[dreambooth lora sdxl] add sdxl micro conditioning (#6795)

* add micro conditioning

* remove redundant lines

* style

* fix missing 's'

* fix missing shape bug due to missing RGB if statement

* remove redundant if, change arg order

---------

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

Author: linoytsaban

examples/dreambooth/train_dreambooth_lora_sdxl.py

@@ -19,6 +19,7 @@
 import logging
 import math
 import os
+import random
 import shutil
 import warnings
 from pathlib import Path
@@ -40,6 +41,7 @@
 from PIL.ImageOps import exif_transpose
 from torch.utils.data import Dataset
 from torchvision import transforms
+from torchvision.transforms.functional import crop
 from tqdm.auto import tqdm
 from transformers import AutoTokenizer, PretrainedConfig
 
@@ -304,18 +306,6 @@ def parse_args(input_args=None):
             " resolution"
         ),
     )
-    parser.add_argument(
-        "--crops_coords_top_left_h",
-        type=int,
-        default=0,
-        help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."),
-    )
-    parser.add_argument(
-        "--crops_coords_top_left_w",
-        type=int,
-        default=0,
-        help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."),
-    )
     parser.add_argument(
         "--center_crop",
         default=False,
@@ -325,6 +315,11 @@ def parse_args(input_args=None):
             " cropped. The images will be resized to the resolution first before cropping."
         ),
     )
+    parser.add_argument(
+        "--random_flip",
+        action="store_true",
+        help="whether to randomly flip images horizontally",
+    )
     parser.add_argument(
         "--train_text_encoder",
         action="store_true",
@@ -669,6 +664,41 @@ def __init__(
         self.instance_images = []
         for img in instance_images:
             self.instance_images.extend(itertools.repeat(img, repeats))
+
+        # image processing to prepare for using SD-XL micro-conditioning
+        self.original_sizes = []
+        self.crop_top_lefts = []
+        self.pixel_values = []
+        train_resize = transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR)
+        train_crop = transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size)
+        train_flip = transforms.RandomHorizontalFlip(p=1.0)
+        train_transforms = transforms.Compose(
+            [
+                transforms.ToTensor(),
+                transforms.Normalize([0.5], [0.5]),
+            ]
+        )
+        for image in self.instance_images:
+            image = exif_transpose(image)
+            if not image.mode == "RGB":
+                image = image.convert("RGB")
+            self.original_sizes.append((image.height, image.width))
+            image = train_resize(image)
+            if args.random_flip and random.random() < 0.5:
+                # flip
+                image = train_flip(image)
+            if args.center_crop:
+                y1 = max(0, int(round((image.height - args.resolution) / 2.0)))
+                x1 = max(0, int(round((image.width - args.resolution) / 2.0)))
+                image = train_crop(image)
+            else:
+                y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))
+                image = crop(image, y1, x1, h, w)
+            crop_top_left = (y1, x1)
+            self.crop_top_lefts.append(crop_top_left)
+            image = train_transforms(image)
+            self.pixel_values.append(image)
+
         self.num_instance_images = len(self.instance_images)
         self._length = self.num_instance_images
 
@@ -698,12 +728,12 @@ def __len__(self):
 
     def __getitem__(self, index):
         example = {}
-        instance_image = self.instance_images[index % self.num_instance_images]
-        instance_image = exif_transpose(instance_image)
-
-        if not instance_image.mode == "RGB":
-            instance_image = instance_image.convert("RGB")
-        example["instance_images"] = self.image_transforms(instance_image)
+        instance_image = self.pixel_values[index % self.num_instance_images]
+        original_size = self.original_sizes[index % self.num_instance_images]
+        crop_top_left = self.crop_top_lefts[index % self.num_instance_images]
+        example["instance_images"] = instance_image
+        example["original_size"] = original_size
+        example["crop_top_left"] = crop_top_left
 
         if self.custom_instance_prompts:
             caption = self.custom_instance_prompts[index % self.num_instance_images]
@@ -730,6 +760,8 @@ def __getitem__(self, index):
 def collate_fn(examples, with_prior_preservation=False):
     pixel_values = [example["instance_images"] for example in examples]
     prompts = [example["instance_prompt"] for example in examples]
+    original_sizes = [example["original_size"] for example in examples]
+    crop_top_lefts = [example["crop_top_left"] for example in examples]
 
     # Concat class and instance examples for prior preservation.
     # We do this to avoid doing two forward passes.
@@ -740,7 +772,12 @@ def collate_fn(examples, with_prior_preservation=False):
     pixel_values = torch.stack(pixel_values)
     pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
 
-    batch = {"pixel_values": pixel_values, "prompts": prompts}
+    batch = {
+        "pixel_values": pixel_values,
+        "prompts": prompts,
+        "original_sizes": original_sizes,
+        "crop_top_lefts": crop_top_lefts,
+    }
     return batch
 
 
@@ -1233,11 +1270,9 @@ def load_model_hook(models, input_dir):
     # pooled text embeddings
     # time ids
 
-    def compute_time_ids():
+    def compute_time_ids(original_size, crops_coords_top_left):
         # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids
-        original_size = (args.resolution, args.resolution)
         target_size = (args.resolution, args.resolution)
-        crops_coords_top_left = (args.crops_coords_top_left_h, args.crops_coords_top_left_w)
         add_time_ids = list(original_size + crops_coords_top_left + target_size)
         add_time_ids = torch.tensor([add_time_ids])
         add_time_ids = add_time_ids.to(accelerator.device, dtype=weight_dtype)
@@ -1254,9 +1289,6 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers):
                 pooled_prompt_embeds = pooled_prompt_embeds.to(accelerator.device)
             return prompt_embeds, pooled_prompt_embeds
 
-    # Handle instance prompt.
-    instance_time_ids = compute_time_ids()
-
     # If no type of tuning is done on the text_encoder and custom instance prompts are NOT
     # provided (i.e. the --instance_prompt is used for all images), we encode the instance prompt once to avoid
     # the redundant encoding.
@@ -1267,7 +1299,6 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers):
 
     # Handle class prompt for prior-preservation.
     if args.with_prior_preservation:
-        class_time_ids = compute_time_ids()
         if not args.train_text_encoder:
             class_prompt_hidden_states, class_pooled_prompt_embeds = compute_text_embeddings(
                 args.class_prompt, text_encoders, tokenizers
@@ -1282,9 +1313,6 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers):
     # If custom instance prompts are NOT provided (i.e. the instance prompt is used for all images),
     # pack the statically computed variables appropriately here. This is so that we don't
     # have to pass them to the dataloader.
-    add_time_ids = instance_time_ids
-    if args.with_prior_preservation:
-        add_time_ids = torch.cat([add_time_ids, class_time_ids], dim=0)
 
     if not train_dataset.custom_instance_prompts:
         if not args.train_text_encoder:
@@ -1436,18 +1464,24 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers):
                 # (this is the forward diffusion process)
                 noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)
 
+                # time ids
+                add_time_ids = torch.cat(
+                    [
+                        compute_time_ids(original_size=s, crops_coords_top_left=c)
+                        for s, c in zip(batch["original_sizes"], batch["crop_top_lefts"])
+                    ]
+                )
+
                 # Calculate the elements to repeat depending on the use of prior-preservation and custom captions.
                 if not train_dataset.custom_instance_prompts:
                     elems_to_repeat_text_embeds = bsz // 2 if args.with_prior_preservation else bsz
-                    elems_to_repeat_time_ids = bsz // 2 if args.with_prior_preservation else bsz
                 else:
                     elems_to_repeat_text_embeds = 1
-                    elems_to_repeat_time_ids = bsz // 2 if args.with_prior_preservation else bsz
 
                 # Predict the noise residual
                 if not args.train_text_encoder:
                     unet_added_conditions = {
-                        "time_ids": add_time_ids.repeat(elems_to_repeat_time_ids, 1),
+                        "time_ids": add_time_ids,
                         "text_embeds": unet_add_text_embeds.repeat(elems_to_repeat_text_embeds, 1),
                     }
                     prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat_text_embeds, 1, 1)
@@ -1459,7 +1493,7 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers):
                         return_dict=False,
                     )[0]
                 else:
-                    unet_added_conditions = {"time_ids": add_time_ids.repeat(elems_to_repeat_time_ids, 1)}
+                    unet_added_conditions = {"time_ids": add_time_ids}
                     prompt_embeds, pooled_prompt_embeds = encode_prompt(
                         text_encoders=[text_encoder_one, text_encoder_two],
                         tokenizers=None,