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Merged
pull merged 6 commits into from
Jan 25, 2026
Merged

[pull] master from Comfy-Org:master #564

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aef4e13
Make empty latent node work with other models. (#12062)
comfyanonymous Jan 25, 2026
bc72d7f
[API Nodes] add TencentHunyuan3D nodes (#12026)
bigcat88 Jan 25, 2026
ed6002c
add support for kwargs inputs to allow arbitrary inputs from frontend…
pythongosssss Jan 25, 2026
635406e
Only enable fp16 on z image models that actually support it. (#12065)
comfyanonymous Jan 25, 2026
a97c980
[Weight-adapter/Trainer] Bypass forward mode in Weight adapter system…
KohakuBlueleaf Jan 25, 2026
26c5bbb
Move nodes from previous PR into their own file. (#12066)
comfyanonymous Jan 25, 2026
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2 changes: 2 additions & 0 deletions comfy/latent_formats.py
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Original file line number Diff line number Diff line change
Expand Up @@ -594,6 +594,7 @@ def __init__(self):
class HunyuanImage21(LatentFormat):
latent_channels = 64
latent_dimensions = 2
spacial_downscale_ratio = 32
scale_factor = 0.75289

latent_rgb_factors = [
Expand Down Expand Up @@ -727,6 +728,7 @@ class HunyuanVideo15(LatentFormat):
latent_rgb_factors_bias = [ 0.0456, -0.0202, -0.0644]
latent_channels = 32
latent_dimensions = 3
spacial_downscale_ratio = 16
scale_factor = 1.03682
taesd_decoder_name = "lighttaehy1_5"

Expand Down
1 change: 1 addition & 0 deletions comfy/ldm/lumina/model.py
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Expand Up @@ -451,6 +451,7 @@ def __init__(
device=None,
dtype=None,
operations=None,
**kwargs,
) -> None:
super().__init__()
self.dtype = dtype
Expand Down
4 changes: 4 additions & 0 deletions comfy/model_detection.py
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Expand Up @@ -444,6 +444,10 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
dit_config["ffn_dim_multiplier"] = (8.0 / 3.0)
dit_config["z_image_modulation"] = True
dit_config["time_scale"] = 1000.0
try:
dit_config["allow_fp16"] = torch.std(state_dict['{}layers.{}.ffn_norm1.weight'.format(key_prefix, dit_config["n_layers"] - 2)], unbiased=False).item() < 0.42
except Exception:
pass
if '{}cap_pad_token'.format(key_prefix) in state_dict_keys:
dit_config["pad_tokens_multiple"] = 32
sig_weight = state_dict.get('{}siglip_embedder.0.weight'.format(key_prefix), None)
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100 changes: 100 additions & 0 deletions comfy/sd.py
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Expand Up @@ -20,6 +20,7 @@
import comfy.ldm.hunyuan_video.vae
import comfy.ldm.mmaudio.vae.autoencoder
import comfy.pixel_space_convert
import comfy.weight_adapter
import yaml
import math
import os
Expand Down Expand Up @@ -101,6 +102,105 @@ def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
return (new_modelpatcher, new_clip)


def load_bypass_lora_for_models(model, clip, lora, strength_model, strength_clip):
"""
Load LoRA in bypass mode without modifying base model weights.

Instead of patching weights, this injects the LoRA computation into the
forward pass: output = base_forward(x) + lora_path(x)

Non-adapter patches (bias diff, weight diff, etc.) are applied as regular patches.

This is useful for training and when model weights are offloaded.
"""
key_map = {}
if model is not None:
key_map = comfy.lora.model_lora_keys_unet(model.model, key_map)
if clip is not None:
key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map)

logging.debug(f"[BypassLoRA] key_map has {len(key_map)} entries")

lora = comfy.lora_convert.convert_lora(lora)
loaded = comfy.lora.load_lora(lora, key_map)

logging.debug(f"[BypassLoRA] loaded has {len(loaded)} entries")

# Separate adapters (for bypass) from other patches (for regular patching)
bypass_patches = {} # WeightAdapterBase instances -> bypass mode
regular_patches = {} # diff, set, bias patches -> regular weight patching

for key, patch_data in loaded.items():
if isinstance(patch_data, comfy.weight_adapter.WeightAdapterBase):
bypass_patches[key] = patch_data
else:
regular_patches[key] = patch_data

logging.debug(f"[BypassLoRA] {len(bypass_patches)} bypass adapters, {len(regular_patches)} regular patches")

k = set()
k1 = set()

if model is not None:
new_modelpatcher = model.clone()

# Apply regular patches (bias diff, weight diff, etc.) via normal patching
if regular_patches:
patched_keys = new_modelpatcher.add_patches(regular_patches, strength_model)
k.update(patched_keys)

# Apply adapter patches via bypass injection
manager = comfy.weight_adapter.BypassInjectionManager()
model_sd_keys = set(new_modelpatcher.model.state_dict().keys())

for key, adapter in bypass_patches.items():
if key in model_sd_keys:
manager.add_adapter(key, adapter, strength=strength_model)
k.add(key)
else:
logging.warning(f"[BypassLoRA] Adapter key not in model state_dict: {key}")

injections = manager.create_injections(new_modelpatcher.model)

if manager.get_hook_count() > 0:
new_modelpatcher.set_injections("bypass_lora", injections)
else:
new_modelpatcher = None

if clip is not None:
new_clip = clip.clone()

# Apply regular patches to clip
if regular_patches:
patched_keys = new_clip.add_patches(regular_patches, strength_clip)
k1.update(patched_keys)

# Apply adapter patches via bypass injection
clip_manager = comfy.weight_adapter.BypassInjectionManager()
clip_sd_keys = set(new_clip.cond_stage_model.state_dict().keys())

for key, adapter in bypass_patches.items():
if key in clip_sd_keys:
clip_manager.add_adapter(key, adapter, strength=strength_clip)
k1.add(key)

clip_injections = clip_manager.create_injections(new_clip.cond_stage_model)
if clip_manager.get_hook_count() > 0:
new_clip.patcher.set_injections("bypass_lora", clip_injections)
else:
new_clip = None

for x in loaded:
if (x not in k) and (x not in k1):
patch_data = loaded[x]
patch_type = type(patch_data).__name__
if isinstance(patch_data, tuple):
patch_type = f"tuple({patch_data[0]})"
logging.warning(f"NOT LOADED: {x} (type={patch_type})")

return (new_modelpatcher, new_clip)


class CLIP:
def __init__(self, target=None, embedding_directory=None, no_init=False, tokenizer_data={}, parameters=0, state_dict=[], model_options={}):
if no_init:
Expand Down
2 changes: 1 addition & 1 deletion comfy/supported_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1093,7 +1093,7 @@ class ZImage(Lumina2):

def __init__(self, unet_config):
super().__init__(unet_config)
if comfy.model_management.extended_fp16_support():
if comfy.model_management.extended_fp16_support() and unet_config.get("allow_fp16", False):
self.supported_inference_dtypes = self.supported_inference_dtypes.copy()
self.supported_inference_dtypes.insert(1, torch.float16)

Expand Down
8 changes: 8 additions & 0 deletions comfy/weight_adapter/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,11 @@
from .glora import GLoRAAdapter
from .oft import OFTAdapter
from .boft import BOFTAdapter
from .bypass import (
BypassInjectionManager,
BypassForwardHook,
create_bypass_injections_from_patches,
)


adapters: list[type[WeightAdapterBase]] = [
Expand All @@ -31,4 +36,7 @@
"WeightAdapterTrainBase",
"adapters",
"adapter_maps",
"BypassInjectionManager",
"BypassForwardHook",
"create_bypass_injections_from_patches",
] + [a.__name__ for a in adapters]
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