convert nodes_lt_upsampler nodes to V3 schema

README.md

@@ -227,7 +227,7 @@ Put your VAE in: models/vae
 
 AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
 
-```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.1```
+```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.4```
 
 This is the command to install the nightly with ROCm 7.1 which might have some performance improvements:
 

comfy/controlnet.py

@@ -297,30 +297,6 @@ class ControlNet(ControlBase):
         self.model_sampling_current = None
         super().cleanup()
 
-
-class QwenFunControlNet(ControlNet):
-    def get_control(self, x_noisy, t, cond, batched_number, transformer_options):
-        # Fun checkpoints are more sensitive to high strengths in the generic
-        # ControlNet merge path. Use a soft response curve so strength=1.0 stays
-        # unchanged while >1 grows more gently.
-        original_strength = self.strength
-        self.strength = math.sqrt(max(self.strength, 0.0))
-        try:
-            return super().get_control(x_noisy, t, cond, batched_number, transformer_options)
-        finally:
-            self.strength = original_strength
-
-    def pre_run(self, model, percent_to_timestep_function):
-        super().pre_run(model, percent_to_timestep_function)
-        self.set_extra_arg("base_model", model.diffusion_model)
-
-    def copy(self):
-        c = QwenFunControlNet(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)
-        c.control_model = self.control_model
-        c.control_model_wrapped = self.control_model_wrapped
-        self.copy_to(c)
-        return c
-
 class ControlLoraOps:
     class Linear(torch.nn.Module, comfy.ops.CastWeightBiasOp):
         def __init__(self, in_features: int, out_features: int, bias: bool = True,
@@ -584,7 +560,6 @@ def load_controlnet_hunyuandit(controlnet_data, model_options={}):
 def load_controlnet_flux_xlabs_mistoline(sd, mistoline=False, model_options={}):
     model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(sd, model_options=model_options)
     control_model = comfy.ldm.flux.controlnet.ControlNetFlux(mistoline=mistoline, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
-    sd = model_config.process_unet_state_dict(sd)
     control_model = controlnet_load_state_dict(control_model, sd)
     extra_conds = ['y', 'guidance']
     control = ControlNet(control_model, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)
@@ -630,53 +605,6 @@ def load_controlnet_qwen_instantx(sd, model_options={}):
     control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)
     return control
 
-
-def load_controlnet_qwen_fun(sd, model_options={}):
-    load_device = comfy.model_management.get_torch_device()
-    weight_dtype = comfy.utils.weight_dtype(sd)
-    unet_dtype = model_options.get("dtype", weight_dtype)
-    manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
-
-    operations = model_options.get("custom_operations", None)
-    if operations is None:
-        operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype, disable_fast_fp8=True)
-
-    in_features = sd["control_img_in.weight"].shape[1]
-    inner_dim = sd["control_img_in.weight"].shape[0]
-
-    block_weight = sd["control_blocks.0.attn.to_q.weight"]
-    attention_head_dim = sd["control_blocks.0.attn.norm_q.weight"].shape[0]
-    num_attention_heads = max(1, block_weight.shape[0] // max(1, attention_head_dim))
-
-    model = comfy.ldm.qwen_image.controlnet.QwenImageFunControlNetModel(
-        control_in_features=in_features,
-        inner_dim=inner_dim,
-        num_attention_heads=num_attention_heads,
-        attention_head_dim=attention_head_dim,
-        num_control_blocks=5,
-        main_model_double=60,
-        injection_layers=(0, 12, 24, 36, 48),
-        operations=operations,
-        device=comfy.model_management.unet_offload_device(),
-        dtype=unet_dtype,
-    )
-    model = controlnet_load_state_dict(model, sd)
-
-    latent_format = comfy.latent_formats.Wan21()
-    control = QwenFunControlNet(
-        model,
-        compression_ratio=1,
-        latent_format=latent_format,
-        # Fun checkpoints already expect their own 33-channel context handling.
-        # Enabling generic concat_mask injects an extra mask channel at apply-time
-        # and breaks the intended fallback packing path.
-        concat_mask=False,
-        load_device=load_device,
-        manual_cast_dtype=manual_cast_dtype,
-        extra_conds=[],
-    )
-    return control
-
 def convert_mistoline(sd):
     return comfy.utils.state_dict_prefix_replace(sd, {"single_controlnet_blocks.": "controlnet_single_blocks."})
 
@@ -754,8 +682,6 @@ def load_controlnet_state_dict(state_dict, model=None, model_options={}):
             return load_controlnet_qwen_instantx(controlnet_data, model_options=model_options)
         elif "controlnet_x_embedder.weight" in controlnet_data:
             return load_controlnet_flux_instantx(controlnet_data, model_options=model_options)
-    elif "control_blocks.0.after_proj.weight" in controlnet_data and "control_img_in.weight" in controlnet_data:
-        return load_controlnet_qwen_fun(controlnet_data, model_options=model_options)
 
     elif "controlnet_blocks.0.linear.weight" in controlnet_data: #mistoline flux
         return load_controlnet_flux_xlabs_mistoline(convert_mistoline(controlnet_data), mistoline=True, model_options=model_options)

comfy/ldm/chroma/layers.py

@@ -3,6 +3,7 @@ from torch import Tensor, nn
 
 from comfy.ldm.flux.layers import (
     MLPEmbedder,
+    RMSNorm,
     ModulationOut,
 )
 
@@ -28,7 +29,7 @@ class Approximator(nn.Module):
         super().__init__()
         self.in_proj = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device)
         self.layers = nn.ModuleList([MLPEmbedder(hidden_dim, hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)])
-        self.norms = nn.ModuleList([operations.RMSNorm(hidden_dim, dtype=dtype, device=device) for x in range( n_layers)])
+        self.norms = nn.ModuleList([RMSNorm(hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)])
         self.out_proj = operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device)
 
     @property

comfy/ldm/chroma_radiance/layers.py

@@ -4,6 +4,8 @@ from functools import lru_cache
 import torch
 from torch import nn
 
+from comfy.ldm.flux.layers import RMSNorm
+
 
 class NerfEmbedder(nn.Module):
     """
@@ -143,7 +145,7 @@ class NerfGLUBlock(nn.Module):
         # We now need to generate parameters for 3 matrices.
         total_params = 3 * hidden_size_x**2 * mlp_ratio
         self.param_generator = operations.Linear(hidden_size_s, total_params, dtype=dtype, device=device)
-        self.norm = operations.RMSNorm(hidden_size_x, dtype=dtype, device=device)
+        self.norm = RMSNorm(hidden_size_x, dtype=dtype, device=device, operations=operations)
         self.mlp_ratio = mlp_ratio
 
 
@@ -176,7 +178,7 @@ class NerfGLUBlock(nn.Module):
 class NerfFinalLayer(nn.Module):
     def __init__(self, hidden_size, out_channels, dtype=None, device=None, operations=None):
         super().__init__()
-        self.norm = operations.RMSNorm(hidden_size, dtype=dtype, device=device)
+        self.norm = RMSNorm(hidden_size, dtype=dtype, device=device, operations=operations)
         self.linear = operations.Linear(hidden_size, out_channels, dtype=dtype, device=device)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -188,7 +190,7 @@ class NerfFinalLayer(nn.Module):
 class NerfFinalLayerConv(nn.Module):
     def __init__(self, hidden_size: int, out_channels: int, dtype=None, device=None, operations=None):
         super().__init__()
-        self.norm = operations.RMSNorm(hidden_size, dtype=dtype, device=device)
+        self.norm = RMSNorm(hidden_size, dtype=dtype, device=device, operations=operations)
         self.conv = operations.Conv2d(
             in_channels=hidden_size,
             out_channels=out_channels,

comfy/ldm/flux/layers.py

@@ -5,9 +5,9 @@ import torch
 from torch import Tensor, nn
 
 from .math import attention, rope
+import comfy.ops
+import comfy.ldm.common_dit
 
-# Fix import for some custom nodes, TODO: delete eventually.
-RMSNorm = None
 
 class EmbedND(nn.Module):
     def __init__(self, dim: int, theta: int, axes_dim: list):
@@ -87,12 +87,20 @@ def build_mlp(hidden_size, mlp_hidden_dim, mlp_silu_act=False, yak_mlp=False, dt
             operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
         )
 
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.scale = nn.Parameter(torch.empty((dim), dtype=dtype, device=device))
+
+    def forward(self, x: Tensor):
+        return comfy.ldm.common_dit.rms_norm(x, self.scale, 1e-6)
+
 
 class QKNorm(torch.nn.Module):
     def __init__(self, dim: int, dtype=None, device=None, operations=None):
         super().__init__()
-        self.query_norm = operations.RMSNorm(dim, dtype=dtype, device=device)
-        self.key_norm = operations.RMSNorm(dim, dtype=dtype, device=device)
+        self.query_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations)
+        self.key_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations)
 
     def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple:
         q = self.query_norm(q)
@@ -161,7 +169,7 @@ class SiLUActivation(nn.Module):
 
 
 class DoubleStreamBlock(nn.Module):
-    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, modulation=True, mlp_silu_act=False, proj_bias=True, yak_mlp=False, dtype=None, device=None, operations=None):
+    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, modulation=True, mlp_silu_act=False, proj_bias=True, yak_mlp=False, dtype=None, device=None, operations=None):
         super().__init__()
 
         mlp_hidden_dim = int(hidden_size * mlp_ratio)
@@ -189,6 +197,8 @@ class DoubleStreamBlock(nn.Module):
 
         self.txt_mlp = build_mlp(hidden_size, mlp_hidden_dim, mlp_silu_act=mlp_silu_act, yak_mlp=yak_mlp, dtype=dtype, device=device, operations=operations)
 
+        self.flipped_img_txt = flipped_img_txt
+
     def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims_img=None, modulation_dims_txt=None, transformer_options={}):
         if self.modulation:
             img_mod1, img_mod2 = self.img_mod(vec)
@@ -214,17 +224,32 @@ class DoubleStreamBlock(nn.Module):
         del txt_qkv
         txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
 
-        q = torch.cat((txt_q, img_q), dim=2)
-        del txt_q, img_q
-        k = torch.cat((txt_k, img_k), dim=2)
-        del txt_k, img_k
-        v = torch.cat((txt_v, img_v), dim=2)
-        del txt_v, img_v
-        # run actual attention
-        attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)
-        del q, k, v
+        if self.flipped_img_txt:
+            q = torch.cat((img_q, txt_q), dim=2)
+            del img_q, txt_q
+            k = torch.cat((img_k, txt_k), dim=2)
+            del img_k, txt_k
+            v = torch.cat((img_v, txt_v), dim=2)
+            del img_v, txt_v
+            # run actual attention
+            attn = attention(q, k, v,
+                             pe=pe, mask=attn_mask, transformer_options=transformer_options)
+            del q, k, v
 
-        txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1]:]
+            img_attn, txt_attn = attn[:, : img.shape[1]], attn[:, img.shape[1]:]
+        else:
+            q = torch.cat((txt_q, img_q), dim=2)
+            del txt_q, img_q
+            k = torch.cat((txt_k, img_k), dim=2)
+            del txt_k, img_k
+            v = torch.cat((txt_v, img_v), dim=2)
+            del txt_v, img_v
+            # run actual attention
+            attn = attention(q, k, v,
+                             pe=pe, mask=attn_mask, transformer_options=transformer_options)
+            del q, k, v
+
+            txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1]:]
 
         # calculate the img bloks
         img += apply_mod(self.img_attn.proj(img_attn), img_mod1.gate, None, modulation_dims_img)

comfy/ldm/flux/model.py

@@ -16,6 +16,7 @@ from .layers import (
     SingleStreamBlock,
     timestep_embedding,
     Modulation,
+    RMSNorm
 )
 
 @dataclass
@@ -80,7 +81,7 @@ class Flux(nn.Module):
         self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device)
 
         if params.txt_norm:
-            self.txt_norm = operations.RMSNorm(params.context_in_dim, dtype=dtype, device=device)
+            self.txt_norm = RMSNorm(params.context_in_dim, dtype=dtype, device=device, operations=operations)
         else:
             self.txt_norm = None
 

comfy/ldm/hunyuan_video/model.py

@@ -241,6 +241,7 @@ class HunyuanVideo(nn.Module):
                     self.num_heads,
                     mlp_ratio=params.mlp_ratio,
                     qkv_bias=params.qkv_bias,
+                    flipped_img_txt=True,
                     dtype=dtype, device=device, operations=operations
                 )
                 for _ in range(params.depth)
@@ -377,14 +378,14 @@ class HunyuanVideo(nn.Module):
             extra_txt_ids = torch.zeros((txt_ids.shape[0], txt_vision_states.shape[1], txt_ids.shape[-1]), device=txt_ids.device, dtype=txt_ids.dtype)
             txt_ids = torch.cat((txt_ids, extra_txt_ids), dim=1)
 
-        ids = torch.cat((txt_ids, img_ids), dim=1)
+        ids = torch.cat((img_ids, txt_ids), dim=1)
         pe = self.pe_embedder(ids)
 
         img_len = img.shape[1]
         if txt_mask is not None:
             attn_mask_len = img_len + txt.shape[1]
             attn_mask = torch.zeros((1, 1, attn_mask_len), dtype=img.dtype, device=img.device)
-            attn_mask[:, 0, :txt.shape[1]] = txt_mask
+            attn_mask[:, 0, img_len:] = txt_mask
         else:
             attn_mask = None
 
@@ -412,7 +413,7 @@ class HunyuanVideo(nn.Module):
                     if add is not None:
                         img += add
 
-        img = torch.cat((txt, img), 1)
+        img = torch.cat((img, txt), 1)
 
         transformer_options["total_blocks"] = len(self.single_blocks)
         transformer_options["block_type"] = "single"
@@ -434,9 +435,9 @@ class HunyuanVideo(nn.Module):
                 if i < len(control_o):
                     add = control_o[i]
                     if add is not None:
-                        img[:, txt.shape[1]: img_len + txt.shape[1]] += add
+                        img[:, : img_len] += add
 
-        img = img[:, txt.shape[1]: img_len + txt.shape[1]]
+        img = img[:, : img_len]
         if ref_latent is not None:
             img = img[:, ref_latent.shape[1]:]
 

comfy/ldm/qwen_image/controlnet.py

@@ -2,196 +2,6 @@ import torch
 import math
 
 from .model import QwenImageTransformer2DModel
-from .model import QwenImageTransformerBlock
-
-
-class QwenImageFunControlBlock(QwenImageTransformerBlock):
-    def __init__(self, dim, num_attention_heads, attention_head_dim, has_before_proj=False, dtype=None, device=None, operations=None):
-        super().__init__(
-            dim=dim,
-            num_attention_heads=num_attention_heads,
-            attention_head_dim=attention_head_dim,
-            dtype=dtype,
-            device=device,
-            operations=operations,
-        )
-        self.has_before_proj = has_before_proj
-        if has_before_proj:
-            self.before_proj = operations.Linear(dim, dim, device=device, dtype=dtype)
-        self.after_proj = operations.Linear(dim, dim, device=device, dtype=dtype)
-
-
-class QwenImageFunControlNetModel(torch.nn.Module):
-    def __init__(
-        self,
-        control_in_features=132,
-        inner_dim=3072,
-        num_attention_heads=24,
-        attention_head_dim=128,
-        num_control_blocks=5,
-        main_model_double=60,
-        injection_layers=(0, 12, 24, 36, 48),
-        dtype=None,
-        device=None,
-        operations=None,
-    ):
-        super().__init__()
-        self.dtype = dtype
-        self.main_model_double = main_model_double
-        self.injection_layers = tuple(injection_layers)
-        # Keep base hint scaling at 1.0 so user-facing strength behaves similarly
-        # to the reference Gen2/VideoX implementation around strength=1.
-        self.hint_scale = 1.0
-        self.control_img_in = operations.Linear(control_in_features, inner_dim, device=device, dtype=dtype)
-
-        self.control_blocks = torch.nn.ModuleList([])
-        for i in range(num_control_blocks):
-            self.control_blocks.append(
-                QwenImageFunControlBlock(
-                    dim=inner_dim,
-                    num_attention_heads=num_attention_heads,
-                    attention_head_dim=attention_head_dim,
-                    has_before_proj=(i == 0),
-                    dtype=dtype,
-                    device=device,
-                    operations=operations,
-                )
-            )
-
-    def _process_hint_tokens(self, hint):
-        if hint is None:
-            return None
-        if hint.ndim == 4:
-            hint = hint.unsqueeze(2)
-
-        # Fun checkpoints are trained with 33 latent channels before 2x2 packing:
-        # [control_latent(16), mask(1), inpaint_latent(16)] -> 132 features.
-        # Default behavior (no inpaint input in stock Apply ControlNet) should use
-        # zeros for mask/inpaint branches, matching VideoX fallback semantics.
-        expected_c = self.control_img_in.weight.shape[1] // 4
-        if hint.shape[1] == 16 and expected_c == 33:
-            zeros_mask = torch.zeros_like(hint[:, :1])
-            zeros_inpaint = torch.zeros_like(hint)
-            hint = torch.cat([hint, zeros_mask, zeros_inpaint], dim=1)
-
-        bs, c, t, h, w = hint.shape
-        hidden_states = torch.nn.functional.pad(hint, (0, w % 2, 0, h % 2))
-        orig_shape = hidden_states.shape
-        hidden_states = hidden_states.view(
-            orig_shape[0],
-            orig_shape[1],
-            orig_shape[-3],
-            orig_shape[-2] // 2,
-            2,
-            orig_shape[-1] // 2,
-            2,
-        )
-        hidden_states = hidden_states.permute(0, 2, 3, 5, 1, 4, 6)
-        hidden_states = hidden_states.reshape(
-            bs,
-            t * ((h + 1) // 2) * ((w + 1) // 2),
-            c * 4,
-        )
-
-        expected_in = self.control_img_in.weight.shape[1]
-        cur_in = hidden_states.shape[-1]
-        if cur_in < expected_in:
-            pad = torch.zeros(
-                (hidden_states.shape[0], hidden_states.shape[1], expected_in - cur_in),
-                device=hidden_states.device,
-                dtype=hidden_states.dtype,
-            )
-            hidden_states = torch.cat([hidden_states, pad], dim=-1)
-        elif cur_in > expected_in:
-            hidden_states = hidden_states[:, :, :expected_in]
-
-        return hidden_states
-
-    def forward(
-        self,
-        x,
-        timesteps,
-        context,
-        attention_mask=None,
-        guidance: torch.Tensor = None,
-        hint=None,
-        transformer_options={},
-        base_model=None,
-        **kwargs,
-    ):
-        if base_model is None:
-            raise RuntimeError("Qwen Fun ControlNet requires a QwenImage base model at runtime.")
-
-        encoder_hidden_states_mask = attention_mask
-        # Keep attention mask disabled inside Fun control blocks to mirror
-        # VideoX behavior (they rely on seq lengths for RoPE, not masked attention).
-        encoder_hidden_states_mask = None
-
-        hidden_states, img_ids, _ = base_model.process_img(x)
-        hint_tokens = self._process_hint_tokens(hint)
-        if hint_tokens is None:
-            raise RuntimeError("Qwen Fun ControlNet requires a control hint image.")
-
-        if hint_tokens.shape[1] != hidden_states.shape[1]:
-            max_tokens = min(hint_tokens.shape[1], hidden_states.shape[1])
-            hint_tokens = hint_tokens[:, :max_tokens]
-            hidden_states = hidden_states[:, :max_tokens]
-            img_ids = img_ids[:, :max_tokens]
-
-        txt_start = round(
-            max(
-                ((x.shape[-1] + (base_model.patch_size // 2)) // base_model.patch_size) // 2,
-                ((x.shape[-2] + (base_model.patch_size // 2)) // base_model.patch_size) // 2,
-            )
-        )
-        txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3)
-        ids = torch.cat((txt_ids, img_ids), dim=1)
-        image_rotary_emb = base_model.pe_embedder(ids).to(x.dtype).contiguous()
-
-        hidden_states = base_model.img_in(hidden_states)
-        encoder_hidden_states = base_model.txt_norm(context)
-        encoder_hidden_states = base_model.txt_in(encoder_hidden_states)
-
-        if guidance is not None:
-            guidance = guidance * 1000
-
-        temb = (
-            base_model.time_text_embed(timesteps, hidden_states)
-            if guidance is None
-            else base_model.time_text_embed(timesteps, guidance, hidden_states)
-        )
-
-        c = self.control_img_in(hint_tokens)
-
-        for i, block in enumerate(self.control_blocks):
-            if i == 0:
-                c_in = block.before_proj(c) + hidden_states
-                all_c = []
-            else:
-                all_c = list(torch.unbind(c, dim=0))
-                c_in = all_c.pop(-1)
-
-            encoder_hidden_states, c_out = block(
-                hidden_states=c_in,
-                encoder_hidden_states=encoder_hidden_states,
-                encoder_hidden_states_mask=encoder_hidden_states_mask,
-                temb=temb,
-                image_rotary_emb=image_rotary_emb,
-                transformer_options=transformer_options,
-            )
-
-            c_skip = block.after_proj(c_out) * self.hint_scale
-            all_c += [c_skip, c_out]
-            c = torch.stack(all_c, dim=0)
-
-        hints = torch.unbind(c, dim=0)[:-1]
-
-        controlnet_block_samples = [None] * self.main_model_double
-        for local_idx, base_idx in enumerate(self.injection_layers):
-            if local_idx < len(hints) and base_idx < len(controlnet_block_samples):
-                controlnet_block_samples[base_idx] = hints[local_idx]
-
-        return {"input": controlnet_block_samples}
 
 
 class QwenImageControlNetModel(QwenImageTransformer2DModel):

comfy/lora_convert.py

@@ -5,7 +5,7 @@ import comfy.utils
 def convert_lora_bfl_control(sd): #BFL loras for Flux
     sd_out = {}
     for k in sd:
-        k_to = "diffusion_model.{}".format(k.replace(".lora_B.bias", ".diff_b").replace("_norm.scale", "_norm.set_weight"))
+        k_to = "diffusion_model.{}".format(k.replace(".lora_B.bias", ".diff_b").replace("_norm.scale", "_norm.scale.set_weight"))
         sd_out[k_to] = sd[k]
 
     sd_out["diffusion_model.img_in.reshape_weight"] = torch.tensor([sd["img_in.lora_B.weight"].shape[0], sd["img_in.lora_A.weight"].shape[1]])

comfy/model_detection.py

@@ -19,12 +19,6 @@ def count_blocks(state_dict_keys, prefix_string):
         count += 1
     return count
 
-def any_suffix_in(keys, prefix, main, suffix_list=[]):
-    for x in suffix_list:
-        if "{}{}{}".format(prefix, main, x) in keys:
-            return True
-    return False
-
 def calculate_transformer_depth(prefix, state_dict_keys, state_dict):
     context_dim = None
     use_linear_in_transformer = False
@@ -192,7 +186,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             dit_config["meanflow_sum"] = False
         return dit_config
 
-    if any_suffix_in(state_dict_keys, key_prefix, 'double_blocks.0.img_attn.norm.key_norm.', ["weight", "scale"]) and ('{}img_in.weight'.format(key_prefix) in state_dict_keys or any_suffix_in(state_dict_keys, key_prefix, 'distilled_guidance_layer.norms.0.', ["weight", "scale"])): #Flux, Chroma or Chroma Radiance (has no img_in.weight)
+    if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys and ('{}img_in.weight'.format(key_prefix) in state_dict_keys or f"{key_prefix}distilled_guidance_layer.norms.0.scale" in state_dict_keys): #Flux, Chroma or Chroma Radiance (has no img_in.weight)
         dit_config = {}
         if '{}double_stream_modulation_img.lin.weight'.format(key_prefix) in state_dict_keys:
             dit_config["image_model"] = "flux2"
@@ -247,8 +241,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
 
         dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.')
         dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.')
-
-        if any_suffix_in(state_dict_keys, key_prefix, 'distilled_guidance_layer.0.norms.0.', ["weight", "scale"]) or any_suffix_in(state_dict_keys, key_prefix, 'distilled_guidance_layer.norms.0.', ["weight", "scale"]): #Chroma
+        if '{}distilled_guidance_layer.0.norms.0.scale'.format(key_prefix) in state_dict_keys or '{}distilled_guidance_layer.norms.0.scale'.format(key_prefix) in state_dict_keys: #Chroma
             dit_config["image_model"] = "chroma"
             dit_config["in_channels"] = 64
             dit_config["out_channels"] = 64
@@ -256,8 +249,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             dit_config["out_dim"] = 3072
             dit_config["hidden_dim"] = 5120
             dit_config["n_layers"] = 5
-
-            if any_suffix_in(state_dict_keys, key_prefix, 'nerf_blocks.0.norm.', ["weight", "scale"]): #Chroma Radiance
+            if f"{key_prefix}nerf_blocks.0.norm.scale" in state_dict_keys: #Chroma Radiance
                 dit_config["image_model"] = "chroma_radiance"
                 dit_config["in_channels"] = 3
                 dit_config["out_channels"] = 3
@@ -267,7 +259,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
                 dit_config["nerf_depth"] = 4
                 dit_config["nerf_max_freqs"] = 8
                 dit_config["nerf_tile_size"] = 512
-                dit_config["nerf_final_head_type"] = "conv" if any_suffix_in(state_dict_keys, key_prefix, 'nerf_final_layer_conv.norm.', ["weight", "scale"]) else "linear"
+                dit_config["nerf_final_head_type"] = "conv" if f"{key_prefix}nerf_final_layer_conv.norm.scale" in state_dict_keys else "linear"
                 dit_config["nerf_embedder_dtype"] = torch.float32
                 if "{}__x0__".format(key_prefix) in state_dict_keys: # x0 pred
                     dit_config["use_x0"] = True
@@ -276,7 +268,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         else:
             dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
             dit_config["yak_mlp"] = '{}double_blocks.0.img_mlp.gate_proj.weight'.format(key_prefix) in state_dict_keys
-            dit_config["txt_norm"] = any_suffix_in(state_dict_keys, key_prefix, 'txt_norm.', ["weight", "scale"])
+            dit_config["txt_norm"] = "{}txt_norm.scale".format(key_prefix) in state_dict_keys
             if dit_config["yak_mlp"] and dit_config["txt_norm"]:  # Ovis model
                 dit_config["txt_ids_dims"] = [1, 2]
 

comfy/model_patcher.py

@@ -679,19 +679,18 @@ class ModelPatcher:
         for key in list(self.pinned):
             self.unpin_weight(key)
 
-    def _load_list(self, prio_comfy_cast_weights=False, default_device=None):
+    def _load_list(self, prio_comfy_cast_weights=False):
         loading = []
         for n, m in self.model.named_modules():
-            default = False
-            params = { name: param for name, param in m.named_parameters(recurse=False) }
+            params = []
+            skip = False
+            for name, param in m.named_parameters(recurse=False):
+                params.append(name)
             for name, param in m.named_parameters(recurse=True):
                 if name not in params:
-                    default = True # default random weights in non leaf modules
+                    skip = True # skip random weights in non leaf modules
                     break
-            if default and default_device is not None:
-                for param in params.values():
-                    param.data = param.data.to(device=default_device)
-            if not default and (hasattr(m, "comfy_cast_weights") or len(params) > 0):
+            if not skip and (hasattr(m, "comfy_cast_weights") or len(params) > 0):
                 module_mem = comfy.model_management.module_size(m)
                 module_offload_mem = module_mem
                 if hasattr(m, "comfy_cast_weights"):
@@ -1496,7 +1495,7 @@ class ModelPatcherDynamic(ModelPatcher):
             #with pin and unpin syncrhonization which can be expensive for small weights
             #with a high layer rate (e.g. autoregressive LLMs).
             #prioritize the non-comfy weights (note the order reverse).
-            loading = self._load_list(prio_comfy_cast_weights=True, default_device=device_to)
+            loading = self._load_list(prio_comfy_cast_weights=True)
             loading.sort(reverse=True)
 
             for x in loading:
@@ -1561,8 +1560,6 @@ class ModelPatcherDynamic(ModelPatcher):
                         allocated_size += weight_size
                     vbar.set_watermark_limit(allocated_size)
 
-                move_weight_functions(m, device_to)
-
             logging.info(f"Model {self.model.__class__.__name__} prepared for dynamic VRAM loading. {allocated_size // (1024 ** 2)}MB Staged. {num_patches} patches attached.")
 
             self.model.device = device_to
@@ -1582,7 +1579,7 @@ class ModelPatcherDynamic(ModelPatcher):
         return 0 if vbar is None else vbar.free_memory(memory_to_free)
 
     def partially_unload_ram(self, ram_to_unload):
-        loading = self._load_list(prio_comfy_cast_weights=True, default_device=self.offload_device)
+        loading = self._load_list(prio_comfy_cast_weights=True)
         for x in loading:
             _, _, _, _, m, _ = x
             ram_to_unload -= comfy.pinned_memory.unpin_memory(m)
@@ -1603,8 +1600,6 @@ class ModelPatcherDynamic(ModelPatcher):
         if unpatch_weights:
             self.partially_unload_ram(1e32)
             self.partially_unload(None, 1e32)
-            for m in self.model.modules():
-                move_weight_functions(m, device_to)
 
     def partially_load(self, device_to, extra_memory=0, force_patch_weights=False):
         assert not force_patch_weights #See above

comfy/sd1_clip.py

@@ -171,9 +171,8 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
 
     def process_tokens(self, tokens, device):
         end_token = self.special_tokens.get("end", None)
-        pad_token = self.special_tokens.get("pad", -1)
         if end_token is None:
-            cmp_token = pad_token
+            cmp_token = self.special_tokens.get("pad", -1)
         else:
             cmp_token = end_token
 
@@ -187,21 +186,15 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
             other_embeds = []
             eos = False
             index = 0
-            left_pad = False
             for y in x:
                 if isinstance(y, numbers.Integral):
-                    token = int(y)
-                    if index == 0 and token == pad_token:
-                        left_pad = True
-
-                    if eos or (left_pad and token == pad_token):
+                    if eos:
                         attention_mask.append(0)
                     else:
                         attention_mask.append(1)
-                        left_pad = False
-
+                    token = int(y)
                     tokens_temp += [token]
-                    if not eos and token == cmp_token and not left_pad:
+                    if not eos and token == cmp_token:
                         if end_token is None:
                             attention_mask[-1] = 0
                         eos = True

comfy/supported_models.py

@@ -710,15 +710,6 @@ class Flux(supported_models_base.BASE):
 
     supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
 
-    def process_unet_state_dict(self, state_dict):
-        out_sd = {}
-        for k in list(state_dict.keys()):
-            key_out = k
-            if key_out.endswith("_norm.scale"):
-                key_out = "{}.weight".format(key_out[:-len(".scale")])
-            out_sd[key_out] = state_dict[k]
-        return out_sd
-
     vae_key_prefix = ["vae."]
     text_encoder_key_prefix = ["text_encoders."]
 
@@ -907,13 +898,11 @@ class HunyuanVideo(supported_models_base.BASE):
             key_out = key_out.replace("txt_in.c_embedder.linear_1.", "txt_in.c_embedder.in_layer.").replace("txt_in.c_embedder.linear_2.", "txt_in.c_embedder.out_layer.")
             key_out = key_out.replace("_mod.linear.", "_mod.lin.").replace("_attn_qkv.", "_attn.qkv.")
             key_out = key_out.replace("mlp.fc1.", "mlp.0.").replace("mlp.fc2.", "mlp.2.")
-            key_out = key_out.replace("_attn_q_norm.weight", "_attn.norm.query_norm.weight").replace("_attn_k_norm.weight", "_attn.norm.key_norm.weight")
-            key_out = key_out.replace(".q_norm.weight", ".norm.query_norm.weight").replace(".k_norm.weight", ".norm.key_norm.weight")
+            key_out = key_out.replace("_attn_q_norm.weight", "_attn.norm.query_norm.scale").replace("_attn_k_norm.weight", "_attn.norm.key_norm.scale")
+            key_out = key_out.replace(".q_norm.weight", ".norm.query_norm.scale").replace(".k_norm.weight", ".norm.key_norm.scale")
             key_out = key_out.replace("_attn_proj.", "_attn.proj.")
             key_out = key_out.replace(".modulation.linear.", ".modulation.lin.")
             key_out = key_out.replace("_in.mlp.2.", "_in.out_layer.").replace("_in.mlp.0.", "_in.in_layer.")
-            if key_out.endswith(".scale"):
-                key_out = "{}.weight".format(key_out[:-len(".scale")])
             out_sd[key_out] = state_dict[k]
         return out_sd
 
@@ -1275,15 +1264,6 @@ class Hunyuan3Dv2(supported_models_base.BASE):
 
     latent_format = latent_formats.Hunyuan3Dv2
 
-    def process_unet_state_dict(self, state_dict):
-        out_sd = {}
-        for k in list(state_dict.keys()):
-            key_out = k
-            if key_out.endswith(".scale"):
-                key_out = "{}.weight".format(key_out[:-len(".scale")])
-            out_sd[key_out] = state_dict[k]
-        return out_sd
-
     def process_unet_state_dict_for_saving(self, state_dict):
         replace_prefix = {"": "model."}
         return utils.state_dict_prefix_replace(state_dict, replace_prefix)
@@ -1361,14 +1341,6 @@ class Chroma(supported_models_base.BASE):
 
     supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
 
-    def process_unet_state_dict(self, state_dict):
-        out_sd = {}
-        for k in list(state_dict.keys()):
-            key_out = k
-            if key_out.endswith(".scale"):
-                key_out = "{}.weight".format(key_out[:-len(".scale")])
-            out_sd[key_out] = state_dict[k]
-        return out_sd
 
     def get_model(self, state_dict, prefix="", device=None):
         out = model_base.Chroma(self, device=device)

comfy/text_encoders/ace15.py

@@ -10,6 +10,7 @@ import comfy.utils
 def sample_manual_loop_no_classes(
     model,
     ids=None,
+    paddings=[],
     execution_dtype=None,
     cfg_scale: float = 2.0,
     temperature: float = 0.85,
@@ -35,6 +36,9 @@ def sample_manual_loop_no_classes(
 
     embeds, attention_mask, num_tokens, embeds_info = model.process_tokens(ids, device)
     embeds_batch = embeds.shape[0]
+    for i, t in enumerate(paddings):
+        attention_mask[i, :t] = 0
+        attention_mask[i, t:] = 1
 
     output_audio_codes = []
     past_key_values = []
@@ -131,11 +135,13 @@ def generate_audio_codes(model, positive, negative, min_tokens=1, max_tokens=102
             pos_pad = (len(negative) - len(positive))
             positive = [model.special_tokens["pad"]] * pos_pad + positive
 
+        paddings = [pos_pad, neg_pad]
         ids = [positive, negative]
     else:
+        paddings = []
         ids = [positive]
 
-    return sample_manual_loop_no_classes(model, ids, cfg_scale=cfg_scale, temperature=temperature, top_p=top_p, top_k=top_k, min_p=min_p, seed=seed, min_tokens=min_tokens, max_new_tokens=max_tokens)
+    return sample_manual_loop_no_classes(model, ids, paddings, cfg_scale=cfg_scale, temperature=temperature, top_p=top_p, top_k=top_k, min_p=min_p, seed=seed, min_tokens=min_tokens, max_new_tokens=max_tokens)
 
 
 class ACE15Tokenizer(sd1_clip.SD1Tokenizer):

comfy/text_encoders/llama.py

@@ -355,6 +355,13 @@ class RMSNorm(nn.Module):
 
 
 
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
 def precompute_freqs_cis(head_dim, position_ids, theta, rope_scale=None, rope_dims=None, device=None):
     if not isinstance(theta, list):
         theta = [theta]
@@ -383,30 +390,20 @@ def precompute_freqs_cis(head_dim, position_ids, theta, rope_scale=None, rope_di
         else:
             cos = cos.unsqueeze(1)
             sin = sin.unsqueeze(1)
-        sin_split = sin.shape[-1] // 2
-        out.append((cos, sin[..., : sin_split], -sin[..., sin_split :]))
+        out.append((cos, sin))
 
     if len(out) == 1:
         return out[0]
 
     return out
 
+
 def apply_rope(xq, xk, freqs_cis):
     org_dtype = xq.dtype
     cos = freqs_cis[0]
     sin = freqs_cis[1]
-    nsin = freqs_cis[2]
-
-    q_embed = (xq * cos)
-    q_split = q_embed.shape[-1] // 2
-    q_embed[..., : q_split].addcmul_(xq[..., q_split :], nsin)
-    q_embed[..., q_split :].addcmul_(xq[..., : q_split], sin)
-
-    k_embed = (xk * cos)
-    k_split = k_embed.shape[-1] // 2
-    k_embed[..., : k_split].addcmul_(xk[..., k_split :], nsin)
-    k_embed[..., k_split :].addcmul_(xk[..., : k_split], sin)
-
+    q_embed = (xq * cos) + (rotate_half(xq) * sin)
+    k_embed = (xk * cos) + (rotate_half(xk) * sin)
     return q_embed.to(org_dtype), k_embed.to(org_dtype)
 
 

comfy/text_encoders/lt.py

@@ -25,7 +25,7 @@ def ltxv_te(*args, **kwargs):
 class Gemma3_12BTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         tokenizer = tokenizer_data.get("spiece_model", None)
-        super().__init__(tokenizer, pad_with_end=False, embedding_size=3840, embedding_key='gemma3_12b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, pad_left=True, disable_weights=True, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=3840, embedding_key='gemma3_12b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, disable_weights=True, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
 
     def state_dict(self):
         return {"spiece_model": self.tokenizer.serialize_model()}
@@ -97,7 +97,6 @@ class LTXAVTEModel(torch.nn.Module):
         token_weight_pairs = token_weight_pairs["gemma3_12b"]
 
         out, pooled, extra = self.gemma3_12b.encode_token_weights(token_weight_pairs)
-        out = out[:, :, -torch.sum(extra["attention_mask"]).item():]
         out_device = out.device
         if comfy.model_management.should_use_bf16(self.execution_device):
             out = out.to(device=self.execution_device, dtype=torch.bfloat16)
@@ -139,7 +138,6 @@ class LTXAVTEModel(torch.nn.Module):
 
         token_weight_pairs = token_weight_pairs.get("gemma3_12b", [])
         num_tokens = sum(map(lambda a: len(a), token_weight_pairs))
-        num_tokens = max(num_tokens, 64)
         return num_tokens * constant * 1024 * 1024
 
 def ltxav_te(dtype_llama=None, llama_quantization_metadata=None):

comfy/utils.py

@@ -675,10 +675,10 @@ def flux_to_diffusers(mmdit_config, output_prefix=""):
                         "ff_context.linear_in.bias": "txt_mlp.0.bias",
                         "ff_context.linear_out.weight": "txt_mlp.2.weight",
                         "ff_context.linear_out.bias": "txt_mlp.2.bias",
-                        "attn.norm_q.weight": "img_attn.norm.query_norm.weight",
-                        "attn.norm_k.weight": "img_attn.norm.key_norm.weight",
-                        "attn.norm_added_q.weight": "txt_attn.norm.query_norm.weight",
-                        "attn.norm_added_k.weight": "txt_attn.norm.key_norm.weight",
+                        "attn.norm_q.weight": "img_attn.norm.query_norm.scale",
+                        "attn.norm_k.weight": "img_attn.norm.key_norm.scale",
+                        "attn.norm_added_q.weight": "txt_attn.norm.query_norm.scale",
+                        "attn.norm_added_k.weight": "txt_attn.norm.key_norm.scale",
                     }
 
         for k in block_map:
@@ -701,8 +701,8 @@ def flux_to_diffusers(mmdit_config, output_prefix=""):
                         "norm.linear.bias": "modulation.lin.bias",
                         "proj_out.weight": "linear2.weight",
                         "proj_out.bias": "linear2.bias",
-                        "attn.norm_q.weight": "norm.query_norm.weight",
-                        "attn.norm_k.weight": "norm.key_norm.weight",
+                        "attn.norm_q.weight": "norm.query_norm.scale",
+                        "attn.norm_k.weight": "norm.key_norm.scale",
                         "attn.to_qkv_mlp_proj.weight": "linear1.weight", # Flux 2
                         "attn.to_out.weight": "linear2.weight", # Flux 2
                     }

comfy_extras/nodes_lt_upsampler.py

@@ -1,32 +1,32 @@
 from comfy import model_management
+from comfy_api.latest import ComfyExtension, IO
+from typing_extensions import override
 import math
 
-class LTXVLatentUpsampler:
+
+class LTXVLatentUpsampler(IO.ComfyNode):
     """
     Upsamples a video latent by a factor of 2.
     """
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "samples": ("LATENT",),
-                "upscale_model": ("LATENT_UPSCALE_MODEL",),
-                "vae": ("VAE",),
-            }
-        }
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="LTXVLatentUpsampler",
+            category="latent/video",
+            is_experimental=True,
+            inputs=[
+                IO.Latent.Input("samples"),
+                IO.LatentUpscaleModel.Input("upscale_model"),
+                IO.Vae.Input("vae"),
+            ],
+            outputs=[
+                IO.Latent.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "upsample_latent"
-    CATEGORY = "latent/video"
-    EXPERIMENTAL = True
-
-    def upsample_latent(
-        self,
-        samples: dict,
-        upscale_model,
-        vae,
-    ) -> tuple:
+    @classmethod
+    def execute(cls, samples, upscale_model, vae) -> IO.NodeOutput:
         """
         Upsample the input latent using the provided model.
 
@@ -34,7 +34,6 @@ class LTXVLatentUpsampler:
             samples (dict): Input latent samples
             upscale_model (LatentUpsampler): Loaded upscale model
             vae: VAE model for normalization
-            auto_tiling (bool): Whether to automatically tile the input for processing
 
         Returns:
             tuple: Tuple containing the upsampled latent
@@ -67,9 +66,16 @@ class LTXVLatentUpsampler:
         return_dict = samples.copy()
         return_dict["samples"] = upsampled_latents
         return_dict.pop("noise_mask", None)
-        return (return_dict,)
+        return IO.NodeOutput(return_dict)
+
+    upsample_latent = execute  # TODO: remove
 
 
-NODE_CLASS_MAPPINGS = {
-    "LTXVLatentUpsampler": LTXVLatentUpsampler,
-}
+class LTXVLatentUpsamplerExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [LTXVLatentUpsampler]
+
+
+async def comfy_entrypoint() -> LTXVLatentUpsamplerExtension:
+    return LTXVLatentUpsamplerExtension()

comfy_extras/nodes_train.py

@@ -1035,7 +1035,7 @@ class TrainLoraNode(io.ComfyNode):
                 io.Boolean.Input(
                     "offloading",
                     default=False,
-                    tooltip="Offload the Model to RAM. Requires Bypass Mode.",
+                    tooltip="Depth level for gradient checkpointing.",
                 ),
                 io.Combo.Input(
                     "existing_lora",
@@ -1124,15 +1124,6 @@ class TrainLoraNode(io.ComfyNode):
         lora_dtype = node_helpers.string_to_torch_dtype(lora_dtype)
         mp.set_model_compute_dtype(dtype)
 
-        if mp.is_dynamic():
-            if not bypass_mode:
-                logging.info("Training MP is Dynamic - forcing bypass mode. Start comfy with --highvram to force weight diff mode")
-                bypass_mode = True
-            offloading = True
-        elif offloading:
-            if not bypass_mode:
-                logging.info("Training Offload selected - forcing bypass mode. Set bypass = True to remove this message")
-
         # Prepare latents and compute counts
         latents, num_images, multi_res = _prepare_latents_and_count(
             latents, dtype, bucket_mode