[advanced dreambooth lora sdxl training script] load pipeline for inference...

[advanced dreambooth lora sdxl training script] load pipeline for inference only if validation prompt is used (#6171)

* load pipeline for inference only if validation prompt is used

* move things outside

* load pipeline for inference only if validation prompt is used

* fix readme when validation prompt is used

---------
Co-authored-by: linoytsaban <linoy@huggingface.co>
Co-authored-by: apolinário <joaopaulo.passos@gmail.com>

examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py

@@ -112,7 +112,7 @@ def save_model_card(
     repo_folder=None,
     vae_path=None,
 ):
-    img_str = "widget:\n" if images else ""
+    img_str = "widget:\n"
     for i, image in enumerate(images):
         image.save(os.path.join(repo_folder, f"image_{i}.png"))
         img_str += f"""
@@ -121,6 +121,10 @@ def save_model_card(
             url:
                 "image_{i}.png"
         """
+    if not images:
+        img_str += f"""
+        - text: '{instance_prompt}'
+        """
 
     trigger_str = f"You should use {instance_prompt} to trigger the image generation."
     diffusers_imports_pivotal = ""
@@ -157,8 +161,6 @@ tags:
 base_model: {base_model}
 instance_prompt: {instance_prompt}
 license: openrail++
-widget:
-    - text: '{validation_prompt if validation_prompt else instance_prompt}'
 ---
 """
 
@@ -2010,43 +2012,42 @@ def main(args):
             text_encoder_lora_layers=text_encoder_lora_layers,
             text_encoder_2_lora_layers=text_encoder_2_lora_layers,
         )
+        images = []
+        if args.validation_prompt and args.num_validation_images > 0:
+            # Final inference
+            # Load previous pipeline
+            vae = AutoencoderKL.from_pretrained(
+                vae_path,
+                subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
+                revision=args.revision,
+                variant=args.variant,
+                torch_dtype=weight_dtype,
+            )
+            pipeline = StableDiffusionXLPipeline.from_pretrained(
+                args.pretrained_model_name_or_path,
+                vae=vae,
+                revision=args.revision,
+                variant=args.variant,
+                torch_dtype=weight_dtype,
+            )
 
-        # Final inference
-        # Load previous pipeline
-        vae = AutoencoderKL.from_pretrained(
-            vae_path,
-            subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
-            revision=args.revision,
-            variant=args.variant,
-            torch_dtype=weight_dtype,
-        )
-        pipeline = StableDiffusionXLPipeline.from_pretrained(
-            args.pretrained_model_name_or_path,
-            vae=vae,
-            revision=args.revision,
-            variant=args.variant,
-            torch_dtype=weight_dtype,
-        )
-
-        # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
-        scheduler_args = {}
+            # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
+            scheduler_args = {}
 
-        if "variance_type" in pipeline.scheduler.config:
-            variance_type = pipeline.scheduler.config.variance_type
+            if "variance_type" in pipeline.scheduler.config:
+                variance_type = pipeline.scheduler.config.variance_type
 
-            if variance_type in ["learned", "learned_range"]:
-                variance_type = "fixed_small"
+                if variance_type in ["learned", "learned_range"]:
+                    variance_type = "fixed_small"
 
-            scheduler_args["variance_type"] = variance_type
+                scheduler_args["variance_type"] = variance_type
 
-        pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, **scheduler_args)
+            pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, **scheduler_args)
 
-        # load attention processors
-        pipeline.load_lora_weights(args.output_dir)
+            # load attention processors
+            pipeline.load_lora_weights(args.output_dir)
 
-        # run inference
-        images = []
-        if args.validation_prompt and args.num_validation_images > 0:
+            # run inference
             pipeline = pipeline.to(accelerator.device)
             generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
             images = [