Merge branch 'v2.3' into security/scan-ckpt-models

docs/features/TEXTUAL_INVERSION.md

@@ -154,8 +154,11 @@ training sets will converge with 2000-3000 steps.
 
 This adjusts how many training images are processed simultaneously in
 each step. Higher values will cause the training process to run more
-quickly, but use more memory. The default size will run with GPUs with
-as little as 12 GB.
+quickly, but use more memory. The default size is selected based on
+whether you have the `xformers` memory-efficient attention library
+installed. If `xformers` is available, the batch size will be 8,
+otherwise 3.  These values were chosen to allow training to run with
+GPUs with as little as 12 GB VRAM.
 
 ### Learning rate
 
@@ -172,8 +175,10 @@ learning rate to improve performance.
 
 ### Use xformers acceleration
 
-This will activate XFormers memory-efficient attention. You need to
-have XFormers installed for this to have an effect.
+This will activate XFormers memory-efficient attention, which will
+reduce memory requirements by half or more and allow you to select a
+higher batch size. You need to have XFormers installed for this to
+have an effect.
 
 ### Learning rate scheduler
 
@@ -250,6 +255,49 @@ invokeai-ti \
        --only_save_embeds
 ```
 
+## Using Distributed Training
+
+If you have multiple GPUs on one machine, or a cluster of GPU-enabled
+machines, you can activate distributed training. See the [HuggingFace
+Accelerate pages](https://huggingface.co/docs/accelerate/index) for
+full information, but the basic recipe is:
+
+1. Enter the InvokeAI developer's console command line by selecting
+option [8] from the `invoke.sh`/`invoke.bat` script.
+
+2. Configurate Accelerate using `accelerate config`:
+```sh
+accelerate config
+```
+This will guide you through the configuration process, including
+specifying how many machines you will run training on and the number
+of GPUs pe rmachine.
+
+You only need to do this once.
+
+3. Launch training from the command line using `accelerate launch`. Be sure
+that your current working directory is the InvokeAI root directory (usually
+named `invokeai` in your home directory):
+
+```sh
+accelerate launch .venv/bin/invokeai-ti \
+       --model=stable-diffusion-1.5 \
+       --resolution=512 \
+       --learnable_property=object \
+       --initializer_token='*' \
+       --placeholder_token='<shraddha>' \
+       --train_data_dir=/home/lstein/invokeai/text-inversion-training-data/shraddha \
+       --output_dir=/home/lstein/invokeai/text-inversion-training/shraddha \
+       --scale_lr \
+       --train_batch_size=10 \
+       --gradient_accumulation_steps=4 \
+       --max_train_steps=2000 \
+       --learning_rate=0.0005 \
+       --lr_scheduler=constant \
+       --mixed_precision=fp16 \
+       --only_save_embeds
+```
+
 ## Using Embeddings
 
 After training completes, the resultant embeddings will be saved into your `$INVOKEAI_ROOT/embeddings/<trigger word>/learned_embeds.bin`.

ldm/invoke/_version.py

@@ -1,2 +1,2 @@
 
-__version__='2.3.2.post1'
+__version__='2.3.3-rc1'

ldm/invoke/dynamic_prompts.py

@@ -157,7 +157,7 @@ def _run_invoke(
 ):
     pid = os.getpid()
     logdir.mkdir(parents=True, exist_ok=True)
-    logfile = Path(logdir, f'{time.strftime("%Y-%m-%d-%H:%M:%S")}-pid={pid}.txt')
+    logfile = Path(logdir, f'{time.strftime("%Y-%m-%d_%H-%M-%S")}-pid={pid}.txt')
     print(
         f">> Process {pid} running on GPU {gpu}; logging to {logfile}", file=sys.stderr
     )

ldm/invoke/pngwriter.py

@@ -30,14 +30,17 @@ class PngWriter:
                 prefix = self._unused_prefix()
             else:
                 with open(next_prefix_file,'r') as file:
-                    prefix=int(file.readline() or int(self._unused_prefix())-1)
-                    prefix+=1
+                    prefix = 0
+                    try:
+                        prefix=int(file.readline())
+                    except (TypeError, ValueError):
+                        prefix=self._unused_prefix()
             with open(next_prefix_file,'w') as file:
-                file.write(str(prefix))
+                file.write(str(prefix+1))
         return f'{prefix:06}'
 
     # gives the next unique prefix in outdir
-    def _unused_prefix(self):
+    def _unused_prefix(self)->int:
         # sort reverse alphabetically until we find max+1
         dirlist = sorted(os.listdir(self.outdir), reverse=True)
         # find the first filename that matches our pattern or return 000000.0.png
@@ -45,8 +48,7 @@ class PngWriter:
             (f for f in dirlist if re.match('^(\d+)\..*\.png', f)),
             '0000000.0.png',
         )
-        basecount = int(existing_name.split('.', 1)[0]) + 1
-        return f'{basecount:06}'
+        return int(existing_name.split('.', 1)[0]) + 1
 
     # saves image named _image_ to outdir/name, writing metadata from prompt
     # returns full path of output

ldm/invoke/training/textual_inversion.py

@@ -17,6 +17,7 @@ from pathlib import Path
 from typing import List, Tuple
 
 import npyscreen
+from diffusers.utils.import_utils import is_xformers_available
 from npyscreen import widget
 from omegaconf import OmegaConf
 
@@ -29,7 +30,7 @@ from ldm.invoke.training.textual_inversion_training import (
 TRAINING_DATA = "text-inversion-training-data"
 TRAINING_DIR = "text-inversion-output"
 CONF_FILE = "preferences.conf"
-
+XFORMERS_AVAILABLE = is_xformers_available()
 
 class textualInversionForm(npyscreen.FormMultiPageAction):
     resolutions = [512, 768, 1024]
@@ -178,7 +179,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
             out_of=10000,
             step=500,
             lowest=1,
-            value=saved_args.get("max_train_steps", 3000),
+            value=saved_args.get("max_train_steps", 2500),
             scroll_exit=True,
         )
         self.train_batch_size = self.add_widget_intelligent(
@@ -187,7 +188,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
             out_of=50,
             step=1,
             lowest=1,
-            value=saved_args.get("train_batch_size", 8),
+            value=saved_args.get("train_batch_size", 8 if XFORMERS_AVAILABLE else 3),
             scroll_exit=True,
         )
         self.gradient_accumulation_steps = self.add_widget_intelligent(
@@ -225,7 +226,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
         self.enable_xformers_memory_efficient_attention = self.add_widget_intelligent(
             npyscreen.Checkbox,
             name="Use xformers acceleration",
-            value=saved_args.get("enable_xformers_memory_efficient_attention", False),
+            value=saved_args.get("enable_xformers_memory_efficient_attention", XFORMERS_AVAILABLE),
             scroll_exit=True,
         )
         self.lr_scheduler = self.add_widget_intelligent(
@@ -428,8 +429,7 @@ def do_front_end(args: Namespace):
             print(str(e))
             print("** DETAILS:")
             print(traceback.format_exc())
-
-
+            
 def main():
     args = parse_args()
     global_set_root(args.root_dir or Globals.root)

ldm/invoke/training/textual_inversion_training.py

@@ -67,7 +67,7 @@ else:
         "nearest": PIL.Image.NEAREST,
     }
 # ------------------------------------------------------------------------------
-
+XFORMERS_AVAILABLE = is_xformers_available
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
 check_min_version("0.10.0.dev0")
@@ -227,7 +227,7 @@ def parse_args():
     training_group.add_argument(
         "--train_batch_size",
         type=int,
-        default=16,
+        default=8 if XFORMERS_AVAILABLE else 3,
         help="Batch size (per device) for the training dataloader.",
     )
     training_group.add_argument("--num_train_epochs", type=int, default=100)
@@ -324,6 +324,7 @@ def parse_args():
     parser.add_argument(
         "--enable_xformers_memory_efficient_attention",
         action="store_true",
+        default=XFORMERS_AVAILABLE,
         help="Whether or not to use xformers.",
     )
 
@@ -536,7 +537,7 @@ def do_textual_inversion_training(
     seed: int = None,
     resolution: int = 512,
     center_crop: bool = False,
-    train_batch_size: int = 16,
+    train_batch_size: int = 4,
     num_train_epochs: int = 100,
     max_train_steps: int = 5000,
     gradient_accumulation_steps: int = 1,

pyproject.toml

@@ -70,7 +70,7 @@ dependencies = [
   "taming-transformers-rom1504",
   "test-tube>=0.7.5",
   "torch-fidelity",
-  "torch>=1.13.1",
+  "torch~=1.13.1",
   "torchmetrics",
   "torchvision>=0.14.1",
   "transformers~=4.26",