please refer to `MinimalDataset` for implementation. I will prepare a sample later.
+- The following features have been added to the generation script.
+  - Added an option `--highres_fix_disable_control_net` to disable ControlNet in the 2nd stage of Highres. Fix. Please try it if the image is disturbed by some ControlNet such as Canny.
+  - Added Variants similar to sd-dynamic-propmpts in the prompt.
+    - If you specify `{spring|summer|autumn|winter}`, one of them will be randomly selected.
+    - If you specify `{2$$chocolate|vanilla|strawberry}`, two of them will be randomly selected.
+    - If you specify `{1-2$$ and $$chocolate|vanilla|strawberry}`, one or two of them will be randomly selected and connected by ` and `.
+    - You can specify the number of candidates in the range `0-2`. You cannot omit one side like `-2` or `1-`.
+    - It can also be specified for the prompt option.
+    - If you specify `e` or `E`, all candidates will be selected and the prompt will be repeated multiple times (`--images_per_prompt` is ignored). It may be useful for creating X/Y plots.
+    - You can also specify `--am {e$$0.2|0.4|0.6|0.8|1.0},{e$$0.4|0.7|1.0} --d 1234`. In this case, 15 prompts will be generated with 5*3.
+    - There is no weighting function.
+- Add pre and posfix to wd14
 * 2023/06/12 (v21.7.7)
 - Add `Print only` button to all training tabs
 - Sort json file vars for easier visual search

docker-compose.yaml

@@ -12,6 +12,7 @@ services:
     environment:
       CLI_ARGS: ""
       SAFETENSORS_FAST_GPU: 1
+      DISPLAY: $DISPLAY
     tmpfs:
       - /tmp      
     volumes:
@@ -21,6 +22,7 @@ services:
       - ./.cache/config:/app/appuser/.config
       - ./.cache/nv:/home/appuser/.nv 
       - ./.cache/keras:/home/appuser/.keras      
+      - /tmp/.X11-unix:/tmp/.X11-unix
     deploy:
       resources:
         reservations:

docs/train_README-ja.md

@@ -622,6 +622,7 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b
     - DAdaptAdanIP : 引数は同上
     - DAdaptLion : 引数は同上
     - DAdaptSGD : 引数は同上
+    - Prodigy : https://github.com/konstmish/prodigy
     - AdaFactor : [Transformers AdaFactor](https://huggingface.co/docs/transformers/main_classes/optimizer_schedules)
     - 任意のオプティマイザ
 

docs/train_README-zh.md

@@ -555,9 +555,10 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b
     - DAdaptAdam : 参数同上
     - DAdaptAdaGrad : 参数同上
     - DAdaptAdan : 参数同上
-    - DAdaptAdanIP : 引数は同上
+    - DAdaptAdanIP : 参数同上
     - DAdaptLion : 参数同上
     - DAdaptSGD : 参数同上
+    - Prodigy : https://github.com/konstmish/prodigy
     - AdaFactor : [Transformers AdaFactor](https://huggingface.co/docs/transformers/main_classes/optimizer_schedules)
     - 任何优化器
 

fine_tune.py

@@ -42,33 +42,37 @@ def train(args):
 
     tokenizer = train_util.load_tokenizer(args)
 
-    blueprint_generator = BlueprintGenerator(ConfigSanitizer(False, True, True))
-    if args.dataset_config is not None:
-        print(f"Load dataset config from {args.dataset_config}")
-        user_config = config_util.load_user_config(args.dataset_config)
-        ignored = ["train_data_dir", "in_json"]
-        if any(getattr(args, attr) is not None for attr in ignored):
-            print(
-                "ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(
-                    ", ".join(ignored)
+    # データセットを準備する
+    if args.dataset_class is None:
+        blueprint_generator = BlueprintGenerator(ConfigSanitizer(False, True, True))
+        if args.dataset_config is not None:
+            print(f"Load dataset config from {args.dataset_config}")
+            user_config = config_util.load_user_config(args.dataset_config)
+            ignored = ["train_data_dir", "in_json"]
+            if any(getattr(args, attr) is not None for attr in ignored):
+                print(
+                    "ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(
+                        ", ".join(ignored)
+                    )
                 )
-            )
+        else:
+            user_config = {
+                "datasets": [
+                    {
+                        "subsets": [
+                            {
+                                "image_dir": args.train_data_dir,
+                                "metadata_file": args.in_json,
+                            }
+                        ]
+                    }
+                ]
+            }
+
+        blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
+        train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
     else:
-        user_config = {
-            "datasets": [
-                {
-                    "subsets": [
-                        {
-                            "image_dir": args.train_data_dir,
-                            "metadata_file": args.in_json,
-                        }
-                    ]
-                }
-            ]
-        }
-
-    blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
-    train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
+        train_dataset_group = train_util.load_arbitrary_dataset(args, tokenizer)
 
     current_epoch = Value("i", 0)
     current_step = Value("i", 0)
@@ -393,7 +397,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
             current_loss = loss.detach().item()  # 平均なのでbatch sizeは関係ないはず
             if args.logging_dir is not None:
                 logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
-                if args.optimizer_type.lower().startswith("DAdapt".lower()):  # tracking d*lr value
+                if args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy":  # tracking d*lr value
                     logs["lr/d*lr"] = (
                         lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]
                     )

finetune/make_captions.py

@@ -3,6 +3,7 @@
 import os
 import json
 import random
+import sys
 
 from pathlib import Path
 from PIL import Image
@@ -11,6 +12,7 @@
 import torch
 from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
+sys.path.append(os.path.dirname(__file__))
 from blip.blip import blip_decoder
 import library.train_util as train_util