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prompt_injection-main/LICENSE

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prompt_injection-main/README.md

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+# Prompt Injection Node for ComfyUI
+
+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.
+
+## Highly Experimental
+
+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. 
+
+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.
+
+## Credits
+
+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.

prompt_injection-main/__init__.py

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+from .prompt_injection import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

prompt_injection-main/prompt_injection.py

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+import comfy.model_patcher
+import comfy.samplers
+import torch
+import torch.nn.functional as F
+
+def build_patch(patchedBlocks, weight=1.0, sigma_start=0.0, sigma_end=1.0, noise=0.0):
+    def prompt_injection_patch(n, context_attn1: torch.Tensor, value_attn1, extra_options):
+        (block, block_index) = extra_options.get('block', (None,None))
+        sigma = extra_options["sigmas"].detach().cpu()[0].item() if 'sigmas' in extra_options else 999999999.9
+        batch_prompt = n.shape[0] // len(extra_options["cond_or_uncond"])
+
+        if sigma <= sigma_start and sigma >= sigma_end:
+            if (block and f'{block}:{block_index}' in patchedBlocks and patchedBlocks[f'{block}:{block_index}']):
+                if context_attn1.dim() == 3:
+                    c = context_attn1[0].unsqueeze(0)
+                else:
+                    c = context_attn1[0][0].unsqueeze(0)
+                b = patchedBlocks[f'{block}:{block_index}'][0][0].repeat(c.shape[0], 1, 1).to(context_attn1.device)
+                if noise != 0.0:
+                    b = b + torch.randn_like(b) * noise
+
+                padding = abs(c.shape[1] - b.shape[1])
+                if c.shape[1] > b.shape[1]:
+                    b = F.pad(b, (0, 0, 0, padding), mode='constant', value=0)
+                elif c.shape[1] < b.shape[1]:
+                    c = F.pad(c, (0, 0, 0, padding), mode='constant', value=0)
+
+                out = torch.stack((c, b)).to(dtype=context_attn1.dtype)
+                out = out.repeat(1, batch_prompt, 1, 1) * weight
+
+                return n, out, out 
+
+        return n, context_attn1, value_attn1
+    return prompt_injection_patch
+
+def build_patch_by_index(patchedBlocks, weight=1.0, sigma_start=0.0, sigma_end=1.0, noise=0.0):
+    def prompt_injection_patch(n, context_attn1: torch.Tensor, value_attn1, extra_options):
+        idx = extra_options["transformer_index"]
+        sigma = extra_options["sigmas"].detach().cpu()[0].item() if 'sigmas' in extra_options else 999999999.9
+        batch_prompt = n.shape[0] // len(extra_options["cond_or_uncond"])
+
+        if sigma <= sigma_start and sigma >= sigma_end:
+            if idx in patchedBlocks and patchedBlocks[idx] is not None:
+                if context_attn1.dim() == 3:
+                    c = context_attn1[0].unsqueeze(0)
+                else:
+                    c = context_attn1[0][0].unsqueeze(0)
+
+                b = patchedBlocks[idx][0][0].repeat(c.shape[0], 1, 1).to(context_attn1.device)
+                if noise != 0.0:
+                    b = b + torch.randn_like(b) * noise
+
+                padding = abs(c.shape[1] - b.shape[1])
+                if c.shape[1] > b.shape[1]:
+                    b = F.pad(b, (0, 0, 0, padding), mode='constant', value=0)
+                elif c.shape[1] < b.shape[1]:
+                    c = F.pad(c, (0, 0, 0, padding), mode='constant', value=0)
+
+                out = torch.stack((c, b)).to(dtype=context_attn1.dtype)
+                out = out.repeat(1, batch_prompt, 1, 1) * weight
+
+                return n, out, out
+
+        return n, context_attn1, value_attn1
+    return prompt_injection_patch
+
+class PromptInjection:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+            },
+            "optional": {
+                "all":  ("CONDITIONING",),
+                "input_4":  ("CONDITIONING",),
+                "input_5":  ("CONDITIONING",),
+                "input_7":  ("CONDITIONING",),
+                "input_8":  ("CONDITIONING",),
+                "middle_0": ("CONDITIONING",),
+                "output_0": ("CONDITIONING",),
+                "output_1": ("CONDITIONING",),
+                "output_2": ("CONDITIONING",),
+                "output_3": ("CONDITIONING",),
+                "output_4": ("CONDITIONING",),
+                "output_5": ("CONDITIONING",),
+                "weight": ("FLOAT", {"default": 1.0, "min": -2.0, "max": 5.0, "step": 0.05}),
+                "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "noise": ("FLOAT", {"default": 0.0, "min": -5.0, "max": 5.0, "step": 0.05}),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    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):
+        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)):
+            return (model,)
+
+        m = model.clone()
+        sigma_start = m.get_model_object("model_sampling").percent_to_sigma(start_at)
+        sigma_end = m.get_model_object("model_sampling").percent_to_sigma(end_at)
+
+        patchedBlocks = {}
+        blocks = {'input': [4, 5, 7, 8], 'middle': [0], 'output': [0, 1, 2, 3, 4, 5]}
+
+        for block in blocks:
+            for index in blocks[block]:
+                value = locals()[f"{block}_{index}"] if locals()[f"{block}_{index}"] is not None else all
+                if value is not None:
+                    patchedBlocks[f"{block}:{index}"] = value
+
+        m.set_model_attn2_patch(build_patch(patchedBlocks, weight=weight, sigma_start=sigma_start, sigma_end=sigma_end, noise=noise))
+
+        return (m,)
+
+class PromptInjectionIdx:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+            },
+            "optional": {
+                "all": ("CONDITIONING",),
+                "idx_0": ("CONDITIONING",),
+                "idx_1": ("CONDITIONING",),
+                "idx_2": ("CONDITIONING",),
+                "idx_3": ("CONDITIONING",),
+                "idx_4": ("CONDITIONING",),
+                "idx_5": ("CONDITIONING",),
+                "idx_6": ("CONDITIONING",),
+                "idx_7": ("CONDITIONING",),
+                "idx_8": ("CONDITIONING",),
+                "idx_9": ("CONDITIONING",),
+                "idx_10": ("CONDITIONING",),
+                "idx_11_sd15": ("CONDITIONING",),
+                "idx_12_sd15": ("CONDITIONING",),
+                "idx_13_sd15": ("CONDITIONING",),
+                "idx_14_sd15": ("CONDITIONING",),
+                "idx_15_sd15": ("CONDITIONING",),
+                "weight": ("FLOAT", {"default": 1.0, "min": -2.0, "max": 5.0, "step": 0.05}),
+                "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "noise": ("FLOAT", {"default": 0.0, "min": -5.0, "max": 5.0, "step": 0.05}),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    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):
+        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)):
+            return (model,)
+        
+        m = model.clone()
+        sigma_start = m.get_model_object("model_sampling").percent_to_sigma(start_at)
+        sigma_end = m.get_model_object("model_sampling").percent_to_sigma(end_at)
+        is_sdxl = isinstance(model.model, (comfy.model_base.SDXL, comfy.model_base.SDXLRefiner, comfy.model_base.SDXL_instructpix2pix))
+
+        patchedBlocks = {
+            0: idx_0 if idx_0 is not None else all,
+            1: idx_1 if idx_1 is not None else all,
+            2: idx_2 if idx_2 is not None else all,
+            3: idx_3 if idx_3 is not None else all,
+            4: idx_4 if idx_4 is not None else all,
+            5: idx_5 if idx_5 is not None else all,
+            6: idx_6 if idx_6 is not None else all,
+            7: idx_7 if idx_7 is not None else all,
+            8: idx_8 if idx_8 is not None else all,
+            9: idx_9 if idx_9 is not None else all,
+            10: idx_10 if idx_10 is not None else all,
+            11: idx_11_sd15 if idx_11_sd15 is not None else all if not is_sdxl else None,
+            12: idx_12_sd15 if idx_12_sd15 is not None else all if not is_sdxl else None,
+            13: idx_13_sd15 if idx_13_sd15 is not None else all if not is_sdxl else None,
+            14: idx_14_sd15 if idx_14_sd15 is not None else all if not is_sdxl else None,
+            15: idx_15_sd15 if idx_15_sd15 is not None else all if not is_sdxl else None,
+        }
+
+        m.set_model_attn2_patch(build_patch_by_index(patchedBlocks, weight=weight, sigma_start=sigma_start, sigma_end=sigma_end, noise=noise))
+
+        return (m,)
+
+
+class SimplePromptInjection:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+            },
+            "optional": {
+                "block": (["input:4", "input:5", "input:7", "input:8", "middle:0", "output:0", "output:1", "output:2", "output:3", "output:4", "output:5"],),
+                "conditioning": ("CONDITIONING",),
+                "weight": ("FLOAT", {"default": 1.0, "min": -2.0, "max": 5.0, "step": 0.05}),
+                "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    def patch(self, model: comfy.model_patcher.ModelPatcher, block, conditioning=None, weight=1.0, start_at=0.0, end_at=1.0):
+        if conditioning is None:
+            return (model,)
+
+        m = model.clone()
+        sigma_start = m.get_model_object("model_sampling").percent_to_sigma(start_at)
+        sigma_end = m.get_model_object("model_sampling").percent_to_sigma(end_at)
+
+        m.set_model_attn2_patch(build_patch({f"{block}": conditioning}, weight=weight, sigma_start=sigma_start, sigma_end=sigma_end))
+
+        return (m,)
+
+class AdvancedPromptInjection:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+            },
+            "optional": {
+                "locations": ("STRING", {"multiline": True, "default": "output:0,1.0\noutput:1,1.0"}),
+                "conditioning": ("CONDITIONING",),
+                "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    def patch(self, model: comfy.model_patcher.ModelPatcher, locations: str, conditioning=None, start_at=0.0, end_at=1.0):
+        if not conditioning:
+            return (model,)
+
+        m = model.clone()
+        sigma_start = m.get_model_object("model_sampling").percent_to_sigma(start_at)
+        sigma_end = m.get_model_object("model_sampling").percent_to_sigma(end_at)
+
+        for line in locations.splitlines():
+            line = line.strip().strip('\n')
+            weight = 1.0
+            if ',' in line:
+                line, weight = line.split(',')
+                line = line.strip()
+                weight = float(weight)
+            if line:
+                m.set_model_attn2_patch(build_patch({f"{line}": conditioning}, weight=weight, sigma_start=sigma_start, sigma_end=sigma_end))
+
+        return (m,)
+
+NODE_CLASS_MAPPINGS = {
+    "PromptInjection": PromptInjection,
+    "PromptInjectionIdx": PromptInjectionIdx,
+    "SimplePromptInjection": SimplePromptInjection,
+    "AdvancedPromptInjection": AdvancedPromptInjection
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "PromptInjection": "Attn2 Prompt Injection",
+    "PromptInjectionIdx": "Attn2 Prompt Injection (by index)",
+    "SimplePromptInjection": "Attn2 Prompt Injection (simple)",
+    "AdvancedPromptInjection": "Attn2 Prompt Injection (advanced)"
+}