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feat(isolation): DynamicVRAM compatibility for process isolation

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DynamicVRAM's on-demand model loading/offloading conflicted with  process isolation in three ways: RPC tensor transport stalls from mid-call GPU offload, race conditions between model lifecycle and active RPC operations, and false positive memory leak detection from changed finalizer patterns.

- Marshal CUDA tensors to CPU before RPC transport for dynamic models
- Add operation state tracking + quiescence waits at workflow boundaries
- Distinguish proxy reference release from actual leaks in cleanup_models_gc
- Fix init order: DynamicVRAM must initialize before isolation proxies
- Add RPC timeouts to prevent indefinite hangs on model unavailability
- Prevent proxy-of-proxy chains from DynamicVRAM model reload cycles
- Add torch.device/torch.dtype serializers for new DynamicVRAM RPC paths
- Guard isolation overhead so non-isolated workflows are unaffected
- Migrate env var to PYISOLATE_CHILD
This commit is contained in:
John Pollock
2026-03-04 23:48:02 -06:00
parent a0f8784e9f
commit 9250191c65
38 changed files with 94595 additions and 307 deletions
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14
comfy/model_detection.py
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@@ -423,7 +423,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
dit_config["extra_per_block_abs_pos_emb_type"] = "learnable"
return dit_config
if '{}cap_embedder.1.weight'.format(key_prefix) in state_dict_keys: # Lumina 2
if '{}cap_embedder.1.weight'.format(key_prefix) in state_dict_keys and '{}noise_refiner.0.attention.k_norm.weight'.format(key_prefix) in state_dict_keys: # Lumina 2
dit_config = {}
dit_config["image_model"] = "lumina2"
dit_config["patch_size"] = 2
@@ -498,6 +498,8 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
dit_config["model_type"] = "humo"
elif '{}face_adapter.fuser_blocks.0.k_norm.weight'.format(key_prefix) in state_dict_keys:
dit_config["model_type"] = "animate"
elif '{}patch_embedding_pose.weight'.format(key_prefix) in state_dict_keys:
dit_config["model_type"] = "scail"
else:
if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys:
dit_config["model_type"] = "i2v"
@@ -531,8 +533,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
return dit_config
if f"{key_prefix}t_embedder.mlp.2.weight" in state_dict_keys: # Hunyuan 3D 2.1
if f"{key_prefix}t_embedder.mlp.2.weight" in state_dict_keys and f"{key_prefix}blocks.0.attn1.k_norm.weight" in state_dict_keys: # Hunyuan 3D 2.1
dit_config = {}
dit_config["image_model"] = "hunyuan3d2_1"
dit_config["in_channels"] = state_dict[f"{key_prefix}x_embedder.weight"].shape[1]
@@ -1053,6 +1054,13 @@ def convert_diffusers_mmdit(state_dict, output_prefix=""):
elif 'adaln_single.emb.timestep_embedder.linear_1.bias' in state_dict and 'pos_embed.proj.bias' in state_dict: # PixArt
num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.')
sd_map = comfy.utils.pixart_to_diffusers({"depth": num_blocks}, output_prefix=output_prefix)
elif 'noise_refiner.0.attention.norm_k.weight' in state_dict:
n_layers = count_blocks(state_dict, 'layers.{}.')
dim = state_dict['noise_refiner.0.attention.to_k.weight'].shape[0]
sd_map = comfy.utils.z_image_to_diffusers({"n_layers": n_layers, "dim": dim}, output_prefix=output_prefix)
for k in state_dict: # For zeta chroma
if k not in sd_map:
sd_map[k] = k
elif 'x_embedder.weight' in state_dict: #Flux
depth = count_blocks(state_dict, 'transformer_blocks.{}.')
depth_single_blocks = count_blocks(state_dict, 'single_transformer_blocks.{}.')