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from nodes import MAX_RESOLUTION, ConditioningZeroOut, ConditioningSetTimestepRange, ConditioningCombine
import re
class CLIPTextEncodeSDXLSimplified:
@classmethod
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:
@classmethod
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:
@classmethod
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:
@classmethod
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:
@classmethod
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:
@classmethod
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:
@classmethod
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",
} |