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__init__.py

@@ -2,41 +2,80 @@ from .nodes import *
 
 NODE_CLASS_MAPPINGS = {}
 
+# try:
+#     from .skimmed_CFG import cond_skimming_pre_cfg_node
+#     NODE_CLASS_MAPPINGS["Skimmed CFG"] = cond_skimming_pre_cfg_node
+# except:
+#     pass
+
 NODE_CLASS_MAPPINGS_ADD = {
     "Pre CFG automatic scale": automatic_pre_cfg,
     "Pre CFG uncond zero": uncondZeroPreCFGNode,
     "Pre CFG perp-neg": pre_cfg_perp_neg,
-    # "Pre CFG re-negative": pre_cfg_re_negative,
+    "Pre CFG re-negative": pre_cfg_re_negative,
     
     "Pre CFG PAG": perturbed_attention_guidance_pre_cfg_node,
     "Pre CFG zero attention": zero_attention_pre_cfg_node,
+    # "Pre CFG color control": latent_color_control_pre_cfg_node,
     "Pre CFG channel multiplier": channel_multiplier_node,
     "Pre CFG multiplier": multiply_cond_pre_cfg_node,
+    "Pre CFG roll latent": PreCFGRollLatentNode,
+    "Pre CFG mirror flip": PreCFGMirrorFlipLatentNode,
 
+    "Pre CFG clamp negative": clamp_sign_uncond_pre_cfg_node,
+    "Pre CFG clamp negative to denoised relation": clamp_uncond_to_denoised_pre_cfg_node,
+
+    "Pre CFG clamp min max": minmax_clamp_pre_cfg_node,
+    "Pre CFG lerp": lerp_conds_pre_cfg_node,
+    
     "Pre CFG norm neg to pos": norm_uncond_to_cond_pre_cfg_node,
     "Pre CFG subtract mean": PreCFGsubtractMeanNode,
     "Pre CFG variable scaling": variable_scale_pre_cfg_node,
     "Pre CFG gradient scaling": gradient_scaling_pre_cfg_node,
-
+    
     "Pre CFG flip flop": flip_flip_conds_pre_cfg_node,
     "Pre CFG replace negative channel": replace_uncond_channel_pre_cfg_node,
-    "Pre CFG merge negative channel": merge_uncond_channel_pre_cfg_node,    
+    "Pre CFG merge negative channel": merge_uncond_channel_pre_cfg_node,
+    "Pre CFG timed CFG rescale": rescale_cfg_during_sigma_pre_cfg_node,
 
     "Pre CFG sharpening": condDiffSharpeningNode,
+    "Pre CFG sharpen/blur": condBlurSharpeningNode,
+    
     "Pre CFG exponentiation": condExpNode,
+    "Pre CFG cond boost": boost_std_pre_cfg_node,
+    "tHe dArK GuiDaNcE": dark_guidance_pre_cfg_node,
 
     "Conditioning set timestep from sigma": ConditioningSetTimestepRangeFromSigma,
     "Support empty uncond": support_empty_uncond_pre_cfg_node,
     "Shape attention": ShapeAttentionNode,
     "Excellent attention": ExlAttentionNode,
     "Post CFG subtract mean": PostCFGsubtractMeanNode,
+    # "Post CFG make a dot": PostCFGDotNode,
     "Individual channel selector": individual_channel_selection_node,
     "Subtract noise mean": latent_noise_subtract_mean_node,
     "Empty RGB image": EmptyRGBImage,
     "Gradient RGB image": GradientRGBImage,
+    "colors test node": colors_test_node,
+    "gradient batch mask": gradientNoisyLatentMaskBatch,
+    "Load latent from path": load_latent_for_guidance,
+    "Latent recombine by channels": latent_recombine_channels,
 }
 
 NODE_CLASS_MAPPINGS.update(NODE_CLASS_MAPPINGS_ADD)
 
 for c in [4,8,16,32,64,128]:
     NODE_CLASS_MAPPINGS[f"Channel selector for {c} channels"] = type("channel_selection_node", (channel_selection_node,), { "CHANNELS_AMOUNT": c})
+
+try:
+    from .tester_nodes import *
+    NODE_CLASS_MAPPINGS["K-K-K-K-KOMBO BREAKER"] = combo_breaker
+    NODE_CLASS_MAPPINGS["K-K-K-K-KOMBO BREAKER X2"] = combo_breaker_x2
+    
+    NODE_CLASS_MAPPINGS["K-K-K-K-KOMBO BREAKER 6 bool"] = combo_breaker_6_bool
+    NODE_CLASS_MAPPINGS["K-K-K-K-KOMBO BREAKER 4 bool"] = combo_breaker_4_bool
+    
+    NODE_CLASS_MAPPINGS["CFG_TEST"] = cfg_test_node
+
+except:
+    pass
+

imported_functions.py

@@ -0,0 +1,66 @@
+import torch
+
+@torch.no_grad()
+def get_skimming_mask(x_orig, cond, uncond, cond_scale, return_denoised=False, disable_flipping_filter=False, release_inner_scaling=False):
+    denoised = x_orig - ((x_orig - uncond) + cond_scale * ((x_orig - cond) - (x_orig - uncond)))
+    matching_pred_signs = (cond - uncond).sign() == cond.sign()
+    matching_diff_after = cond.sign() == (cond * cond_scale - uncond * (cond_scale - 1)).sign()
+    if disable_flipping_filter:
+        outer_influence = matching_pred_signs & matching_diff_after
+    else:
+        deviation_influence = (denoised.sign() == (denoised - x_orig).sign())
+        outer_influence = matching_pred_signs & matching_diff_after & deviation_influence
+    if return_denoised:
+        return outer_influence, denoised
+    else:
+        return outer_influence
+    
+@torch.no_grad()
+def skimmed_CFG(x_orig, cond, uncond, cond_scale, skimming_scale, disable_flipping_filter=False):
+    outer_influence, denoised = get_skimming_mask(x_orig, cond, uncond, cond_scale, return_denoised=True, disable_flipping_filter=disable_flipping_filter)
+    low_cfg_denoised_outer = x_orig - ((x_orig - uncond) + skimming_scale * ((x_orig - cond) - (x_orig - uncond)))
+    low_cfg_denoised_outer_difference = denoised - low_cfg_denoised_outer
+    cond[outer_influence] = cond[outer_influence] - (low_cfg_denoised_outer_difference[outer_influence] / cond_scale)
+    return cond
+
+def skimmed_CFG_patch_wrap(model,Skimming_CFG=-1,end_proportion=1,full_skim_negative=True,disable_flipping_filter=False):
+    @torch.no_grad()
+    def skimmed_CFG_patch(args):
+        conds_out  = args["conds_out"]
+        cond_scale = args["cond_scale"]
+        x_orig     = args['input']
+        if not torch.any(conds_out[1]):
+            return conds_out
+        if end_proportion != 1:
+            c0,c1=conds_out[0].clone(),conds_out[1].clone()
+        practical_scale = cond_scale if Skimming_CFG < 0 else Skimming_CFG
+        conds_out[1] = skimmed_CFG(x_orig, conds_out[1], conds_out[0], cond_scale, practical_scale if not full_skim_negative else 0, disable_flipping_filter)
+        conds_out[0] = skimmed_CFG(x_orig, conds_out[0], conds_out[1], cond_scale, practical_scale, disable_flipping_filter)
+        if end_proportion != 1:
+            conds_out[0] = conds_out[0] * end_proportion + c0 * (1 - end_proportion)
+            conds_out[1] = conds_out[1] * end_proportion + c1 * (1 - end_proportion)
+        return conds_out
+    m = model.clone()
+    m.set_model_sampler_pre_cfg_function(skimmed_CFG_patch)
+    return m,
+
+# def skimmed_CFG_patch_wrap(model,Skimming_CFG=-1,end_proportion=1,full_skim_negative=False,disable_flipping_filter=False):
+#     @torch.no_grad()
+#     def skimmed_CFG_patch(args):
+#         conds_out  = args["conds_out"]
+#         cond_scale = args["cond_scale"]
+#         x_orig     = args['input']
+#         if not torch.any(conds_out[1]):
+#             return conds_out
+#         if end_proportion != 1:
+#             c0,c1=conds_out[0].clone(),conds_out[1].clone()
+#         practical_scale = cond_scale if Skimming_CFG < 0 else Skimming_CFG
+#         conds_out[1] = skimmed_CFG(x_orig, conds_out[1], conds_out[0], cond_scale, practical_scale if not full_skim_negative else 0, disable_flipping_filter)
+#         conds_out[0] = skimmed_CFG(x_orig, conds_out[0], conds_out[1], cond_scale, practical_scale, disable_flipping_filter)
+#         if end_proportion != 1:
+#             conds_out[0] = conds_out[0] * end_proportion + c0 * (1 - end_proportion)
+#             conds_out[1] = conds_out[1] * end_proportion + c1 * (1 - end_proportion)
+#         return conds_out
+#     m = model.clone()
+#     m.set_model_sampler_pre_cfg_function(skimmed_CFG_patch)
+#     return m,