├── __init__.py ├── README.md ├── advanced_model_merging.py ├── sampler_rescalecfg.py ├── sampler_tonemap.py ├── reference_only.py ├── sampler_tonemap_rescalecfg.py ├── sdxl_model_merging.py ├── workflows └── reference_only_simple.json └── LICENSE /__init__.py: -------------------------------------------------------------------------------- 1 | import importlib 2 | import os 3 | 4 | node_list = [ #Add list of .py files containing nodes here 5 | "advanced_model_merging", 6 | "reference_only", 7 | "sampler_rescalecfg", 8 | "sampler_tonemap", 9 | "sampler_tonemap_rescalecfg", 10 | "sdxl_model_merging" 11 | ] 12 | 13 | NODE_CLASS_MAPPINGS = {} 14 | NODE_DISPLAY_NAME_MAPPINGS = {} 15 | 16 | for module_name in node_list: 17 | imported_module = importlib.import_module(".{}".format(module_name), __name__) 18 | 19 | NODE_CLASS_MAPPINGS = {**NODE_CLASS_MAPPINGS, **imported_module.NODE_CLASS_MAPPINGS} 20 | if hasattr(imported_module, "NODE_DISPLAY_NAME_MAPPINGS"): 21 | NODE_DISPLAY_NAME_MAPPINGS = {**NODE_DISPLAY_NAME_MAPPINGS, **imported_module.NODE_DISPLAY_NAME_MAPPINGS} 22 | 23 | __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] 24 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## Some experimental custom nodes for [ComfyUI](https://github.com/comfyanonymous/ComfyUI) 2 | 3 | Copy the .py files to your custom_nodes directory to use them. 4 | 5 | They will show up in: custom_node_experiments/ 6 | 7 | ### sampler_tonemap.py 8 | 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. 9 | 10 | ### sampler_rescalecfg.py 11 | contains an implementation of the Rescale Classifier-Free Guidance from: https://arxiv.org/pdf/2305.08891.pdf 12 | 13 | ### advanced_model_merging.py 14 | 15 | Node for merging models by block. 16 | 17 | ### sdxl_model_merging.py 18 | 19 | Node for merging SDXL base models. 20 | 21 | ### reference_only.py 22 | 23 | Contains a node that implements the "reference only controlnet". An example workflow can be found in the workflows folder. 24 | -------------------------------------------------------------------------------- /advanced_model_merging.py: -------------------------------------------------------------------------------- 1 | import comfy_extras.nodes_model_merging 2 | 3 | class ModelMergeBlockNumber(comfy_extras.nodes_model_merging.ModelMergeBlocks): 4 | @classmethod 5 | def INPUT_TYPES(s): 6 | arg_dict = { "model1": ("MODEL",), 7 | "model2": ("MODEL",)} 8 | 9 | argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) 10 | 11 | arg_dict["time_embed."] = argument 12 | arg_dict["label_emb."] = argument 13 | 14 | for i in range(12): 15 | arg_dict["input_blocks.{}.".format(i)] = argument 16 | 17 | for i in range(3): 18 | arg_dict["middle_block.{}.".format(i)] = argument 19 | 20 | for i in range(12): 21 | arg_dict["output_blocks.{}.".format(i)] = argument 22 | 23 | arg_dict["out."] = argument 24 | 25 | return {"required": arg_dict} 26 | 27 | 28 | NODE_CLASS_MAPPINGS = { 29 | "ModelMergeBlockNumber": ModelMergeBlockNumber, 30 | } 31 | -------------------------------------------------------------------------------- /sampler_rescalecfg.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | class RescaleClassifierFreeGuidance: 5 | @classmethod 6 | def INPUT_TYPES(s): 7 | return {"required": { "model": ("MODEL",), 8 | "multiplier": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}), 9 | }} 10 | RETURN_TYPES = ("MODEL",) 11 | FUNCTION = "patch" 12 | 13 | CATEGORY = "custom_node_experiments" 14 | 15 | def patch(self, model, multiplier): 16 | 17 | def rescale_cfg(args): 18 | cond = args["cond"] 19 | uncond = args["uncond"] 20 | cond_scale = args["cond_scale"] 21 | 22 | x_cfg = uncond + cond_scale * (cond - uncond) 23 | ro_pos = torch.std(cond, dim=(1,2,3), keepdim=True) 24 | ro_cfg = torch.std(x_cfg, dim=(1,2,3), keepdim=True) 25 | 26 | x_rescaled = x_cfg * (ro_pos / ro_cfg) 27 | x_final = multiplier * x_rescaled + (1.0 - multiplier) * x_cfg 28 | 29 | return x_final 30 | 31 | m = model.clone() 32 | m.set_model_sampler_cfg_function(rescale_cfg) 33 | return (m, ) 34 | 35 | 36 | NODE_CLASS_MAPPINGS = { 37 | "RescaleClassifierFreeGuidanceTest": RescaleClassifierFreeGuidance, 38 | } 39 | -------------------------------------------------------------------------------- /sampler_tonemap.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | class ModelSamplerTonemapNoiseTest: 5 | @classmethod 6 | def INPUT_TYPES(s): 7 | return {"required": { "model": ("MODEL",), 8 | "multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), 9 | }} 10 | RETURN_TYPES = ("MODEL",) 11 | FUNCTION = "patch" 12 | 13 | CATEGORY = "custom_node_experiments" 14 | 15 | def patch(self, model, multiplier): 16 | 17 | def sampler_tonemap_reinhard(args): 18 | cond = args["cond"] 19 | uncond = args["uncond"] 20 | cond_scale = args["cond_scale"] 21 | noise_pred = (cond - uncond) 22 | noise_pred_vector_magnitude = (torch.linalg.vector_norm(noise_pred, dim=(1)) + 0.0000000001)[:,None] 23 | noise_pred /= noise_pred_vector_magnitude 24 | 25 | mean = torch.mean(noise_pred_vector_magnitude, dim=(1,2,3), keepdim=True) 26 | std = torch.std(noise_pred_vector_magnitude, dim=(1,2,3), keepdim=True) 27 | 28 | top = (std * 3 + mean) * multiplier 29 | 30 | #reinhard 31 | noise_pred_vector_magnitude *= (1.0 / top) 32 | new_magnitude = noise_pred_vector_magnitude / (noise_pred_vector_magnitude + 1.0) 33 | new_magnitude *= top 34 | 35 | return uncond + noise_pred * new_magnitude * cond_scale 36 | 37 | m = model.clone() 38 | m.set_model_sampler_cfg_function(sampler_tonemap_reinhard) 39 | return (m, ) 40 | 41 | 42 | NODE_CLASS_MAPPINGS = { 43 | "ModelSamplerTonemapNoiseTest": ModelSamplerTonemapNoiseTest, 44 | } 45 | -------------------------------------------------------------------------------- /reference_only.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | class ReferenceOnlySimple: 4 | @classmethod 5 | def INPUT_TYPES(s): 6 | return {"required": { "model": ("MODEL",), 7 | "reference": ("LATENT",), 8 | "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}) 9 | }} 10 | 11 | RETURN_TYPES = ("MODEL", "LATENT") 12 | FUNCTION = "reference_only" 13 | 14 | CATEGORY = "custom_node_experiments" 15 | 16 | def reference_only(self, model, reference, batch_size): 17 | model_reference = model.clone() 18 | size_latent = list(reference["samples"].shape) 19 | size_latent[0] = batch_size 20 | latent = {} 21 | latent["samples"] = torch.zeros(size_latent) 22 | 23 | batch = latent["samples"].shape[0] + reference["samples"].shape[0] 24 | def reference_apply(q, k, v, extra_options): 25 | k = k.clone().repeat(1, 2, 1) 26 | offset = 0 27 | if q.shape[0] > batch: 28 | offset = batch 29 | 30 | for o in range(0, q.shape[0], batch): 31 | for x in range(1, batch): 32 | k[x + o, q.shape[1]:] = q[o,:] 33 | 34 | return q, k, k 35 | 36 | model_reference.set_model_attn1_patch(reference_apply) 37 | out_latent = torch.cat((reference["samples"], latent["samples"])) 38 | if "noise_mask" in latent: 39 | mask = latent["noise_mask"] 40 | else: 41 | mask = torch.ones((64,64), dtype=torch.float32, device="cpu") 42 | 43 | if len(mask.shape) < 3: 44 | mask = mask.unsqueeze(0) 45 | if mask.shape[0] < latent["samples"].shape[0]: 46 | print(latent["samples"].shape, mask.shape) 47 | mask = mask.repeat(latent["samples"].shape[0], 1, 1) 48 | 49 | out_mask = torch.zeros((1,mask.shape[1],mask.shape[2]), dtype=torch.float32, device="cpu") 50 | return (model_reference, {"samples": out_latent, "noise_mask": torch.cat((out_mask, mask))}) 51 | 52 | NODE_CLASS_MAPPINGS = { 53 | "ReferenceOnlySimple": ReferenceOnlySimple, 54 | } 55 | -------------------------------------------------------------------------------- /sampler_tonemap_rescalecfg.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | class TonemapNoiseWithRescaleCFG: 5 | @classmethod 6 | def INPUT_TYPES(s): 7 | return {"required": {"model": ("MODEL",), 8 | "tonemap_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), 9 | "rescale_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), 10 | }} 11 | RETURN_TYPES = ("MODEL",) 12 | FUNCTION = "patch" 13 | 14 | CATEGORY = "custom_node_experiments" 15 | 16 | def patch(self, model, tonemap_multiplier, rescale_multiplier): 17 | 18 | def tonemap_noise_rescale_cfg(args): 19 | cond = args["cond"] 20 | uncond = args["uncond"] 21 | cond_scale = args["cond_scale"] 22 | 23 | # Tonemap 24 | noise_pred = (cond - uncond) 25 | noise_pred_vector_magnitude = (torch.linalg.vector_norm(noise_pred, dim=(1)) + 0.0000000001)[:, None] 26 | noise_pred /= noise_pred_vector_magnitude 27 | 28 | mean = torch.mean(noise_pred_vector_magnitude, dim=(1, 2, 3), keepdim=True) 29 | std = torch.std(noise_pred_vector_magnitude, dim=(1, 2, 3), keepdim=True) 30 | 31 | top = (std * 3 + mean) * tonemap_multiplier 32 | 33 | # Reinhard 34 | noise_pred_vector_magnitude *= (1.0 / top) 35 | new_magnitude = noise_pred_vector_magnitude / (noise_pred_vector_magnitude + 1.0) 36 | new_magnitude *= top 37 | 38 | # Rescale CFG 39 | x_cfg = uncond + (noise_pred * new_magnitude * cond_scale) 40 | ro_pos = torch.std(cond, dim=(1, 2, 3), keepdim=True) 41 | ro_cfg = torch.std(x_cfg, dim=(1, 2, 3), keepdim=True) 42 | 43 | x_rescaled = x_cfg * (ro_pos / ro_cfg) 44 | x_final = rescale_multiplier * x_rescaled + (1.0 - rescale_multiplier) * x_cfg 45 | 46 | return x_final 47 | 48 | m = model.clone() 49 | m.set_model_sampler_cfg_function(tonemap_noise_rescale_cfg) 50 | return (m, ) 51 | 52 | 53 | NODE_CLASS_MAPPINGS = { 54 | "TonemapNoiseWithRescaleCFG": TonemapNoiseWithRescaleCFG, 55 | } 56 | -------------------------------------------------------------------------------- /sdxl_model_merging.py: -------------------------------------------------------------------------------- 1 | import comfy_extras.nodes_model_merging 2 | 3 | class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks): 4 | @classmethod 5 | def INPUT_TYPES(s): 6 | arg_dict = { "model1": ("MODEL",), 7 | "model2": ("MODEL",)} 8 | 9 | argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) 10 | 11 | arg_dict["time_embed."] = argument 12 | arg_dict["label_emb."] = argument 13 | 14 | for i in range(9): 15 | arg_dict["input_blocks.{}".format(i)] = argument 16 | 17 | for i in range(3): 18 | arg_dict["middle_block.{}".format(i)] = argument 19 | 20 | for i in range(9): 21 | arg_dict["output_blocks.{}".format(i)] = argument 22 | 23 | arg_dict["out."] = argument 24 | 25 | return {"required": arg_dict} 26 | 27 | 28 | class ModelMergeSDXLTransformers(comfy_extras.nodes_model_merging.ModelMergeBlocks): 29 | @classmethod 30 | def INPUT_TYPES(s): 31 | arg_dict = { "model1": ("MODEL",), 32 | "model2": ("MODEL",)} 33 | 34 | argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) 35 | 36 | arg_dict["time_embed."] = argument 37 | arg_dict["label_emb."] = argument 38 | 39 | transformers = {4: 2, 5:2, 7:10, 8:10} 40 | 41 | for i in range(9): 42 | arg_dict["input_blocks.{}.0.".format(i)] = argument 43 | if i in transformers: 44 | arg_dict["input_blocks.{}.1.".format(i)] = argument 45 | for j in range(transformers[i]): 46 | arg_dict["input_blocks.{}.1.transformer_blocks.{}.".format(i, j)] = argument 47 | 48 | for i in range(3): 49 | arg_dict["middle_block.{}.".format(i)] = argument 50 | if i == 1: 51 | for j in range(10): 52 | arg_dict["middle_block.{}.transformer_blocks.{}.".format(i, j)] = argument 53 | 54 | transformers = {3:2, 4: 2, 5:2, 6:10, 7:10, 8:10} 55 | for i in range(9): 56 | arg_dict["output_blocks.{}.0.".format(i)] = argument 57 | t = 8 - i 58 | if t in transformers: 59 | arg_dict["output_blocks.{}.1.".format(i)] = argument 60 | for j in range(transformers[t]): 61 | arg_dict["output_blocks.{}.1.transformer_blocks.{}.".format(i, j)] = argument 62 | 63 | arg_dict["out."] = argument 64 | 65 | return {"required": arg_dict} 66 | 67 | class ModelMergeSDXLDetailedTransformers(comfy_extras.nodes_model_merging.ModelMergeBlocks): 68 | @classmethod 69 | def INPUT_TYPES(s): 70 | arg_dict = { "model1": ("MODEL",), 71 | "model2": ("MODEL",)} 72 | 73 | argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) 74 | 75 | arg_dict["time_embed."] = argument 76 | arg_dict["label_emb."] = argument 77 | 78 | transformers = {4: 2, 5:2, 7:10, 8:10} 79 | 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"] 80 | 81 | for i in range(9): 82 | arg_dict["input_blocks.{}.0.".format(i)] = argument 83 | if i in transformers: 84 | arg_dict["input_blocks.{}.1.".format(i)] = argument 85 | for j in range(transformers[i]): 86 | for x in transformers_args: 87 | arg_dict["input_blocks.{}.1.transformer_blocks.{}.{}".format(i, j, x)] = argument 88 | 89 | for i in range(3): 90 | arg_dict["middle_block.{}.".format(i)] = argument 91 | if i == 1: 92 | for j in range(10): 93 | for x in transformers_args: 94 | arg_dict["middle_block.{}.transformer_blocks.{}.{}".format(i, j, x)] = argument 95 | 96 | transformers = {3:2, 4: 2, 5:2, 6:10, 7:10, 8:10} 97 | for i in range(9): 98 | arg_dict["output_blocks.{}.0.".format(i)] = argument 99 | t = 8 - i 100 | if t in transformers: 101 | arg_dict["output_blocks.{}.1.".format(i)] = argument 102 | for j in range(transformers[t]): 103 | for x in transformers_args: 104 | arg_dict["output_blocks.{}.1.transformer_blocks.{}.{}".format(i, j, x)] = argument 105 | 106 | arg_dict["out."] = argument 107 | 108 | return {"required": arg_dict} 109 | 110 | NODE_CLASS_MAPPINGS = { 111 | "ModelMergeSDXL": ModelMergeSDXL, 112 | "ModelMergeSDXLTransformers": ModelMergeSDXLTransformers, 113 | "ModelMergeSDXLDetailedTransformers": ModelMergeSDXLDetailedTransformers, 114 | } 115 | -------------------------------------------------------------------------------- /workflows/reference_only_simple.json: -------------------------------------------------------------------------------- 1 | { 2 | "last_node_id": 15, 3 | "last_link_id": 37, 4 | "nodes": [ 5 | { 6 | "id": 8, 7 | "type": "VAEDecode", 8 | "pos": [ 9 | 1209, 10 | 188 11 | ], 12 | "size": { 13 | "0": 210, 14 | "1": 46 15 | }, 16 | "flags": {}, 17 | "order": 8, 18 | "mode": 0, 19 | "inputs": [ 20 | { 21 | "name": "samples", 22 | "type": "LATENT", 23 | "link": 7 24 | }, 25 | { 26 | "name": "vae", 27 | "type": "VAE", 28 | "link": 8 29 | } 30 | ], 31 | "outputs": [ 32 | { 33 | "name": "IMAGE", 34 | "type": "IMAGE", 35 | "links": [ 36 | 9 37 | ], 38 | "slot_index": 0 39 | } 40 | ], 41 | "properties": { 42 | "Node name for S&R": "VAEDecode" 43 | } 44 | }, 45 | { 46 | "id": 6, 47 | "type": "CLIPTextEncode", 48 | "pos": [ 49 | 233, 50 | 117 51 | ], 52 | "size": { 53 | "0": 422.84503173828125, 54 | "1": 164.31304931640625 55 | }, 56 | "flags": {}, 57 | "order": 2, 58 | "mode": 0, 59 | "inputs": [ 60 | { 61 | "name": "clip", 62 | "type": "CLIP", 63 | "link": 3 64 | } 65 | ], 66 | "outputs": [ 67 | { 68 | "name": "CONDITIONING", 69 | "type": "CONDITIONING", 70 | "links": [ 71 | 4 72 | ], 73 | "slot_index": 0 74 | } 75 | ], 76 | "properties": { 77 | "Node name for S&R": "CLIPTextEncode" 78 | }, 79 | "widgets_values": [ 80 | "crude drawing of girl" 81 | ] 82 | }, 83 | { 84 | "id": 7, 85 | "type": "CLIPTextEncode", 86 | "pos": [ 87 | 237, 88 | 370 89 | ], 90 | "size": { 91 | "0": 425.27801513671875, 92 | "1": 180.6060791015625 93 | }, 94 | "flags": {}, 95 | "order": 3, 96 | "mode": 0, 97 | "inputs": [ 98 | { 99 | "name": "clip", 100 | "type": "CLIP", 101 | "link": 5 102 | } 103 | ], 104 | "outputs": [ 105 | { 106 | "name": "CONDITIONING", 107 | "type": "CONDITIONING", 108 | "links": [ 109 | 6 110 | ], 111 | "slot_index": 0 112 | } 113 | ], 114 | "properties": { 115 | "Node name for S&R": "CLIPTextEncode" 116 | }, 117 | "widgets_values": [ 118 | "text, watermark" 119 | ] 120 | }, 121 | { 122 | "id": 3, 123 | "type": "KSampler", 124 | "pos": [ 125 | 863, 126 | 186 127 | ], 128 | "size": { 129 | "0": 315, 130 | "1": 262 131 | }, 132 | "flags": {}, 133 | "order": 7, 134 | "mode": 0, 135 | "inputs": [ 136 | { 137 | "name": "model", 138 | "type": "MODEL", 139 | "link": 37 140 | }, 141 | { 142 | "name": "positive", 143 | "type": "CONDITIONING", 144 | "link": 4 145 | }, 146 | { 147 | "name": "negative", 148 | "type": "CONDITIONING", 149 | "link": 6 150 | }, 151 | { 152 | "name": "latent_image", 153 | "type": "LATENT", 154 | "link": 34 155 | } 156 | ], 157 | "outputs": [ 158 | { 159 | "name": "LATENT", 160 | "type": "LATENT", 161 | "links": [ 162 | 7 163 | ], 164 | "slot_index": 0 165 | } 166 | ], 167 | "properties": { 168 | "Node name for S&R": "KSampler" 169 | }, 170 | "widgets_values": [ 171 | 719286772344905, 172 | "fixed", 173 | 20, 174 | 8, 175 | "euler", 176 | "normal", 177 | 1 178 | ] 179 | }, 180 | { 181 | "id": 9, 182 | "type": "SaveImage", 183 | "pos": [ 184 | 1548, 185 | 180 186 | ], 187 | "size": [ 188 | 1454.6668601568254, 189 | 548.2885143635223 190 | ], 191 | "flags": {}, 192 | "order": 9, 193 | "mode": 0, 194 | "inputs": [ 195 | { 196 | "name": "images", 197 | "type": "IMAGE", 198 | "link": 9 199 | } 200 | ], 201 | "properties": {}, 202 | "widgets_values": [ 203 | "refer/ComfyUI" 204 | ] 205 | }, 206 | { 207 | "id": 4, 208 | "type": "CheckpointLoaderSimple", 209 | "pos": [ 210 | -563, 211 | 510 212 | ], 213 | "size": { 214 | "0": 315, 215 | "1": 98 216 | }, 217 | "flags": {}, 218 | "order": 0, 219 | "mode": 0, 220 | "outputs": [ 221 | { 222 | "name": "MODEL", 223 | "type": "MODEL", 224 | "links": [ 225 | 32 226 | ], 227 | "slot_index": 0 228 | }, 229 | { 230 | "name": "CLIP", 231 | "type": "CLIP", 232 | "links": [ 233 | 3, 234 | 5 235 | ], 236 | "slot_index": 1 237 | }, 238 | { 239 | "name": "VAE", 240 | "type": "VAE", 241 | "links": [ 242 | 8, 243 | 20 244 | ], 245 | "slot_index": 2 246 | } 247 | ], 248 | "properties": { 249 | "Node name for S&R": "CheckpointLoaderSimple" 250 | }, 251 | "widgets_values": [ 252 | "sd_xl_1.0.safetensors" 253 | ] 254 | }, 255 | { 256 | "id": 14, 257 | "type": "ImageScale", 258 | "pos": [ 259 | -129, 260 | 763 261 | ], 262 | "size": { 263 | "0": 315, 264 | "1": 130 265 | }, 266 | "flags": {}, 267 | "order": 4, 268 | "mode": 0, 269 | "inputs": [ 270 | { 271 | "name": "image", 272 | "type": "IMAGE", 273 | "link": 19 274 | } 275 | ], 276 | "outputs": [ 277 | { 278 | "name": "IMAGE", 279 | "type": "IMAGE", 280 | "links": [ 281 | 18 282 | ], 283 | "shape": 3, 284 | "slot_index": 0 285 | } 286 | ], 287 | "properties": { 288 | "Node name for S&R": "ImageScale" 289 | }, 290 | "widgets_values": [ 291 | "nearest-exact", 292 | 768, 293 | 768, 294 | "center" 295 | ] 296 | }, 297 | { 298 | "id": 13, 299 | "type": "LoadImage", 300 | "pos": [ 301 | -483, 302 | 777 303 | ], 304 | "size": { 305 | "0": 315, 306 | "1": 314 307 | }, 308 | "flags": {}, 309 | "order": 1, 310 | "mode": 0, 311 | "outputs": [ 312 | { 313 | "name": "IMAGE", 314 | "type": "IMAGE", 315 | "links": [ 316 | 19 317 | ], 318 | "shape": 3, 319 | "slot_index": 0 320 | }, 321 | { 322 | "name": "MASK", 323 | "type": "MASK", 324 | "links": null, 325 | "shape": 3 326 | } 327 | ], 328 | "properties": { 329 | "Node name for S&R": "LoadImage" 330 | }, 331 | "widgets_values": [ 332 | "example.png", 333 | "image" 334 | ] 335 | }, 336 | { 337 | "id": 15, 338 | "type": "ReferenceOnlySimple", 339 | "pos": [ 340 | 515, 341 | 675 342 | ], 343 | "size": { 344 | "0": 315, 345 | "1": 78 346 | }, 347 | "flags": {}, 348 | "order": 6, 349 | "mode": 0, 350 | "inputs": [ 351 | { 352 | "name": "model", 353 | "type": "MODEL", 354 | "link": 32, 355 | "slot_index": 0 356 | }, 357 | { 358 | "name": "reference", 359 | "type": "LATENT", 360 | "link": 35 361 | } 362 | ], 363 | "outputs": [ 364 | { 365 | "name": "MODEL", 366 | "type": "MODEL", 367 | "links": [ 368 | 37 369 | ], 370 | "shape": 3, 371 | "slot_index": 0 372 | }, 373 | { 374 | "name": "LATENT", 375 | "type": "LATENT", 376 | "links": [ 377 | 34 378 | ], 379 | "shape": 3, 380 | "slot_index": 1 381 | } 382 | ], 383 | "properties": { 384 | "Node name for S&R": "ReferenceOnlySimple" 385 | }, 386 | "widgets_values": [ 387 | 2 388 | ] 389 | }, 390 | { 391 | "id": 12, 392 | "type": "VAEEncode", 393 | "pos": [ 394 | 248, 395 | 732 396 | ], 397 | "size": { 398 | "0": 210, 399 | "1": 46 400 | }, 401 | "flags": {}, 402 | "order": 5, 403 | "mode": 0, 404 | "inputs": [ 405 | { 406 | "name": "pixels", 407 | "type": "IMAGE", 408 | "link": 18, 409 | "slot_index": 0 410 | }, 411 | { 412 | "name": "vae", 413 | "type": "VAE", 414 | "link": 20, 415 | "slot_index": 1 416 | } 417 | ], 418 | "outputs": [ 419 | { 420 | "name": "LATENT", 421 | "type": "LATENT", 422 | "links": [ 423 | 35 424 | ], 425 | "shape": 3, 426 | "slot_index": 0 427 | } 428 | ], 429 | "properties": { 430 | "Node name for S&R": "VAEEncode" 431 | } 432 | } 433 | ], 434 | "links": [ 435 | [ 436 | 3, 437 | 4, 438 | 1, 439 | 6, 440 | 0, 441 | "CLIP" 442 | ], 443 | [ 444 | 4, 445 | 6, 446 | 0, 447 | 3, 448 | 1, 449 | "CONDITIONING" 450 | ], 451 | [ 452 | 5, 453 | 4, 454 | 1, 455 | 7, 456 | 0, 457 | "CLIP" 458 | ], 459 | [ 460 | 6, 461 | 7, 462 | 0, 463 | 3, 464 | 2, 465 | "CONDITIONING" 466 | ], 467 | [ 468 | 7, 469 | 3, 470 | 0, 471 | 8, 472 | 0, 473 | "LATENT" 474 | ], 475 | [ 476 | 8, 477 | 4, 478 | 2, 479 | 8, 480 | 1, 481 | "VAE" 482 | ], 483 | [ 484 | 9, 485 | 8, 486 | 0, 487 | 9, 488 | 0, 489 | "IMAGE" 490 | ], 491 | [ 492 | 18, 493 | 14, 494 | 0, 495 | 12, 496 | 0, 497 | "IMAGE" 498 | ], 499 | [ 500 | 19, 501 | 13, 502 | 0, 503 | 14, 504 | 0, 505 | "IMAGE" 506 | ], 507 | [ 508 | 20, 509 | 4, 510 | 2, 511 | 12, 512 | 1, 513 | "VAE" 514 | ], 515 | [ 516 | 32, 517 | 4, 518 | 0, 519 | 15, 520 | 0, 521 | "MODEL" 522 | ], 523 | [ 524 | 34, 525 | 15, 526 | 1, 527 | 3, 528 | 3, 529 | "LATENT" 530 | ], 531 | [ 532 | 35, 533 | 12, 534 | 0, 535 | 15, 536 | 1, 537 | "LATENT" 538 | ], 539 | [ 540 | 37, 541 | 15, 542 | 0, 543 | 3, 544 | 0, 545 | "MODEL" 546 | ] 547 | ], 548 | "groups": [], 549 | "config": {}, 550 | "extra": {}, 551 | "version": 0.4 552 | } 553 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------