Merge branch 'comfyanonymous:master' into master

comfy/ldm/hidream/model.py

@@ -8,26 +8,12 @@ from einops import repeat
 from comfy.ldm.lightricks.model import TimestepEmbedding, Timesteps
 import torch.nn.functional as F
 
-from comfy.ldm.flux.math import apply_rope
+from comfy.ldm.flux.math import apply_rope, rope
+from comfy.ldm.flux.layers import LastLayer
+
 from comfy.ldm.modules.attention import optimized_attention
 import comfy.model_management
 
-# Copied from https://github.com/black-forest-labs/flux/blob/main/src/flux/math.py
-def rope(pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor:
-    assert dim % 2 == 0, "The dimension must be even."
-
-    scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
-    omega = 1.0 / (theta**scale)
-
-    batch_size, seq_length = pos.shape
-    out = torch.einsum("...n,d->...nd", pos, omega)
-    cos_out = torch.cos(out)
-    sin_out = torch.sin(out)
-
-    stacked_out = torch.stack([cos_out, -sin_out, sin_out, cos_out], dim=-1)
-    out = stacked_out.view(batch_size, -1, dim // 2, 2, 2)
-    return out.float()
-
 
 # Copied from https://github.com/black-forest-labs/flux/blob/main/src/flux/modules/layers.py
 class EmbedND(nn.Module):
@@ -84,23 +70,6 @@ class TimestepEmbed(nn.Module):
         return t_emb
 
 
-class OutEmbed(nn.Module):
-    def __init__(self, hidden_size, patch_size, out_channels, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
-        self.adaLN_modulation = nn.Sequential(
-            nn.SiLU(),
-            operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device)
-        )
-
-    def forward(self, x, adaln_input):
-        shift, scale = self.adaLN_modulation(adaln_input).chunk(2, dim=1)
-        x = self.norm_final(x) * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
-        x = self.linear(x)
-        return x
-
-
 def attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor):
     return optimized_attention(query.view(query.shape[0], -1, query.shape[-1] * query.shape[-2]), key.view(key.shape[0], -1, key.shape[-1] * key.shape[-2]), value.view(value.shape[0], -1, value.shape[-1] * value.shape[-2]), query.shape[2])
 
@@ -663,7 +632,7 @@ class HiDreamImageTransformer2DModel(nn.Module):
             ]
         )
 
-        self.final_layer = OutEmbed(self.inner_dim, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations)
+        self.final_layer = LastLayer(self.inner_dim, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations)
 
         caption_channels = [caption_channels[1], ] * (num_layers + num_single_layers) + [caption_channels[0], ]
         caption_projection = []

comfy/text_encoders/hidream.py

@@ -11,14 +11,15 @@ class HiDreamTokenizer:
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
         self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
-        self.t5xxl = sd3_clip.T5XXLTokenizer(embedding_directory=embedding_directory, min_length=128, tokenizer_data=tokenizer_data)
+        self.t5xxl = sd3_clip.T5XXLTokenizer(embedding_directory=embedding_directory, min_length=128, max_length=128, tokenizer_data=tokenizer_data)
         self.llama = hunyuan_video.LLAMA3Tokenizer(embedding_directory=embedding_directory, min_length=128, pad_token=128009, tokenizer_data=tokenizer_data)
 
     def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
         out = {}
         out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids)
         out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
-        out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids)
+        t5xxl = self.t5xxl.tokenize_with_weights(text, return_word_ids)
+        out["t5xxl"] = [t5xxl[0]]  # Use only first 128 tokens
         out["llama"] = self.llama.tokenize_with_weights(text, return_word_ids)
         return out
 

comfy/text_encoders/sd3_clip.py

@@ -32,9 +32,9 @@ def t5_xxl_detect(state_dict, prefix=""):
     return out
 
 class T5XXLTokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, embedding_directory=None, tokenizer_data={}, min_length=77):
+    def __init__(self, embedding_directory=None, tokenizer_data={}, min_length=77, max_length=99999999):
         tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
-        super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=min_length, tokenizer_data=tokenizer_data)
+        super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=max_length, min_length=min_length, tokenizer_data=tokenizer_data)
 
 
 class SD3Tokenizer: