[Training] QoL improvements in the Flux Control training scripts (#10461

)

* qol improvements to the Flux script.

* propagate the dataloader changes.

examples/flux-control/README.md

@@ -121,7 +121,7 @@ prompt = "A couple, 4k photo, highly detailed"
 
 gen_images = pipe(
   prompt=prompt,
-  condition_image=image,
+  control_image=image,
   num_inference_steps=50,
   joint_attention_kwargs={"scale": 0.9},
   guidance_scale=25., 
@@ -190,7 +190,7 @@ prompt = "A couple, 4k photo, highly detailed"
 
 gen_images = pipe(
   prompt=prompt,
-  condition_image=image,
+  control_image=image,
   num_inference_steps=50,
   guidance_scale=25., 
 ).images[0]
@@ -200,5 +200,5 @@ gen_images.save("output.png")
 ## Things to note
 
 * The scripts provided in this directory are experimental and educational. This means we may have to tweak things around to get good results on a given condition. We believe this is best done with the community 🤗
-* The scripts are not memory-optimized but we offload the VAE and the text encoders to CPU when they are not used. 
+* The scripts are not memory-optimized but we offload the VAE and the text encoders to CPU when they are not used if `--offload` is specified. 
 * We can extract LoRAs from the fully fine-tuned model. While we currently don't provide any utilities for that, users are welcome to refer to [this script](https://github.com/Stability-AI/stability-ComfyUI-nodes/blob/master/control_lora_create.py) that provides a similar functionality. 

examples/flux-control/train_control_flux.py

@@ -122,7 +122,6 @@ def log_validation(flux_transformer, args, accelerator, weight_dtype, step, is_f
 
         for _ in range(args.num_validation_images):
             with autocast_ctx:
-                # need to fix in pipeline_flux_controlnet
                 image = pipeline(
                     prompt=validation_prompt,
                     control_image=validation_image,
@@ -159,7 +158,7 @@ def log_validation(flux_transformer, args, accelerator, weight_dtype, step, is_f
                 images = log["images"]
                 validation_prompt = log["validation_prompt"]
                 validation_image = log["validation_image"]
-                formatted_images.append(wandb.Image(validation_image, caption="Controlnet conditioning"))
+                formatted_images.append(wandb.Image(validation_image, caption="Conditioning"))
                 for image in images:
                     image = wandb.Image(image, caption=validation_prompt)
                     formatted_images.append(image)
@@ -188,7 +187,7 @@ def save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=N
             img_str += f"![images_{i})](./images_{i}.png)\n"
 
     model_description = f"""
-# control-lora-{repo_id}
+# flux-control-{repo_id}
 
 These are Control weights trained on {base_model} with new type of conditioning.
 {img_str}
@@ -434,14 +433,15 @@ def parse_args(input_args=None):
         "--conditioning_image_column",
         type=str,
         default="conditioning_image",
-        help="The column of the dataset containing the controlnet conditioning image.",
+        help="The column of the dataset containing the control conditioning image.",
     )
     parser.add_argument(
         "--caption_column",
         type=str,
         default="text",
         help="The column of the dataset containing a caption or a list of captions.",
     )
+    parser.add_argument("--log_dataset_samples", action="store_true", help="Whether to log somple dataset samples.")
     parser.add_argument(
         "--max_train_samples",
         type=int,
@@ -468,7 +468,7 @@ def parse_args(input_args=None):
         default=None,
         nargs="+",
         help=(
-            "A set of paths to the controlnet conditioning image be evaluated every `--validation_steps`"
+            "A set of paths to the control conditioning image be evaluated every `--validation_steps`"
             " and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a"
             " a single `--validation_prompt` to be used with all `--validation_image`s, or a single"
             " `--validation_image` that will be used with all `--validation_prompt`s."
@@ -505,7 +505,11 @@ def parse_args(input_args=None):
         default=None,
         help="Path to the jsonl file containing the training data.",
     )
-
+    parser.add_argument(
+        "--only_target_transformer_blocks",
+        action="store_true",
+        help="If we should only target the transformer blocks to train along with the input layer (`x_embedder`).",
+    )
     parser.add_argument(
         "--guidance_scale",
         type=float,
@@ -581,7 +585,7 @@ def parse_args(input_args=None):
 
     if args.resolution % 8 != 0:
         raise ValueError(
-            "`--resolution` must be divisible by 8 for consistently sized encoded images between the VAE and the controlnet encoder."
+            "`--resolution` must be divisible by 8 for consistently sized encoded images between the VAE and the Flux transformer."
         )
 
     return args
@@ -665,7 +669,12 @@ def preprocess_train(examples):
         conditioning_images = [image_transforms(image) for image in conditioning_images]
         examples["pixel_values"] = images
         examples["conditioning_pixel_values"] = conditioning_images
-        examples["captions"] = list(examples[args.caption_column])
+
+        is_caption_list = isinstance(examples[args.caption_column][0], list)
+        if is_caption_list:
+            examples["captions"] = [max(example, key=len) for example in examples[args.caption_column]]
+        else:
+            examples["captions"] = list(examples[args.caption_column])
 
         return examples
 
@@ -765,7 +774,8 @@ def main(args):
         subfolder="scheduler",
     )
     noise_scheduler_copy = copy.deepcopy(noise_scheduler)
-    flux_transformer.requires_grad_(True)
+    if not args.only_target_transformer_blocks:
+        flux_transformer.requires_grad_(True)
     vae.requires_grad_(False)
 
     # cast down and move to the CPU
@@ -797,6 +807,12 @@ def main(args):
     assert torch.all(flux_transformer.x_embedder.weight[:, initial_input_channels:].data == 0)
     flux_transformer.register_to_config(in_channels=initial_input_channels * 2, out_channels=initial_input_channels)
 
+    if args.only_target_transformer_blocks:
+        flux_transformer.x_embedder.requires_grad_(True)
+        for name, module in flux_transformer.named_modules():
+            if "transformer_blocks" in name:
+                module.requires_grad_(True)
+
     def unwrap_model(model):
         model = accelerator.unwrap_model(model)
         model = model._orig_mod if is_compiled_module(model) else model
@@ -974,6 +990,32 @@ def load_model_hook(models, input_dir):
     else:
         initial_global_step = 0
 
+    if accelerator.is_main_process and args.report_to == "wandb" and args.log_dataset_samples:
+        logger.info("Logging some dataset samples.")
+        formatted_images = []
+        formatted_control_images = []
+        all_prompts = []
+        for i, batch in enumerate(train_dataloader):
+            images = (batch["pixel_values"] + 1) / 2
+            control_images = (batch["conditioning_pixel_values"] + 1) / 2
+            prompts = batch["captions"]
+
+            if len(formatted_images) > 10:
+                break
+
+            for img, control_img, prompt in zip(images, control_images, prompts):
+                formatted_images.append(img)
+                formatted_control_images.append(control_img)
+                all_prompts.append(prompt)
+
+        logged_artifacts = []
+        for img, control_img, prompt in zip(formatted_images, formatted_control_images, all_prompts):
+            logged_artifacts.append(wandb.Image(control_img, caption="Conditioning"))
+            logged_artifacts.append(wandb.Image(img, caption=prompt))
+
+        wandb_tracker = [tracker for tracker in accelerator.trackers if tracker.name == "wandb"]
+        wandb_tracker[0].log({"dataset_samples": logged_artifacts})
+
     progress_bar = tqdm(
         range(0, args.max_train_steps),
         initial=initial_global_step,

examples/flux-control/train_control_lora_flux.py

@@ -132,7 +132,6 @@ def log_validation(flux_transformer, args, accelerator, weight_dtype, step, is_f
 
         for _ in range(args.num_validation_images):
             with autocast_ctx:
-                # need to fix in pipeline_flux_controlnet
                 image = pipeline(
                     prompt=validation_prompt,
                     control_image=validation_image,
@@ -169,7 +168,7 @@ def log_validation(flux_transformer, args, accelerator, weight_dtype, step, is_f
                 images = log["images"]
                 validation_prompt = log["validation_prompt"]
                 validation_image = log["validation_image"]
-                formatted_images.append(wandb.Image(validation_image, caption="Controlnet conditioning"))
+                formatted_images.append(wandb.Image(validation_image, caption="Conditioning"))
                 for image in images:
                     image = wandb.Image(image, caption=validation_prompt)
                     formatted_images.append(image)
@@ -198,7 +197,7 @@ def save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=N
             img_str += f"![images_{i})](./images_{i}.png)\n"
 
     model_description = f"""
-# controlnet-lora-{repo_id}
+# control-lora-{repo_id}
 
 These are Control LoRA weights trained on {base_model} with new type of conditioning.
 {img_str}
@@ -256,7 +255,7 @@ def parse_args(input_args=None):
     parser.add_argument(
         "--output_dir",
         type=str,
-        default="controlnet-lora",
+        default="control-lora",
         help="The output directory where the model predictions and checkpoints will be written.",
     )
     parser.add_argument(
@@ -466,14 +465,15 @@ def parse_args(input_args=None):
         "--conditioning_image_column",
         type=str,
         default="conditioning_image",
-        help="The column of the dataset containing the controlnet conditioning image.",
+        help="The column of the dataset containing the control conditioning image.",
     )
     parser.add_argument(
         "--caption_column",
         type=str,
         default="text",
         help="The column of the dataset containing a caption or a list of captions.",
     )
+    parser.add_argument("--log_dataset_samples", action="store_true", help="Whether to log somple dataset samples.")
     parser.add_argument(
         "--max_train_samples",
         type=int,
@@ -500,7 +500,7 @@ def parse_args(input_args=None):
         default=None,
         nargs="+",
         help=(
-            "A set of paths to the controlnet conditioning image be evaluated every `--validation_steps`"
+            "A set of paths to the control conditioning image be evaluated every `--validation_steps`"
             " and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a"
             " a single `--validation_prompt` to be used with all `--validation_image`s, or a single"
             " `--validation_image` that will be used with all `--validation_prompt`s."
@@ -613,7 +613,7 @@ def parse_args(input_args=None):
 
     if args.resolution % 8 != 0:
         raise ValueError(
-            "`--resolution` must be divisible by 8 for consistently sized encoded images between the VAE and the controlnet encoder."
+            "`--resolution` must be divisible by 8 for consistently sized encoded images between the VAE and the Flux transformer."
         )
 
     return args
@@ -697,7 +697,12 @@ def preprocess_train(examples):
         conditioning_images = [image_transforms(image) for image in conditioning_images]
         examples["pixel_values"] = images
         examples["conditioning_pixel_values"] = conditioning_images
-        examples["captions"] = list(examples[args.caption_column])
+
+        is_caption_list = isinstance(examples[args.caption_column][0], list)
+        if is_caption_list:
+            examples["captions"] = [max(example, key=len) for example in examples[args.caption_column]]
+        else:
+            examples["captions"] = list(examples[args.caption_column])
 
         return examples
 
@@ -1132,6 +1137,32 @@ def load_model_hook(models, input_dir):
     else:
         initial_global_step = 0
 
+    if accelerator.is_main_process and args.report_to == "wandb" and args.log_dataset_samples:
+        logger.info("Logging some dataset samples.")
+        formatted_images = []
+        formatted_control_images = []
+        all_prompts = []
+        for i, batch in enumerate(train_dataloader):
+            images = (batch["pixel_values"] + 1) / 2
+            control_images = (batch["conditioning_pixel_values"] + 1) / 2
+            prompts = batch["captions"]
+
+            if len(formatted_images) > 10:
+                break
+
+            for img, control_img, prompt in zip(images, control_images, prompts):
+                formatted_images.append(img)
+                formatted_control_images.append(control_img)
+                all_prompts.append(prompt)
+
+        logged_artifacts = []
+        for img, control_img, prompt in zip(formatted_images, formatted_control_images, all_prompts):
+            logged_artifacts.append(wandb.Image(control_img, caption="Conditioning"))
+            logged_artifacts.append(wandb.Image(img, caption=prompt))
+
+        wandb_tracker = [tracker for tracker in accelerator.trackers if tracker.name == "wandb"]
+        wandb_tracker[0].log({"dataset_samples": logged_artifacts})
+
     progress_bar = tqdm(
         range(0, args.max_train_steps),
         initial=initial_global_step,