Merge 780b7d7d28017b25203a4ca39f2de72a6eedcb7f into cd66d72b464fd9d344baa426b50a5f0e5e512f99

comfy/sd.py

@@ -836,7 +836,7 @@ class CLIPType(Enum):
     OMNIGEN2 = 17
     QWEN_IMAGE = 18
     HUNYUAN_IMAGE = 19
-
+    HUNYUAN_IMAGE_REFINER = 20
 
 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
     clip_data = []
@@ -995,6 +995,9 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             if clip_type == CLIPType.HUNYUAN_IMAGE:
                 clip_target.clip = comfy.text_encoders.hunyuan_image.te(byt5=False, **llama_detect(clip_data))
                 clip_target.tokenizer = comfy.text_encoders.hunyuan_image.HunyuanImageTokenizer
+            elif clip_type == CLIPType.HUNYUAN_IMAGE_REFINER:
+                clip_target.clip = comfy.text_encoders.hunyuan_image.te(byt5=False, refiner=True, **llama_detect(clip_data))
+                clip_target.tokenizer = comfy.text_encoders.hunyuan_image.HunyuanImageRefinerTokenizer
             else:
                 clip_target.clip = comfy.text_encoders.qwen_image.te(**llama_detect(clip_data))
                 clip_target.tokenizer = comfy.text_encoders.qwen_image.QwenImageTokenizer

comfy/text_encoders/hunyuan_image.py

@@ -4,6 +4,8 @@ from .qwen_image import QwenImageTokenizer, QwenImageTEModel
 from transformers import ByT5Tokenizer
 import os
 import re
+import torch
+import numbers
 
 class ByT5SmallTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
@@ -38,6 +40,13 @@ class HunyuanImageTokenizer(QwenImageTokenizer):
             out['byt5'] = self.byt5.tokenize_with_weights(''.join(map(lambda a: 'Text "{}". '.format(a), text_prompt_texts)), return_word_ids, **kwargs)
         return out
 
+class HunyuanImageRefinerTokenizer(HunyuanImageTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+        self.llama_template = "<|start_header_id|>system<|end_header_id|>\n\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|eot_id|>\n<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
+
+
+
 class Qwen25_7BVLIModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="hidden", layer_idx=-3, dtype=None, attention_mask=True, model_options={}):
         llama_scaled_fp8 = model_options.get("qwen_scaled_fp8", None)
@@ -53,9 +62,9 @@ class ByT5SmallModel(sd1_clip.SDClipModel):
         super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, model_options=model_options, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True)
 
 
-class HunyuanImageTEModel(QwenImageTEModel):
+class HunyuanImageTEModel(sd1_clip.SD1ClipModel):
     def __init__(self, byt5=True, device="cpu", dtype=None, model_options={}):
-        super(QwenImageTEModel, self).__init__(device=device, dtype=dtype, name="qwen25_7b", clip_model=Qwen25_7BVLIModel, model_options=model_options)
+        super().__init__(device=device, dtype=dtype, name="qwen25_7b", clip_model=Qwen25_7BVLIModel, model_options=model_options)
 
         if byt5:
             self.byt5_small = ByT5SmallModel(device=device, dtype=dtype, model_options=model_options)
@@ -63,11 +72,35 @@ class HunyuanImageTEModel(QwenImageTEModel):
             self.byt5_small = None
 
     def encode_token_weights(self, token_weight_pairs):
-        cond, p, extra = super().encode_token_weights(token_weight_pairs)
+        out, pooled, extra = super().encode_token_weights(token_weight_pairs)
+        tok_pairs = token_weight_pairs["qwen25_7b"][0]
+        count_im_start = 0
+        for i, v in enumerate(tok_pairs):
+            elem = v[0]
+            if not torch.is_tensor(elem):
+                if isinstance(elem, numbers.Integral):
+                    if elem == 151644 and count_im_start < 2:
+                        template_end = i
+                        count_im_start += 1
+
+        if out.shape[1] > (template_end + 3):
+            if tok_pairs[template_end + 1][0] == 872:
+                if tok_pairs[template_end + 2][0] == 198:
+                    template_end += 3
+
+        out = out[:, template_end:]
+
+        extra["attention_mask"] = extra["attention_mask"][:, template_end:]
+        if extra["attention_mask"].sum() == torch.numel(extra["attention_mask"]):
+            extra.pop("attention_mask")  # attention mask is useless if no masked elements
+  # noqa: W293
+
         if self.byt5_small is not None and "byt5" in token_weight_pairs:
-            out = self.byt5_small.encode_token_weights(token_weight_pairs["byt5"])
+            byt5_out = self.byt5_small.encode_token_weights(token_weight_pairs["byt5"])
-            extra["conditioning_byt5small"] = out[0]
+            extra["conditioning_byt5small"] = byt5_out[0]
-        return cond, p, extra
+        return out, pooled, extra
+
+
 
     def set_clip_options(self, options):
         super().set_clip_options(options)
@@ -84,9 +117,33 @@ class HunyuanImageTEModel(QwenImageTEModel):
             return self.byt5_small.load_sd(sd)
         else:
             return super().load_sd(sd)
+class HunyuanImageRefinerTEModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, name="qwen25_7b", clip_model=Qwen25_7BVLIModel, model_options=model_options)
+
+    def encode_token_weights(self, token_weight_pairs):
+        out, pooled, extra = super().encode_token_weights(token_weight_pairs)
+        tok_pairs = token_weight_pairs["qwen25_7b"][0]
+        for i, v in enumerate(tok_pairs):
+            elem = v[0]
+            if not torch.is_tensor(elem):
+                if isinstance(elem, numbers.Integral):
+                    if elem == 6171:
+                        template_end = i
+                        break
+
+        out = out[:, template_end-1:]
+
+        extra["attention_mask"] = extra["attention_mask"][:, template_end-1:]
+        if extra["attention_mask"].sum() == torch.numel(extra["attention_mask"]):
+            extra.pop("attention_mask")  # attention mask is useless if no masked elements
+
+        return out, pooled, extra
+
+
+def te(byt5=True, dtype_llama=None, llama_scaled_fp8=None, refiner=False):
+    class HunyuanImageTEModel_(HunyuanImageTEModel):
 
-def te(byt5=True, dtype_llama=None, llama_scaled_fp8=None):
-    class QwenImageTEModel_(HunyuanImageTEModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):
             if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
                 model_options = model_options.copy()
@@ -94,4 +151,14 @@ def te(byt5=True, dtype_llama=None, llama_scaled_fp8=None):
             if dtype_llama is not None:
                 dtype = dtype_llama
             super().__init__(byt5=byt5, device=device, dtype=dtype, model_options=model_options)
-    return QwenImageTEModel_
+    class HunyuanImageTEModel_refiner(HunyuanImageRefinerTEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
+                model_options = model_options.copy()
+                model_options["qwen_scaled_fp8"] = llama_scaled_fp8
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            assert refiner, "refiner must be True"
+            assert not byt5, "byt5 must be False"
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return HunyuanImageTEModel_refiner if refiner else HunyuanImageTEModel_

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 +26,249 @@ 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", "STRING")
+    RETURN_NAMES = ("conditioning", "has_quoted_text", "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)
+
+        c = []
+        for t in conditioning:
+            n = [t[0], t[1].copy()]
+            n[1]['has_quoted_text'] = has_quoted_text
+            c.append(n)
+
+        return (conditioning, has_quoted_text, text)
+
+
+class CLIPTextEncodeHunyuanImageRefiner:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {
+            "clip": ("CLIP", ),
+            "text": ("STRING", ),
+            }}
+    RETURN_TYPES = ("CONDITIONING",)
+    RETURN_NAMES = ("conditioning",)
+    FUNCTION = "encode"
+
+    CATEGORY = "advanced/conditioning/hunyuan"
+
+
+    def encode(self, clip, text):
+        tokens = clip.tokenize(text)
+
+        conditioning = clip.encode_from_tokens_scheduled(tokens)
+
+        c = []
+        for t in conditioning:
+            n = [t[0], t[1].copy()]
+            c.append(n)
+
+        return (c, )
+
 class EmptyHunyuanLatentVideo:
     @classmethod
     def INPUT_TYPES(s):
@@ -151,8 +397,16 @@ class HunyuanRefinerLatent:
         return (positive, negative, out_latent)
 
 
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "HunyuanMixModeAPG": "Hunyuan Mix Mode APG",
+}
+
 NODE_CLASS_MAPPINGS = {
+    "HunyuanMixModeAPG": HunyuanMixModeAPG,
     "CLIPTextEncodeHunyuanDiT": CLIPTextEncodeHunyuanDiT,
+    "CLIPTextEncodeHunyuanDiTWithTextDetection": CLIPTextEncodeHunyuanDiTWithTextDetection,
+    "CLIPTextEncodeHunyuanImageRefiner": CLIPTextEncodeHunyuanImageRefiner,
     "TextEncodeHunyuanVideo_ImageToVideo": TextEncodeHunyuanVideo_ImageToVideo,
     "EmptyHunyuanLatentVideo": EmptyHunyuanLatentVideo,
     "HunyuanImageToVideo": HunyuanImageToVideo,

nodes.py

@@ -929,7 +929,7 @@ class CLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ),
-                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image"], ),
+                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image","hunyuan_image_refiner"], ),
                               },
                 "optional": {
                               "device": (["default", "cpu"], {"advanced": True}),