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Add debug logging to Florence2 model loading path

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Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Gang Wen
2026-04-07 08:20:03 +02:00
co-authored by Claude Opus 4.6
parent 810e34d859
commit fd34c3d5d8
1 changed files with 19 additions and 1 deletions
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py/florence2_ultra.py
+19 -1
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@@ -47,6 +47,7 @@ def fixed_get_imports(filename) -> list[str]:
def _load_model_v5(model_path, attention, dtype):
"""Load Florence2 model for transformers >= 5.0.0"""
log(f"[DEBUG] _load_model_v5 called with model_path={model_path}, attention={attention}, dtype={dtype}")
from florence2_models.modeling_florence2 import Florence2ForConditionalGeneration, Florence2Config
from transformers import CLIPImageProcessor, BartTokenizerFast
from florence2_models.processing_florence2 import Florence2Processor
@@ -55,9 +56,12 @@ def _load_model_v5(model_path, attention, dtype):
from comfy.utils import load_torch_file
offload_device = comfy.model_management.unet_offload_device()
log(f"[DEBUG] offload_device={offload_device}")
log(f"[DEBUG] Loading Florence2Config from {model_path}")
config = Florence2Config.from_pretrained(model_path)
config._attn_implementation = attention
log(f"[DEBUG] Config loaded, initializing empty model")
with init_empty_weights():
model = Florence2ForConditionalGeneration(config)
@@ -65,7 +69,9 @@ def _load_model_v5(model_path, attention, dtype):
if not os.path.exists(checkpoint_path):
checkpoint_path = os.path.join(model_path, "pytorch_model.bin")
if os.path.exists(checkpoint_path):
log(f"[DEBUG] Loading weights from {checkpoint_path}")
state_dict = load_torch_file(checkpoint_path)
log(f"[DEBUG] Loaded {len(state_dict)} keys from checkpoint")
else:
raise FileNotFoundError(f"No model weights found at {model_path}")
@@ -74,13 +80,17 @@ def _load_model_v5(model_path, attention, dtype):
key_mapping["language_model.model.encoder.embed_tokens.weight"] = "language_model.model.shared.weight"
key_mapping["language_model.model.decoder.embed_tokens.weight"] = "language_model.model.shared.weight"
missing_keys = []
for name, param in model.named_parameters():
actual_key = key_mapping.get(name, name)
if actual_key in state_dict:
set_module_tensor_to_device(model, name, offload_device, value=state_dict[actual_key].to(dtype))
else:
print(f"Parameter {name} not found in state_dict.")
missing_keys.append(name)
if missing_keys:
log(f"[DEBUG] {len(missing_keys)} parameters not found in state_dict: {missing_keys[:5]}{'...' if len(missing_keys) > 5 else ''}", message_type='warning')
log(f"[DEBUG] Tying weights and finalizing model")
model.language_model.tie_weights()
model = model.eval().to(dtype).to(offload_device)
@@ -97,8 +107,11 @@ def _load_model_v5(model_path, attention, dtype):
)
image_processor.image_seq_length = 577
log(f"[DEBUG] Loading tokenizer from {model_path}")
tokenizer = BartTokenizerFast.from_pretrained(model_path)
log(f"[DEBUG] Creating Florence2Processor")
processor = Florence2Processor(image_processor=image_processor, tokenizer=tokenizer)
log(f"[DEBUG] _load_model_v5 completed successfully")
return model, processor
def load_model(ver):
@@ -114,8 +127,13 @@ def load_model(ver):
from huggingface_hub import snapshot_download
snapshot_download(repo_id=repo_id, local_dir=model_path, ignore_patterns=["*.md", "*.txt"])
log(f"[DEBUG] transformers version: {transformers.__version__}, v5+ path: {version.parse(transformers.__version__) >= version.parse('5.0.0')}")
log(f"[DEBUG] model_path: {model_path}, exists: {os.path.exists(model_path)}")
if version.parse(transformers.__version__) >= version.parse('5.0.0'):
log(f"[DEBUG] Using transformers v5 loading path")
model, processor = _load_model_v5(model_path, attention, torch.float32)
log(f"[DEBUG] Model loaded, model type: {type(model)}, processor type: {type(processor)}")
return (model.to(device), processor)
try: