Automatic CFG - Custom attentions¶
Documentation¶
- Class name:
Automatic CFG - Custom attentions - Category:
model_patches/Automatic_CFG/experimental_attention_modifiers - Output node:
False
This node is designed to concatenate attention modifier parameters from two sources, creating a unified set of parameters for further processing. It serves as a utility within the context of configuring or enhancing model attention mechanisms, particularly in scenarios requiring the combination of distinct attention modification strategies.
Input types¶
Required¶
model- unknown
- Comfy dtype:
MODEL - Python dtype:
unknown
Auto_CFG- unknown
- Comfy dtype:
BOOLEAN - Python dtype:
unknown
cond_mode- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
uncond_mode- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
cond_diff_multiplier- unknown
- Comfy dtype:
FLOAT - Python dtype:
unknown
uncond_diff_multiplier- unknown
- Comfy dtype:
FLOAT - Python dtype:
unknown
uncond_sigma_end- unknown
- Comfy dtype:
FLOAT - Python dtype:
unknown
save_as_preset- unknown
- Comfy dtype:
BOOLEAN - Python dtype:
unknown
preset_name- unknown
- Comfy dtype:
STRING - Python dtype:
unknown
Optional¶
attn_mod_for_positive_operation- unknown
- Comfy dtype:
ATTNMOD - Python dtype:
unknown
attn_mod_for_negative_operation- unknown
- Comfy dtype:
ATTNMOD - Python dtype:
unknown
Output types¶
Model- Comfy dtype:
MODEL - unknown
- Python dtype:
unknown
- Comfy dtype:
Usage tips¶
- Infra type:
CPU - Common nodes: unknown
Source code¶
class simpleDynamicCFGCustomAttentionPatch:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"model": ("MODEL",),
"Auto_CFG": ("BOOLEAN", {"default": True}),
"cond_mode" : (["replace_by_custom","normal+(normal-custom_cond)*multiplier","normal+(normal-custom_uncond)*multiplier"],),
"uncond_mode" : (["replace_by_custom","normal+(normal-custom_cond)*multiplier","normal+(normal-custom_uncond)*multiplier"],),
"cond_diff_multiplier": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.1, "round": 0.01}),
"uncond_diff_multiplier": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.1, "round": 0.01}),
"uncond_sigma_end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10000, "step": 0.1, "round": 0.01}),
"save_as_preset": ("BOOLEAN", {"default": False}),
"preset_name": ("STRING", {"multiline": False}),
},
"optional":{
"attn_mod_for_positive_operation": ("ATTNMOD", {"forceInput": True}),
"attn_mod_for_negative_operation": ("ATTNMOD", {"forceInput": True}),
}}
RETURN_TYPES = ("MODEL",)
RETURN_NAMES = ("Model",)
FUNCTION = "patch"
CATEGORY = "model_patches/Automatic_CFG/experimental_attention_modifiers"
def patch(self, model, Auto_CFG, cond_mode, uncond_mode, cond_diff_multiplier, uncond_diff_multiplier, uncond_sigma_end, save_as_preset, preset_name,
attn_mod_for_positive_operation = [], attn_mod_for_negative_operation = []):
with open(os.path.join(json_preset_path, "do_not_delete.json"), 'r', encoding='utf-8') as f:
patch_parameters = json.load(f)
patch_parameters["cond_exp_value"] = cond_diff_multiplier
patch_parameters["uncond_exp_value"] = uncond_diff_multiplier
if cond_mode != "replace_by_custom":
patch_parameters["disable_cond"] = False
if cond_mode == "normal+(normal-custom_cond)*multiplier":
patch_parameters["cond_exp_method"] = "subtract_attention_modifiers_input_using_cond"
elif cond_mode == "normal+(normal-custom_uncond)*multiplier":
patch_parameters["cond_exp_method"] = "subtract_attention_modifiers_input_using_uncond"
if uncond_mode != "replace_by_custom":
patch_parameters["uncond_sigma_start"] = 1000.0
patch_parameters["fake_uncond_exp"] = False
patch_parameters["uncond_exp"] = True
if uncond_mode == "normal+(normal-custom_cond)*multiplier":
patch_parameters["uncond_exp_method"] = "subtract_attention_modifiers_input_using_cond"
elif uncond_mode == "normal+(normal-custom_uncond)*multiplier":
patch_parameters["uncond_exp_method"] = "subtract_attention_modifiers_input_using_uncond"
if cond_mode != "replace_by_custom" and attn_mod_for_positive_operation != []:
smallest_sigma = min([float(x['sigma_end']) for x in attn_mod_for_positive_operation])
patch_parameters["disable_cond_sigma_end"] = smallest_sigma
patch_parameters["cond_exp_sigma_end"] = smallest_sigma
if uncond_mode != "replace_by_custom" and attn_mod_for_negative_operation != []:
smallest_sigma = min([float(x['sigma_end']) for x in attn_mod_for_negative_operation])
patch_parameters["uncond_exp_sigma_end"] = smallest_sigma
patch_parameters["fake_uncond_start"] = False
# else:
# biggest_sigma = max([float(x['sigma_start']) for x in attn_mod_for_negative_operation])
# patch_parameters["fake_uncond_sigma_start"] = biggest_sigma
patch_parameters["automatic_cfg"] = "hard" if Auto_CFG else "None"
patch_parameters['attention_modifiers_positive'] = attn_mod_for_positive_operation
patch_parameters['attention_modifiers_negative'] = attn_mod_for_negative_operation
patch_parameters['attention_modifiers_fake_negative'] = attn_mod_for_negative_operation
patch_parameters["uncond_sigma_end"] = uncond_sigma_end
patch_parameters["fake_uncond_sigma_end"] = uncond_sigma_end
patch_parameters["save_as_preset"] = save_as_preset
patch_parameters["preset_name"] = preset_name
advcfg = advancedDynamicCFG()
m = advcfg.patch(model, **patch_parameters)[0]
return (m, )