nodes/kijai-ComfyUI-Florence2
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Simpler check

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kijai
2026-03-12 18:23:05 +02:00
parent 606bc5cd34
commit 2752591c30
1 changed files with 8 additions and 8 deletions
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nodes.py
+8 -8
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@@ -206,11 +206,11 @@ class DownloadAndLoadFlorence2Model:
if version.parse(transformers.__version__) >= version.parse('5.0.0'):
model, processor = load_model(model_path, attention, dtype, offload_device)
else:
import inspect
from .modeling_florence2 import Florence2ForConditionalGeneration
_sig = inspect.signature(Florence2ForConditionalGeneration.from_pretrained)
_dtype_kwarg = 'torch_dtype' if 'torch_dtype' in _sig.parameters else 'dtype'
model = Florence2ForConditionalGeneration.from_pretrained(model_path, attn_implementation=attention, **{_dtype_kwarg: dtype}).to(offload_device)
try:
model = Florence2ForConditionalGeneration.from_pretrained(model_path, attn_implementation=attention, torch_dtype=dtype).to(offload_device)
except TypeError:
model = Florence2ForConditionalGeneration.from_pretrained(model_path, attn_implementation=attention, dtype=dtype).to(offload_device)
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
if lora is not None:
@@ -309,11 +309,11 @@ class Florence2ModelLoader:
if version.parse(transformers.__version__) >= version.parse('5.0.0'):
model, processor = load_model(model_path, attention, dtype, offload_device)
else:
import inspect
from .modeling_florence2 import Florence2ForConditionalGeneration
_sig = inspect.signature(Florence2ForConditionalGeneration.from_pretrained)
_dtype_kwarg = 'torch_dtype' if 'torch_dtype' in _sig.parameters else 'dtype'
model = Florence2ForConditionalGeneration.from_pretrained(model_path, attn_implementation=attention, **{_dtype_kwarg: dtype}).to(offload_device)
try:
model = Florence2ForConditionalGeneration.from_pretrained(model_path, attn_implementation=attention, torch_dtype=dtype).to(offload_device)
except TypeError:
model = Florence2ForConditionalGeneration.from_pretrained(model_path, attn_implementation=attention, dtype=dtype).to(offload_device)
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
if lora is not None: