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| from nodes import MAX_RESOLUTION, ConditioningZeroOut, ConditioningSetTimestepRange, ConditioningCombine | |
| import re | |
| class CLIPTextEncodeSDXLSimplified: | |
| def INPUT_TYPES(s): | |
| return {"required": { | |
| "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), | |
| "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), | |
| "size_cond_factor": ("INT", {"default": 4, "min": 1, "max": 16 }), | |
| "text": ("STRING", {"multiline": True, "dynamicPrompts": True, "default": ""}), | |
| "clip": ("CLIP", ), | |
| }} | |
| RETURN_TYPES = ("CONDITIONING",) | |
| FUNCTION = "execute" | |
| CATEGORY = "essentials/conditioning" | |
| def execute(self, clip, width, height, size_cond_factor, text): | |
| crop_w = 0 | |
| crop_h = 0 | |
| width = width*size_cond_factor | |
| height = height*size_cond_factor | |
| target_width = width | |
| target_height = height | |
| text_g = text_l = text | |
| tokens = clip.tokenize(text_g) | |
| tokens["l"] = clip.tokenize(text_l)["l"] | |
| if len(tokens["l"]) != len(tokens["g"]): | |
| empty = clip.tokenize("") | |
| while len(tokens["l"]) < len(tokens["g"]): | |
| tokens["l"] += empty["l"] | |
| while len(tokens["l"]) > len(tokens["g"]): | |
| tokens["g"] += empty["g"] | |
| cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) | |
| return ([[cond, {"pooled_output": pooled, "width": width, "height": height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}]], ) | |
| class ConditioningCombineMultiple: | |
| def INPUT_TYPES(s): | |
| return { | |
| "required": { | |
| "conditioning_1": ("CONDITIONING",), | |
| "conditioning_2": ("CONDITIONING",), | |
| }, "optional": { | |
| "conditioning_3": ("CONDITIONING",), | |
| "conditioning_4": ("CONDITIONING",), | |
| "conditioning_5": ("CONDITIONING",), | |
| }, | |
| } | |
| RETURN_TYPES = ("CONDITIONING",) | |
| FUNCTION = "execute" | |
| CATEGORY = "essentials/conditioning" | |
| def execute(self, conditioning_1, conditioning_2, conditioning_3=None, conditioning_4=None, conditioning_5=None): | |
| c = conditioning_1 + conditioning_2 | |
| if conditioning_3 is not None: | |
| c += conditioning_3 | |
| if conditioning_4 is not None: | |
| c += conditioning_4 | |
| if conditioning_5 is not None: | |
| c += conditioning_5 | |
| return (c,) | |
| class SD3NegativeConditioning: | |
| def INPUT_TYPES(s): | |
| return {"required": { | |
| "conditioning": ("CONDITIONING",), | |
| "end": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.001 }), | |
| }} | |
| RETURN_TYPES = ("CONDITIONING",) | |
| FUNCTION = "execute" | |
| CATEGORY = "essentials/conditioning" | |
| def execute(self, conditioning, end): | |
| zero_c = ConditioningZeroOut().zero_out(conditioning)[0] | |
| if end == 0: | |
| return (zero_c, ) | |
| c = ConditioningSetTimestepRange().set_range(conditioning, 0, end)[0] | |
| zero_c = ConditioningSetTimestepRange().set_range(zero_c, end, 1.0)[0] | |
| c = ConditioningCombine().combine(zero_c, c)[0] | |
| return (c, ) | |
| class FluxAttentionSeeker: | |
| def INPUT_TYPES(s): | |
| return {"required": { | |
| "clip": ("CLIP",), | |
| "apply_to_query": ("BOOLEAN", { "default": True }), | |
| "apply_to_key": ("BOOLEAN", { "default": True }), | |
| "apply_to_value": ("BOOLEAN", { "default": True }), | |
| "apply_to_out": ("BOOLEAN", { "default": True }), | |
| **{f"clip_l_{s}": ("FLOAT", { "display": "slider", "default": 1.0, "min": 0, "max": 5, "step": 0.05 }) for s in range(12)}, | |
| **{f"t5xxl_{s}": ("FLOAT", { "display": "slider", "default": 1.0, "min": 0, "max": 5, "step": 0.05 }) for s in range(24)}, | |
| }} | |
| RETURN_TYPES = ("CLIP",) | |
| FUNCTION = "execute" | |
| CATEGORY = "essentials/conditioning" | |
| def execute(self, clip, apply_to_query, apply_to_key, apply_to_value, apply_to_out, **values): | |
| if not apply_to_key and not apply_to_query and not apply_to_value and not apply_to_out: | |
| return (clip, ) | |
| m = clip.clone() | |
| sd = m.patcher.model_state_dict() | |
| for k in sd: | |
| if "self_attn" in k: | |
| layer = re.search(r"\.layers\.(\d+)\.", k) | |
| layer = int(layer.group(1)) if layer else None | |
| if layer is not None and values[f"clip_l_{layer}"] != 1.0: | |
| if (apply_to_query and "q_proj" in k) or (apply_to_key and "k_proj" in k) or (apply_to_value and "v_proj" in k) or (apply_to_out and "out_proj" in k): | |
| m.add_patches({k: (None,)}, 0.0, values[f"clip_l_{layer}"]) | |
| elif "SelfAttention" in k: | |
| block = re.search(r"\.block\.(\d+)\.", k) | |
| block = int(block.group(1)) if block else None | |
| if block is not None and values[f"t5xxl_{block}"] != 1.0: | |
| if (apply_to_query and ".q." in k) or (apply_to_key and ".k." in k) or (apply_to_value and ".v." in k) or (apply_to_out and ".o." in k): | |
| m.add_patches({k: (None,)}, 0.0, values[f"t5xxl_{block}"]) | |
| return (m, ) | |
| class SD3AttentionSeekerLG: | |
| def INPUT_TYPES(s): | |
| return {"required": { | |
| "clip": ("CLIP",), | |
| "apply_to_query": ("BOOLEAN", { "default": True }), | |
| "apply_to_key": ("BOOLEAN", { "default": True }), | |
| "apply_to_value": ("BOOLEAN", { "default": True }), | |
| "apply_to_out": ("BOOLEAN", { "default": True }), | |
| **{f"clip_l_{s}": ("FLOAT", { "display": "slider", "default": 1.0, "min": 0, "max": 5, "step": 0.05 }) for s in range(12)}, | |
| **{f"clip_g_{s}": ("FLOAT", { "display": "slider", "default": 1.0, "min": 0, "max": 5, "step": 0.05 }) for s in range(32)}, | |
| }} | |
| RETURN_TYPES = ("CLIP",) | |
| FUNCTION = "execute" | |
| CATEGORY = "essentials/conditioning" | |
| def execute(self, clip, apply_to_query, apply_to_key, apply_to_value, apply_to_out, **values): | |
| if not apply_to_key and not apply_to_query and not apply_to_value and not apply_to_out: | |
| return (clip, ) | |
| m = clip.clone() | |
| sd = m.patcher.model_state_dict() | |
| for k in sd: | |
| if "self_attn" in k: | |
| layer = re.search(r"\.layers\.(\d+)\.", k) | |
| layer = int(layer.group(1)) if layer else None | |
| if layer is not None: | |
| if "clip_l" in k and values[f"clip_l_{layer}"] != 1.0: | |
| if (apply_to_query and "q_proj" in k) or (apply_to_key and "k_proj" in k) or (apply_to_value and "v_proj" in k) or (apply_to_out and "out_proj" in k): | |
| m.add_patches({k: (None,)}, 0.0, values[f"clip_l_{layer}"]) | |
| elif "clip_g" in k and values[f"clip_g_{layer}"] != 1.0: | |
| if (apply_to_query and "q_proj" in k) or (apply_to_key and "k_proj" in k) or (apply_to_value and "v_proj" in k) or (apply_to_out and "out_proj" in k): | |
| m.add_patches({k: (None,)}, 0.0, values[f"clip_g_{layer}"]) | |
| return (m, ) | |
| class SD3AttentionSeekerT5: | |
| def INPUT_TYPES(s): | |
| return {"required": { | |
| "clip": ("CLIP",), | |
| "apply_to_query": ("BOOLEAN", { "default": True }), | |
| "apply_to_key": ("BOOLEAN", { "default": True }), | |
| "apply_to_value": ("BOOLEAN", { "default": True }), | |
| "apply_to_out": ("BOOLEAN", { "default": True }), | |
| **{f"t5xxl_{s}": ("FLOAT", { "display": "slider", "default": 1.0, "min": 0, "max": 5, "step": 0.05 }) for s in range(24)}, | |
| }} | |
| RETURN_TYPES = ("CLIP",) | |
| FUNCTION = "execute" | |
| CATEGORY = "essentials/conditioning" | |
| def execute(self, clip, apply_to_query, apply_to_key, apply_to_value, apply_to_out, **values): | |
| if not apply_to_key and not apply_to_query and not apply_to_value and not apply_to_out: | |
| return (clip, ) | |
| m = clip.clone() | |
| sd = m.patcher.model_state_dict() | |
| for k in sd: | |
| if "SelfAttention" in k: | |
| block = re.search(r"\.block\.(\d+)\.", k) | |
| block = int(block.group(1)) if block else None | |
| if block is not None and values[f"t5xxl_{block}"] != 1.0: | |
| if (apply_to_query and ".q." in k) or (apply_to_key and ".k." in k) or (apply_to_value and ".v." in k) or (apply_to_out and ".o." in k): | |
| m.add_patches({k: (None,)}, 0.0, values[f"t5xxl_{block}"]) | |
| return (m, ) | |
| class FluxBlocksBuster: | |
| def INPUT_TYPES(s): | |
| return {"required": { | |
| "model": ("MODEL",), | |
| "blocks": ("STRING", {"default": "## 0 = 1.0\n## 1 = 1.0\n## 2 = 1.0\n## 3 = 1.0\n## 4 = 1.0\n## 5 = 1.0\n## 6 = 1.0\n## 7 = 1.0\n## 8 = 1.0\n## 9 = 1.0\n## 10 = 1.0\n## 11 = 1.0\n## 12 = 1.0\n## 13 = 1.0\n## 14 = 1.0\n## 15 = 1.0\n## 16 = 1.0\n## 17 = 1.0\n## 18 = 1.0\n# 0 = 1.0\n# 1 = 1.0\n# 2 = 1.0\n# 3 = 1.0\n# 4 = 1.0\n# 5 = 1.0\n# 6 = 1.0\n# 7 = 1.0\n# 8 = 1.0\n# 9 = 1.0\n# 10 = 1.0\n# 11 = 1.0\n# 12 = 1.0\n# 13 = 1.0\n# 14 = 1.0\n# 15 = 1.0\n# 16 = 1.0\n# 17 = 1.0\n# 18 = 1.0\n# 19 = 1.0\n# 20 = 1.0\n# 21 = 1.0\n# 22 = 1.0\n# 23 = 1.0\n# 24 = 1.0\n# 25 = 1.0\n# 26 = 1.0\n# 27 = 1.0\n# 28 = 1.0\n# 29 = 1.0\n# 30 = 1.0\n# 31 = 1.0\n# 32 = 1.0\n# 33 = 1.0\n# 34 = 1.0\n# 35 = 1.0\n# 36 = 1.0\n# 37 = 1.0", "multiline": True, "dynamicPrompts": True}), | |
| #**{f"double_block_{s}": ("FLOAT", { "display": "slider", "default": 1.0, "min": 0, "max": 5, "step": 0.05 }) for s in range(19)}, | |
| #**{f"single_block_{s}": ("FLOAT", { "display": "slider", "default": 1.0, "min": 0, "max": 5, "step": 0.05 }) for s in range(38)}, | |
| }} | |
| RETURN_TYPES = ("MODEL", "STRING") | |
| RETURN_NAMES = ("MODEL", "patched_blocks") | |
| FUNCTION = "patch" | |
| CATEGORY = "essentials/conditioning" | |
| def patch(self, model, blocks): | |
| if blocks == "": | |
| return (model, ) | |
| m = model.clone() | |
| sd = model.model_state_dict() | |
| patched_blocks = [] | |
| """ | |
| Also compatible with the following format: | |
| double_blocks\.0\.(img|txt)_(mod|attn|mlp)\.(lin|qkv|proj|0|2)\.(weight|bias)=1.1 | |
| single_blocks\.0\.(linear[12]|modulation\.lin)\.(weight|bias)=1.1 | |
| The regex is used to match the block names | |
| """ | |
| blocks = blocks.split("\n") | |
| blocks = [b.strip() for b in blocks if b.strip()] | |
| for k in sd: | |
| for block in blocks: | |
| block = block.split("=") | |
| value = float(block[1].strip()) if len(block) > 1 else 1.0 | |
| block = block[0].strip() | |
| if block.startswith("##"): | |
| block = r"double_blocks\." + block[2:].strip() + r"\.(img|txt)_(mod|attn|mlp)\.(lin|qkv|proj|0|2)\.(weight|bias)" | |
| elif block.startswith("#"): | |
| block = r"single_blocks\." + block[1:].strip() + r"\.(linear[12]|modulation\.lin)\.(weight|bias)" | |
| if value != 1.0 and re.search(block, k): | |
| m.add_patches({k: (None,)}, 0.0, value) | |
| patched_blocks.append(f"{k}: {value}") | |
| patched_blocks = "\n".join(patched_blocks) | |
| return (m, patched_blocks,) | |
| COND_CLASS_MAPPINGS = { | |
| "CLIPTextEncodeSDXL+": CLIPTextEncodeSDXLSimplified, | |
| "ConditioningCombineMultiple+": ConditioningCombineMultiple, | |
| "SD3NegativeConditioning+": SD3NegativeConditioning, | |
| "FluxAttentionSeeker+": FluxAttentionSeeker, | |
| "SD3AttentionSeekerLG+": SD3AttentionSeekerLG, | |
| "SD3AttentionSeekerT5+": SD3AttentionSeekerT5, | |
| "FluxBlocksBuster+": FluxBlocksBuster, | |
| } | |
| COND_NAME_MAPPINGS = { | |
| "CLIPTextEncodeSDXL+": "🔧 SDXL CLIPTextEncode", | |
| "ConditioningCombineMultiple+": "🔧 Cond Combine Multiple", | |
| "SD3NegativeConditioning+": "🔧 SD3 Negative Conditioning", | |
| "FluxAttentionSeeker+": "🔧 Flux Attention Seeker", | |
| "SD3AttentionSeekerLG+": "🔧 SD3 Attention Seeker L/G", | |
| "SD3AttentionSeekerT5+": "🔧 SD3 Attention Seeker T5", | |
| "FluxBlocksBuster+": "🔧 Flux Model Blocks Buster", | |
| } |