Merge fd20999994bb287411c2b06e9b7c8fc8b82cdfe1 into 95ca2e56c82c1c714dba685bd81ebf3f7baf8efa

comfy_extras/nodes_hunyuan.py

@@ -1,12 +1,15 @@
+import math
 import nodes
 import node_helpers
 import torch
+import re
 import comfy.model_management
+import comfy.patcher_extension
 
 
 class CLIPTextEncodeHunyuanDiT:
     @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
         return {"required": {
             "clip": ("CLIP", ),
             "bert": ("STRING", {"multiline": True, "dynamicPrompts": True}),
@@ -23,6 +26,216 @@ class CLIPTextEncodeHunyuanDiT:
 
         return (clip.encode_from_tokens_scheduled(tokens), )
 
+class MomentumBuffer:
+    def __init__(self, momentum: float):
+        self.momentum = momentum
+        self.running_average = 0
+
+    def update(self, update_value: torch.Tensor):
+        new_average = self.momentum * self.running_average
+        self.running_average = update_value + new_average
+
+def normalized_guidance_apg(
+    pred_cond: torch.Tensor,
+    pred_uncond: torch.Tensor,
+    guidance_scale: float,
+    momentum_buffer,
+    eta: float = 1.0,
+    norm_threshold: float = 0.0,
+    use_original_formulation: bool = False,
+):
+    diff = pred_cond - pred_uncond
+    dim = [-i for i in range(1, len(diff.shape))]
+
+    if momentum_buffer is not None:
+        momentum_buffer.update(diff)
+        diff = momentum_buffer.running_average
+
+    if norm_threshold > 0:
+        ones = torch.ones_like(diff)
+        diff_norm = diff.norm(p=2, dim=dim, keepdim=True)
+        scale_factor = torch.minimum(ones, norm_threshold / diff_norm)
+        diff = diff * scale_factor
+
+    v0, v1 = diff.double(), pred_cond.double()
+    v1 = torch.nn.functional.normalize(v1, dim=dim)
+    v0_parallel = (v0 * v1).sum(dim=dim, keepdim=True) * v1
+    v0_orthogonal = v0 - v0_parallel
+    diff_parallel, diff_orthogonal = v0_parallel.type_as(diff), v0_orthogonal.type_as(diff)
+
+    normalized_update = diff_orthogonal + eta * diff_parallel
+    pred = pred_cond if use_original_formulation else pred_uncond
+    pred = pred + guidance_scale * normalized_update
+
+    return pred
+
+class AdaptiveProjectedGuidance:
+    def __init__(
+        self,
+        guidance_scale: float = 7.5,
+        adaptive_projected_guidance_momentum=None,
+        adaptive_projected_guidance_rescale: float = 15.0,
+        # eta: float = 1.0,
+        eta: float = 0.0,
+        guidance_rescale: float = 0.0,
+        use_original_formulation: bool = False,
+        start: float = 0.0,
+        stop: float = 1.0,
+    ):
+        super().__init__()
+
+        self.guidance_scale = guidance_scale
+        self.adaptive_projected_guidance_momentum = adaptive_projected_guidance_momentum
+        self.adaptive_projected_guidance_rescale = adaptive_projected_guidance_rescale
+        self.eta = eta
+        self.guidance_rescale = guidance_rescale
+        self.use_original_formulation = use_original_formulation
+        self.momentum_buffer = None
+
+    def __call__(self, pred_cond: torch.Tensor, pred_uncond=None, is_first_step=False) -> torch.Tensor:
+
+        if is_first_step and self.adaptive_projected_guidance_momentum is not None:
+            self.momentum_buffer = MomentumBuffer(self.adaptive_projected_guidance_momentum)
+
+        pred = normalized_guidance_apg(
+            pred_cond,
+            pred_uncond,
+            self.guidance_scale,
+            self.momentum_buffer,
+            self.eta,
+            self.adaptive_projected_guidance_rescale,
+            self.use_original_formulation,
+        )
+
+        return pred
+
+class HunyuanMixModeAPG:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL", ),
+                "has_quoted_text": ("BOOLEAN", ),
+
+                "guidance_scale": ("FLOAT", {"default": 10.0, "min": 1.0, "max": 30.0, "step": 0.1}),
+
+                "general_eta": ("FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+                "general_norm_threshold": ("FLOAT", {"default": 10.0, "min": 0.0, "max": 50.0, "step": 0.1}),
+                "general_momentum": ("FLOAT", {"default": -0.5, "min": -5.0, "max": 1.0, "step": 0.01}),
+                "general_start_percent": ("FLOAT", {"default": 0.10, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The relative sampling step to begin use of general APG."}),
+
+                "ocr_eta": ("FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+                "ocr_norm_threshold": ("FLOAT", {"default": 10.0, "min": 0.0, "max": 50.0, "step": 0.1}),
+                "ocr_momentum": ("FLOAT", {"default": -0.5, "min": -5.0, "max": 1.0, "step": 0.01}),
+                "ocr_start_percent": ("FLOAT", {"default": 0.75, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The relative sampling step to begin use of OCR APG."}),
+
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "apply_mix_mode_apg"
+    CATEGORY = "sampling/custom_sampling/hunyuan"
+
+
+    def apply_mix_mode_apg(self, model,  has_quoted_text, guidance_scale, general_eta, general_norm_threshold, general_momentum, general_start_percent,
+                          ocr_eta, ocr_norm_threshold, ocr_momentum, ocr_start_percent):
+
+        general_apg = AdaptiveProjectedGuidance(
+            guidance_scale=guidance_scale,
+            eta=general_eta,
+            adaptive_projected_guidance_rescale=general_norm_threshold,
+            adaptive_projected_guidance_momentum=general_momentum
+        )
+
+        ocr_apg = AdaptiveProjectedGuidance(
+            eta=ocr_eta,
+            adaptive_projected_guidance_rescale=ocr_norm_threshold,
+            adaptive_projected_guidance_momentum=ocr_momentum
+        )
+
+        m = model.clone()
+
+
+        model_sampling = m.model.model_sampling
+        general_start_t = model_sampling.percent_to_sigma(general_start_percent)
+        ocr_start_t = model_sampling.percent_to_sigma(ocr_start_percent)
+
+
+        def cfg_function(args):
+            sigma = args["sigma"].to(torch.float32)
+            is_first_step = math.isclose(sigma.item(), args['model_options']['transformer_options']['sample_sigmas'][0].item())
+            cond = args["cond"]
+            uncond = args["uncond"]
+            cond_scale = args["cond_scale"]
+
+            sigma = sigma[:, None, None, None]
+
+
+            if not has_quoted_text:
+                if sigma[0] <= general_start_t:
+                    modified_cond = general_apg(cond / sigma, uncond / sigma, is_first_step=is_first_step)
+                    return modified_cond * sigma
+                else:
+                    if cond_scale > 1:
+                        _ = general_apg(cond / sigma, uncond / sigma, is_first_step=is_first_step) # track momentum
+                        return uncond + (cond - uncond) * cond_scale
+            else:
+                if sigma[0] <= ocr_start_t:
+                    modified_cond = ocr_apg(cond / sigma, uncond / sigma, is_first_step=is_first_step)
+                    return modified_cond * sigma
+                else:
+                    if cond_scale > 1:
+                        _ = ocr_apg(cond / sigma, uncond / sigma, is_first_step=is_first_step) # track momentum
+                        return uncond + (cond - uncond) * cond_scale
+
+            return cond
+
+        m.set_model_sampler_cfg_function(cfg_function, disable_cfg1_optimization=True)
+        return (m,)
+
+class CLIPTextEncodeHunyuanDiTWithTextDetection:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {
+            "clip": ("CLIP", ),
+            "text": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+            }}
+
+    RETURN_TYPES = ("CONDITIONING", "BOOLEAN")
+    RETURN_NAMES = ("conditioning", "has_quoted_text")
+    FUNCTION = "encode"
+
+    CATEGORY = "advanced/conditioning/hunyuan"
+
+    def detect_quoted_text(self, text):
+        """Detect quoted text in the prompt"""
+        text_prompt_texts = []
+
+        # Patterns to match different quote styles
+        pattern_quote_double = r'\"(.*?)\"'
+        pattern_quote_chinese_single = r'‘(.*?)’'
+        pattern_quote_chinese_double = r'“(.*?)”'
+
+        matches_quote_double = re.findall(pattern_quote_double, text)
+        matches_quote_chinese_single = re.findall(pattern_quote_chinese_single, text)
+        matches_quote_chinese_double = re.findall(pattern_quote_chinese_double, text)
+
+        text_prompt_texts.extend(matches_quote_double)
+        text_prompt_texts.extend(matches_quote_chinese_single)
+        text_prompt_texts.extend(matches_quote_chinese_double)
+
+        return len(text_prompt_texts) > 0
+
+    def encode(self, clip, text):
+        tokens = clip.tokenize(text)
+        has_quoted_text = self.detect_quoted_text(text)
+
+        conditioning = clip.encode_from_tokens_scheduled(tokens)
+
+        return (conditioning, has_quoted_text)
+
 class EmptyHunyuanLatentVideo:
     @classmethod
     def INPUT_TYPES(s):
@@ -151,8 +364,16 @@ class HunyuanRefinerLatent:
         return (positive, negative, out_latent)
 
 
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "HunyuanMixModeAPG": "Hunyuan Mix Mode APG",
+    "HunyuanStepBasedAPG": "Hunyuan Step Based APG",
+}
+
 NODE_CLASS_MAPPINGS = {
+    "HunyuanMixModeAPG": HunyuanMixModeAPG,
     "CLIPTextEncodeHunyuanDiT": CLIPTextEncodeHunyuanDiT,
+    "CLIPTextEncodeHunyuanDiTWithTextDetection": CLIPTextEncodeHunyuanDiTWithTextDetection,
     "TextEncodeHunyuanVideo_ImageToVideo": TextEncodeHunyuanVideo_ImageToVideo,
     "EmptyHunyuanLatentVideo": EmptyHunyuanLatentVideo,
     "HunyuanImageToVideo": HunyuanImageToVideo,