[Examples] Multiple enhancements to the ControlNet training scripts (huggingface#7096)

* log_validation unification for controlnet.

* additional fixes.

* remove print.

* better reuse and loading

* make final inference run conditional.

* Update examples/controlnet/README_sdxl.md

Co-authored-by: Pedro Cuenca <[email protected]>

* resize the control image in the snippet.

---------

Co-authored-by: Pedro Cuenca <[email protected]>

Author: sayakpaul

examples/controlnet/README_sdxl.md

@@ -113,7 +113,7 @@ pipe.enable_xformers_memory_efficient_attention()
 # memory optimization.
 pipe.enable_model_cpu_offload()
 
-control_image = load_image("./conditioning_image_1.png")
+control_image = load_image("./conditioning_image_1.png").resize((1024, 1024))
 prompt = "pale golden rod circle with old lace background"
 
 # generate image
@@ -128,4 +128,14 @@ image.save("./output.png")
 
 ### Specifying a better VAE
 
-SDXL's VAE is known to suffer from numerical instability issues. This is why we also expose a CLI argument namely `--pretrained_vae_model_name_or_path` that lets you specify the location of a better VAE (such as [this one](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)).
+SDXL's VAE is known to suffer from numerical instability issues. This is why we also expose a CLI argument namely `--pretrained_vae_model_name_or_path` that lets you specify the location of an alternative VAE (such as [`madebyollin/sdxl-vae-fp16-fix`](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)).
+
+If you're using this VAE during training, you need to ensure you're using it during inference too. You do so by:
+
+```diff
++ vae = AutoencoderKL.from_pretrained(vae_path_or_repo_id, torch_dtype=torch.float16)
+controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
+pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+    base_model_path, controlnet=controlnet, torch_dtype=torch.float16,
++   vae=vae,
+)

examples/controlnet/train_controlnet.py

@@ -14,6 +14,8 @@
 # See the License for the specific language governing permissions and
 
 import argparse
+import contextlib
+import gc
 import logging
 import math
 import os
@@ -74,10 +76,15 @@ def image_grid(imgs, rows, cols):
     return grid
 
 
-def log_validation(vae, text_encoder, tokenizer, unet, controlnet, args, accelerator, weight_dtype, step):
+def log_validation(
+    vae, text_encoder, tokenizer, unet, controlnet, args, accelerator, weight_dtype, step, is_final_validation=False
+):
     logger.info("Running validation... ")
 
-    controlnet = accelerator.unwrap_model(controlnet)
+    if not is_final_validation:
+        controlnet = accelerator.unwrap_model(controlnet)
+    else:
+        controlnet = ControlNetModel.from_pretrained(args.output_dir, torch_dtype=weight_dtype)
 
     pipeline = StableDiffusionControlNetPipeline.from_pretrained(
         args.pretrained_model_name_or_path,
@@ -118,14 +125,15 @@ def log_validation(vae, text_encoder, tokenizer, unet, controlnet, args, acceler
         )
 
     image_logs = []
+    inference_ctx = contextlib.nullcontext() if is_final_validation else torch.autocast("cuda")
 
     for validation_prompt, validation_image in zip(validation_prompts, validation_images):
         validation_image = Image.open(validation_image).convert("RGB")
 
         images = []
 
         for _ in range(args.num_validation_images):
-            with torch.autocast("cuda"):
+            with inference_ctx:
                 image = pipeline(
                     validation_prompt, validation_image, num_inference_steps=20, generator=generator
                 ).images[0]
@@ -136,6 +144,7 @@ def log_validation(vae, text_encoder, tokenizer, unet, controlnet, args, acceler
             {"validation_image": validation_image, "images": images, "validation_prompt": validation_prompt}
         )
 
+    tracker_key = "test" if is_final_validation else "validation"
     for tracker in accelerator.trackers:
         if tracker.name == "tensorboard":
             for log in image_logs:
@@ -167,10 +176,14 @@ def log_validation(vae, text_encoder, tokenizer, unet, controlnet, args, acceler
                     image = wandb.Image(image, caption=validation_prompt)
                     formatted_images.append(image)
 
-            tracker.log({"validation": formatted_images})
+            tracker.log({tracker_key: formatted_images})
         else:
             logger.warn(f"image logging not implemented for {tracker.name}")
 
+        del pipeline
+        gc.collect()
+        torch.cuda.empty_cache()
+
         return image_logs
 
 
@@ -197,7 +210,7 @@ def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: st
 def save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=None):
     img_str = ""
     if image_logs is not None:
-        img_str = "You can find some example images below.\n"
+        img_str = "You can find some example images below.\n\n"
         for i, log in enumerate(image_logs):
             images = log["images"]
             validation_prompt = log["validation_prompt"]
@@ -1131,6 +1144,22 @@ def load_model_hook(models, input_dir):
         controlnet = unwrap_model(controlnet)
         controlnet.save_pretrained(args.output_dir)
 
+        # Run a final round of validation.
+        image_logs = None
+        if args.validation_prompt is not None:
+            image_logs = log_validation(
+                vae=vae,
+                text_encoder=text_encoder,
+                tokenizer=tokenizer,
+                unet=unet,
+                controlnet=None,
+                args=args,
+                accelerator=accelerator,
+                weight_dtype=weight_dtype,
+                step=global_step,
+                is_final_validation=True,
+            )
+
         if args.push_to_hub:
             save_model_card(
                 repo_id,

examples/controlnet/train_controlnet_sdxl.py

@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions and
 
 import argparse
+import contextlib
 import functools
 import gc
 import logging
@@ -65,20 +66,38 @@
 logger = get_logger(__name__)
 
 
-def log_validation(vae, unet, controlnet, args, accelerator, weight_dtype, step):
+def log_validation(vae, unet, controlnet, args, accelerator, weight_dtype, step, is_final_validation=False):
     logger.info("Running validation... ")
 
-    controlnet = accelerator.unwrap_model(controlnet)
+    if not is_final_validation:
+        controlnet = accelerator.unwrap_model(controlnet)
+        pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(
+            args.pretrained_model_name_or_path,
+            vae=vae,
+            unet=unet,
+            controlnet=controlnet,
+            revision=args.revision,
+            variant=args.variant,
+            torch_dtype=weight_dtype,
+        )
+    else:
+        controlnet = ControlNetModel.from_pretrained(args.output_dir, torch_dtype=weight_dtype)
+        if args.pretrained_vae_model_name_or_path is not None:
+            vae = AutoencoderKL.from_pretrained(args.pretrained_vae_model_name_or_path, torch_dtype=weight_dtype)
+        else:
+            vae = AutoencoderKL.from_pretrained(
+                args.pretrained_model_name_or_path, subfolder="vae", torch_dtype=weight_dtype
+            )
+
+        pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(
+            args.pretrained_model_name_or_path,
+            vae=vae,
+            controlnet=controlnet,
+            revision=args.revision,
+            variant=args.variant,
+            torch_dtype=weight_dtype,
+        )
 
-    pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(
-        args.pretrained_model_name_or_path,
-        vae=vae,
-        unet=unet,
-        controlnet=controlnet,
-        revision=args.revision,
-        variant=args.variant,
-        torch_dtype=weight_dtype,
-    )
     pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)
     pipeline = pipeline.to(accelerator.device)
     pipeline.set_progress_bar_config(disable=True)
@@ -106,6 +125,7 @@ def log_validation(vae, unet, controlnet, args, accelerator, weight_dtype, step)
         )
 
     image_logs = []
+    inference_ctx = contextlib.nullcontext() if is_final_validation else torch.autocast("cuda")
 
     for validation_prompt, validation_image in zip(validation_prompts, validation_images):
         validation_image = Image.open(validation_image).convert("RGB")
@@ -114,7 +134,7 @@ def log_validation(vae, unet, controlnet, args, accelerator, weight_dtype, step)
         images = []
 
         for _ in range(args.num_validation_images):
-            with torch.autocast("cuda"):
+            with inference_ctx:
                 image = pipeline(
                     prompt=validation_prompt, image=validation_image, num_inference_steps=20, generator=generator
                 ).images[0]
@@ -124,6 +144,7 @@ def log_validation(vae, unet, controlnet, args, accelerator, weight_dtype, step)
             {"validation_image": validation_image, "images": images, "validation_prompt": validation_prompt}
         )
 
+    tracker_key = "test" if is_final_validation else "validation"
     for tracker in accelerator.trackers:
         if tracker.name == "tensorboard":
             for log in image_logs:
@@ -155,7 +176,7 @@ def log_validation(vae, unet, controlnet, args, accelerator, weight_dtype, step)
                     image = wandb.Image(image, caption=validation_prompt)
                     formatted_images.append(image)
 
-            tracker.log({"validation": formatted_images})
+            tracker.log({tracker_key: formatted_images})
         else:
             logger.warn(f"image logging not implemented for {tracker.name}")
 
@@ -189,7 +210,7 @@ def import_model_class_from_model_name_or_path(
 def save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=None):
     img_str = ""
     if image_logs is not None:
-        img_str = "You can find some example images below.\n"
+        img_str = "You can find some example images below.\n\n"
         for i, log in enumerate(image_logs):
             images = log["images"]
             validation_prompt = log["validation_prompt"]
@@ -1228,7 +1249,13 @@ def compute_embeddings(batch, proportion_empty_prompts, text_encoders, tokenizer
 
                     if args.validation_prompt is not None and global_step % args.validation_steps == 0:
                         image_logs = log_validation(
-                            vae, unet, controlnet, args, accelerator, weight_dtype, global_step
+                            vae=vae,
+                            unet=unet,
+                            controlnet=controlnet,
+                            args=args,
+                            accelerator=accelerator,
+                            weight_dtype=weight_dtype,
+                            step=global_step,
                         )
 
             logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
@@ -1244,6 +1271,21 @@ def compute_embeddings(batch, proportion_empty_prompts, text_encoders, tokenizer
         controlnet = unwrap_model(controlnet)
         controlnet.save_pretrained(args.output_dir)
 
+        # Run a final round of validation.
+        # Setting `vae`, `unet`, and `controlnet` to None to load automatically from `args.output_dir`.
+        image_logs = None
+        if args.validation_prompt is not None:
+            image_logs = log_validation(
+                vae=None,
+                unet=None,
+                controlnet=None,
+                args=args,
+                accelerator=accelerator,
+                weight_dtype=weight_dtype,
+                step=global_step,
+                is_final_validation=True,
+            )
+
         if args.push_to_hub:
             save_model_card(
                 repo_id,