Merge pull request #15 from pollockjj/issue_80

Fix Trellis VAE decode memory management

comfy_extras/nodes_trellis2.py

@@ -8,6 +8,21 @@ import torch
 import scipy
 import copy
 
+def prepare_trellis_vae_for_decode(vae, sample_shape):
+    memory_required = vae.memory_used_decode(sample_shape, vae.vae_dtype)
+    if len(sample_shape) == 5:
+        memory_required *= max(1, int(sample_shape[4]))
+    memory_required = max(1, int(memory_required))
+    device = comfy.model_management.get_torch_device()
+    comfy.model_management.load_models_gpu(
+        [vae.patcher],
+        memory_required=memory_required,
+        force_full_load=getattr(vae, "disable_offload", False),
+    )
+    free_memory = vae.patcher.get_free_memory(device)
+    batch_number = max(1, int(free_memory / memory_required))
+    return batch_number
+
 
 def pack_variable_mesh_batch(vertices, faces, colors=None):
     batch_size = len(vertices)
@@ -271,19 +286,18 @@ class VaeDecodeShapeTrellis(IO.ComfyNode):
     def execute(cls, samples, vae, resolution):
 
         resolution = int(resolution)
-        patcher = vae.patcher
+        sample_tensor = samples["samples"]
         device = comfy.model_management.get_torch_device()
-        comfy.model_management.load_model_gpu(patcher)
-
-        vae = vae.first_stage_model
         coords = samples["coords"]
+        prepare_trellis_vae_for_decode(vae, sample_tensor.shape)
+        trellis_vae = vae.first_stage_model
         coord_counts = samples.get("coord_counts")
 
         samples = samples["samples"]
         if coord_counts is None:
             samples, coords = flatten_batched_sparse_latent(samples, coords, coord_counts)
             samples = shape_norm(samples.to(device), coords.to(device))
-            mesh, subs = vae.decode_shape_slat(samples, resolution)
+            mesh, subs = trellis_vae.decode_shape_slat(samples, resolution)
         else:
             split_items = split_batched_sparse_latent(samples, coords, coord_counts)
             mesh = []
@@ -292,7 +306,7 @@ class VaeDecodeShapeTrellis(IO.ComfyNode):
                 coords_i = coords_i.to(device).clone()
                 coords_i[:, 0] = 0
                 sample_i = shape_norm(feats_i.to(device), coords_i)
-                mesh_i, subs_i = vae.decode_shape_slat(sample_i, resolution)
+                mesh_i, subs_i = trellis_vae.decode_shape_slat(sample_i, resolution)
                 mesh.append(mesh_i[0])
                 subs_per_sample.append(subs_i)
 
@@ -332,13 +346,12 @@ class VaeDecodeTextureTrellis(IO.ComfyNode):
     @classmethod
     def execute(cls, shape_mesh, samples, vae, shape_subs, resolution):
 
+        sample_tensor = samples["samples"]
         resolution = int(resolution)
-        patcher = vae.patcher
         device = comfy.model_management.get_torch_device()
-        comfy.model_management.load_model_gpu(patcher)
-
-        vae = vae.first_stage_model
         coords = samples["coords"]
+        prepare_trellis_vae_for_decode(vae, sample_tensor.shape)
+        trellis_vae = vae.first_stage_model
         coord_counts = samples.get("coord_counts")
 
         samples = samples["samples"]
@@ -349,7 +362,7 @@ class VaeDecodeTextureTrellis(IO.ComfyNode):
         samples = SparseTensor(feats = samples, coords=coords.to(device))
         samples = samples * std + mean
 
-        voxel = vae.decode_tex_slat(samples, shape_subs)
+        voxel = trellis_vae.decode_tex_slat(samples, shape_subs)
         color_feats = voxel.feats[:, :3]
         voxel_coords = voxel.coords[:, 1:]
         voxel_batch_idx = voxel.coords[:, 0]
@@ -397,22 +410,16 @@ class VaeDecodeStructureTrellis2(IO.ComfyNode):
     @classmethod
     def execute(cls, samples, vae, resolution):
         resolution = int(resolution)
-        vae = vae.first_stage_model
+        sample_tensor = samples["samples"]
-        decoder = vae.struct_dec
+        batch_number = prepare_trellis_vae_for_decode(vae, sample_tensor.shape)
+        decoder = vae.first_stage_model.struct_dec
         load_device = comfy.model_management.get_torch_device()
-        offload_device = comfy.model_management.vae_offload_device()
-        decoder = decoder.to(load_device)
         batch_index = normalize_batch_index(samples.get("batch_index"))
-        samples = samples["samples"]
+        decoded_batches = []
-        samples = samples.to(load_device)
+        for start in range(0, sample_tensor.shape[0], batch_number):
-        if samples.shape[0] > 1:
+            sample_chunk = sample_tensor[start:start + batch_number].to(load_device)
-            decoded_items = []
+            decoded_batches.append(decoder(sample_chunk) > 0)
-            for i in range(samples.shape[0]):
+        decoded = torch.cat(decoded_batches, dim=0)
-                decoded_items.append(decoder(samples[i:i + 1]) > 0)
-            decoded = torch.cat(decoded_items, dim=0)
-        else:
-            decoded = decoder(samples) > 0
-        decoder.to(offload_device)
         current_res = decoded.shape[2]
 
         if current_res != resolution:
@@ -443,7 +450,7 @@ class Trellis2UpsampleCascade(IO.ComfyNode):
     @classmethod
     def execute(cls, shape_latent_512, vae, target_resolution, max_tokens):
         device = comfy.model_management.get_torch_device()
-        comfy.model_management.load_model_gpu(vae.patcher)
+        prepare_trellis_vae_for_decode(vae, shape_latent_512["samples"].shape)
 
         coord_counts = shape_latent_512.get("coord_counts")
         batch_index = normalize_batch_index(shape_latent_512.get("batch_index"))

tests-unit/comfy_extras_test/nodes_trellis2_test.py

@@ -73,6 +73,57 @@ class DummyModel:
         self.model = inner_model
 
 
+class DummyPatcher:
+    def __init__(self, free_memory):
+        self.free_memory = free_memory
+
+    def get_free_memory(self, device):
+        return self.free_memory
+
+
+class DummyVAE:
+    vae_dtype = torch.float16
+
+    def __init__(self, free_memory, memory_factor=2):
+        self.patcher = DummyPatcher(free_memory)
+        self.memory_factor = memory_factor
+
+    def memory_used_decode(self, shape, dtype):
+        return shape[2] * shape[3] * self.memory_factor
+
+
+class TestPrepareTrellisVaeForDecode(unittest.TestCase):
+    def test_uses_load_models_gpu_without_pre_freeing_memory(self):
+        vae = DummyVAE(free_memory=1000)
+
+        with patch.object(nodes_trellis2.comfy.model_management, "get_torch_device", return_value="cuda"):
+            with patch.object(nodes_trellis2.comfy.model_management, "free_memory") as free_memory:
+                with patch.object(nodes_trellis2.comfy.model_management, "load_models_gpu") as load_models_gpu:
+                    batch_number = nodes_trellis2.prepare_trellis_vae_for_decode(vae, (3, 32, 10, 1))
+
+        free_memory.assert_not_called()
+        load_models_gpu.assert_called_once_with(
+            [vae.patcher],
+            memory_required=20,
+            force_full_load=False,
+        )
+        self.assertEqual(batch_number, 50)
+
+    def test_scales_memory_estimate_for_5d_structure_latents(self):
+        vae = DummyVAE(free_memory=40960, memory_factor=1)
+
+        with patch.object(nodes_trellis2.comfy.model_management, "get_torch_device", return_value="cuda"):
+            with patch.object(nodes_trellis2.comfy.model_management, "load_models_gpu") as load_models_gpu:
+                batch_number = nodes_trellis2.prepare_trellis_vae_for_decode(vae, (2, 8, 16, 16, 16))
+
+        load_models_gpu.assert_called_once_with(
+            [vae.patcher],
+            memory_required=4096,
+            force_full_load=False,
+        )
+        self.assertEqual(batch_number, 10)
+
+
 class TestRunConditioningRestore(unittest.TestCase):
     def setUp(self):
         self.intermediate_patch = patch.object(