feat: ming-image support - #16482
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feat: ming-image support#16482
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September 23, 2026 22:55
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Source excerpt: Treat `execution.py` as one example of this rule: it should consume the prompt graph and execution-relevant state, produce execution results and errors, and not know about workflow ids, frontend ids, persistence ids, or API-...📄 CodeRabbit inference engine (AGENTS.md) Files:
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📝 WalkthroughWalkthroughThe change adds Ming Image text encoding, checkpoint detection and loading, model and latent support, and a node for prompt and reference-image conditioning. Lumina now accepts extra context and reference frames and supports frame-aware inputs and outputs. Shared MoE expert execution now handles quantized expert banks, and GPT-OSS uses the shared helper. Priority: ➖ Normal Merge Risk: 🔵 Low · up to A Z-Image checkpoint with malformed optional config metadata can fail to load. This is a bounded loading risk; the separate metadata-free Ming-Image report could not be confirmed against this head. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
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Inline comments:
In `@comfy/model_detection.py`:
- Around line 615-618: In the metadata handling block, apply the transformer
config override only when its image_model is "ming_image"; keep detected
dit_config fields unchanged for other checkpoints with config metadata. Use the
existing metadata and dit_config flow to make this targeted change.
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comfy/latent_formats.pycomfy/ldm/lumina/model.pycomfy/model_base.pycomfy/model_detection.pycomfy/ops.pycomfy/sd.pycomfy/supported_models.pycomfy/text_encoders/gpt_oss.pycomfy/text_encoders/llama.pycomfy/text_encoders/ming_image.pycomfy_extras/nodes_ming.pynodes.py
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Community-contributed extra nodes.
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comfy_extras/nodes_ming.py
Core node definitions (2500+ lines).
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nodes.py
Core ML/diffusion engine.
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comfy/model_detection.pycomfy/latent_formats.pycomfy/supported_models.pycomfy/text_encoders/gpt_oss.pycomfy/model_base.pycomfy/text_encoders/llama.pycomfy/ops.pycomfy/sd.pycomfy/ldm/lumina/model.pycomfy/text_encoders/ming_image.py
IMPORTANT: Only comment on issues directly introduced by this PR's code changes.
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comfy/model_detection.pycomfy/latent_formats.pycomfy/supported_models.pycomfy/text_encoders/gpt_oss.pynodes.pycomfy/model_base.pycomfy/text_encoders/llama.pycomfy_extras/nodes_ming.pycomfy/ops.pycomfy/sd.pycomfy/ldm/lumina/model.pycomfy/text_encoders/ming_image.py
Source excerpt: Treat `execution.py` as one example of this rule: it should consume the prompt graph and execution-relevant state, produce execution results and errors, and not know about workflow ids, frontend ids, persistence ids, or API-...
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comfy/model_detection.pycomfy/latent_formats.pycomfy/supported_models.pycomfy/text_encoders/gpt_oss.pynodes.pycomfy/model_base.pycomfy/text_encoders/llama.pycomfy_extras/nodes_ming.pycomfy/ops.pycomfy/sd.pycomfy/ldm/lumina/model.pycomfy/text_encoders/ming_image.py
🪛 ast-grep (0.45.3)
comfy/ops.py
[warning] 1347-1747: Do not use an empty list as a default parameter
Context: def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_precision_mm=False, disabled=[]):
class MixedPrecisionOps(manual_cast):
_quant_config = quant_config
_compute_dtype = compute_dtype
_full_precision_mm = full_precision_mm
_disabled = disabled
class Linear(torch.nn.Module, MixedPrecisionOp):
_disabled_formats = disabled
def __init__(self, in_features: int, out_features: int, bias: bool = True, device=None, dtype=None):
super().__init__()
self.factory_kwargs = {"device": device, "dtype": MixedPrecisionOps._compute_dtype}
self.in_features = in_features
self.out_features = out_features
self._orig_shape = (out_features, in_features)
if bias:
self.bias = torch.nn.Parameter(torch.empty(out_features, **self.factory_kwargs))
else:
self.register_parameter("bias", None)
self.tensor_class = None
self._full_precision_mm = MixedPrecisionOps._full_precision_mm
self._full_precision_mm_config = False
def reset_parameters(self):
return None
def _load_from_state_dict(self, *args):
_load_quantized_module(self, super()._load_from_state_dict, *args, load_extra_params=True)
def state_dict(self, *args, destination=None, prefix="", **kwargs):
sd = destination if destination is not None else {}
return _quantized_weight_state_dict(self, sd, prefix, extra_quant_params=("input_scale", "pre_quant_scale"))
def _forward(self, input, weight, bias):
return torch.nn.functional.linear(input, weight, bias)
def forward_comfy_cast_weights(
self,
input,
compute_dtype=None,
want_requant=False,
weight_only_quant=False,
):
if not weight_only_quant:
with CastBiasWeightContext(
self,
input,
offloadable=True,
compute_dtype=compute_dtype,
want_requant=want_requant,
) as (weight, bias):
if self._full_precision_mm and isinstance(weight, QuantizedTensor):
weight = weight.dequantize()
return self._forward(input, weight, bias)
with CastBiasWeightContext(
self,
input=None,
dtype=self.weight.dtype,
device=input.device,
bias_dtype=input.dtype,
offloadable=True,
compute_dtype=compute_dtype,
want_requant=True,
) as (weight, bias):
weight = weight.to(dtype=input.dtype)
return self._forward(input, weight, bias)
def forward(self, input, *args, **kwargs):
run_every_op()
# ModelOpt AWQ-style smoothing
pre_quant_scale = getattr(self, 'pre_quant_scale', None)
if pre_quant_scale is not None:
input = input * comfy.model_management.cast_to_device(pre_quant_scale, input.device, input.dtype)
input_shape = input.shape
reshaped_nd = False
`#If` cast needs to apply lora, it should be done in the compute dtype
compute_dtype = input.dtype
_use_quantized = (
getattr(self, 'layout_type', None) is not None and
not isinstance(input, QuantizedTensor) and not self._full_precision_mm and
not getattr(self, 'comfy_force_cast_weights', False) and
len(self.weight_function) == 0 and len(self.bias_function) == 0
)
quantize_input = QUANT_ALGOS.get(getattr(self, 'quant_format', None), {}).get("quantize_input", True)
# Training path: quantized forward with compute_dtype backward via autograd function
if (input.requires_grad and _use_quantized and quantize_input):
with CastBiasWeightContext(
self,
input,
offloadable=True,
compute_dtype=compute_dtype,
want_requant=True
) as (weight, bias):
scale = getattr(self, 'input_scale', None)
if scale is not None:
scale = comfy.model_management.cast_to_device(scale, input.device, None)
return QuantLinearFunc.apply(
input, weight, bias, self.layout_type, scale, compute_dtype
)
# Inference path (unchanged)
if _use_quantized and quantize_input:
# Reshape >=3D tensors to 2D for quantization (needed for NVFP4 and others)
input_reshaped = input.reshape(-1, input_shape[-1]) if input.ndim >= 3 else input
# Fall back to non-quantized for non-2D tensors
if input_reshaped.ndim == 2:
reshaped_nd = input.ndim >= 3
# dtype is now implicit in the layout class
scale = getattr(self, 'input_scale', None)
if scale is not None:
scale = comfy.model_management.cast_to_device(scale, input.device, None)
input = QuantizedTensor.from_float(input_reshaped, self.layout_type, scale=scale)
weight_only_quant = _use_quantized and not quantize_input and isinstance(self.weight, QuantizedTensor)
output = self.forward_comfy_cast_weights(
input,
compute_dtype,
want_requant=isinstance(input, QuantizedTensor),
weight_only_quant=weight_only_quant,
)
# Reshape output back to original rank if input was >2D
if reshaped_nd:
output = output.reshape((*input_shape[:-1], self.weight.shape[0]))
return output
def convert_weight(self, weight, inplace=False, **kwargs):
if isinstance(weight, QuantizedTensor):
return weight.dequantize()
else:
return weight
def set_weight(self, weight, inplace_update=False, seed=None, return_weight=False, **kwargs):
if getattr(self, 'layout_type', None) is not None:
weight = self.weight.requantize_from_float(weight, scale="recalculate", stochastic_rounding=seed, inplace_ops=True).to(self.weight.dtype)
else:
weight = weight.to(self.weight.dtype)
if return_weight:
return weight
assert inplace_update is False # TODO: eventually remove the inplace_update stuff
self.weight = torch.nn.Parameter(weight, requires_grad=False)
def _apply(self, fn, recurse=True): # This is to get torch.compile + moving weights to another device working
return _quantized_apply(self, fn, recurse)
class MoEExperts(torch.nn.Module, MixedPrecisionOp):
"""Container for E quantized expert weights, indexed via expert_weight(i).
The bank lives on self.weight as a single 3D tensor — either a
compute_dtype Parameter or a Parameter wrapping a QuantizedTensor
with leading expert dim.
State-dict layout matches mixed_precision_ops.Linear with a leading
expert dim:
{prefix}.weight quant data (storage_t), leading dim = E
{prefix}.weight_scale block / per-tensor scale
{prefix}.weight_scale_2 [E] or scalar NVFP4 only
{prefix}.bias [E, out_features] optional, compute_dtype
{prefix}.comfy_quant json -> {{"format": "...", "num_experts": E}}
Without comfy_quant the weight loads as a plain compute_dtype 3D Parameter [E, out, in].
"""
_disabled_formats = disabled
def __init__(self, num_experts: int, in_features: int, out_features: int, bias: bool = True, device=None, dtype=None):
super().__init__()
self.num_experts = num_experts
self.in_features = in_features
self.out_features = out_features
self._orig_shape = (num_experts, out_features, in_features)
self.factory_kwargs = {"device": device, "dtype": MixedPrecisionOps._compute_dtype}
if bias:
self.bias = torch.nn.Parameter(torch.empty(num_experts, out_features, **self.factory_kwargs))
else:
self.register_parameter("bias", None)
# Populated by _load_from_state_dict:
self.weight = None
self.quant_format = None
self.layout_type = None
self._full_precision_mm = MixedPrecisionOps._full_precision_mm
self._full_precision_mm_config = False
self._resident_bank = None
def reset_parameters(self):
return None
def _apply(self, fn, recurse=True):
return _quantized_apply(self, fn, recurse)
def _load_from_state_dict(self, state_dict, prefix, *args):
layer_conf = state_dict.get(f"{prefix}comfy_quant", None)
quant_format = json.loads(layer_conf.numpy().tobytes()).get("format") if layer_conf is not None else None
scale = state_dict.get(f"{prefix}weight_scale", None)
if quant_format == "asym_w4a8_int8" or (quant_format == "int8_tensorwise" and scale is not None and scale.ndim == 3):
# per-row scaled layouts are 2-D only: keep the bank as [E * out, in] and slice rows per expert
for name in ("weight", "weight_scale", "weight_s_rel", "weight_s_channel"):
t = state_dict.get(f"{prefix}{name}", None)
if t is not None and t.ndim > 1 and t.shape[0] == self.num_experts:
state_dict[f"{prefix}{name}"] = t.reshape(self.num_experts * t.shape[1], *t.shape[2:])
self._orig_shape = (self.num_experts * self.out_features, self.in_features)
_load_quantized_module(self, super()._load_from_state_dict, state_dict, prefix, *args, load_extra_params=False)
def expert_weight(self, i: int):
"""Expert i's weight (Tensor or per-expert QuantizedTensor view)."""
if isinstance(self.weight, QuantizedTensor):
return self._expert_qt_from(self.weight, i)
return self.weight[i]
def _cast_bank(self, input):
# A quantized bank stays quantized: the layouts dequantize one [out, in] matrix at a time, per expert.
if isinstance(self.weight, QuantizedTensor):
return CastBiasWeightContext(self, input=None, dtype=self.weight.dtype, device=input.device, bias_dtype=input.dtype, offloadable=True)
return CastBiasWeightContext(self, input, offloadable=True)
def _dequantize_bank(self, weight, dtype):
# in expert chunks: the W4A8 dequantize kernel allocates over twice its output in temporaries
flat = QuantizedTensor(weight._qdata, weight._layout_cls, dataclasses.replace(weight._params, orig_dtype=dtype))
out = torch.empty((self.num_experts, self.out_features, self.in_features), dtype=dtype, device=weight.device)
step = max(1, (256 << 20) // (self.out_features * self.in_features * out.element_size()))
for first in range(0, self.num_experts, step):
last = min(first + step, self.num_experts)
out[first:last] = self._bank_rows(flat, first, last).dequantize().view(last - first, self.out_features, self.in_features)
return out
def _bank_rows(self, weight: QuantizedTensor, first: int, last: int) -> QuantizedTensor:
"""Experts [first, last) of a flat [E * out, in] bank as one QuantizedTensor."""
params = weight._params
rows = slice(first * self.out_features, last * self.out_features)
per_row = {f.name: getattr(params, f.name)[rows] for f in dataclasses.fields(params) if torch.is_tensor(getattr(params, f.name)) and getattr(params, f.name).ndim >= 1 and getattr(params, f.name).shape[0] == weight._qdata.shape[0]}
return QuantizedTensor(weight._qdata[rows], weight._layout_cls, dataclasses.replace(params, orig_shape=((last - first) * self.out_features, self.in_features), **per_row))
`@contextlib.contextmanager`
def bank_resident(self, input):
"""Cast the whole bank once; expert_linear inside reuses the cast.
Not re-entrant — do not nest calls on the same instance.
"""
with self._cast_bank(input) as (weight, bias):
if self._full_precision_mm and isinstance(weight, QuantizedTensor) and weight._qdata.ndim == 2: # flat per-row banks; 3-D banks dequantize per expert
weight = self._dequantize_bank(weight, input.dtype)
self._resident_bank = (weight, bias)
try:
yield self
finally:
self._resident_bank = None
def expert_linear(self, input: torch.Tensor, i: int) -> torch.Tensor:
"""Linear against expert i's weight (with optional bias)."""
resident = getattr(self, "_resident_bank", None)
if resident is not None:
weight, bias = resident
return self._expert_linear_impl(input, weight, bias, i)
with self._cast_bank(input) as (weight, bias):
return self._expert_linear_impl(input, weight, bias, i)
def _expert_linear_impl(self, input, weight, bias, i):
if isinstance(weight, QuantizedTensor):
qw = self._expert_qt_from(weight, i)
else:
qw = cast_to_input(weight[i], input, copy=False)
b = cast_to_input(bias[i], input, copy=False) if bias is not None else None
if isinstance(qw, QuantizedTensor):
use_fast = (
not self._full_precision_mm
and qw.layout_cls.supports_fast_matmul()
and input.dim() == 2
)
if use_fast:
qin = QuantizedTensor.from_float(input, self.layout_type)
return torch.nn.functional.linear(qin, qw, b)
qw = cast_to_input(qw.dequantize(), input, copy=False)
return torch.nn.functional.linear(input, qw, b)
def _expert_qt_from(self, weight: QuantizedTensor, i: int) -> QuantizedTensor:
"""Build a per-expert QuantizedTensor by indexing into a resident bank."""
if weight._qdata.ndim == 2:
return self._bank_rows(weight, i, i + 1)
params = weight._params
kwargs = {
"scale": params.scale[i] if params.scale.dim() else params.scale,
"orig_dtype": params.orig_dtype,
"orig_shape": (self.out_features, self.in_features),
}
if hasattr(params, "block_scale"): # NVFP4
kwargs["block_scale"] = params.block_scale[i]
if hasattr(params, "quant_group_size"):
kwargs["quant_group_size"] = params.quant_group_size
if hasattr(params, "convrot"):
kwargs["convrot"] = params.convrot
if hasattr(params, "convrot_groupsize"):
kwargs["convrot_groupsize"] = params.convrot_groupsize
if hasattr(params, "linear_dtype"):
kwargs["linear_dtype"] = params.linear_dtype
return QuantizedTensor(weight._qdata[i], weight._layout_cls, type(params)(**kwargs))
def state_dict(self, *args, destination=None, prefix="", **kwargs):
sd = destination if destination is not None else {}
return _quantized_weight_state_dict(self, sd, prefix, extra_quant_conf={"num_experts": self.num_experts})
class Embedding(manual_cast.Embedding):
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
weight_key = f"{prefix}weight"
layer_conf = state_dict.pop(f"{prefix}comfy_quant", None)
if layer_conf is not None:
layer_conf = json.loads(layer_conf.numpy().tobytes())
# Only fp8 and int8_tensorwise support per-row dequant via index select.
# Block-scaled formats (NVFP4, MXFP8) can't do per-row lookup efficiently.
quant_format = layer_conf.get("format") if layer_conf is not None else None
manually_loaded_keys = []
if quant_format in ("float8_e4m3fn", "float8_e5m2", "int8_tensorwise") and weight_key in state_dict:
self.quant_format = quant_format
qconfig = QUANT_ALGOS[quant_format]
self.layout_type = qconfig["comfy_tensor_layout"]
layout_cls = get_layout_class(self.layout_type)
weight = state_dict.pop(weight_key)
manually_loaded_keys.append(weight_key)
scale_key = f"{prefix}weight_scale"
scale = state_dict.pop(scale_key, None)
if scale is not None:
scale = scale.float()
manually_loaded_keys.append(scale_key)
extra = {}
if quant_format == "int8_tensorwise" and layer_conf.get("convrot", False):
# rotated embedding table: record it so the forward un-rotates after lookup
extra["convrot"] = True
extra["convrot_groupsize"] = int(layer_conf.get("convrot_groupsize", 256))
params = layout_cls.Params(
scale=scale if scale is not None else torch.ones((), dtype=torch.float32),
orig_dtype=MixedPrecisionOps._compute_dtype,
orig_shape=(self.num_embeddings, self.embedding_dim),
**extra,
)
self.weight = torch.nn.Parameter(
QuantizedTensor(weight.to(dtype=qconfig["storage_t"]), qconfig["comfy_tensor_layout"], params),
requires_grad=False)
elif layer_conf is not None:
# Unsupported format — restore the marker so it round-trips; fall through to default load.
state_dict[f"{prefix}comfy_quant"] = torch.tensor(
list(json.dumps(layer_conf).encode('utf-8')), dtype=torch.uint8)
super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
for k in manually_loaded_keys:
if k in missing_keys:
missing_keys.remove(k)
def state_dict(self, *args, destination=None, prefix="", **kwargs):
sd = destination if destination is not None else {}
return _quantized_weight_state_dict(self, sd, prefix)
def forward_comfy_cast_weights(self, input, out_dtype=None):
weight = self.weight
# Optimized path: lookup in fp8/int8, dequantize only the selected rows.
if isinstance(weight, QuantizedTensor) and len(self.weight_function) == 0:
with CastBiasWeightContext(self, device=input.device, dtype=weight.dtype, offloadable=True) as (qdata, _bias):
if isinstance(qdata, QuantizedTensor):
params = qdata._params
scale = params.scale
qdata = qdata._qdata
else:
params = weight._params
scale = None
# int8: per-row scale possible ConvRot, so let the layout do the gather
if self.quant_format == "int8_tensorwise":
x = get_layout_class(self.layout_type).dequantize_embedding(qdata, params, input)
return x if out_dtype is None else x.to(dtype=out_dtype)
x = torch.nn.functional.embedding(
input, qdata, self.padding_idx, self.max_norm,
self.norm_type, self.scale_grad_by_freq, self.sparse)
target_dtype = out_dtype if out_dtype is not None else weight._params.orig_dtype
x = x.to(dtype=target_dtype)
if scale is not None:
x = x * scale.to(dtype=target_dtype)
return x
# Fallback for non-quantized or weight_function (LoRA) case
return super().forward_comfy_cast_weights(input, out_dtype=out_dtype)
return MixedPrecisionOps
Note: [CWE-710] Improper Adherence to Coding Standards (mutable default argument).
(no-empty-list-as-parameter)
comfy/sd.py
[warning] 1758-2101: Do not use an empty list as a default parameter
Context: def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}, disable_dynamic=False):
clip_data = state_dicts
class EmptyClass:
pass
for i in range(len(clip_data)):
if "transformer.resblocks.0.ln_1.weight" in clip_data[i]:
clip_data[i] = comfy.utils.clip_text_transformers_convert(clip_data[i], "", "")
else:
if "text_projection" in clip_data[i]:
clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) `#old` models saved with the CLIPSave node
if "lm_head.weight" in clip_data[i]:
clip_data[i]["model.lm_head.weight"] = clip_data[i].pop("lm_head.weight") # prefix missing in some models
tokenizer_data = {}
clip_target = EmptyClass()
clip_target.params = {}
if len(clip_data) == 1:
te_model = detect_te_model(clip_data[0])
if clip_type == CLIPType.YUE2 and "yue2_tokenizer_json" in clip_data[0]:
tokenizer_data["yue2_tokenizer_json"] = clip_data[0].pop("yue2_tokenizer_json")
detect = comfy.text_encoders.hunyuan_video.llama_detect(clip_data[0])
clip_target.clip = comfy.text_encoders.yue2.te(**detect)
clip_target.tokenizer = comfy.text_encoders.yue2.YuE2Tokenizer
elif clip_type == CLIPType.MINIMAX and "model.audio_decoder.projection.weight" in clip_data[0]:
tokenizer_data["tokenizer_json"] = clip_data[0].pop("tokenizer_json", None)
quant = comfy.utils.detect_layer_quantization(clip_data[0], "")
if quant is not None:
model_options = model_options.copy()
model_options["quantization_metadata"] = quant
clip_target.params["projection_config"] = comfy.text_encoders.minimax_music.detect_merged_config(clip_data[0])
clip_target.clip = comfy.text_encoders.minimax_music.MiniMaxMusic3TEModel
clip_target.tokenizer = comfy.text_encoders.minimax_music.MiniMaxMusic3Tokenizer
elif te_model == TEModel.CLIP_G:
if clip_type == CLIPType.STABLE_CASCADE:
clip_target.clip = sdxl_clip.StableCascadeClipModel
clip_target.tokenizer = sdxl_clip.StableCascadeTokenizer
elif clip_type == CLIPType.SD3:
clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=True, t5=False)
clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
elif clip_type == CLIPType.HIDREAM:
clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=False, clip_g=True, t5=False, llama=False, dtype_t5=None, dtype_llama=None)
clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
else:
clip_target.clip = sdxl_clip.SDXLRefinerClipModel
clip_target.tokenizer = sdxl_clip.SDXLTokenizer
elif te_model == TEModel.CLIP_H:
clip_target.clip = comfy.text_encoders.sd2_clip.SD2ClipModel
clip_target.tokenizer = comfy.text_encoders.sd2_clip.SD2Tokenizer
elif te_model == TEModel.T5_XXL:
if clip_type == CLIPType.SD3:
clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, **t5xxl_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
elif clip_type == CLIPType.LTXV:
clip_target.clip = comfy.text_encoders.lt.ltxv_te(**t5xxl_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.lt.LTXVT5Tokenizer
elif clip_type == CLIPType.PIXART or clip_type == CLIPType.CHROMA:
clip_target.clip = comfy.text_encoders.pixart_t5.pixart_te(**t5xxl_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.pixart_t5.PixArtTokenizer
elif clip_type == CLIPType.WAN:
clip_target.clip = comfy.text_encoders.wan.te(**t5xxl_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.wan.WanT5Tokenizer
tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
elif clip_type == CLIPType.HIDREAM:
clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**t5xxl_detect(clip_data),
clip_l=False, clip_g=False, t5=True, llama=False, dtype_llama=None)
clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
elif clip_type == CLIPType.COGVIDEOX:
clip_target.clip = comfy.text_encoders.cogvideo.cogvideo_te(**t5xxl_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.cogvideo.CogVideoXTokenizer
else: `#CLIPType.MOCHI`
clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.genmo.MochiT5Tokenizer
elif te_model == TEModel.T5_XXL_OLD:
clip_target.clip = comfy.text_encoders.cosmos.te(**t5xxl_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.cosmos.CosmosT5Tokenizer
elif te_model == TEModel.T5_XL:
clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model
clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer
elif te_model == TEModel.T5_BASE:
if clip_type == CLIPType.ACE or "spiece_model" in clip_data[0]:
clip_target.clip = comfy.text_encoders.ace.AceT5Model
clip_target.tokenizer = comfy.text_encoders.ace.AceT5Tokenizer
tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
else:
clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model
clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer
elif te_model == TEModel.T5_GEMMA:
clip_target.clip = comfy.text_encoders.sa3.SAT5GemmaModel
clip_target.tokenizer = comfy.text_encoders.sa3.SAT5GemmaTokenizer
tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
elif te_model in (TEModel.GEMMA_4_E4B, TEModel.GEMMA_4_E2B, TEModel.GEMMA_4_31B, TEModel.GEMMA_4_12B):
if te_model == TEModel.GEMMA_4_12B and "text_embedding_projection.video_aggregate_embed.weight" in clip_data[0]:
clip_target.clip = comfy.text_encoders.lt.ltxav_te(
**llama_detect(clip_data),
**comfy.text_encoders.lt.sd_detect(clip_data),
text_encoder_model=comfy.text_encoders.gemma4.gemma4_text_encoder_model(comfy.text_encoders.gemma4.Gemma4_12B),
text_encoder_key="gemma4",
)
clip_target.tokenizer = comfy.text_encoders.lt.ltxav_gemma4_tokenizer(comfy.text_encoders.gemma4.Gemma4_12B.tokenizer)
else:
variant = {TEModel.GEMMA_4_E4B: comfy.text_encoders.gemma4.Gemma4_E4B,
TEModel.GEMMA_4_E2B: comfy.text_encoders.gemma4.Gemma4_E2B,
TEModel.GEMMA_4_31B: comfy.text_encoders.gemma4.Gemma4_31B,
TEModel.GEMMA_4_12B: comfy.text_encoders.gemma4.Gemma4_12B}[te_model]
clip_target.clip = comfy.text_encoders.gemma4.gemma4_te(**llama_detect(clip_data), model_class=variant)
clip_target.tokenizer = variant.tokenizer
tokenizer_data["tokenizer_json"] = clip_data[0].get("tokenizer_json", None)
elif te_model == TEModel.GEMMA_2_2B:
if clip_type == CLIPType.PIXELDIT:
clip_target.clip = comfy.text_encoders.pixeldit.pixeldit_te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.pixeldit.PixelDiTGemma2Tokenizer
else:
clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.lumina2.LuminaTokenizer
tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
elif te_model == TEModel.GEMMA_3_4B:
clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data), model_type="gemma3_4b")
clip_target.tokenizer = comfy.text_encoders.lumina2.NTokenizer
tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
elif te_model == TEModel.GEMMA_3_4B_VISION:
clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data), model_type="gemma3_4b_vision")
clip_target.tokenizer = comfy.text_encoders.lumina2.NTokenizer
tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
elif te_model == TEModel.GEMMA_3_12B:
clip_target.clip = comfy.text_encoders.lt.gemma3_te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.lt.Gemma3_12BTokenizer
tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
elif te_model == TEModel.LLAMA3_8:
clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**llama_detect(clip_data),
clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None)
clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
elif te_model == TEModel.QWEN25_3B:
clip_target.clip = comfy.text_encoders.omnigen2.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.omnigen2.Omnigen2Tokenizer
elif te_model == TEModel.QWEN25_7B:
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.LONGCAT_IMAGE:
clip_target.clip = comfy.text_encoders.longcat_image.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.longcat_image.LongCatImageTokenizer
else:
clip_target.clip = comfy.text_encoders.qwen_image.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.qwen_image.QwenImageTokenizer
elif te_model == TEModel.MISTRAL3_24B or te_model == TEModel.MISTRAL3_24B_PRUNED_FLUX2:
clip_target.clip = comfy.text_encoders.flux.flux2_te(**llama_detect(clip_data), pruned=te_model == TEModel.MISTRAL3_24B_PRUNED_FLUX2)
clip_target.tokenizer = comfy.text_encoders.flux.Flux2Tokenizer
tokenizer_data["tekken_model"] = clip_data[0].get("tekken_model", None)
elif te_model == TEModel.MING_IMAGE:
tokenizer_data["tokenizer_json"] = clip_data[0].get("tokenizer_json", None)
quant = comfy.utils.detect_layer_quantization(clip_data[0], "")
clip_target.clip = comfy.text_encoders.ming_image.te(dtype_llama=clip_data[0]["thinker.norm.weight"].dtype, llama_quantization_metadata=quant)
clip_target.tokenizer = comfy.text_encoders.ming_image.MingImageTokenizer
elif te_model == TEModel.GPT_OSS_20B:
clip_target.clip = comfy.text_encoders.gpt_oss.lens_te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.gpt_oss.LensTokenizer
tokenizer_data["tokenizer_json"] = clip_data[0].get("tokenizer_json", None)
elif te_model == TEModel.QWEN3_4B:
if clip_type == CLIPType.FLUX or clip_type == CLIPType.FLUX2:
clip_target.clip = comfy.text_encoders.flux.klein_te(**llama_detect(clip_data), model_type="qwen3_4b")
clip_target.tokenizer = comfy.text_encoders.flux.KleinTokenizer
else:
clip_target.clip = comfy.text_encoders.z_image.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.z_image.ZImageTokenizer
elif te_model == TEModel.QWEN3_2B:
clip_target.clip = comfy.text_encoders.ovis.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.ovis.OvisTokenizer
elif te_model == TEModel.QWEN3_8B:
if clip_type == CLIPType.IDEOGRAM4:
clip_target.clip = comfy.text_encoders.ideogram4.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.ideogram4.Ideogram4Tokenizer
else:
clip_target.clip = comfy.text_encoders.flux.klein_te(**llama_detect(clip_data), model_type="qwen3_8b")
clip_target.tokenizer = comfy.text_encoders.flux.KleinTokenizer8B
elif te_model == TEModel.JINA_CLIP_2:
clip_target.clip = comfy.text_encoders.jina_clip_2.JinaClip2TextModelWrapper
clip_target.tokenizer = comfy.text_encoders.jina_clip_2.JinaClip2TokenizerWrapper
elif te_model in (TEModel.QWEN35_08B, TEModel.QWEN35_2B, TEModel.QWEN35_4B, TEModel.QWEN35_9B, TEModel.QWEN35_27B):
clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
qwen35_type = {TEModel.QWEN35_08B: "qwen35_08b", TEModel.QWEN35_2B: "qwen35_2b", TEModel.QWEN35_4B: "qwen35_4b", TEModel.QWEN35_9B: "qwen35_9b", TEModel.QWEN35_27B: "qwen35_27b"}[te_model]
clip_target.clip = comfy.text_encoders.qwen35.te(**llama_detect(clip_data), model_type=qwen35_type, mtp="mtp.fc.weight" in clip_data[0])
clip_target.tokenizer = comfy.text_encoders.qwen35.tokenizer(model_type=qwen35_type)
elif te_model in (TEModel.QWEN3VL_4B, TEModel.QWEN3VL_8B):
if clip_type == CLIPType.IDEOGRAM4 and te_model == TEModel.QWEN3VL_8B: # Ideogram4 reuses the full Qwen3-VL-8B (13-layer tap for conditioning + multimodal generate).
clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
clip_target.clip = comfy.text_encoders.ideogram4.te_qwen3vl(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.ideogram4.Ideogram4Qwen3VLTokenizer
elif clip_type == CLIPType.BOOGU and te_model == TEModel.QWEN3VL_8B: # Boogu-Image: full Qwen3-VL-8B, last hidden state, no-think template.
clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
clip_target.clip = comfy.text_encoders.boogu.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.boogu.BooguTokenizer
elif clip_type == CLIPType.KREA2 and te_model == TEModel.QWEN3VL_4B: # Krea2: full Qwen3-VL-4B (12-layer tap for conditioning + multimodal generate).
clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
clip_target.clip = comfy.text_encoders.krea2.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.krea2.Krea2Tokenizer
elif clip_type == CLIPType.MAGE and te_model == TEModel.QWEN3VL_4B: # Mage-Flow: full Qwen3-VL-4B, last hidden state, Qwen-Image-style templates.
clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
clip_target.clip = comfy.text_encoders.mage_flow.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.mage_flow.MageFlowTokenizer
elif clip_type == CLIPType.JOYIMAGE and te_model == TEModel.QWEN3VL_8B: # JoyImageEdit: full Qwen3-VL-8B, edit-conditioning template + drop_idx.
clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
clip_target.clip = comfy.text_encoders.joyimage.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.joyimage.JoyImageTokenizer
elif clip_type == CLIPType.QWEN_IMAGE and te_model == TEModel.QWEN3VL_8B: # Qwen-Image 2.1: full Qwen3-VL-8B, last hidden state, image slots spliced by the DiT.
clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
clip_target.clip = comfy.text_encoders.qwen_image21.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.qwen_image21.QwenImage21Tokenizer
elif clip_type in (CLIPType.FLUX, CLIPType.FLUX2): # Flux2 Klein reuses the Qwen3-VL LM (3-layer tap -> 12288); visual unused.
klein_model_type = "qwen3_8b" if te_model == TEModel.QWEN3VL_8B else "qwen3_4b"
clip_target.clip = comfy.text_encoders.flux.klein_te(**llama_detect(clip_data), model_type=klein_model_type)
clip_target.tokenizer = comfy.text_encoders.flux.KleinTokenizer8B if te_model == TEModel.QWEN3VL_8B else comfy.text_encoders.flux.KleinTokenizer
else:
clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
qwen3vl_type = {TEModel.QWEN3VL_4B: "qwen3vl_4b", TEModel.QWEN3VL_8B: "qwen3vl_8b"}[te_model]
clip_target.clip = comfy.text_encoders.qwen3vl.te(**llama_detect(clip_data), model_type=qwen3vl_type)
clip_target.tokenizer = comfy.text_encoders.qwen3vl.tokenizer(model_type=qwen3vl_type)
elif te_model == TEModel.QWEN3VL_32B:
clip_target.clip = comfy.text_encoders.minimax.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.minimax.MiniMaxH3Tokenizer
elif te_model == TEModel.QWEN3_06B:
clip_target.clip = comfy.text_encoders.anima.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.anima.AnimaTokenizer
elif te_model == TEModel.MINISTRAL_3_3B:
clip_target.clip = comfy.text_encoders.ernie.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.ernie.ErnieTokenizer
tokenizer_data["tekken_model"] = clip_data[0].get("tekken_model", None)
else:
# clip_l
if clip_type == CLIPType.SD3:
clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=False, t5=False)
clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
elif clip_type == CLIPType.HIDREAM:
clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=True, clip_g=False, t5=False, llama=False, dtype_t5=None, dtype_llama=None)
clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
else:
clip_target.clip = sd1_clip.SD1ClipModel
clip_target.tokenizer = sd1_clip.SD1Tokenizer
elif len(clip_data) == 2:
if clip_type == CLIPType.SD3:
te_models = [detect_te_model(clip_data[0]), detect_te_model(clip_data[1])]
clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=TEModel.CLIP_L in te_models, clip_g=TEModel.CLIP_G in te_models, t5=TEModel.T5_XXL in te_models, **t5xxl_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
elif clip_type == CLIPType.HUNYUAN_DIT:
clip_target.clip = comfy.text_encoders.hydit.HyditModel
clip_target.tokenizer = comfy.text_encoders.hydit.HyditTokenizer
elif clip_type == CLIPType.FLUX:
clip_target.clip = comfy.text_encoders.flux.flux_clip(**t5xxl_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.flux.FluxTokenizer
elif clip_type == CLIPType.HUNYUAN_VIDEO:
clip_target.clip = comfy.text_encoders.hunyuan_video.hunyuan_video_clip(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.hunyuan_video.HunyuanVideoTokenizer
elif clip_type == CLIPType.HIDREAM:
# Detect
hidream_dualclip_classes = []
for hidream_te in clip_data:
te_model = detect_te_model(hidream_te)
hidream_dualclip_classes.append(te_model)
clip_l = TEModel.CLIP_L in hidream_dualclip_classes
clip_g = TEModel.CLIP_G in hidream_dualclip_classes
t5 = TEModel.T5_XXL in hidream_dualclip_classes
llama = TEModel.LLAMA3_8 in hidream_dualclip_classes
# Initialize t5xxl_detect and llama_detect kwargs if needed
t5_kwargs = t5xxl_detect(clip_data) if t5 else {}
llama_kwargs = llama_detect(clip_data) if llama else {}
clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, llama=llama, **t5_kwargs, **llama_kwargs)
clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
elif clip_type == CLIPType.HUNYUAN_IMAGE:
clip_target.clip = comfy.text_encoders.hunyuan_image.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.hunyuan_image.HunyuanImageTokenizer
elif clip_type == CLIPType.HUNYUAN_VIDEO_15:
clip_target.clip = comfy.text_encoders.hunyuan_image.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.hunyuan_video.HunyuanVideo15Tokenizer
elif clip_type == CLIPType.KANDINSKY5:
clip_target.clip = comfy.text_encoders.kandinsky5.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.kandinsky5.Kandinsky5Tokenizer
elif clip_type == CLIPType.KANDINSKY5_IMAGE:
clip_target.clip = comfy.text_encoders.kandinsky5.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.kandinsky5.Kandinsky5TokenizerImage
elif clip_type == CLIPType.LTXV:
te_models = [detect_te_model(sd) for sd in clip_data]
gemma4_models = {
TEModel.GEMMA_4_E4B: comfy.text_encoders.gemma4.Gemma4_E4B,
TEModel.GEMMA_4_E2B: comfy.text_encoders.gemma4.Gemma4_E2B,
TEModel.GEMMA_4_31B: comfy.text_encoders.gemma4.Gemma4_31B,
TEModel.GEMMA_4_12B: comfy.text_encoders.gemma4.Gemma4_12B,
}
gemma4_type = next((model for model in te_models if model in gemma4_models), None)
if gemma4_type is None:
clip_target.clip = comfy.text_encoders.lt.ltxav_te(**llama_detect(clip_data), **comfy.text_encoders.lt.sd_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.lt.LTXAVGemmaTokenizer
gemma_sd = clip_data[te_models.index(TEModel.GEMMA_3_12B)] if TEModel.GEMMA_3_12B in te_models else clip_data[0]
tokenizer_data["spiece_model"] = gemma_sd.get("spiece_model", None)
else:
variant = gemma4_models[gemma4_type]
clip_target.clip = comfy.text_encoders.lt.ltxav_te(
**llama_detect(clip_data),
**comfy.text_encoders.lt.sd_detect(clip_data),
text_encoder_model=comfy.text_encoders.gemma4.gemma4_text_encoder_model(variant),
text_encoder_key="gemma4",
)
clip_target.tokenizer = comfy.text_encoders.lt.ltxav_gemma4_tokenizer(variant.tokenizer)
gemma_sd = clip_data[te_models.index(gemma4_type)]
tokenizer_data["tokenizer_json"] = gemma_sd.get("tokenizer_json", None)
elif clip_type == CLIPType.NEWBIE:
clip_target.clip = comfy.text_encoders.newbie.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.newbie.NewBieTokenizer
if "model.layers.0.self_attn.q_norm.weight" in clip_data[0]:
clip_data_gemma = clip_data[0]
clip_data_jina = clip_data[1]
else:
clip_data_gemma = clip_data[1]
clip_data_jina = clip_data[0]
tokenizer_data["gemma_spiece_model"] = clip_data_gemma.get("spiece_model", None)
tokenizer_data["jina_spiece_model"] = clip_data_jina.get("spiece_model", None)
elif clip_type == CLIPType.ACE:
te_models = [detect_te_model(clip_data[0]), detect_te_model(clip_data[1])]
if TEModel.QWEN3_4B in te_models:
model_type = "qwen3_4b"
else:
model_type = "qwen3_2b"
clip_target.clip = comfy.text_encoders.ace15.te(lm_model=model_type, **llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.ace15.ACE15Tokenizer
else:
clip_target.clip = sdxl_clip.SDXLClipModel
clip_target.tokenizer = sdxl_clip.SDXLTokenizer
elif len(clip_data) == 3:
clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(**t5xxl_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
elif len(clip_data) == 4:
clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**t5xxl_detect(clip_data), **llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
parameters = 0
for c in clip_data:
parameters += comfy.utils.calculate_parameters(c)
tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options)
clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, state_dict=clip_data, model_options=model_options, disable_dynamic=disable_dynamic)
return clip
Note: [CWE-710] Improper Adherence to Coding Standards (mutable default argument).
(no-empty-list-as-parameter)
comfy/ldm/lumina/model.py
[warning] 668-723: Do not use an empty list as a default parameter
Context: def embed_all(self, x, cap_feats=None, siglip_feats=None, offset=0, omni=False, transformer_options={}, cap_extra=None, ref_frames=[]):
bsz = 1
pH = pW = self.patch_size
device = x.device
embeds, freqs_cis, cap_feats_len = self.embed_cap(cap_feats, offset=offset, bsz=bsz, device=device, dtype=x.dtype, cap_extra=cap_extra)
if (not omni) or self.siglip_embedder is None:
cap_feats_len = embeds[0].shape[1]
if self.masked_pad_multiple is not None: # zero-masked context padding only shifts the image positions
cap_feats_len = -(-cap_feats_len // self.masked_pad_multiple) * self.masked_pad_multiple
cap_feats_len += offset
embeds += (None,)
freqs_cis += (None,)
else:
cap_feats_len += offset
if siglip_feats is not None:
b, h, w, c = siglip_feats.shape
siglip_feats = siglip_feats.permute(0, 3, 1, 2).reshape(b, h * w, c)
siglip_feats = self.siglip_embedder(siglip_feats)
siglip_pos_ids = torch.zeros((bsz, siglip_feats.shape[1], 3), dtype=torch.float32, device=device)
siglip_pos_ids[:, :, 0] = cap_feats_len + 2
siglip_pos_ids[:, :, 1] = (torch.linspace(0, h * 8 - 1, steps=h, dtype=torch.float32, device=device).floor()).view(-1, 1).repeat(1, w).flatten()
siglip_pos_ids[:, :, 2] = (torch.linspace(0, w * 8 - 1, steps=w, dtype=torch.float32, device=device).floor()).view(1, -1).repeat(h, 1).flatten()
if self.siglip_pad_token is not None:
siglip_feats, pad_extra = pad_zimage(siglip_feats, self.siglip_pad_token, self.pad_tokens_multiple) # TODO: double check
siglip_pos_ids = torch.nn.functional.pad(siglip_pos_ids, (0, 0, 0, pad_extra))
else:
if self.siglip_pad_token is not None:
siglip_feats = self.siglip_pad_token.to(device=device, dtype=x.dtype, copy=True).unsqueeze(0).repeat(bsz, self.pad_tokens_multiple, 1)
siglip_pos_ids = torch.zeros((bsz, siglip_feats.shape[1], 3), dtype=torch.float32, device=device)
if siglip_feats is None:
embeds += (None,)
freqs_cis += (None,)
else:
embeds += (siglip_feats,)
freqs_cis += (self.rope_embedder(siglip_pos_ids).movedim(1, 2),)
if x.ndim == 4:
x = x.unsqueeze(2)
B, C, F, H, W = x.shape
x = self.x_embedder(x.view(B, C, F, H // pH, pH, W // pW, pW).permute(0, 2, 3, 5, 4, 6, 1).flatten(4).flatten(1, 3))
x_pos_ids = torch.cat([pos_ids_x(cap_feats_len + 1 + f, H // pH, W // pW, bsz, device, transformer_options=transformer_options) for f in range(F)], dim=1)
for f, ref in enumerate(ref_frames): # Ming-Image: clean reference frames follow the target frames on the t axis
ref = comfy.ldm.common_dit.pad_to_patch_size(ref, (pH, pW))
rH, rW = ref.shape[-2], ref.shape[-1]
ref = self.x_embedder(ref.view(ref.shape[0], C, rH // pH, pH, rW // pW, pW).permute(0, 2, 4, 3, 5, 1).flatten(3).flatten(1, 2))
x = torch.cat((x, comfy.utils.repeat_to_batch_size(ref, B)), dim=1)
x_pos_ids = torch.cat((x_pos_ids, pos_ids_x(cap_feats_len + 1 + F + f, rH // pH, rW // pW, bsz, device, transformer_options=transformer_options)), dim=1)
if self.pad_tokens_multiple is not None:
x, pad_extra = pad_zimage(x, self.x_pad_token, self.pad_tokens_multiple)
x_pos_ids = torch.nn.functional.pad(x_pos_ids, (0, 0, 0, pad_extra))
embeds += (x,)
freqs_cis += (self.rope_embedder(x_pos_ids).movedim(1, 2),)
return embeds, freqs_cis, cap_feats_len + len(freqs_cis) - 1
Note: [CWE-710] Improper Adherence to Coding Standards (mutable default argument).
(no-empty-list-as-parameter)
[warning] 834-884: Do not use an empty list as a default parameter
Context: def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, ref_latents=[], ref_contexts=[], siglip_feats=[], direct_context=None, ref_frames=[], transformer_options={}, **kwargs):
omni = len(ref_latents) > 0
if omni:
timesteps = torch.cat([timesteps * 0, timesteps], dim=0)
t = 1.0 - timesteps
cap_feats = context
cap_mask = attention_mask
frames = x.shape[2] if x.ndim == 5 else None # Ming-Image latents carry a frame axis: the target, or composite + layers for Ming-Image-Layer
h, w = x.shape[-2], x.shape[-1]
x = comfy.ldm.common_dit.pad_to_patch_size(x, (1, self.patch_size, self.patch_size) if frames else (self.patch_size, self.patch_size))
"""
Forward pass of NextDiT.
t: (N,) tensor of diffusion timesteps
y: (N,) tensor of text tokens/features
"""
t = self.t_embedder(t * self.time_scale, dtype=x.dtype) # (N, D)
adaln_input = t
if self.clip_text_pooled_proj is not None:
pooled = kwargs.get("clip_text_pooled", None)
if pooled is not None:
pooled = self.clip_text_pooled_proj(pooled)
else:
pooled = torch.zeros((x.shape[0], self.clip_text_dim), device=x.device, dtype=x.dtype)
adaln_input = self.time_text_embed(torch.cat((t, pooled), dim=-1))
patches = transformer_options.get("patches", {})
x_is_tensor = isinstance(x, torch.Tensor)
img, mask, img_size, cap_size, freqs_cis, timestep_zero_index = self.patchify_and_embed(x, cap_feats, cap_mask, adaln_input, num_tokens, ref_latents=ref_latents, ref_contexts=ref_contexts, siglip_feats=siglip_feats, direct_context=direct_context, ref_frames=ref_frames, transformer_options=transformer_options)
freqs_cis = freqs_cis.to(img.device)
transformer_options["total_blocks"] = len(self.layers)
transformer_options["block_type"] = "double"
img_input = img
for i, layer in enumerate(self.layers):
transformer_options["block_index"] = i
img = layer(img, mask, freqs_cis, adaln_input, timestep_zero_index=timestep_zero_index, transformer_options=transformer_options)
if "double_block" in patches:
for p in patches["double_block"]:
out = p({"img": img[:, cap_size[0]:], "img_input": img_input[:, cap_size[0]:], "txt": img[:, :cap_size[0]], "pe": freqs_cis[:, cap_size[0]:], "vec": adaln_input, "x": x, "block_index": i, "transformer_options": transformer_options})
if "img" in out:
img[:, cap_size[0]:] = out["img"]
if "txt" in out:
img[:, :cap_size[0]] = out["txt"]
img = self.final_layer(img, adaln_input, timestep_zero_index=timestep_zero_index)
img = self.unpatchify(img, img_size, cap_size, return_tensor=x_is_tensor, frames=frames)[..., :h, :w]
return -img
Note: [CWE-710] Improper Adherence to Coding Standards (mutable default argument).
(no-empty-list-as-parameter)
comfy/text_encoders/ming_image.py
[warning] 298-310: Do not use an empty list as a default parameter
Context: def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], **kwargs):
if llama_template is None:
llama_template = self.llama_template
if len(images) > 0: # reference images lead the user turn, separated by blank lines like the vendor processor
llama_template = llama_template.replace("{}", "\n\n".join([IMAGE_BLOCK] * len(images)) + "\n{}")
tokens = super().tokenize_with_weights(llama_template.format(text), return_word_ids=return_word_ids, **kwargs)
images = iter(images)
for r in tokens["ming_image"]:
for i in range(len(r)):
if r[i][0] == IMAGE_PATCH_TOKEN:
image = next(images, None)
r[i] = ({"type": "query"} if image is None else {"type": "image", "data": image},) + r[i][1:]
return tokens
Note: [CWE-710] Improper Adherence to Coding Standards (mutable default argument).
(no-empty-list-as-parameter)
🔇 Additional comments (11)
comfy/ops.py (1)
22-22: LGTM!Also applies to: 1553-1564, 1572-1594, 1600-1603, 1615-1615, 1622-1622, 1634-1634, 1639-1640, 1651-1652
comfy/text_encoders/llama.py (1)
561-583: LGTM!comfy/text_encoders/ming_image.py (1)
1-349: LGTM!comfy/latent_formats.py (1)
779-804: LGTM!comfy/ldm/lumina/model.py (1)
463-463: LGTM!Also applies to: 480-480, 632-632, 636-636, 643-645, 651-651, 654-655, 669-669, 673-673, 676-679, 707-717, 728-728, 762-762, 835-835, 843-845, 866-866, 884-884
comfy/model_base.py (1)
1562-1572: LGTM!comfy/sd.py (1)
70-70: LGTM!Also applies to: 1644-1644, 1648-1649, 1909-1913, 2358-2358
comfy/supported_models.py (1)
30-30: LGTM!Also applies to: 1246-1263, 2624-2624
comfy_extras/nodes_ming.py (1)
1-54: LGTM!nodes.py (1)
296-298: LGTM!Also applies to: 2493-2493
comfy/text_encoders/gpt_oss.py (1)
208-208: 🎯 Functional CorrectnessThe concern is refuted.
GptOssTopKRouter.forwardreturns softmaxedtop_valswith shape[T, top_k]andtop_idxwith the same shape.GptOssMLP.forwardflattenshidden_statesto[T, H]before callingGptOssExperts. No gather is required.
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Inline comments:
In `@comfy/model_detection.py`:
- Line 615: Update the Ming-Image detection condition in the metadata check to
handle invalid JSON and non-object config or transformer values; skip Ming-Image
detection for invalid shapes while preserving the existing check for valid
metadata.
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Core ML/diffusion engine.
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comfy/model_detection.py
Source excerpt: Treat `execution.py` as one example of this rule: it should consume the prompt graph and execution-relevant state, produce execution results and errors, and not know about workflow ids, frontend ids, persistence ids, or API-...
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|
The uploaded diffusion_models/ming_image_0.1_design_int8_convrot.safetensors on HF has no metadata at all, so the config → transformer.image_model == "ming_image" check in model_detection.py never matches. The model gets detected as plain Z-Image (Model Lumina2 prepared... in the log), sampling finishes, then VAE Decode fails because the latent is 4D and the Wan 2.1 style VAE expects 5D: File "comfy/sd.py", line 877, in This was on commit 8f7f6c2 with your ming_image_test_01.json workflow and ming_image_vae_bf16.safetensors. Adding {"transformer": {"image_model": "ming_image"}} as the config metadata and re-saving the file fixed it; it then loads as MingImage and works. I haven't checked the design_bf16 or design_layer_int8 uploads, but they may need the same fix. It might also be worth a fallback detection that doesn't rely only on metadata, since other repacks will likely lose it. |
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* [Partner Nodes] feat(Quiver): add Arrow 2 models with reasoning effort to the SVG nodes (Comfy-Org#16478) Signed-off-by: bigcat88 <bigcat88@icloud.com> * [Partner Nodes] feat(Anthropic): add Claude Opus 5.5 to the Claude node (Comfy-Org#16479) Signed-off-by: bigcat88 <bigcat88@icloud.com> * [Partner Nodes] feat(Recraft): add Recraft V4.1 Flash to the V4 text-to-image node (Comfy-Org#16501) Signed-off-by: bigcat88 <bigcat88@icloud.com> * chore: update workflow templates to v0.11.69 (Comfy-Org#16503) * Fix ImageUpscaleWithModel crashing on RGBA images (Comfy-Org#16500) Upscale models loaded via Spandrel expect exactly 3 input channels, so a 4-channel IMAGE crashed in the model's first conv. Split off the alpha channel before upscaling, then resize it to the output resolution and concatenate it back so transparency survives the upscale. Fixes Comfy-Org#16499 * Take the SQLite write lock up front for asset scan and output-registration writes (Comfy-Org#16480) * Take the SQLite write lock up front for scan and output-registration writes The scanner's seeding and reference sync, and executed-output registration, write through a separate engine whose transactions open with BEGIN IMMEDIATE, and the database runs in WAL mode. On those paths stat, hashing and metadata extraction now happen before the write transaction opens, and reference-sync results are applied only to rows unchanged since they were observed. busy_timeout stays at pysqlite's 5s default. A fast-scan batch now commits as one transaction, so an unexpected error partway through discards the whole batch; the next scan recreates it. Enrichment, verification, uploads and tagging still write through the existing sessions. Migration backups use SQLite's backup API, and a legacy database is checkpointed before it is relocated, since in WAL mode committed pages can live in the -wal file that a plain file copy misses. * Skip relocating a legacy database whose WAL cannot be checkpointed The checkpoint can report busy without raising; moving the file then would leave committed pages behind in the -wal. Also keep the source's file mode on SQLite backups, as the plain copy did. * Replace run_write_txn with a create_write_session factory Write paths open the write engine's session the same way the rest of the code opens create_session(), and commit explicitly. Executed-output registration reads the new record's fields before committing, so expiry does not reload them in a second write transaction. * Document the scanner's pre-transaction observation types * Document that write sessions must not nest * Warn that a nested write session looks like lock contention * Seed a hashed spec with the stat its hash was verified against * Bound the SQLite backup so a locked destination cannot hang startup * Time out a backup only while it is blocked * Move file reads out of the remaining asset write transactions (Comfy-Org#16486) * Take the SQLite write lock up front for scan and output-registration writes The scanner's seeding and reference sync, and executed-output registration, write through a separate engine whose transactions open with BEGIN IMMEDIATE, and the database runs in WAL mode. On those paths stat, hashing and metadata extraction now happen before the write transaction opens, and reference-sync results are applied only to rows unchanged since they were observed. busy_timeout stays at pysqlite's 5s default. A fast-scan batch now commits as one transaction, so an unexpected error partway through discards the whole batch; the next scan recreates it. Enrichment, verification, uploads and tagging still write through the existing sessions. Migration backups use SQLite's backup API, and a legacy database is checkpointed before it is relocated, since in WAL mode committed pages can live in the -wal file that a plain file copy misses. * Skip relocating a legacy database whose WAL cannot be checkpointed The checkpoint can report busy without raising; moving the file then would leave committed pages behind in the -wal. Also keep the source's file mode on SQLite backups, as the plain copy did. * Replace run_write_txn with a create_write_session factory Write paths open the write engine's session the same way the rest of the code opens create_session(), and commit explicitly. Executed-output registration reads the new record's fields before committing, so expiry does not reload them in a second write transaction. * Document the scanner's pre-transaction observation types * Document that write sessions must not nest * Warn that a nested write session looks like lock contention * Seed a hashed spec with the stat its hash was verified against * Bound the SQLite backup so a locked destination cannot hang startup * Time out a backup only while it is blocked * Commit each drained entry before hashing the next drain_pending_verifications and drain_transition_queue ran every entry in one session, so an entry that wrote (marking a vanished file missing, say) left a transaction open while the next entry's file was stat'ed and hashed. Commit at the top of each entry instead, so the hash runs with no transaction open. * Seed settled watch-list entries through insert_asset_specs tick_watch_list seeded each settled file through seed_asset_specs in the caller's session, where the enrich phase still held drain_pending's writes open, and each seed ran in a deferred savepoint that reads before it writes. Collect the settled specs and hand them to insert_asset_specs, which stats and hashes before opening one write session. The seeder commits the pending verifications before ticking, so no transaction is open while the watch list stats or waits for the write lock. * Read upload metadata before the claim transaction _create_upload_record read the file for system metadata after the content claim had opened a write transaction. Callers now extract the metadata before opening their session (or, when reusing content, before claiming it) and pass it in. The claim's own stat re-check stays inside the transaction: that is what makes the claim sound. * Keep the upgrade error when restoring the backup also fails If restoring the pre-upgrade backup raised, that exception replaced the upgrade's, and the backup's location was never logged. Log the upgrade error first, log where the pre-upgrade copy is kept if the restore or its cleanup fails, and re-raise the upgrade error either way. * Note the drains' session requirement and word the restore log for either failure The drains' per-entry commit only leaves no transaction open on a create_session() session. The restore log now covers a failed backup removal as well as a failed restore. The watch-list admission test patches insert_asset_specs, the seam tick_watch_list now calls. * Log the real error when a seed spec cannot be read observe_asset_specs treated every OSError as a vanished file, so a permission error or an I/O error on a file that still exists was reported only as "Skipping vanished asset during scan". A missing file is still handled as before; any other OSError is now also logged through _log_scan_error before the spec is skipped. The vanished-path test's fake now raises FileNotFoundError, the error a vanished file produces. * Report an unreadable seed spec once, without also calling it vanished * Skip a pending verification whose row changed while it was hashed Committing per entry means no lock is held while the file is hashed, so another writer can retire or replace the row in that time. The drain would then store a hash on a missing row, or split it and attach a record to the other writer's row. Re-read the row after hashing and skip it unless it is still live with the hash, size and mtime it was loaded with, as apply_reference_observations does. * Remove the stored upload file in the reupload claim test The test cleaned up its temp files but left the first upload's stored file in the output directory. * feat: ming-image support (Comfy-Org#16482) * Fix saving/loading ming image model breaking detection. (Comfy-Org#16514) * Lower memory usage and .comfy_attention support for lumina family models. (Comfy-Org#16515) * [Partner Nodes] feat(OpenAI): add GPT-6 Sol and Luna models (Comfy-Org#16520) Signed-off-by: bigcat88 <bigcat88@icloud.com> * [Partner Nodes] feat(ByteDance): add Seedream 5.0 Flash (Comfy-Org#16522) * [Partner Nodes] feat(ByteDance): add Seedream 5.0 Flash and raise Seedream 5.0 Pro max resolution to 4.62MP Signed-off-by: bigcat88 <bigcat88@icloud.com> * [Partner Nodes] feat(ByteDance): add Seedream 5.0 Flash to Layer Separation Signed-off-by: bigcat88 <bigcat88@icloud.com> * [Partner Nodes] fix(ByteDance): raise Seedream 5.0 Pro and Flash custom size limit to 4514 Signed-off-by: bigcat88 <bigcat88@icloud.com> --------- Signed-off-by: bigcat88 <bigcat88@icloud.com> * fix(assets): harden the asset catalogue's failure paths (Comfy-Org#16393) * chore(assets): drop the unused asset_meta table from migration 0007 * fix(assets): drop asset_meta when downgrading a database that already created it * docs(assets): clarify asset schema docstring * test(assets): assert alembic and ORM index parity for the surviving asset tables * test(assets): cover asset system state index parity * refactor(tests): hoist migration-0007 test imports to module scope * fix(assets): guard the hashing dependency and chain the real import error * fix(assets): always resume background scanning when prompt handling fails * fix(api): derive the assets feature flag from the selected manager * fix(assets): paginate enrichment by id cursor so failures cannot starve or overflow the query * refactor(assets): extract prompt_worker so its resume contract is testable in-process * refactor(assets): test the blake3 import guard in-process instead of via subprocess * fix(assets): advance the enrichment cursor only past rows the batch attempted * fix(assets): track the scan pause across prompt worker iterations * chore(assets): address review follow-ups in the hashing guard, feature flags, and pagination pin * docs(assets): describe enrichment rows as attempted rather than selected The cursor holds at the last row a batch actually attempted, so a pause ends a batch early and the rows behind it are selected again when the scan resumes. Only the attempt is capped at once per pass. * test(api): derive the expected assets flag from the manager under test The assertion asked for the flag with no argument, so it read the parameter default rather than anything the manager reported - in a test whose subject is the two agreeing. Passing the manager's own state keeps it honest if the setup ever yields an enabled manager. * test(assets): let a broken prompt worker import fail instead of skipping The fixture wrapped importlib.import_module in a bare except that called pytest.skip, so a circular import, a missing dependency or a syntax error in app/prompt_worker.py would retire all four resume-contract tests while CI stayed green. The whole premise of extracting the module is that main.py can import it, so an import failure has to be a collection error. The CPU guard is genuinely load-bearing — comfy.model_management selects its device at import time and a CUDA build with no driver raises there — so it is kept, but as a precondition rather than an exception handler, matching the args.cpu-before-import convention already used by the comfy_test and comfy_api_nodes_test modules. Nothing is caught now. * docs(assets): name the unattempted rows instead of the ones behind the cursor The cursor moves forward through ascending ids, so 'the rows behind it' reads as the rows already passed - the opposite of what is selected again. * fix(assets): only absorb duplicate-path races when seeding scanned assets * fix(db): copy the legacy database inside the process lock * fix(assets): drop watch-list entries on stat errors instead of aborting the scan * fix(assets): clean up temp uploads on validation failures Multipart parsing writes the uploaded bytes to a temporary file before it validates the remaining form fields, so a request rejected after its file part had already been read left the temp file and its uuid directory on disk. Routing those removals through delete_temp_file_if_exists also changes the success path. The previous helper returned early when the temp file was already gone, so it never reached the parent rmdir; the shared helper attempts the rmdir unconditionally. Moving the upload to its destination leaves the temp path absent, so a successful upload now also discards its empty uuid directory, closing a pre-existing leak. * fix(assets): keep updated_at stable on no-op renames * docs(assets): make module docstrings and the rebuild warning truthful * fix(assets): correct event-log status snapshots and failure telemetry * test(assets): make the keyset tie-breaker and temp-exclusion tests falsifiable * chore: comment cleanup Comment-Gate: 3 quarantined * fix(assets): keep unreadable filesystem metadata from failing a whole scan batch * fix(assets): remove temporary uploads on non-UploadError failures * fix(assets): preserve successful specs when a scan batch fault propagates * test(db): drop the inert legacy-copy patch from the path preparation tests prepare_file_db_path no longer copies the legacy database - that moved inside the process lock in _init_file_db - so patching copy_legacy_default_db here did nothing. Leaving it implied a side effect the function does not have, and would have masked one if it were reintroduced. * fix(assets): report specs committed before a batch fault and preserve the fault itself * fix(assets): distinguish partial batch insert failures * refactor(assets): collapse the duplicated batch fault deferral into seed_asset_specs * fix(assets): reject duplicate file parts instead of stranding the first upload * refactor(assets): reap empty upload directories without importing the API layer * fix(assets): emit the invalid-mtime event once per scan Every other per-file emit on this scan path is gated -- mark_emitted( "stat_failed:enrich"), "hash_discarded_modified", "hash_failed", "enrich_failed" -- but scanner.invalid_mtime fired per file, so a restored archive or a FAT volume of pre-epoch mtimes put one structured event per file into the stream the closed vocabulary exists to keep parseable. Counted and emitted once, carrying the count. seed_asset_specs receives no _ScanProgress object and neither does insert_asset_specs above it, so routing this through mark_emitted would mean changing both signatures plus the seeder call site; the count form needs neither and the event is now strictly more informative than N identical fieldless lines. The per-file logging.warning is unchanged, and the emit stays inside seed_asset_specs so the static call-site manifest still matches. test_seed_skips_negative_fresh_mtime_with_warning_and_telemetry now pins the full list of invalid_mtime lines to exactly ["... count=1"] instead of asserting one such line exists -- a strictly stronger assertion, and the only change the new field required. * fix(assets): keep spec construction failures from wedging the watch list get_name_and_tags_from_asset_path raises ValueError by contract when a path stops resolving to a configured root, and it sat outside the guard, as did compute_loader_path and mimetypes.guess_type. An escape skipped the _WATCH_LIST[:] = remaining write at the end, so drained entries stayed on the list and were re-attempted every tick while entries past the fault never reached the increment _WATCH_SCAN_RETRIES needs to retire them. The list wedged permanently. Spec construction is now inside a guard that drops just the offending entry, and the list write moved into a finally so no future escape can skip it. The loop walks an iterator rather than the list, so the finally can put back the entries it never reached instead of discarding them. New event name rather than reusing one: scanner.watch_seed_failed is emitted only when seed_asset_specs returns an error, and widening it to also mean "never got as far as seeding" would make it lie -- a consumer treating it as a database-health signal would get false positives from what is really a path layout problem. scanner.watch_spec_failed is registered in ALLOWED_EVENTS and in the static call-site manifest. * refactor(assets): export the live-path conflict check as public API scanner.py reached past the package's own re-export surface to import _is_live_path_conflict directly out of records.py. The underscore said module-private while the import said otherwise, and records.py deliberately publishes its public names through app.assets.database.queries -- which the same import block three lines above was already using. The use is correct and unchanged; only the name and the route change. Renamed to is_live_path_conflict, listed in the package __init__ import and __all__ alongside its siblings, and scanner.py now takes it from the package like everything else it imports from there. * docs(db): restore the rationale for locking before migration Commit 1dbcdcd and the comment-cleanup pass 8205022 reduced this to "All database reads and writes, including the legacy import, run under the lock", dropping the part that did the work: upstream master locks after migrating and justifies it with "Alembic uses its own connection, so we must wait until it's done before locking -- otherwise our own lock blocks the migration". That is false, the lock is on a separate <db>.lock file, and the surviving sentence said nothing to stop a contributor "fixing" the ordering back. Restored and adapted rather than pasted: the legacy copy and the db_exists probe now happen inside the lock, which the original text predates, so both are named in the list of things the ordering makes mutually exclusive. * fix(assets): stop a scan on memory exhaustion instead of deferring it MemoryError is an Exception, so the per-spec and per-batch handlers stored it alongside ordinary faults and carried on - allocating for every remaining spec and then every remaining batch while the process was already out of memory. Both handlers now let it through, and the scan records a failure and stops. * docs(assets): document the prune failure response and its None result The route gained a 500 PRUNE_FAILED branch and the seeder method gained a None return, both so a prune that did not run cannot be reported as a clean one. Neither contract was written down. * test(assets): assert the surviving spec count after a propagated fault * docs(db): shorten the lock-ordering comment while keeping its rationale * docs(tests): drop the cross-module justification from the import-order comment * test(assets): restore the cpu flag after the guarded prompt worker import * test(assets): restore the cpu flag even when the prompt worker import fails * fix(assets): drop asset_meta in a new migration instead of editing 0007 0007 shipped in v0.36.0, v0.37.0 and v0.37.1, so editing it would leave two installs at that revision with different schemas depending on when they upgraded. Restore 0007 to its released form and drop the unused asset_meta table in 0008 instead. Nothing reads or writes asset_meta; asset metadata lives in the JSON columns on assets. 0008 downgrades by recreating the table and its four indexes exactly as 0007 creates them. * refactor(assets): keep prompt_worker in main.py Custom nodes may reference main.prompt_worker, and tests can already import main (test_db_init_locking does), so the function stays where it was. Its body keeps the resume-on-failure handling and the pause flag that persists across loop iterations; the tests now call main.prompt_worker. * fix(assets): report the selected manager through the existing feature flags * fix(assets): record a failed prune in the scan status A failed prune left the scan's error list empty, so the run looked clean. Record it instead and let discovery continue as before. * test(assets): test the feature flag API directly instead of copying the startup write Both parity tests wrote SERVER_FEATURE_FLAGS["assets"] themselves, so they passed whatever startup did. Pin the one real claim - a no-argument get_server_features() reports the flag - in the feature flags tests, and drop the disabled case, which default_asset_manager already covers. * Bring Comfy-Org#16486's watch-list batching and seed logging into this branch The merge before this commit is `git merge -X ours origin/master`: in conflicting hunks it keeps this branch's side. This commit ports what Comfy-Org#16486 changed in those hunks. - tick_watch_list takes no session. It collects settled entries and seeds them in one batch through insert_asset_specs, keeping this branch's per-entry stat and spec handling and the finally that always rewrites the watch list. A failed seed is reported once per batch, since the batch only returns its first error. - seed_asset_specs no longer warns again for a skipped spec; observe_asset_specs already logged why. - Tests call tick_watch_list() without a session, bind the write session where they seed for real, and fake insert_asset_specs with its (created, error) return. The mid-drain fault now comes from stat, because seeding runs after the loop. * Note where the assets core flag is set and why prompt_worker catches BaseException --------- Co-authored-by: guill <jacob.e.segal@gmail.com> * Add missing RDNA2 arches to list. (Comfy-Org#16537) * feat(npu): support async weight offload streams (Comfy-Org#16057) Add device-aware torch-npu stream creation, current-stream lookup, and synchronization so the existing async offload path can run on Ascend NPU devices. Keep the feature opt-in and cover stream rotation and disabled behavior in the unit-test suite. * Small fix: skip unnecessary work. (Comfy-Org#16540) * Fix Qwen VL image preprocessing crash on 4-channel (RGBA) input (Comfy-Org#16548) process_qwen2vl_images() computed the patch grid from height/width only but flatten_patches kept every input channel, so a 4-channel image (e.g. Qwen-Image-2.1's RGBA VAEDecode output fed back as a reference image) inflated the patch count by channels/3 and crashed with a shape mismatch against the position embeddings. Drop extra channels up front so only RGB reaches the patch embed. * [Partner Nodes] feat(ByteDance): add Seedance 2.5 Draft mode (Comfy-Org#16529) Signed-off-by: bigcat88 <bigcat88@icloud.com> * chore: update workflow templates to v0.11.70 (Comfy-Org#16557) Co-authored-by: Purz <97489706+purzbeats@users.noreply.github.com> * Add HDR LogC3 and ACEScct to Convert Color Space node. (Comfy-Org#16541) * Fix HDR output issue with negative values in Convert Image Color Space. (Comfy-Org#16565) * Update sheetsage2 abc creation from upstream yue code. (Comfy-Org#16569) * perf(assets): replace pathlib prefix checks in the asset scan (Comfy-Org#16543) * Speed up the startup prune's owned-prefix check mark_contents_missing_outside_prefixes tested every live AssetContent row against every owned prefix with Path.is_relative_to, which walks the path's parents on each call: rows x prefixes x depth. At 9k rows and 150 prefixes that was 9.5s (15.5s with deeper output paths). path_prefix_matcher normalizes the prefixes once and checks each row with a normcase'd, separator-bounded str.startswith over a tuple, keeping is_relative_to's component bounds and platform case rules. The same case now takes about 0.04s, flat in prefix count and depth. * Move path_prefix_matcher next to the SQL containment predicate path_utils needs the same check for per-file tagging, and scanner_changes already imports path_utils, so the matcher moves to app.assets.helpers beside sql_path_under_prefix. * Speed up asset tag derivation during scans get_backend_system_tags_from_path checked every scanned file against every model base with Path.is_relative_to, which dominated the fast scan: 37s of build_asset_specs for 100k output files. Using path_prefix_matcher, the separator-bounded string check the startup prune already uses, brings that to about 7s. * Keep path_prefix_matcher's parity for paths starting with two slashes abspath keeps exactly two leading slashes, and pathlib treats that '//' as an anchor of its own, so Path('//server/f').is_relative_to('/') is False, but the string check accepted it. Prefixes are now split once by whether their anchor is '//', and a candidate is only checked against prefixes with the same anchor. The per-row check is still a single str.startswith. * Build tag-derivation prefix matchers once per folder config get_backend_system_tags_from_path built a path_prefix_matcher for input, output, temp and every model category on each call, so most of its cost was normalizing the same prefixes again for every scanned file. cached_prefix_matcher memoizes construction on the raw prefix tuple, which the callers pass as absolute folder paths; a folder-config change is a new key. 100k files, 58 model bases: tag derivation 81.8 -> 27.7 us/file, and build_asset_specs CPU 11.55 -> 6.40s. * State cached_prefix_matcher's absolute-prefix precondition The docstring presented absolute prefixes as a property of the callers. It is a precondition the caller must meet: folder_paths stores what it is given, and a relative prefix is resolved once, on first use, and then frozen. --------- Co-authored-by: guill <jacob.e.segal@gmail.com> * Mention some ComfyUI optimization details in README. (Comfy-Org#16576) * Support tiny VAE for Qwen-Image 2.1 (Comfy-Org#16552) * Support ID-V2V (Comfy-Org#15139) * Fix potential regression with previous PR. (Comfy-Org#16596) --------- Signed-off-by: bigcat88 <bigcat88@icloud.com> Co-authored-by: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Co-authored-by: Daxiong (Lin) <contact@comfyui-wiki.com> Co-authored-by: Jialong(Bruce) Li <chelsealong@126.com> Co-authored-by: Simon Pinfold <synap5e@users.noreply.github.com> Co-authored-by: Jukka Seppänen <40791699+kijai@users.noreply.github.com> Co-authored-by: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Co-authored-by: guill <jacob.e.segal@gmail.com> Co-authored-by: yulun <100981785+Big2Wheel@users.noreply.github.com> Co-authored-by: Purz <97489706+purzbeats@users.noreply.github.com>
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Support for https://huggingface.co/inclusionAI/Ming-Image-0.1-Design
In addition to the model support, MoE experts now share one loop that groups tokens by expert with a single host sync, and quantized expert banks whose kernels only take 2-D weights are stored flat and sliced per expert, dequantized in bounded chunks when running in full precision.
Test models: https://huggingface.co/Kijai/Ming-Image-ComfyUI/tree/main
Text to image with prompt enhancer:
ming_image_test_01.json
Image editing:
ming_image_edit_test_01.json
