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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Prompt Injection Node for ComfyUI 2 | 3 | This custom node for ComfyUI allows you to inject specific prompts at specific blocks of the Stable Diffusion UNet, providing fine-grained control over the generated image. 4 | 5 | ## Highly Experimental 6 | 7 | The code is very basic, experimental and prossibly buggy. It's a very interesting proof of concept and I will expand it if anything good can be done with it. 8 | 9 | At the moment this is a fork of [DataCTE](https://github.com/DataCTE/prompt_injection)'s repository, I'm in contact with them and we'll evaluate a merge when the code is stable. 10 | 11 | ## Credits 12 | 13 | This code is based on [DataCTE](https://github.com/DataCTE/prompt_injection), [Perturbed Attention](https://github.com/pamparamm/sd-perturbed-attention), [B-Lora](https://github.com/yardenfren1996/B-LoRA/) and my previous experiments with the [IPAdapter](https://github.com/cubiq/ComfyUI_IPAdapter_plus?tab=readme-ov-file) style/composition. -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | from .prompt_injection import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS 2 | 3 | __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] 4 | -------------------------------------------------------------------------------- /prompt_injection.py: -------------------------------------------------------------------------------- 1 | import comfy.model_patcher 2 | import comfy.samplers 3 | import torch 4 | import torch.nn.functional as F 5 | 6 | def build_patch(patchedBlocks, weight=1.0, sigma_start=0.0, sigma_end=1.0, noise=0.0): 7 | def prompt_injection_patch(n, context_attn1: torch.Tensor, value_attn1, extra_options): 8 | (block, block_index) = extra_options.get('block', (None,None)) 9 | sigma = extra_options["sigmas"].detach().cpu()[0].item() if 'sigmas' in extra_options else 999999999.9 10 | batch_prompt = n.shape[0] // len(extra_options["cond_or_uncond"]) 11 | 12 | if sigma <= sigma_start and sigma >= sigma_end: 13 | if (block and f'{block}:{block_index}' in patchedBlocks and patchedBlocks[f'{block}:{block_index}']): 14 | if context_attn1.dim() == 3: 15 | c = context_attn1[0].unsqueeze(0) 16 | else: 17 | c = context_attn1[0][0].unsqueeze(0) 18 | b = patchedBlocks[f'{block}:{block_index}'][0][0].repeat(c.shape[0], 1, 1).to(context_attn1.device) 19 | if noise != 0.0: 20 | b = b + torch.randn_like(b) * noise 21 | 22 | padding = abs(c.shape[1] - b.shape[1]) 23 | if c.shape[1] > b.shape[1]: 24 | b = F.pad(b, (0, 0, 0, padding), mode='constant', value=0) 25 | elif c.shape[1] < b.shape[1]: 26 | c = F.pad(c, (0, 0, 0, padding), mode='constant', value=0) 27 | 28 | out = torch.stack((c, b)).to(dtype=context_attn1.dtype) 29 | out = out.repeat(1, batch_prompt, 1, 1) * weight 30 | 31 | return n, out, out 32 | 33 | return n, context_attn1, value_attn1 34 | return prompt_injection_patch 35 | 36 | def build_patch_by_index(patchedBlocks, weight=1.0, sigma_start=0.0, sigma_end=1.0, noise=0.0): 37 | def prompt_injection_patch(n, context_attn1: torch.Tensor, value_attn1, extra_options): 38 | idx = extra_options["transformer_index"] 39 | sigma = extra_options["sigmas"].detach().cpu()[0].item() if 'sigmas' in extra_options else 999999999.9 40 | batch_prompt = n.shape[0] // len(extra_options["cond_or_uncond"]) 41 | 42 | if sigma <= sigma_start and sigma >= sigma_end: 43 | if idx in patchedBlocks and patchedBlocks[idx] is not None: 44 | if context_attn1.dim() == 3: 45 | c = context_attn1[0].unsqueeze(0) 46 | else: 47 | c = context_attn1[0][0].unsqueeze(0) 48 | 49 | b = patchedBlocks[idx][0][0].repeat(c.shape[0], 1, 1).to(context_attn1.device) 50 | if noise != 0.0: 51 | b = b + torch.randn_like(b) * noise 52 | 53 | padding = abs(c.shape[1] - b.shape[1]) 54 | if c.shape[1] > b.shape[1]: 55 | b = F.pad(b, (0, 0, 0, padding), mode='constant', value=0) 56 | elif c.shape[1] < b.shape[1]: 57 | c = F.pad(c, (0, 0, 0, padding), mode='constant', value=0) 58 | 59 | out = torch.stack((c, b)).to(dtype=context_attn1.dtype) 60 | out = out.repeat(1, batch_prompt, 1, 1) * weight 61 | 62 | return n, out, out 63 | 64 | return n, context_attn1, value_attn1 65 | return prompt_injection_patch 66 | 67 | class PromptInjection: 68 | @classmethod 69 | def INPUT_TYPES(s): 70 | return { 71 | "required": { 72 | "model": ("MODEL",), 73 | }, 74 | "optional": { 75 | "all": ("CONDITIONING",), 76 | "input_4": ("CONDITIONING",), 77 | "input_5": ("CONDITIONING",), 78 | "input_7": ("CONDITIONING",), 79 | "input_8": ("CONDITIONING",), 80 | "middle_0": ("CONDITIONING",), 81 | "output_0": ("CONDITIONING",), 82 | "output_1": ("CONDITIONING",), 83 | "output_2": ("CONDITIONING",), 84 | "output_3": ("CONDITIONING",), 85 | "output_4": ("CONDITIONING",), 86 | "output_5": ("CONDITIONING",), 87 | "weight": ("FLOAT", {"default": 1.0, "min": -2.0, "max": 5.0, "step": 0.05}), 88 | "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), 89 | "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), 90 | "noise": ("FLOAT", {"default": 0.0, "min": -5.0, "max": 5.0, "step": 0.05}), 91 | } 92 | } 93 | 94 | RETURN_TYPES = ("MODEL",) 95 | FUNCTION = "patch" 96 | 97 | CATEGORY = "advanced/model" 98 | 99 | def patch(self, model: comfy.model_patcher.ModelPatcher, all=None, input_4=None, input_5=None, input_7=None, input_8=None, middle_0=None, output_0=None, output_1=None, output_2=None, output_3=None, output_4=None, output_5=None, weight=1.0, start_at=0.0, end_at=1.0, noise=0.0): 100 | if not any((all, input_4, input_5, input_7, input_8, middle_0, output_0, output_1, output_2, output_3, output_4, output_5)): 101 | return (model,) 102 | 103 | m = model.clone() 104 | sigma_start = m.get_model_object("model_sampling").percent_to_sigma(start_at) 105 | sigma_end = m.get_model_object("model_sampling").percent_to_sigma(end_at) 106 | 107 | patchedBlocks = {} 108 | blocks = {'input': [4, 5, 7, 8], 'middle': [0], 'output': [0, 1, 2, 3, 4, 5]} 109 | 110 | for block in blocks: 111 | for index in blocks[block]: 112 | value = locals()[f"{block}_{index}"] if locals()[f"{block}_{index}"] is not None else all 113 | if value is not None: 114 | patchedBlocks[f"{block}:{index}"] = value 115 | 116 | m.set_model_attn2_patch(build_patch(patchedBlocks, weight=weight, sigma_start=sigma_start, sigma_end=sigma_end, noise=noise)) 117 | 118 | return (m,) 119 | 120 | class PromptInjectionIdx: 121 | @classmethod 122 | def INPUT_TYPES(s): 123 | return { 124 | "required": { 125 | "model": ("MODEL",), 126 | }, 127 | "optional": { 128 | "all": ("CONDITIONING",), 129 | "idx_0": ("CONDITIONING",), 130 | "idx_1": ("CONDITIONING",), 131 | "idx_2": ("CONDITIONING",), 132 | "idx_3": ("CONDITIONING",), 133 | "idx_4": ("CONDITIONING",), 134 | "idx_5": ("CONDITIONING",), 135 | "idx_6": ("CONDITIONING",), 136 | "idx_7": ("CONDITIONING",), 137 | "idx_8": ("CONDITIONING",), 138 | "idx_9": ("CONDITIONING",), 139 | "idx_10": ("CONDITIONING",), 140 | "idx_11_sd15": ("CONDITIONING",), 141 | "idx_12_sd15": ("CONDITIONING",), 142 | "idx_13_sd15": ("CONDITIONING",), 143 | "idx_14_sd15": ("CONDITIONING",), 144 | "idx_15_sd15": ("CONDITIONING",), 145 | "weight": ("FLOAT", {"default": 1.0, "min": -2.0, "max": 5.0, "step": 0.05}), 146 | "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), 147 | "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), 148 | "noise": ("FLOAT", {"default": 0.0, "min": -5.0, "max": 5.0, "step": 0.05}), 149 | } 150 | } 151 | 152 | RETURN_TYPES = ("MODEL",) 153 | FUNCTION = "patch" 154 | 155 | CATEGORY = "advanced/model" 156 | 157 | def patch(self, model, all=None, idx_0=None, idx_1=None, idx_2=None, idx_3=None, idx_4=None, idx_5=None, idx_6=None, idx_7=None, idx_8=None, idx_9=None, idx_10=None, idx_11_sd15=None, idx_12_sd15=None, idx_13_sd15=None, idx_14_sd15=None, idx_15_sd15=None, weight=1.0, start_at=0.0, end_at=1.0, noise=0.0): 158 | if not any((all, idx_0, idx_1, idx_2, idx_3, idx_4, idx_5, idx_6, idx_7, idx_8, idx_9, idx_10, idx_11_sd15, idx_12_sd15, idx_13_sd15, idx_14_sd15, idx_15_sd15)): 159 | return (model,) 160 | 161 | m = model.clone() 162 | sigma_start = m.get_model_object("model_sampling").percent_to_sigma(start_at) 163 | sigma_end = m.get_model_object("model_sampling").percent_to_sigma(end_at) 164 | is_sdxl = isinstance(model.model, (comfy.model_base.SDXL, comfy.model_base.SDXLRefiner, comfy.model_base.SDXL_instructpix2pix)) 165 | 166 | patchedBlocks = { 167 | 0: idx_0 if idx_0 is not None else all, 168 | 1: idx_1 if idx_1 is not None else all, 169 | 2: idx_2 if idx_2 is not None else all, 170 | 3: idx_3 if idx_3 is not None else all, 171 | 4: idx_4 if idx_4 is not None else all, 172 | 5: idx_5 if idx_5 is not None else all, 173 | 6: idx_6 if idx_6 is not None else all, 174 | 7: idx_7 if idx_7 is not None else all, 175 | 8: idx_8 if idx_8 is not None else all, 176 | 9: idx_9 if idx_9 is not None else all, 177 | 10: idx_10 if idx_10 is not None else all, 178 | 11: idx_11_sd15 if idx_11_sd15 is not None else all if not is_sdxl else None, 179 | 12: idx_12_sd15 if idx_12_sd15 is not None else all if not is_sdxl else None, 180 | 13: idx_13_sd15 if idx_13_sd15 is not None else all if not is_sdxl else None, 181 | 14: idx_14_sd15 if idx_14_sd15 is not None else all if not is_sdxl else None, 182 | 15: idx_15_sd15 if idx_15_sd15 is not None else all if not is_sdxl else None, 183 | } 184 | 185 | m.set_model_attn2_patch(build_patch_by_index(patchedBlocks, weight=weight, sigma_start=sigma_start, sigma_end=sigma_end, noise=noise)) 186 | 187 | return (m,) 188 | 189 | 190 | class SimplePromptInjection: 191 | @classmethod 192 | def INPUT_TYPES(s): 193 | return { 194 | "required": { 195 | "model": ("MODEL",), 196 | }, 197 | "optional": { 198 | "block": (["input:4", "input:5", "input:7", "input:8", "middle:0", "output:0", "output:1", "output:2", "output:3", "output:4", "output:5"],), 199 | "conditioning": ("CONDITIONING",), 200 | "weight": ("FLOAT", {"default": 1.0, "min": -2.0, "max": 5.0, "step": 0.05}), 201 | "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), 202 | "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), 203 | } 204 | } 205 | 206 | RETURN_TYPES = ("MODEL",) 207 | FUNCTION = "patch" 208 | 209 | CATEGORY = "advanced/model" 210 | 211 | def patch(self, model: comfy.model_patcher.ModelPatcher, block, conditioning=None, weight=1.0, start_at=0.0, end_at=1.0): 212 | if conditioning is None: 213 | return (model,) 214 | 215 | m = model.clone() 216 | sigma_start = m.get_model_object("model_sampling").percent_to_sigma(start_at) 217 | sigma_end = m.get_model_object("model_sampling").percent_to_sigma(end_at) 218 | 219 | m.set_model_attn2_patch(build_patch({f"{block}": conditioning}, weight=weight, sigma_start=sigma_start, sigma_end=sigma_end)) 220 | 221 | return (m,) 222 | 223 | class AdvancedPromptInjection: 224 | @classmethod 225 | def INPUT_TYPES(s): 226 | return { 227 | "required": { 228 | "model": ("MODEL",), 229 | }, 230 | "optional": { 231 | "locations": ("STRING", {"multiline": True, "default": "output:0,1.0\noutput:1,1.0"}), 232 | "conditioning": ("CONDITIONING",), 233 | "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), 234 | "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), 235 | } 236 | } 237 | 238 | RETURN_TYPES = ("MODEL",) 239 | FUNCTION = "patch" 240 | 241 | CATEGORY = "advanced/model" 242 | 243 | def patch(self, model: comfy.model_patcher.ModelPatcher, locations: str, conditioning=None, start_at=0.0, end_at=1.0): 244 | if not conditioning: 245 | return (model,) 246 | 247 | m = model.clone() 248 | sigma_start = m.get_model_object("model_sampling").percent_to_sigma(start_at) 249 | sigma_end = m.get_model_object("model_sampling").percent_to_sigma(end_at) 250 | 251 | for line in locations.splitlines(): 252 | line = line.strip().strip('\n') 253 | weight = 1.0 254 | if ',' in line: 255 | line, weight = line.split(',') 256 | line = line.strip() 257 | weight = float(weight) 258 | if line: 259 | m.set_model_attn2_patch(build_patch({f"{line}": conditioning}, weight=weight, sigma_start=sigma_start, sigma_end=sigma_end)) 260 | 261 | return (m,) 262 | 263 | NODE_CLASS_MAPPINGS = { 264 | "PromptInjection": PromptInjection, 265 | "PromptInjectionIdx": PromptInjectionIdx, 266 | "SimplePromptInjection": SimplePromptInjection, 267 | "AdvancedPromptInjection": AdvancedPromptInjection 268 | } 269 | 270 | NODE_DISPLAY_NAME_MAPPINGS = { 271 | "PromptInjection": "Attn2 Prompt Injection", 272 | "PromptInjectionIdx": "Attn2 Prompt Injection (by index)", 273 | "SimplePromptInjection": "Attn2 Prompt Injection (simple)", 274 | "AdvancedPromptInjection": "Attn2 Prompt Injection (advanced)" 275 | } --------------------------------------------------------------------------------