Resolution bucketing and Trainer implementation refactoring (#11117)

Author: KohakuBlueleaf

comfy/sampler_helpers.py

@@ -122,20 +122,21 @@ def estimate_memory(model, noise_shape, conds):
     minimum_memory_required = model.model.memory_required([noise_shape[0]] + list(noise_shape[1:]), cond_shapes=cond_shapes_min)
     return memory_required, minimum_memory_required
 
-def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None):
+def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None, skip_load_model=False):
     executor = comfy.patcher_extension.WrapperExecutor.new_executor(
         _prepare_sampling,
         comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.PREPARE_SAMPLING, model_options, is_model_options=True)
     )
-    return executor.execute(model, noise_shape, conds, model_options=model_options)
+    return executor.execute(model, noise_shape, conds, model_options=model_options, skip_load_model=skip_load_model)
 
-def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None):
+def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None, skip_load_model=False):
     real_model: BaseModel = None
     models, inference_memory = get_additional_models(conds, model.model_dtype())
     models += get_additional_models_from_model_options(model_options)
     models += model.get_nested_additional_models()  # TODO: does this require inference_memory update?
     memory_required, minimum_memory_required = estimate_memory(model, noise_shape, conds)
-    comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required + inference_memory, minimum_memory_required=minimum_memory_required + inference_memory)
+    models_list = [model] if not skip_load_model else []
+    comfy.model_management.load_models_gpu(models_list + models, memory_required=memory_required + inference_memory, minimum_memory_required=minimum_memory_required + inference_memory)
     real_model = model.model
 
     return real_model, conds, models

comfy_extras/nodes_dataset.py

@@ -1125,6 +1125,99 @@ def _group_process(cls, texts):
 # ========== Training Dataset Nodes ==========
 
 
+class ResolutionBucket(io.ComfyNode):
+    """Bucket latents and conditions by resolution for efficient batch training."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ResolutionBucket",
+            display_name="Resolution Bucket",
+            category="dataset",
+            is_experimental=True,
+            is_input_list=True,
+            inputs=[
+                io.Latent.Input(
+                    "latents",
+                    tooltip="List of latent dicts to bucket by resolution.",
+                ),
+                io.Conditioning.Input(
+                    "conditioning",
+                    tooltip="List of conditioning lists (must match latents length).",
+                ),
+            ],
+            outputs=[
+                io.Latent.Output(
+                    display_name="latents",
+                    is_output_list=True,
+                    tooltip="List of batched latent dicts, one per resolution bucket.",
+                ),
+                io.Conditioning.Output(
+                    display_name="conditioning",
+                    is_output_list=True,
+                    tooltip="List of condition lists, one per resolution bucket.",
+                ),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, latents, conditioning):
+        # latents: list[{"samples": tensor}] where tensor is (B, C, H, W), typically B=1
+        # conditioning: list[list[cond]]
+
+        # Validate lengths match
+        if len(latents) != len(conditioning):
+            raise ValueError(
+                f"Number of latents ({len(latents)}) does not match number of conditions ({len(conditioning)})."
+            )
+
+        # Flatten latents and conditions to individual samples
+        flat_latents = []  # list of (C, H, W) tensors
+        flat_conditions = []  # list of condition lists
+
+        for latent_dict, cond in zip(latents, conditioning):
+            samples = latent_dict["samples"]  # (B, C, H, W)
+            batch_size = samples.shape[0]
+
+            # cond is a list of conditions with length == batch_size
+            for i in range(batch_size):
+                flat_latents.append(samples[i])  # (C, H, W)
+                flat_conditions.append(cond[i])  # single condition
+
+        # Group by resolution (H, W)
+        buckets = {}  # (H, W) -> {"latents": list, "conditions": list}
+
+        for latent, cond in zip(flat_latents, flat_conditions):
+            # latent shape is (..., H, W) (B, C, H, W) or (B, T, C, H ,W)
+            h, w = latent.shape[-2], latent.shape[-1]
+            key = (h, w)
+
+            if key not in buckets:
+                buckets[key] = {"latents": [], "conditions": []}
+
+            buckets[key]["latents"].append(latent)
+            buckets[key]["conditions"].append(cond)
+
+        # Convert buckets to output format
+        output_latents = []  # list[{"samples": tensor}] where tensor is (Bi, ..., H, W)
+        output_conditions = []  # list[list[cond]] where each inner list has Bi conditions
+
+        for (h, w), bucket_data in buckets.items():
+            # Stack latents into batch: list of (..., H, W) -> (Bi, ..., H, W)
+            stacked_latents = torch.stack(bucket_data["latents"], dim=0)
+            output_latents.append({"samples": stacked_latents})
+
+            # Conditions stay as list of condition lists
+            output_conditions.append(bucket_data["conditions"])
+
+            logging.info(
+                f"Resolution bucket ({h}x{w}): {len(bucket_data['latents'])} samples"
+            )
+
+        logging.info(f"Created {len(buckets)} resolution buckets from {len(flat_latents)} samples")
+        return io.NodeOutput(output_latents, output_conditions)
+
+
 class MakeTrainingDataset(io.ComfyNode):
     """Encode images with VAE and texts with CLIP to create a training dataset."""
 
@@ -1373,7 +1466,7 @@ def execute(cls, folder_name):
             shard_path = os.path.join(dataset_dir, shard_file)
 
             with open(shard_path, "rb") as f:
-                shard_data = torch.load(f, weights_only=True)
+                shard_data = torch.load(f)
 
             all_latents.extend(shard_data["latents"])
             all_conditioning.extend(shard_data["conditioning"])
@@ -1425,6 +1518,7 @@ async def get_node_list(self) -> list[type[io.ComfyNode]]:
             MakeTrainingDataset,
             SaveTrainingDataset,
             LoadTrainingDataset,
+            ResolutionBucket,
         ]
 
 

comfy_extras/nodes_post_processing.py

@@ -221,22 +221,23 @@ def define_schema(cls):
                 io.Image.Input("image"),
                 io.Combo.Input("upscale_method", options=cls.upscale_methods),
                 io.Float.Input("megapixels", default=1.0, min=0.01, max=16.0, step=0.01),
+                io.Int.Input("resolution_steps", default=1, min=1, max=256),
             ],
             outputs=[
                 io.Image.Output(),
             ],
         )
 
     @classmethod
-    def execute(cls, image, upscale_method, megapixels) -> io.NodeOutput:
+    def execute(cls, image, upscale_method, megapixels, resolution_steps) -> io.NodeOutput:
         samples = image.movedim(-1,1)
-        total = int(megapixels * 1024 * 1024)
+        total = megapixels * 1024 * 1024
 
         scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
-        width = round(samples.shape[3] * scale_by)
-        height = round(samples.shape[2] * scale_by)
+        width = round(samples.shape[3] * scale_by / resolution_steps) * resolution_steps
+        height = round(samples.shape[2] * scale_by / resolution_steps) * resolution_steps
 
-        s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled")
+        s = comfy.utils.common_upscale(samples, int(width), int(height), upscale_method, "disabled")
         s = s.movedim(1,-1)
         return io.NodeOutput(s)