Merge pull request #3 from LykosAI/add-experiments

Add comfyui_experiments module

Author: ionite34

src/inference_core_nodes/__init__.py

@@ -1,6 +1,6 @@
 __all__ = ("__version__", "NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS")
 
-__version__ = "0.2.1"
+__version__ = "0.3.0"
 
 
 def _get_node_mappings():

src/inference_core_nodes/comfyui_experiments/LICENSE

@@ -0,0 +1,7 @@
+## ComfyUI Experiments
+
+Based on or modified from: [comfyanonymous/ComfyUI_experiments](https://github.com/comfyanonymous/ComfyUI_experiments) @ 934dba9d206e4738e0dac26a09b51f1dffcb4e44
+
+License: GPL-3.0
+
+

src/inference_core_nodes/comfyui_experiments/README.md

@@ -0,0 +1,23 @@
+import importlib
+import os
+
+node_list = [ #Add list of .py files containing nodes here
+    "advanced_model_merging",
+    "reference_only",
+    "sampler_rescalecfg",
+    "sampler_tonemap",
+    "sampler_tonemap_rescalecfg",
+    "sdxl_model_merging"
+]
+
+NODE_CLASS_MAPPINGS = {}
+NODE_DISPLAY_NAME_MAPPINGS = {}
+
+for module_name in node_list:
+    imported_module = importlib.import_module(".{}".format(module_name), __name__)
+
+    NODE_CLASS_MAPPINGS = {**NODE_CLASS_MAPPINGS, **imported_module.NODE_CLASS_MAPPINGS}
+    if hasattr(imported_module, "NODE_DISPLAY_NAME_MAPPINGS"):
+        NODE_DISPLAY_NAME_MAPPINGS = {**NODE_DISPLAY_NAME_MAPPINGS, **imported_module.NODE_DISPLAY_NAME_MAPPINGS}
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

src/inference_core_nodes/comfyui_experiments/__init__.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,
+}

src/inference_core_nodes/comfyui_experiments/advanced_model_merging.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,
+}

src/inference_core_nodes/comfyui_experiments/reference_only.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,
+}

src/inference_core_nodes/comfyui_experiments/sampler_rescalecfg.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,
+}

src/inference_core_nodes/comfyui_experiments/sampler_tonemap.py

@@ -0,0 +1,55 @@
+import torch
+
+
+class TonemapNoiseWithRescaleCFG:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"model": ("MODEL",),
+                             "tonemap_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
+                             "rescale_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                             }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "custom_node_experiments"
+
+    def patch(self, model, tonemap_multiplier, rescale_multiplier):
+
+        def tonemap_noise_rescale_cfg(args):
+            cond = args["cond"]
+            uncond = args["uncond"]
+            cond_scale = args["cond_scale"]
+
+            # Tonemap
+            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) * tonemap_multiplier
+
+            # Reinhard
+            noise_pred_vector_magnitude *= (1.0 / top)
+            new_magnitude = noise_pred_vector_magnitude / (noise_pred_vector_magnitude + 1.0)
+            new_magnitude *= top
+
+            # Rescale CFG
+            x_cfg = uncond + (noise_pred * new_magnitude * cond_scale)
+            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 = rescale_multiplier * x_rescaled + (1.0 - rescale_multiplier) * x_cfg
+
+            return x_final
+
+        m = model.clone()
+        m.set_model_sampler_cfg_function(tonemap_noise_rescale_cfg)
+        return (m, )
+
+
+NODE_CLASS_MAPPINGS = {
+    "TonemapNoiseWithRescaleCFG": TonemapNoiseWithRescaleCFG,
+}

src/inference_core_nodes/comfyui_experiments/sampler_tonemap_rescalecfg.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,
+}