Add some more transformer hooks and move tomesd to comfy_extras.

Tomesd now uses q instead of x to decide which tokens to merge because
it seems to give better results.

comfy/ldm/modules/attention.py

@@ -12,8 +12,6 @@ from .sub_quadratic_attention import efficient_dot_product_attention
 from comfy import model_management
 import comfy.ops
 
-from . import tomesd
-
 if model_management.xformers_enabled():
     import xformers
     import xformers.ops
@@ -519,23 +517,39 @@ class BasicTransformerBlock(nn.Module):
         self.norm2 = nn.LayerNorm(dim, dtype=dtype)
         self.norm3 = nn.LayerNorm(dim, dtype=dtype)
         self.checkpoint = checkpoint
+        self.n_heads = n_heads
+        self.d_head = d_head
 
     def forward(self, x, context=None, transformer_options={}):
         return checkpoint(self._forward, (x, context, transformer_options), self.parameters(), self.checkpoint)
 
     def _forward(self, x, context=None, transformer_options={}):
         extra_options = {}
+        block = None
+        block_index = 0
         if "current_index" in transformer_options:
             extra_options["transformer_index"] = transformer_options["current_index"]
         if "block_index" in transformer_options:
-            extra_options["block_index"] = transformer_options["block_index"]
+            block_index = transformer_options["block_index"]
+            extra_options["block_index"] = block_index
         if "original_shape" in transformer_options:
             extra_options["original_shape"] = transformer_options["original_shape"]
+        if "block" in transformer_options:
+            block = transformer_options["block"]
+            extra_options["block"] = block
         if "patches" in transformer_options:
             transformer_patches = transformer_options["patches"]
         else:
             transformer_patches = {}
 
+        extra_options["n_heads"] = self.n_heads
+        extra_options["dim_head"] = self.d_head
+
+        if "patches_replace" in transformer_options:
+            transformer_patches_replace = transformer_options["patches_replace"]
+        else:
+            transformer_patches_replace = {}
+
         n = self.norm1(x)
         if self.disable_self_attn:
             context_attn1 = context
@@ -551,12 +565,29 @@ class BasicTransformerBlock(nn.Module):
             for p in patch:
                 n, context_attn1, value_attn1 = p(n, context_attn1, value_attn1, extra_options)
 
-        if "tomesd" in transformer_options:
-            m, u = tomesd.get_functions(x, transformer_options["tomesd"]["ratio"], transformer_options["original_shape"])
-            n = u(self.attn1(m(n), context=context_attn1, value=value_attn1))
+        transformer_block = (block[0], block[1], block_index)
+        attn1_replace_patch = transformer_patches_replace.get("attn1", {})
+        block_attn1 = transformer_block
+        if block_attn1 not in attn1_replace_patch:
+            block_attn1 = block
+
+        if block_attn1 in attn1_replace_patch:
+            if context_attn1 is None:
+                context_attn1 = n
+                value_attn1 = n
+            n = self.attn1.to_q(n)
+            context_attn1 = self.attn1.to_k(context_attn1)
+            value_attn1 = self.attn1.to_v(value_attn1)
+            n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options)
+            n = self.attn1.to_out(n)
         else:
             n = self.attn1(n, context=context_attn1, value=value_attn1)
 
+        if "attn1_output_patch" in transformer_patches:
+            patch = transformer_patches["attn1_output_patch"]
+            for p in patch:
+                n = p(n, extra_options)
+
         x += n
         if "middle_patch" in transformer_patches:
             patch = transformer_patches["middle_patch"]
@@ -573,7 +604,21 @@ class BasicTransformerBlock(nn.Module):
             for p in patch:
                 n, context_attn2, value_attn2 = p(n, context_attn2, value_attn2, extra_options)
 
-        n = self.attn2(n, context=context_attn2, value=value_attn2)
+        attn2_replace_patch = transformer_patches_replace.get("attn2", {})
+        block_attn2 = transformer_block
+        if block_attn2 not in attn2_replace_patch:
+            block_attn2 = block
+
+        if block_attn2 in attn2_replace_patch:
+            if value_attn2 is None:
+                value_attn2 = context_attn2
+            n = self.attn2.to_q(n)
+            context_attn2 = self.attn2.to_k(context_attn2)
+            value_attn2 = self.attn2.to_v(value_attn2)
+            n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options)
+            n = self.attn2.to_out(n)
+        else:
+            n = self.attn2(n, context=context_attn2, value=value_attn2)
 
         if "attn2_output_patch" in transformer_patches:
             patch = transformer_patches["attn2_output_patch"]

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -830,17 +830,20 @@ class UNetModel(nn.Module):
 
         h = x.type(self.dtype)
         for id, module in enumerate(self.input_blocks):
+            transformer_options["block"] = ("input", id)
             h = forward_timestep_embed(module, h, emb, context, transformer_options)
             if control is not None and 'input' in control and len(control['input']) > 0:
                 ctrl = control['input'].pop()
                 if ctrl is not None:
                     h += ctrl
             hs.append(h)
+        transformer_options["block"] = ("middle", 0)
         h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options)
         if control is not None and 'middle' in control and len(control['middle']) > 0:
             h += control['middle'].pop()
 
-        for module in self.output_blocks:
+        for id, module in enumerate(self.output_blocks):
+            transformer_options["block"] = ("output", id)
             hsp = hs.pop()
             if control is not None and 'output' in control and len(control['output']) > 0:
                 ctrl = control['output'].pop()

comfy/sd.py

@@ -315,9 +315,6 @@ class ModelPatcher:
         n.model_keys = self.model_keys
         return n
 
-    def set_model_tomesd(self, ratio):
-        self.model_options["transformer_options"]["tomesd"] = {"ratio": ratio}
-
     def set_model_sampler_cfg_function(self, sampler_cfg_function):
         if len(inspect.signature(sampler_cfg_function).parameters) == 3:
             self.model_options["sampler_cfg_function"] = lambda args: sampler_cfg_function(args["cond"], args["uncond"], args["cond_scale"]) #Old way
@@ -330,12 +327,29 @@ class ModelPatcher:
             to["patches"] = {}
         to["patches"][name] = to["patches"].get(name, []) + [patch]
 
+    def set_model_patch_replace(self, patch, name, block_name, number):
+        to = self.model_options["transformer_options"]
+        if "patches_replace" not in to:
+            to["patches_replace"] = {}
+        if name not in to["patches_replace"]:
+            to["patches_replace"][name] = {}
+        to["patches_replace"][name][(block_name, number)] = patch
+
     def set_model_attn1_patch(self, patch):
         self.set_model_patch(patch, "attn1_patch")
 
     def set_model_attn2_patch(self, patch):
         self.set_model_patch(patch, "attn2_patch")
 
+    def set_model_attn1_replace(self, patch, block_name, number):
+        self.set_model_patch_replace(patch, "attn1", block_name, number)
+
+    def set_model_attn2_replace(self, patch, block_name, number):
+        self.set_model_patch_replace(patch, "attn2", block_name, number)
+
+    def set_model_attn1_output_patch(self, patch):
+        self.set_model_patch(patch, "attn1_output_patch")
+
     def set_model_attn2_output_patch(self, patch):
         self.set_model_patch(patch, "attn2_output_patch")
 
@@ -348,6 +362,13 @@ class ModelPatcher:
                 for i in range(len(patch_list)):
                     if hasattr(patch_list[i], "to"):
                         patch_list[i] = patch_list[i].to(device)
+        if "patches_replace" in to:
+            patches = to["patches_replace"]
+            for name in patches:
+                patch_list = patches[name]
+                for k in patch_list:
+                    if hasattr(patch_list[k], "to"):
+                        patch_list[k] = patch_list[k].to(device)
 
     def model_dtype(self):
         return self.model.get_dtype()

comfy/ldm/modules/tomesd.py → comfy_extras/nodes_tomesd.py

@@ -142,3 +142,36 @@ def get_functions(x, ratio, original_shape):
 
     nothing = lambda y: y
     return nothing, nothing
+
+
+
+class TomePatchModel:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "_for_testing"
+
+    def patch(self, model, ratio):
+        self.u = None
+        def tomesd_m(q, k, v, extra_options):
+            #NOTE: In the reference code get_functions takes x (input of the transformer block) as the argument instead of q
+            #however from my basic testing it seems that using q instead gives better results
+            m, self.u = get_functions(q, ratio, extra_options["original_shape"])
+            return m(q), k, v
+        def tomesd_u(n, extra_options):
+            return self.u(n)
+
+        m = model.clone()
+        m.set_model_attn1_patch(tomesd_m)
+        m.set_model_attn1_output_patch(tomesd_u)
+        return (m, )
+
+
+NODE_CLASS_MAPPINGS = {
+    "TomePatchModel": TomePatchModel,
+}

nodes.py

@@ -437,22 +437,6 @@ class LoraLoader:
         model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora_path, strength_model, strength_clip)
         return (model_lora, clip_lora)
 
-class TomePatchModel:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model": ("MODEL",),
-                              "ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
-                              }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch"
-
-    CATEGORY = "_for_testing"
-
-    def patch(self, model, ratio):
-        m = model.clone()
-        m.set_model_tomesd(ratio)
-        return (m, )
-
 class VAELoader:
     @classmethod
     def INPUT_TYPES(s):
@@ -1341,7 +1325,6 @@ NODE_CLASS_MAPPINGS = {
     "CLIPVisionLoader": CLIPVisionLoader,
     "VAEDecodeTiled": VAEDecodeTiled,
     "VAEEncodeTiled": VAEEncodeTiled,
-    "TomePatchModel": TomePatchModel,
     "unCLIPCheckpointLoader": unCLIPCheckpointLoader,
     "GLIGENLoader": GLIGENLoader,
     "GLIGENTextBoxApply": GLIGENTextBoxApply,
@@ -1466,4 +1449,5 @@ def init_custom_nodes():
     load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_mask.py"))
     load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_rebatch.py"))
     load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_model_merging.py"))
+    load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_tomesd.py"))
     load_custom_nodes()