Add model and vae loader nodes

mochi_preview/dit/joint_model/asymm_models_joint.py

@@ -46,8 +46,10 @@ COMPILE_FINAL_LAYER = False #os.environ.get("COMPILE_DIT") == "1"
 COMPILE_MMDIT_BLOCK = False #os.environ.get("COMPILE_DIT") == "1"
 
 backends = []
-if torch.cuda.get_device_properties(0).major < 7:
+if torch.cuda.get_device_properties(0).major <= 7.5:
     backends.append(SDPBackend.MATH)
+if torch.cuda.get_device_properties(0).major >= 9.0:
+    backends.append(SDPBackend.CUDNN_ATTENTION)
 else:
     backends.append(SDPBackend.EFFICIENT_ATTENTION)
 

nodes.py

@@ -149,6 +149,94 @@ class DownloadAndLoadMochiModel:
 
         return (model, vae,)
     
+class MochiModelLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": { 
+                "model_name": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "The name of the checkpoint (model) to load.",}),
+                "precision": (["fp8_e4m3fn","fp8_e4m3fn_fast","fp16", "fp32", "bf16"], {"default": "fp8_e4m3fn"}),
+                "attention_mode": (["sdpa","flash_attn","sage_attn", "comfy"],),
+            },
+            "optional": {
+                "trigger": ("CONDITIONING", {"tooltip": "Dummy input for forcing execution order",}),
+            },
+        }
+    RETURN_TYPES = ("MOCHIMODEL",)
+    RETURN_NAMES = ("mochi_model",)
+    FUNCTION = "loadmodel"
+    CATEGORY = "MochiWrapper"
+
+    def loadmodel(self, model_name, precision, attention_mode, trigger=None):
+
+        device = mm.get_torch_device()
+        offload_device = mm.unet_offload_device()
+        mm.soft_empty_cache()
+
+        dtype = {"fp8_e4m3fn": torch.float8_e4m3fn, "fp8_e4m3fn_fast": torch.float8_e4m3fn, "bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
+        model_path = folder_paths.get_full_path_or_raise("diffusion_models", model_name)
+
+        model = T2VSynthMochiModel(
+            device=device,
+            offload_device=offload_device,
+            vae_stats_path=os.path.join(script_directory, "configs", "vae_stats.json"),
+            dit_checkpoint_path=model_path,
+            weight_dtype=dtype,
+            fp8_fastmode = True if precision == "fp8_e4m3fn_fast" else False,
+            attention_mode=attention_mode
+        )
+
+        return (model, )
+
+class MochiVAELoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": { 
+                "model_name": (folder_paths.get_filename_list("vae"), {"tooltip": "The name of the checkpoint (vae) to load."}),
+            },
+        }
+
+    RETURN_TYPES = ("MOCHIVAE",)
+    RETURN_NAMES = ("mochi_vae", )
+    FUNCTION = "loadmodel"
+    CATEGORY = "MochiWrapper"
+
+    def loadmodel(self, model_name):
+
+        device = mm.get_torch_device()
+        offload_device = mm.unet_offload_device()
+        mm.soft_empty_cache()
+
+        vae_path = folder_paths.get_full_path_or_raise("vae", model_name)
+
+        with (init_empty_weights() if is_accelerate_available else nullcontext()):
+            vae = Decoder(
+                    out_channels=3,
+                    base_channels=128,
+                    channel_multipliers=[1, 2, 4, 6],
+                    temporal_expansions=[1, 2, 3],
+                    spatial_expansions=[2, 2, 2],
+                    num_res_blocks=[3, 3, 4, 6, 3],
+                    latent_dim=12,
+                    has_attention=[False, False, False, False, False],
+                    padding_mode="replicate",
+                    output_norm=False,
+                    nonlinearity="silu",
+                    output_nonlinearity="silu",
+                    causal=True,
+                )
+        vae_sd = load_torch_file(vae_path)
+        if is_accelerate_available:
+            for key in vae_sd:
+                set_module_tensor_to_device(vae, key, dtype=torch.float32, device=device, value=vae_sd[key])
+        else:
+            vae.load_state_dict(vae_sd, strict=True)
+            vae.eval().to(torch.bfloat16).to("cpu")
+        del vae_sd
+
+        return (vae,)
+    
 class MochiTextEncode:
     @classmethod
     def INPUT_TYPES(s):
@@ -381,10 +469,14 @@ NODE_CLASS_MAPPINGS = {
     "MochiSampler": MochiSampler,
     "MochiDecode": MochiDecode,
     "MochiTextEncode": MochiTextEncode,
+    "MochiModelLoader": MochiModelLoader,
+    "MochiVAELoader": MochiVAELoader,
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
     "DownloadAndLoadMochiModel": "(Down)load Mochi Model",
     "MochiSampler": "Mochi Sampler",
     "MochiDecode": "Mochi Decode",
     "MochiTextEncode": "Mochi TextEncode",
+    "MochiModelLoader": "Mochi Model Loader",
+    "MochiVAELoader": "Mochi VAE Loader",
     }