Swap out PatchCublasLinear in favor of checkpoint loader that patches it

Because the patch needs to happen before model loading

__init__.py

@@ -150,7 +150,7 @@ NODE_CONFIG = {
     "FluxBlockLoraLoader": {"class": FluxBlockLoraLoader, "name": "Flux Block Lora Loader"},
     "FluxBlockLoraSelect": {"class": FluxBlockLoraSelect, "name": "Flux Block Lora Select"},
     "CustomControlNetWeightsFluxFromList": {"class": CustomControlNetWeightsFluxFromList, "name": "Custom ControlNet Weights Flux From List"},
-    "PatchCublasLinear": {"class": PatchCublasLinear, "name": "Patch Cublas Linear"},
+    "CheckpointLoaderKJ": {"class": CheckpointLoaderKJ, "name": "CheckpointLoaderKJ"},
 
     #instance diffusion
     "CreateInstanceDiffusionTracking": {"class": CreateInstanceDiffusionTracking},

nodes/nodes.py

@@ -2124,29 +2124,58 @@ class ModelSaveKJ:
             os.makedirs(full_output_folder)
         save_torch_file(new_sd, os.path.join(full_output_folder, output_checkpoint))
         return {}
-        
-class PatchCublasLinear:
+
+
+from comfy.ldm.modules import attention as comfy_attention
+orig_attention = comfy_attention.optimized_attention
+
+class CheckpointLoaderKJ:
     original_linear = None
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "model": ("MODEL",),
-                              "enabled": ("BOOLEAN", {"default": True, "tooltip": "Enable or disable the patching, won't take effect on already loaded models!"}),
+        return {"required": {  
+                            "ckpt_name": (folder_paths.get_filename_list("checkpoints"), {"tooltip": "The name of the checkpoint (model) to load."}),
+                            "patch_cublaslinear": ("BOOLEAN", {"default": True, "tooltip": "Enable or disable the patching, won't take effect on already loaded models!"}),
+                            "sage_attention": ("BOOLEAN", {"default": False, "tooltip": "Patch comfy attention to use sageattn."}),
                               },
                 }
-    RETURN_TYPES = ("MODEL",)
+    RETURN_TYPES = ("MODEL", "CLIP", "VAE")
     FUNCTION = "patch"
     OUTPUT_NODE = True
-    DESCRIPTION = "Highly experimental node that simply patches the Linear layer to use torch-cublas-hgemm, won't take effect on already loaded models!"
+    DESCRIPTION = "Exemplar node for patching torch.nn.Linear with CublasLinear: https://github.com/aredden/torch-cublas-hgemm"
 
     CATEGORY = "KJNodes/experimental"
 
-    def patch(self, model, enabled):
+    def patch(self, ckpt_name, patch_cublaslinear, sage_attention):
         from comfy.ops import disable_weight_init, CastWeightBiasOp, cast_bias_weight
+        from nodes import CheckpointLoaderSimple
         try:
             from cublas_ops import CublasLinear
         except ImportError:
             raise Exception("Can't import 'torch-cublas-hgemm', install it from here https://github.com/aredden/torch-cublas-hgemm")
         
+        if sage_attention:
+            from sageattention import sageattn
+        
+            def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
+                if skip_reshape:
+                    b, _, _, dim_head = q.shape
+                else:
+                    b, _, dim_head = q.shape
+                    dim_head //= heads
+                if dim_head not in (64, 96, 128) or not (k.shape == q.shape and v.shape == q.shape):
+                    return orig_attention(q, k, v, heads, mask=mask, attn_precision=attn_precision, skip_reshape=skip_reshape)
+                if not skip_reshape:
+                    q, k, v = map(
+                        lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
+                        (q, k, v),
+                    )
+                return (
+                    sageattn(q, k, v, is_causal=False, attn_mask=mask, dropout_p=0.0, smooth_k=True)
+                    .transpose(1, 2)
+                    .reshape(b, -1, heads * dim_head)
+        )
+        
         class OriginalLinear(torch.nn.Linear, CastWeightBiasOp):
             def reset_parameters(self):
                 return None
@@ -2175,10 +2204,17 @@ class PatchCublasLinear:
                 else:
                     return super().forward(*args, **kwargs)
 
-        if enabled:
+        if patch_cublaslinear:
             disable_weight_init.Linear = PatchedLinear
         else:
             disable_weight_init.Linear = OriginalLinear
+        if sage_attention:
+            comfy_attention.optimized_attention = attention_sage
+        else:
+            comfy_attention.optimized_attention = orig_attention
+
+        model, clip, vae = CheckpointLoaderSimple.load_checkpoint(self, ckpt_name)
         
-        return model,
+        
+        return model, clip, vae