Merge branch 'comfyanonymous:master' into master

comfy_extras/nodes_sd3.py

@@ -3,7 +3,9 @@ import comfy.sd
 import comfy.model_management
 import nodes
 import torch
-import re
+import comfy_extras.nodes_slg
+
+
 class TripleCLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
@@ -23,6 +25,7 @@ class TripleCLIPLoader:
         clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3], embedding_directory=folder_paths.get_folder_paths("embeddings"))
         return (clip,)
 
+
 class EmptySD3LatentImage:
     def __init__(self):
         self.device = comfy.model_management.intermediate_device()
@@ -41,6 +44,7 @@ class EmptySD3LatentImage:
         latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=self.device)
         return ({"samples":latent}, )
 
+
 class CLIPTextEncodeSD3:
     @classmethod
     def INPUT_TYPES(s):
@@ -97,7 +101,8 @@ class ControlNetApplySD3(nodes.ControlNetApplyAdvanced):
     CATEGORY = "conditioning/controlnet"
     DEPRECATED = True
 
-class SkipLayerGuidanceSD3:
+
+class SkipLayerGuidanceSD3(comfy_extras.nodes_slg.SkipLayerGuidanceDiT):
     '''
     Enhance guidance towards detailed dtructure by having another set of CFG negative with skipped layers.
     Inspired by Perturbed Attention Guidance (https://arxiv.org/abs/2403.17377)
@@ -112,48 +117,12 @@ class SkipLayerGuidanceSD3:
                              "end_percent": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.001})
                                 }}
     RETURN_TYPES = ("MODEL",)
-    FUNCTION = "skip_guidance"
+    FUNCTION = "skip_guidance_sd3"
 
     CATEGORY = "advanced/guidance"
 
-
-    def skip_guidance(self, model, layers, scale, start_percent, end_percent):
-        if layers == "" or layers == None:
-            return (model, )
-        # check if layer is comma separated integers
-        def skip(args, extra_args):
-            return args
-
-        model_sampling = model.get_model_object("model_sampling")
-        sigma_start = model_sampling.percent_to_sigma(start_percent)
-        sigma_end = model_sampling.percent_to_sigma(end_percent)
-
-        layers = re.findall(r'\d+', layers)
-        layers = [int(i) for i in layers]
-
-        def post_cfg_function(args):
-            model = args["model"]
-            cond_pred = args["cond_denoised"]
-            cond = args["cond"]
-            cfg_result = args["denoised"]
-            sigma = args["sigma"]
-            x = args["input"]
-            model_options = args["model_options"].copy()
-        
-            for layer in layers:
-                model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, skip, "dit", "double_block", layer)
-            model_sampling.percent_to_sigma(start_percent)
-
-            sigma_ = sigma[0].item()
-            if scale > 0 and sigma_ >= sigma_end and sigma_ <= sigma_start:
-                (slg,) = comfy.samplers.calc_cond_batch(model, [cond], x, sigma, model_options)
-                cfg_result = cfg_result + (cond_pred - slg) * scale
-            return cfg_result
-
-        m = model.clone()
-        m.set_model_sampler_post_cfg_function(post_cfg_function)
-
-        return (m, )
+    def skip_guidance_sd3(self, model, layers, scale, start_percent, end_percent):
+        return self.skip_guidance(model=model, scale=scale, start_percent=start_percent, end_percent=end_percent, double_layers=layers)
 
 
 NODE_CLASS_MAPPINGS = {

comfy_extras/nodes_slg.py

@@ -0,0 +1,78 @@
+import comfy.model_patcher
+import comfy.samplers
+import re
+
+
+class SkipLayerGuidanceDiT:
+    '''
+    Enhance guidance towards detailed dtructure by having another set of CFG negative with skipped layers.
+    Inspired by Perturbed Attention Guidance (https://arxiv.org/abs/2403.17377)
+    Original experimental implementation for SD3 by Dango233@StabilityAI.
+    '''
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"model": ("MODEL", ),
+                             "double_layers": ("STRING", {"default": "7, 8, 9", "multiline": False}),
+                             "single_layers": ("STRING", {"default": "7, 8, 9", "multiline": False}),
+                             "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 10.0, "step": 0.1}),
+                             "start_percent": ("FLOAT", {"default": 0.01, "min": 0.0, "max": 1.0, "step": 0.001}),
+                             "end_percent": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.001})
+                                }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "skip_guidance"
+    EXPERIMENTAL = True
+
+    DESCRIPTION = "Generic version of SkipLayerGuidance node that can be used on every DiT model."
+
+    CATEGORY = "advanced/guidance"
+
+    def skip_guidance(self, model, scale, start_percent, end_percent, double_layers="", single_layers=""):
+        # check if layer is comma separated integers
+        def skip(args, extra_args):
+            return args
+
+        model_sampling = model.get_model_object("model_sampling")
+        sigma_start = model_sampling.percent_to_sigma(start_percent)
+        sigma_end = model_sampling.percent_to_sigma(end_percent)
+
+        double_layers = re.findall(r'\d+', double_layers)
+        double_layers = [int(i) for i in double_layers]
+
+        single_layers = re.findall(r'\d+', single_layers)
+        single_layers = [int(i) for i in single_layers]
+
+        if len(double_layers) == 0 and len(single_layers) == 0:
+            return (model, )
+
+        def post_cfg_function(args):
+            model = args["model"]
+            cond_pred = args["cond_denoised"]
+            cond = args["cond"]
+            cfg_result = args["denoised"]
+            sigma = args["sigma"]
+            x = args["input"]
+            model_options = args["model_options"].copy()
+
+            for layer in double_layers:
+                model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, skip, "dit", "double_block", layer)
+
+            for layer in single_layers:
+                model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, skip, "dit", "single_block", layer)
+
+            model_sampling.percent_to_sigma(start_percent)
+
+            sigma_ = sigma[0].item()
+            if scale > 0 and sigma_ >= sigma_end and sigma_ <= sigma_start:
+                (slg,) = comfy.samplers.calc_cond_batch(model, [cond], x, sigma, model_options)
+                cfg_result = cfg_result + (cond_pred - slg) * scale
+            return cfg_result
+
+        m = model.clone()
+        m.set_model_sampler_post_cfg_function(post_cfg_function)
+
+        return (m, )
+
+
+NODE_CLASS_MAPPINGS = {
+    "SkipLayerGuidanceDiT": SkipLayerGuidanceDiT,
+}

nodes.py

@@ -2133,6 +2133,7 @@ def init_builtin_extra_nodes():
         "nodes_lora_extract.py",
         "nodes_torch_compile.py",
         "nodes_mochi.py",
+        "nodes_slg.py",
     ]
 
     import_failed = []