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Merge branch 'master' into Import-blueprint

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Daxiong (Lin) 2026-04-17 01:02:52 +08:00 committed by GitHub
commit a8c758dab4
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3 changed files with 5 additions and 4 deletions
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2
comfy/ops.py
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@ -1151,7 +1151,7 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
if param is None:
continue
p = fn(param)
if p.is_inference():
if (not torch.is_inference_mode_enabled()) and p.is_inference():
p = p.clone()
self.register_parameter(key, torch.nn.Parameter(p, requires_grad=False))
for key, buf in self._buffers.items():

2
comfy/text_encoders/ernie.py
View File

@ -35,4 +35,4 @@ def te(dtype_llama=None, llama_quantization_metadata=None):
model_options = model_options.copy()
model_options["quantization_metadata"] = llama_quantization_metadata
super().__init__(device=device, dtype=dtype, model_options=model_options)
return ErnieTEModel
return ErnieTEModel_

5
comfy_extras/nodes_textgen.py
View File

@ -35,6 +35,7 @@ class TextGenerate(io.ComfyNode):
io.Int.Input("max_length", default=256, min=1, max=2048),
io.DynamicCombo.Input("sampling_mode", options=sampling_options, display_name="Sampling Mode"),
io.Boolean.Input("thinking", optional=True, default=False, tooltip="Operate in thinking mode if the model supports it."),
io.Boolean.Input("use_default_template", optional=True, default=True, tooltip="Use the built in system prompt/template if the model has one.", advanced=True),
],
outputs=[
io.String.Output(display_name="generated_text"),
@ -42,9 +43,9 @@ class TextGenerate(io.ComfyNode):
)
@classmethod
def execute(cls, clip, prompt, max_length, sampling_mode, image=None, thinking=False) -> io.NodeOutput:
def execute(cls, clip, prompt, max_length, sampling_mode, image=None, thinking=False, use_default_template=True) -> io.NodeOutput:
tokens = clip.tokenize(prompt, image=image, skip_template=False, min_length=1, thinking=thinking)
tokens = clip.tokenize(prompt, image=image, skip_template=not use_default_template, min_length=1, thinking=thinking)
# Get sampling parameters from dynamic combo
do_sample = sampling_mode.get("sampling_mode") == "on"