diff --git a/examples/wannvideowrapper/wanvideo_1_3B_control_lora_example_01_MultiGPU.json b/examples/wannvideowrapper/wanvideo_1_3B_control_lora_example_01_MultiGPU.json new file mode 100755 index 0000000..9bf08bf --- /dev/null +++ b/examples/wannvideowrapper/wanvideo_1_3B_control_lora_example_01_MultiGPU.json @@ -0,0 +1,1165 @@ +{ + "id": "04f1de76-e363-4c27-bec7-eb184ac6e476", + "revision": 0, + "last_node_id": 111, + "last_link_id": 161, + "nodes": [ + { + "id": 36, + "type": "Note", + "pos": [ + 160, + -1010 + ], + "size": [ + 374.3061828613281, + 171.9547576904297 + ], + "flags": {}, + "order": 0, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "fp8_fast seems to cause huge quality degradation\n\nfp_16_fast enables \"Full FP16 Accmumulation in FP16 GEMMs\" feature available in the very latest pytorch nightly, this is around 20% speed boost. \n\nSageattn if you have it installed can be used for almost double inference speed" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 35, + "type": "WanVideoTorchCompileSettings", + "pos": [ + -276.8500671386719, + -1050.6326904296875 + ], + "size": [ + 390.5999755859375, + 202 + ], + "flags": {}, + "order": 1, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "torch_compile_args", + "type": "WANCOMPILEARGS", + "slot_index": 0, + "links": [] + } + ], + "properties": { + "Node name for S&R": "WanVideoTorchCompileSettings" + }, + "widgets_values": [ + "inductor", + false, + "default", + false, + 64, + true, + 128 + ], + "color": "#223", + "bgcolor": "#335" + }, + { + "id": 44, + "type": "Note", + "pos": [ + -620.9041137695312, + -1049.732421875 + ], + "size": [ + 303.0501403808594, + 88 + ], + "flags": {}, + "order": 2, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "If you have Triton installed, connect this for ~30% speed increase" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 53, + "type": "Note", + "pos": [ + 1329.0076904296875, + -810.7708740234375 + ], + "size": [ + 324.64129638671875, + 159.47401428222656 + ], + "flags": {}, + "order": 3, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "TeaCache could be considered to be sort of an automated step skipper \n\nThe relative l1 threshold -value determines how aggressive this is, higher values are faster but quality suffers more. 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"5a2383621a05825d0d0437781afcb8552d9590fd", + "ComfyUI-VideoHelperSuite": "0a75c7958fe320efcb052f1d9f8451fd20c730a8", + "comfy-core": "0.3.26", + "ComfyUI-KJNodes": "a5bd3c86c8ed6b83c55c2d0e7a59515b15a0137f" + }, + "VHS_latentpreview": false, + "VHS_latentpreviewrate": 0, + "VHS_MetadataImage": true, + "VHS_KeepIntermediate": true, + "frontendVersion": "1.25.11" + }, + "version": 0.4 +} \ No newline at end of file diff --git a/wanvideo.py b/wanvideo.py index a49c2b0..3ddb952 100644 --- a/wanvideo.py +++ b/wanvideo.py @@ -158,6 +158,10 @@ class WanVideoVAELoader: result = original_loader.loadmodel(model_name, precision, compile_args) + # Attach device info to VAE object for downstream nodes + if result and len(result) > 0: + result[0].load_device = selected_device + logging.info(f"[MultiGPU] WanVideo VAE loaded on {selected_device}") return result else: @@ -383,6 +387,33 @@ class WanVideoSampler: original_sampler = NODE_CLASS_MAPPINGS["WanVideoSampler"]() return original_sampler.process(model, **kwargs) +class WanVideoVACEEncode: + @classmethod + def INPUT_TYPES(s): + from nodes import NODE_CLASS_MAPPINGS + original_types = NODE_CLASS_MAPPINGS["WanVideoVACEEncode"].INPUT_TYPES() + return original_types + + RETURN_TYPES = ("LATENT",) + RETURN_NAMES = ("latent",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "MultiGPU-aware VACE encoder that uses device from input VAE" + + def process(self, vae, **kwargs): + # Get device from VAE object + vae_device = vae.load_device + logging.info(f"[MultiGPU] WanVideoVACEEncode: Processing on device: {vae_device}") + + # Patch all WanVideo modules to use the VAE's device + for module_name in sys.modules.keys(): + if 'WanVideoWrapper' in module_name and hasattr(sys.modules[module_name], 'device'): + sys.modules[module_name].device = vae_device + + from nodes import NODE_CLASS_MAPPINGS + original_encoder = NODE_CLASS_MAPPINGS["WanVideoVACEEncode"]() + return original_encoder.process(vae, **kwargs) + class WanVideoBlockSwap: @classmethod def INPUT_TYPES(s):