fixes from comfyui testing

comfy/ldm/hunyuan_image_3/model.py

@@ -596,7 +596,7 @@ class LazyMoELoader(nn.Module):
 
     def get_checkpoint(self):
         comfyui_dir = Path.home() / "ComfyUI"
-        checkpoint = comfyui_dir / "models" / "checkpoint" / "hunyuan_image_3.safetensors"
+        checkpoint = comfyui_dir / "models" / "checkpoints" / "hunyuan_image_3.safetensors"
         checkpoint = checkpoint.resolve()
         if not os.path.exists(checkpoint):
             raise ValueError(f"Hunyuan Image 3 Checkpoint on one GPU should have the path: {checkpoint}")
@@ -998,6 +998,7 @@ class HunyuanImage3ForCausalMM(nn.Module):
         super().__init__()
         config = kwargs
         self.config = config
+        self.dtype = dtype
         factory_kwargs = {"device": device, "dtype": dtype, "operations": operations}
 
         self.timestep_emb = TimestepEmbedder(hidden_size=config["hidden_size"], **factory_kwargs)
@@ -1065,7 +1066,7 @@ class HunyuanImage3ForCausalMM(nn.Module):
         cond_exists = (joint_image[:, 0, :] != -100.0).any(dim=1).any()
 
         height, width = x.size(2) * 16, x.size(3) * 16
-        gen_timestep_scatter_index = 4
+        gen_timestep_scatter_index = 3
 
         def fn(string, func = self.encode_tok):
             return self.model.wte(torch.tensor(func(string) if not isinstance(func, dict) else func[string], device=inputs_embeds.device))\
@@ -1175,7 +1176,7 @@ class HunyuanImage3ForCausalMM(nn.Module):
 
         hidden_states = hidden_states.to(inputs_embeds.device)
         img_mask = torch.zeros(hidden_states.size(1))
-        img_mask[seq_len + x.size(1)+4:] = 1; img_mask[-1] = 0
+        img_mask[seq_len + 5:seq_len + 5 + x.size(1)] = 1
 
         diffusion_prediction = self.ragged_final_layer(
             hidden_states, img_mask, timestep, int(token_height), int(token_width), self.first_step)

comfy/model_detection.py

@@ -483,7 +483,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["text_emb_dim"] = 2048
         return dit_config
     
-    if "{}layers.32.mlp.gate_and_up_proj.weight".format(key_prefix) in state_dict_keys:
+    if "__SKIP__{}model.layers.0.mlp.experts.0.down_proj.weight" or "{}model.layers.0.mlp.experts.0.down_proj.weight".format(key_prefix) in state_dict_keys: # Hunyaun Image 3
         dit_config = {}
         dit_config["image_model"] = "hunyuan_image_3"
         dit_config["hidden_size"] = 4096

comfy/text_encoders/hunyuan_image.py

@@ -1,7 +1,7 @@
 from comfy import sd1_clip
 import comfy.text_encoders.llama
 from .qwen_image import QwenImageTokenizer, QwenImageTEModel
-from transformers import ByT5Tokenizer
+from transformers import ByT5Tokenizer, AutoTokenizer
 import torch
 import os
 import re
@@ -20,8 +20,9 @@ class HunyuanImage3(sd1_clip.SDClipModel):
         super().__init__(device, max_length, freeze, layer, layer_idx, textmodel_json_config, dtype, model_class, layer_norm_hidden_state, enable_attention_masks, zero_out_masked, return_projected_pooled, return_attention_masks, model_options)
 
 class HunyuanImage3Tokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, tokenizer_path="hunyuan_image_3", max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=..., has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, tokenizer_data=..., tokenizer_args=...):
-        super().__init__(tokenizer_path, max_length, pad_with_end, embedding_directory, embedding_size, embedding_key, tokenizer_class, has_start_token, has_end_token, pad_to_max_length, min_length, pad_token, end_token, min_padding, tokenizer_data, tokenizer_args)
+    def __init__(self, tokenizer_path="hunyuan3_tokenizer", max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=4096, embedding_key='clip_l', tokenizer_class=AutoTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=False, min_length=None, pad_token=128009, end_token=None, min_padding=None):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), tokenizer_path)
+        super().__init__(tokenizer_path, max_length, pad_with_end, embedding_directory, embedding_size, embedding_key, tokenizer_class, has_start_token, has_end_token, pad_to_max_length, min_length, pad_token, end_token, min_padding)
 
 class ByT5SmallTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):

comfy_extras/nodes_hunyuan_image.py

@@ -40,7 +40,6 @@ class HunyuanImage3Conditioning(io.ComfyNode):
             category="conditioning/video_models",
             inputs = [
                 io.Conditioning.Input("text_encoding_positive"),
-                io.Clip.Input("clip"),
                 io.Model.Input("model"),
                 io.Conditioning.Input("vae_encoding", optional=True),
                 io.Conditioning.Input("vit_encoding", optional=True),
@@ -50,11 +49,13 @@ class HunyuanImage3Conditioning(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, text_encoding, clip, model, text_encoding_negative=None, vae_encoding = None, vit_encoding = None):
-        encode_fn = clip.tokenizer.tokenizer.convert_tokens_to_ids
-        special_fn = clip.tokenizer.tokenizer.added_tokens_encoder
+    def execute(cls, text_encoding, model, text_encoding_negative=None, vae_encoding = None, vit_encoding = None):
+        model = model.diffusion_model
+
+        encode_fn = model.encode_tok
+        special_fn = model.special_tok
+        word_embed = model.wte
 
-        word_embed = clip.wte
         patch_embed = model.patch_embed
         t_embed = model.time_embed
 
@@ -80,12 +81,7 @@ class HunyuanImage3Conditioning(io.ComfyNode):
             vae_mask = torch.empty_like(joint_image)
 
         ragged_tensors = torch.nested.nested_tensor([joint_image, vae_mask, text_tokens.to(joint_image.dtype)])
-
-        uncond_ragged_tensors = None
-        if text_encoding_negative is not None:
-            uncond_ragged_tensors, _ = cls.execute(vae_encoding, vit_encoding, text_encoding_negative, clip=clip, text_encoding_negative = None)
-        else:
-            uncond_ragged_tensors = torch.nested.nested_tensor([torch.zeros_like(t) for t in ragged_tensors.unbind()])
+        uncond_ragged_tensors = torch.nested.nested_tensor([torch.zeros_like(t) for t in ragged_tensors.unbind()])
 
         if uncond_ragged_tensors is not None:
             positive = [[ragged_tensors, {}]]