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+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
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+                            Preamble
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+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

ComfyUI_experiments/README.md

@@ -0,0 +1,23 @@
+## Some experimental custom nodes for [ComfyUI](https://github.com/comfyanonymous/ComfyUI)
+
+Copy the .py files to your custom_nodes directory to use them.
+
+They will show up in: custom_node_experiments/
+
+### sampler_tonemap.py
+contains ModelSamplerTonemapNoiseTest a node that makes the sampler use a simple tonemapping algorithm to tonemap the noise. It will let you use higher CFG without breaking the image. To using higher CFG lower the multiplier value.
+
+### sampler_rescalecfg.py
+contains an implementation of the Rescale Classifier-Free Guidance from: https://arxiv.org/pdf/2305.08891.pdf
+
+### advanced_model_merging.py
+
+Node for merging models by block.
+
+### sdxl_model_merging.py 
+
+Node for merging SDXL base models.
+
+### reference_only.py
+
+Contains a node that implements the "reference only controlnet". An example workflow can be found in the workflows folder.

ComfyUI_experiments/advanced_model_merging.py

@@ -0,0 +1,30 @@
+import comfy_extras.nodes_model_merging
+
+class ModelMergeBlockNumber(comfy_extras.nodes_model_merging.ModelMergeBlocks):
+    @classmethod
+    def INPUT_TYPES(s):
+        arg_dict = { "model1": ("MODEL",),
+                              "model2": ("MODEL",)}
+
+        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+
+        arg_dict["time_embed."] = argument
+        arg_dict["label_emb."] = argument
+
+        for i in range(12):
+            arg_dict["input_blocks.{}.".format(i)] = argument
+
+        for i in range(3):
+            arg_dict["middle_block.{}.".format(i)] = argument
+
+        for i in range(12):
+            arg_dict["output_blocks.{}.".format(i)] = argument
+
+        arg_dict["out."] = argument
+
+        return {"required": arg_dict}
+
+
+NODE_CLASS_MAPPINGS = {
+    "ModelMergeBlockNumber": ModelMergeBlockNumber,
+}

ComfyUI_experiments/reference_only.py

@@ -0,0 +1,54 @@
+import torch
+
+class ReferenceOnlySimple:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "reference": ("LATENT",),
+                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 64})
+                              }}
+
+    RETURN_TYPES = ("MODEL", "LATENT")
+    FUNCTION = "reference_only"
+
+    CATEGORY = "custom_node_experiments"
+
+    def reference_only(self, model, reference, batch_size):
+        model_reference = model.clone()
+        size_latent = list(reference["samples"].shape)
+        size_latent[0] = batch_size
+        latent = {}
+        latent["samples"] = torch.zeros(size_latent)
+
+        batch = latent["samples"].shape[0] + reference["samples"].shape[0]
+        def reference_apply(q, k, v, extra_options):
+            k = k.clone().repeat(1, 2, 1)
+            offset = 0
+            if q.shape[0] > batch:
+                offset = batch
+
+            for o in range(0, q.shape[0], batch):
+                for x in range(1, batch):
+                    k[x + o, q.shape[1]:] = q[o,:]
+
+            return q, k, k
+
+        model_reference.set_model_attn1_patch(reference_apply)
+        out_latent = torch.cat((reference["samples"], latent["samples"]))
+        if "noise_mask" in latent:
+            mask = latent["noise_mask"]
+        else:
+            mask = torch.ones((64,64), dtype=torch.float32, device="cpu")
+
+        if len(mask.shape) < 3:
+            mask = mask.unsqueeze(0)
+        if mask.shape[0] < latent["samples"].shape[0]:
+            print(latent["samples"].shape, mask.shape)
+            mask = mask.repeat(latent["samples"].shape[0], 1, 1)
+
+        out_mask = torch.zeros((1,mask.shape[1],mask.shape[2]), dtype=torch.float32, device="cpu")
+        return (model_reference, {"samples": out_latent, "noise_mask": torch.cat((out_mask, mask))})
+
+NODE_CLASS_MAPPINGS = {
+    "ReferenceOnlySimple": ReferenceOnlySimple,
+}

ComfyUI_experiments/sampler_rescalecfg.py

@@ -0,0 +1,38 @@
+import torch
+
+
+class RescaleClassifierFreeGuidance:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "multiplier": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "custom_node_experiments"
+
+    def patch(self, model, multiplier):
+        
+        def rescale_cfg(args):
+            cond = args["cond"]
+            uncond = args["uncond"]
+            cond_scale = args["cond_scale"]
+
+            x_cfg = uncond + cond_scale * (cond - uncond)
+            ro_pos = torch.std(cond, dim=(1,2,3), keepdim=True)
+            ro_cfg = torch.std(x_cfg, dim=(1,2,3), keepdim=True)
+
+            x_rescaled = x_cfg * (ro_pos / ro_cfg)
+            x_final = multiplier * x_rescaled + (1.0 - multiplier) * x_cfg
+
+            return x_final
+
+        m = model.clone()
+        m.set_model_sampler_cfg_function(rescale_cfg)
+        return (m, )
+
+
+NODE_CLASS_MAPPINGS = {
+    "RescaleClassifierFreeGuidanceTest": RescaleClassifierFreeGuidance,
+}

ComfyUI_experiments/sampler_tonemap.py

@@ -0,0 +1,44 @@
+import torch
+
+
+class ModelSamplerTonemapNoiseTest:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "custom_node_experiments"
+
+    def patch(self, model, multiplier):
+        
+        def sampler_tonemap_reinhard(args):
+            cond = args["cond"]
+            uncond = args["uncond"]
+            cond_scale = args["cond_scale"]
+            noise_pred = (cond - uncond)
+            noise_pred_vector_magnitude = (torch.linalg.vector_norm(noise_pred, dim=(1)) + 0.0000000001)[:,None]
+            noise_pred /= noise_pred_vector_magnitude
+
+            mean = torch.mean(noise_pred_vector_magnitude, dim=(1,2,3), keepdim=True)
+            std = torch.std(noise_pred_vector_magnitude, dim=(1,2,3), keepdim=True)
+
+            top = (std * 3 + mean) * multiplier
+
+            #reinhard
+            noise_pred_vector_magnitude *= (1.0 / top)
+            new_magnitude = noise_pred_vector_magnitude / (noise_pred_vector_magnitude + 1.0)
+            new_magnitude *= top
+
+            return uncond + noise_pred * new_magnitude * cond_scale
+
+        m = model.clone()
+        m.set_model_sampler_cfg_function(sampler_tonemap_reinhard)
+        return (m, )
+
+
+NODE_CLASS_MAPPINGS = {
+    "ModelSamplerTonemapNoiseTest": ModelSamplerTonemapNoiseTest,
+}

ComfyUI_experiments/sdxl_model_merging.py

@@ -0,0 +1,114 @@
+import comfy_extras.nodes_model_merging
+
+class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks):
+    @classmethod
+    def INPUT_TYPES(s):
+        arg_dict = { "model1": ("MODEL",),
+                              "model2": ("MODEL",)}
+
+        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+
+        arg_dict["time_embed."] = argument
+        arg_dict["label_emb."] = argument
+
+        for i in range(9):
+            arg_dict["input_blocks.{}".format(i)] = argument
+
+        for i in range(3):
+            arg_dict["middle_block.{}".format(i)] = argument
+
+        for i in range(9):
+            arg_dict["output_blocks.{}".format(i)] = argument
+
+        arg_dict["out."] = argument
+
+        return {"required": arg_dict}
+
+
+class ModelMergeSDXLTransformers(comfy_extras.nodes_model_merging.ModelMergeBlocks):
+    @classmethod
+    def INPUT_TYPES(s):
+        arg_dict = { "model1": ("MODEL",),
+                              "model2": ("MODEL",)}
+
+        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+
+        arg_dict["time_embed."] = argument
+        arg_dict["label_emb."] = argument
+
+        transformers = {4: 2, 5:2, 7:10, 8:10}
+
+        for i in range(9):
+            arg_dict["input_blocks.{}.0.".format(i)] = argument
+            if i in transformers:
+                arg_dict["input_blocks.{}.1.".format(i)] = argument
+                for j in range(transformers[i]):
+                    arg_dict["input_blocks.{}.1.transformer_blocks.{}.".format(i, j)] = argument
+
+        for i in range(3):
+            arg_dict["middle_block.{}.".format(i)] = argument
+            if i == 1:
+                for j in range(10):
+                    arg_dict["middle_block.{}.transformer_blocks.{}.".format(i, j)] = argument
+
+        transformers = {3:2, 4: 2, 5:2, 6:10, 7:10, 8:10}
+        for i in range(9):
+            arg_dict["output_blocks.{}.0.".format(i)] = argument
+            t = 8 - i
+            if t in transformers:
+                arg_dict["output_blocks.{}.1.".format(i)] = argument
+                for j in range(transformers[t]):
+                    arg_dict["output_blocks.{}.1.transformer_blocks.{}.".format(i, j)] = argument
+
+        arg_dict["out."] = argument
+
+        return {"required": arg_dict}
+
+class ModelMergeSDXLDetailedTransformers(comfy_extras.nodes_model_merging.ModelMergeBlocks):
+    @classmethod
+    def INPUT_TYPES(s):
+        arg_dict = { "model1": ("MODEL",),
+                              "model2": ("MODEL",)}
+
+        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+
+        arg_dict["time_embed."] = argument
+        arg_dict["label_emb."] = argument
+
+        transformers = {4: 2, 5:2, 7:10, 8:10}
+        transformers_args = ["norm1", "attn1.to_q", "attn1.to_k", "attn1.to_v", "attn1.to_out", "ff.net", "norm2", "attn2.to_q", "attn2.to_k", "attn2.to_v", "attn2.to_out", "norm3"]
+
+        for i in range(9):
+            arg_dict["input_blocks.{}.0.".format(i)] = argument
+            if i in transformers:
+                arg_dict["input_blocks.{}.1.".format(i)] = argument
+                for j in range(transformers[i]):
+                    for x in transformers_args:
+                        arg_dict["input_blocks.{}.1.transformer_blocks.{}.{}".format(i, j, x)] = argument
+
+        for i in range(3):
+            arg_dict["middle_block.{}.".format(i)] = argument
+            if i == 1:
+                for j in range(10):
+                    for x in transformers_args:
+                        arg_dict["middle_block.{}.transformer_blocks.{}.{}".format(i, j, x)] = argument
+
+        transformers = {3:2, 4: 2, 5:2, 6:10, 7:10, 8:10}
+        for i in range(9):
+            arg_dict["output_blocks.{}.0.".format(i)] = argument
+            t = 8 - i
+            if t in transformers:
+                arg_dict["output_blocks.{}.1.".format(i)] = argument
+                for j in range(transformers[t]):
+                    for x in transformers_args:
+                        arg_dict["output_blocks.{}.1.transformer_blocks.{}.{}".format(i, j, x)] = argument
+
+        arg_dict["out."] = argument
+
+        return {"required": arg_dict}
+
+NODE_CLASS_MAPPINGS = {
+    "ModelMergeSDXL": ModelMergeSDXL,
+    "ModelMergeSDXLTransformers": ModelMergeSDXLTransformers,
+    "ModelMergeSDXLDetailedTransformers": ModelMergeSDXLDetailedTransformers,
+}

ComfyUI_experiments/workflows/reference_only_simple.json

@@ -0,0 +1,552 @@
+{
+  "last_node_id": 15,
+  "last_link_id": 37,
+  "nodes": [
+    {
+      "id": 8,
+      "type": "VAEDecode",
+      "pos": [
+        1209,
+        188
+      ],
+      "size": {
+        "0": 210,
+        "1": 46
+      },
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 7
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 8
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            9
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAEDecode"
+      }
+    },
+    {
+      "id": 6,
+      "type": "CLIPTextEncode",
+      "pos": [
+        233,
+        117
+      ],
+      "size": {
+        "0": 422.84503173828125,
+        "1": 164.31304931640625
+      },
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 3
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            4
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "crude drawing of girl"
+      ]
+    },
+    {
+      "id": 7,
+      "type": "CLIPTextEncode",
+      "pos": [
+        237,
+        370
+      ],
+      "size": {
+        "0": 425.27801513671875,
+        "1": 180.6060791015625
+      },
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 5
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            6
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "text, watermark"
+      ]
+    },
+    {
+      "id": 3,
+      "type": "KSampler",
+      "pos": [
+        863,
+        186
+      ],
+      "size": {
+        "0": 315,
+        "1": 262
+      },
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 37
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 4
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 6
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 34
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            7
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "KSampler"
+      },
+      "widgets_values": [
+        719286772344905,
+        "fixed",
+        20,
+        8,
+        "euler",
+        "normal",
+        1
+      ]
+    },
+    {
+      "id": 9,
+      "type": "SaveImage",
+      "pos": [
+        1548,
+        180
+      ],
+      "size": [
+        1454.6668601568254,
+        548.2885143635223
+      ],
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 9
+        }
+      ],
+      "properties": {},
+      "widgets_values": [
+        "refer/ComfyUI"
+      ]
+    },
+    {
+      "id": 4,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        -563,
+        510
+      ],
+      "size": {
+        "0": 315,
+        "1": 98
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            32
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            3,
+            5
+          ],
+          "slot_index": 1
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            8,
+            20
+          ],
+          "slot_index": 2
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CheckpointLoaderSimple"
+      },
+      "widgets_values": [
+        "sd_xl_1.0.safetensors"
+      ]
+    },
+    {
+      "id": 14,
+      "type": "ImageScale",
+      "pos": [
+        -129,
+        763
+      ],
+      "size": {
+        "0": 315,
+        "1": 130
+      },
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 19
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            18
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ImageScale"
+      },
+      "widgets_values": [
+        "nearest-exact",
+        768,
+        768,
+        "center"
+      ]
+    },
+    {
+      "id": 13,
+      "type": "LoadImage",
+      "pos": [
+        -483,
+        777
+      ],
+      "size": {
+        "0": 315,
+        "1": 314
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            19
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "example.png",
+        "image"
+      ]
+    },
+    {
+      "id": 15,
+      "type": "ReferenceOnlySimple",
+      "pos": [
+        515,
+        675
+      ],
+      "size": {
+        "0": 315,
+        "1": 78
+      },
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 32,
+          "slot_index": 0
+        },
+        {
+          "name": "reference",
+          "type": "LATENT",
+          "link": 35
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            37
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