diff --git a/tome-patcher/LICENSE b/tome-patcher/LICENSE
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+MIT License
+
+Copyright (c) 2023 Daniel Bolya
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
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diff --git a/tome-patcher/README.md b/tome-patcher/README.md
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+Adds a TomePatch node that you can use before the KSampler
+This will patch the model using tome:
+https://github.com/dbolya/tomesd
+
+It will not currently unpatch the model when it has finished.
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diff --git a/tome-patcher/tome_patcher.py b/tome-patcher/tome_patcher.py
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+# https://github.com/dbolya/tomesd
+
+# MIT License
+
+# Copyright (c) 2023 Daniel Bolya
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+# @article{bolya2023tomesd,
+#   title={Token Merging for Fast Stable Diffusion},
+#   author={Bolya, Daniel and Hoffman, Judy},
+#   journal={arXiv},
+#   year={2023}
+# }
+
+import torch
+
+
+def isinstance_str(x: object, cls_name: str):
+    """
+    Checks whether x has any class *named* cls_name in its ancestry.
+    Doesn't require access to the class's implementation.
+    
+    Useful for patching!
+    """
+
+    for _cls in x.__class__.__mro__:
+        if _cls.__name__ == cls_name:
+            return True
+    
+    return False
+def do_nothing(x: torch.Tensor, mode:str=None):
+    return x
+
+import math
+
+def make_tome_block(
+        block_class,
+        ratio: float,
+        max_downsample: int,
+        merge_attn: bool,
+        merge_crossattn: bool,
+        merge_mlp: bool,
+        sx: int, sy: int, no_rand: bool):
+    """
+    Make a patched class on the fly so we don't have to import any specific modules.
+    This patch applies ToMe to the forward function of the block.
+    """
+
+    class ToMeBlock(block_class):
+        # Save for unpatching later
+        _parent = block_class
+
+        def _forward(self, x: torch.Tensor, context: torch.Tensor = None) -> torch.Tensor:
+            original_h, original_w = self._tome_info["size"]
+            original_tokens = original_h * original_w
+            downsample = int(math.sqrt(original_tokens // x.shape[1]))
+
+            if downsample <= max_downsample:
+                w = original_w // downsample
+                h = original_h // downsample
+                r = int(x.shape[1] * ratio)
+                m, u = bipartite_soft_matching_random2d(x, w, h, sx, sy, r, no_rand)
+            else:
+                m, u = (do_nothing, do_nothing)
+
+            m_a, u_a = (m, u) if merge_attn      else (do_nothing, do_nothing)
+            m_c, u_c = (m, u) if merge_crossattn else (do_nothing, do_nothing)
+            m_m, u_m = (m, u) if merge_mlp       else (do_nothing, do_nothing)
+
+            # This is where the meat of the computation happens
+            x = u_a(self.attn1(m_a(self.norm1(x)), context=context if self.disable_self_attn else None)) + x
+            x = u_c(self.attn2(m_c(self.norm2(x)), context=context)) + x
+            x = u_m(self.ff(m_m(self.norm3(x)))) + x
+
+            return x
+    
+    return ToMeBlock
+
+
+
+def make_tome_model(model_class):
+    """
+    Make a patched class on the fly so we don't have to import any specific modules.
+    
+    This patches the forward function of the model only to get the current image size.
+    Probably would be better off finding a way to get the size some other way.
+    """
+
+    if model_class.__name__ == "ToMeDiffusionModel":
+        model_class = model_class._parent
+    
+    class ToMeDiffusionModel(model_class):
+        # Save for later
+        _parent = model_class
+
+        def forward(self, *args, **kwdargs):
+            self._tome_info["size"] = (args[0].shape[2], args[0].shape[3])
+            return super().forward(*args, **kwdargs)
+
+    return ToMeDiffusionModel
+
+def apply_patch(
+        model: torch.nn.Module,
+        ratio: float = 0.5,
+        max_downsample: int = 1,
+        sx: int = 2, sy: int = 2,
+        use_rand: bool = True,
+        merge_attn: bool = True,
+        merge_crossattn: bool = False,
+        merge_mlp: bool = False):
+    """
+    Patches a stable diffusion model with ToMe.
+    Apply this to the highest level stable diffusion object (i.e., it should have a .model.diffusion_model).
+    Important Args:
+     - model: A top level Stable Diffusion module to patch in place. Should have a ".model.diffusion_model"
+     - ratio: The ratio of tokens to merge. I.e., 0.4 would reduce the total number of tokens by 40%.
+              The maximum value for this is 1-(1/(sx*sy)). By default, the max is 0.75 (I recommend <= 0.5 though).
+              Higher values result in more speed-up, but with more visual quality loss.
+    
+    Args to tinker with if you want:
+     - max_downsample [1, 2, 4, or 8]: Apply ToMe to layers with at most this amount of downsampling.
+                                       E.g., 1 only applies to layers with no downsampling (4/15) while
+                                       8 applies to all layers (15/15). I recommend a value of 1 or 2.
+     - sx, sy: The stride for computing dst sets (see paper). A higher stride means you can merge more tokens,
+               but the default of (2, 2) works well in most cases. Must divide the image size.
+     - use_rand: Whether or not to allow random perturbations when computing dst sets (see paper). Usually
+                 you'd want to leave this on, but if you're having weird artifacts try turning this off.
+     - merge_attn: Whether or not to merge tokens for attention (recommended).
+     - merge_crossattn: Whether or not to merge tokens for cross attention (not recommended).
+     - merge_mlp: Whether or not to merge tokens for the mlp layers (very not recommended).
+    """
+
+    # Make sure the module is not currently patched
+    remove_patch(model)
+
+    if not hasattr(model, "model") or not hasattr(model.model, "diffusion_model"):
+        # Provided model not supported
+        raise RuntimeError("Provided model was not a Stable Diffusion / Latent Diffusion model, as expected.")
+
+    diffusion_model = model.model.diffusion_model
+    diffusion_model._tome_info = { "size": None, }
+    diffusion_model.__class__ = make_tome_model(diffusion_model.__class__)
+
+    for _, module in diffusion_model.named_modules():
+        # If for some reason this has a different name, create an issue and I'll fix it
+        if isinstance_str(module, "BasicTransformerBlock"):
+            module.__class__ = make_tome_block(
+                module.__class__, ratio, max_downsample,
+                merge_attn, merge_crossattn, merge_mlp,
+                sx, sy, not use_rand
+            )
+            module._tome_info = diffusion_model._tome_info
+
+            # Something introduced in SD 2.0
+            if not hasattr(module, "disable_self_attn"):
+                module.disable_self_attn = False
+
+    return model
+
+def remove_patch(model: torch.nn.Module):
+    """ Removes a patch from a ToMe Diffusion module if it was already patched. """\
+    
+    for _, module in model.named_modules():
+        if module.__class__.__name__ == "ToMeBlock":
+            module.__class__ = module._parent
+        elif module.__class__.__name__ == "ToMeDiffusionModel":
+            module.__class__ = module._parent
+    
+    return model
+
+def bipartite_soft_matching_random2d(metric: torch.Tensor,
+                                     w: int, h: int, sx: int, sy: int, r: int,
+                                     no_rand: bool = False):
+    """
+    Partitions the tokens into src and dst and merges r tokens from src to dst.
+    Dst tokens are partitioned by choosing one randomy in each (sx, sy) region.
+    Args:
+     - metric [B, N, C]: metric to use for similarity
+     - w: image width in tokens
+     - h: image height in tokens
+     - sx: stride in the x dimension for dst, must divide w
+     - sy: stride in the y dimension for dst, must divide h
+     - r: number of tokens to remove (by merging)
+     - no_rand: if true, disable randomness (use top left corner only)
+    """
+    B, N, _ = metric.shape
+
+    if r <= 0:
+        return do_nothing, do_nothing
+    
+    with torch.no_grad():
+        
+        hsy, wsx = h // sy, w // sx
+
+        # For each sy by sx kernel, randomly assign one token to be dst and the rest src
+        idx_buffer = torch.zeros(1, hsy, wsx, sy*sx, 1, device=metric.device)
+
+        if no_rand:
+            rand_idx = torch.zeros(1, hsy, wsx, 1, 1, device=metric.device, dtype=torch.int64)
+        else:
+            rand_idx = torch.randint(sy*sx, size=(1, hsy, wsx, 1, 1), device=metric.device)
+        
+        idx_buffer.scatter_(dim=3, index=rand_idx, src=-torch.ones_like(rand_idx, dtype=idx_buffer.dtype))
+        idx_buffer = idx_buffer.view(1, hsy, wsx, sy, sx, 1).transpose(2, 3).reshape(1, N, 1)
+        rand_idx   = idx_buffer.argsort(dim=1)
+
+        num_dst = int((1 / (sx*sy)) * N)
+        a_idx = rand_idx[:, num_dst:, :] # src
+        b_idx = rand_idx[:, :num_dst, :] # dst
+
+        def split(x):
+            C = x.shape[-1]
+            src = x.gather(dim=1, index=a_idx.expand(B, N - num_dst, C))
+            dst = x.gather(dim=1, index=b_idx.expand(B, num_dst, C))
+            return src, dst
+
+        metric = metric / metric.norm(dim=-1, keepdim=True)
+        a, b = split(metric)
+        scores = a @ b.transpose(-1, -2)
+
+        # Can't reduce more than the # tokens in src
+        r = min(a.shape[1], r)
+
+        node_max, node_idx = scores.max(dim=-1)
+        edge_idx = node_max.argsort(dim=-1, descending=True)[..., None]
+
+        unm_idx = edge_idx[..., r:, :]  # Unmerged Tokens
+        src_idx = edge_idx[..., :r, :]  # Merged Tokens
+        dst_idx = node_idx[..., None].gather(dim=-2, index=src_idx)
+
+    def merge(x: torch.Tensor, mode="mean") -> torch.Tensor:
+        src, dst = split(x)
+        n, t1, c = src.shape
+        
+        unm = src.gather(dim=-2, index=unm_idx.expand(n, t1 - r, c))
+        src = src.gather(dim=-2, index=src_idx.expand(n, r, c))
+        dst = dst.scatter_reduce(-2, dst_idx.expand(n, r, c), src, reduce=mode)
+
+        return torch.cat([unm, dst], dim=1)
+
+    def unmerge(x: torch.Tensor) -> torch.Tensor:
+        unm_len = unm_idx.shape[1]
+        unm, dst = x[..., :unm_len, :], x[..., unm_len:, :]
+        _, _, c = unm.shape
+
+        src = dst.gather(dim=-2, index=dst_idx.expand(B, r, c))
+
+        # Combine back to the original shape
+        out = torch.zeros(B, N, c, device=x.device, dtype=x.dtype)
+        out.scatter_(dim=-2, index=b_idx.expand(B, num_dst, c), src=dst)
+        out.scatter_(dim=-2, index=a_idx.expand(B, a_idx.shape[1], 1).gather(dim=1, index=unm_idx).expand(B, unm_len, c), src=unm)
+        out.scatter_(dim=-2, index=a_idx.expand(B, a_idx.shape[1], 1).gather(dim=1, index=src_idx).expand(B, r, c), src=src)
+
+        return out
+
+    return merge, unmerge
+
+class TomePatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+           "model": ("MODEL",),
+           "ratio": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0}),
+        }}
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "utils"
+
+    def patch(self, model, ratio):   
+        model.model = apply_patch(model.model, ratio=ratio)
+        return (model,)
+
+# class TomeUnpatch:
+#     @classmethod
+#     def INPUT_TYPES(s):
+#         return {"required": {
+#            "model": ("MODEL",),
+#         }}
+
+#     RETURN_TYPES = ("MODEL",)
+#     FUNCTION = "unpatch"
+
+#     CATEGORY = "utils"
+
+#     def unpatch(self, model, ratio):   
+#         model.model = remove_patch(model.model)
+#         return (model,)
+
+NODE_CLASS_MAPPINGS = {
+    "TomePatch": TomePatch,
+    # "TomeUnpatch": TomeUnpatch,
+}