Passed data in for validation.

finetrainers/args.py

@@ -316,7 +316,11 @@ class Args:
     validation_prompts: List[str] = None
     validation_images: List[str] = None
     validation_videos: List[str] = None
+
+    # Condition
     validation_pose_videos: List[str] = None
+    validation_img_ref_videos: List[str] = None
+    
     validation_heights: List[int] = None
     validation_widths: List[int] = None
     validation_num_frames: List[int] = None
@@ -888,6 +892,13 @@ def _add_validation_arguments(parser: argparse.ArgumentParser) -> None:
         help="One or more pose_videos path(s)/URLs that is used during validation to verify that the model is learning. Multiple validation paths should be separated by the '--validation_prompt_seperator' string. These should correspond to the order of the validation prompts.",
     )
 
+    parser.add_argument(
+        "--validation_img_ref_videos",
+        type=str,
+        default=None,
+        help="One or more img_ref video or img will just take the first frame and create a video from it path(s)/URLs that is used during validation to verify that the model is learning. Multiple validation paths should be separated by the '--validation_prompt_seperator' string. These should correspond to the order of the validation prompts.",
+    )
+
     parser.add_argument(
         "--validation_videos",
         type=str,
@@ -1089,7 +1100,10 @@ def _map_to_args_type(args: Dict[str, Any]) -> Args:
     validation_images = args.validation_images.split(args.validation_separator) if args.validation_images else None
     validation_videos = args.validation_videos.split(args.validation_separator) if args.validation_videos else None
     
+    # extend with pose conditioning ...
     validation_pose_videos = args.validation_pose_videos.split(args.validation_separator) if args.validation_pose_videos else None
+    validation_img_ref_videos = args.validation_img_ref_videos.split(args.validation_separator) if args.validation_img_ref_videos else None
+
 
     stripped_validation_prompts = []
     validation_heights = []
@@ -1118,7 +1132,11 @@ def _map_to_args_type(args: Dict[str, Any]) -> Args:
     result_args.validation_num_frames = validation_num_frames
     result_args.validation_images = validation_images
     result_args.validation_videos = validation_videos
+
+    # extend with pose conditioning ...
     result_args.validation_pose_videos = validation_pose_videos
+    result_args.validation_img_ref_videos = validation_img_ref_videos
+    
     result_args.num_validation_videos_per_prompt = args.num_validation_videos
     result_args.validation_every_n_epochs = args.validation_epochs
     result_args.validation_every_n_steps = args.validation_steps

finetrainers/conditioning/conditioned_pipeline.py

@@ -62,7 +62,15 @@ class LTXConditionedPipeline(LTXPipeline):
         callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
         callback_on_step_end_tensor_inputs: List[str] = ["latents"],
         max_sequence_length: int = 128,
+        pose_video=None,
+        image_ref_video=None,
     ):
+        # These are the raw videos
+        if pose_video is None:
+            raise ValueError("pose_video cannot be None.")
+        if image_ref_video is None:
+            raise ValueError("image_ref_video cannot be None.")
+
         if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
             callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
 

finetrainers/trainer.py

@@ -1010,7 +1010,12 @@ class Trainer:
             prompt = self.args.validation_prompts[i]
             image = self.args.validation_images[i]
             video = self.args.validation_videos[i]
-            pose_video = self.args.validation_pose_videos[i]
+            # Condition extension
+
+            if self.pose_condition:
+                pose_video = self.args.validation_pose_videos[i]
+                img_ref_video = self.args.validation_img_ref_videos[i]
+
             height = self.args.validation_heights[i]
             width = self.args.validation_widths[i]
             num_frames = self.args.validation_num_frames[i]
@@ -1019,30 +1024,51 @@ class Trainer:
                 image = load_image(image)
             if video is not None:
                 video = load_video(video)
-            if pose_video is not None:
-                pose_video = load_video(pose_video)
 
+            if self.pose_condition:
+                if pose_video is not None:
+                    pose_video = load_video(pose_video)
+                if img_ref_video is not None:
+                    img_ref_video = load_video(img_ref_video)
+                
             logger.debug(
                 f"Validating sample {i + 1}/{num_validation_samples} on process {accelerator.process_index}. Prompt: {prompt}",
                 main_process_only=False,
             )
             
-            validation_artifacts = self.model_config["validation"](
-                pipeline=pipeline,
-                prompt=prompt,
-                image=image,
-                video=video,
-                pose_video=pose_video,
-                height=height,
-                width=width,
-                num_frames=num_frames,
-                frame_rate=frame_rate,
-                num_videos_per_prompt=self.args.num_validation_videos_per_prompt,
-                generator=torch.Generator(device=accelerator.device).manual_seed(
-                    self.args.seed if self.args.seed is not None else 0
-                ),
-                # todo support passing `fps` for supported pipelines.
-            )
+            if self.pose_condition:
+                validation_artifacts = self.model_config["validation"](
+                    pipeline=pipeline,
+                    prompt=prompt,
+                    image=image,
+                    video=video,
+                    height=height,
+                    width=width,
+                    num_frames=num_frames,
+                    frame_rate=frame_rate,
+                    pose_video=pose_video,
+                    img_ref_video=img_ref_video,
+                    num_videos_per_prompt=self.args.num_validation_videos_per_prompt,
+                    generator=torch.Generator(device=accelerator.device).manual_seed(
+                        self.args.seed if self.args.seed is not None else 0
+                    ),
+                )
+            else:
+                validation_artifacts = self.model_config["validation"](
+                    pipeline=pipeline,
+                    prompt=prompt,
+                    image=image,
+                    video=video,
+                    height=height,
+                    width=width,
+                    num_frames=num_frames,
+                    frame_rate=frame_rate,
+                    num_videos_per_prompt=self.args.num_validation_videos_per_prompt,
+                    generator=torch.Generator(device=accelerator.device).manual_seed(
+                        self.args.seed if self.args.seed is not None else 0
+                    ),
+                    # todo support passing `fps` for supported pipelines.
+                )
 
             prompt_filename = string_to_filename(prompt)[:25]
             artifacts = {