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[wwb] Fix from_onnx for text gen #2959

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[wwb] Fix from_onnx for text gen #2959

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b740d87
[wwb] Fix from_onnx for text gen
sbalandi Nov 3, 2025
83c17fd
add from_onnx for image models
sbalandi Nov 7, 2025
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31 changes: 17 additions & 14 deletions tools/who_what_benchmark/whowhatbench/model_loaders.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -210,6 +210,7 @@ def load_text_model(
use_cache=True,
device=device,
ov_config=ov_config,
**kwargs
)
except Exception:
config = AutoConfig.from_pretrained(model_id)
Expand All @@ -219,6 +220,7 @@ def load_text_model(
use_cache=True,
device=device,
ov_config=ov_config,
**kwargs
)

return model
Expand Down Expand Up @@ -270,9 +272,10 @@ def load_text2image_model(
if 'adapters' in kwargs and kwargs['adapters'] is not None:
raise ValueError("Adapters are not supported for OVPipelineForText2Image.")

model_kwargs = {"from_onnx": kwargs.get('from_onnx', False), "safety_checker": None}
try:
model = TEXT2IMAGEPipeline.from_pretrained(
model_id, device=device, ov_config=ov_config, safety_checker=None,
model_id, device=device, ov_config=ov_config, **model_kwargs
)
except ValueError:
model = TEXT2IMAGEPipeline.from_pretrained(
Expand All @@ -281,7 +284,7 @@ def load_text2image_model(
use_cache=True,
device=device,
ov_config=ov_config,
safety_checker=None,
**model_kwargs
)

return model
Expand Down Expand Up @@ -410,7 +413,7 @@ def load_image2image_genai_pipeline(model_dir, device="CPU", ov_config=None):


def load_imagetext2image_model(
model_id, device="CPU", ov_config=None, use_hf=False, use_genai=False
model_id, device="CPU", ov_config=None, use_hf=False, use_genai=False, **kwargs
):
if use_hf:
from diffusers import AutoPipelineForImage2Image
Expand All @@ -424,9 +427,10 @@ def load_imagetext2image_model(
else:
logger.info("Using Optimum API")
from optimum.intel.openvino import OVPipelineForImage2Image
model_kwargs = {"from_onnx": kwargs.get('from_onnx', False), "safety_checker": None}
try:
model = OVPipelineForImage2Image.from_pretrained(
model_id, device=device, ov_config=ov_config, safety_checker=None,
model_id, device=device, ov_config=ov_config, **model_kwargs
)
except ValueError:
model = OVPipelineForImage2Image.from_pretrained(
Expand All @@ -435,7 +439,7 @@ def load_imagetext2image_model(
use_cache=True,
device=device,
ov_config=ov_config,
safety_checker=None,
**model_kwargs
)
return model

Expand All @@ -455,7 +459,7 @@ def load_inpainting_genai_pipeline(model_dir, device="CPU", ov_config=None):


def load_inpainting_model(
model_id, device="CPU", ov_config=None, use_hf=False, use_genai=False
model_id, device="CPU", ov_config=None, use_hf=False, use_genai=False, **kwargs
):
if use_hf:
from diffusers import AutoPipelineForInpainting
Expand All @@ -469,9 +473,10 @@ def load_inpainting_model(
else:
logger.info("Using Optimum API")
from optimum.intel.openvino import OVPipelineForInpainting
model_kwargs = {"from_onnx": kwargs.get('from_onnx', False), "safety_checker": None}
try:
model = OVPipelineForInpainting.from_pretrained(
model_id, device=device, ov_config=ov_config, safety_checker=None,
model_id, device=device, ov_config=ov_config, **model_kwargs
)
except ValueError as e:
logger.error("Failed to load inpaiting pipeline. Details:\n", e)
Expand All @@ -481,7 +486,7 @@ def load_inpainting_model(
use_cache=True,
device=device,
ov_config=ov_config,
safety_checker=None,
**model_kwargs
)
return model

Expand Down Expand Up @@ -530,8 +535,7 @@ def load_embedding_model(model_id, device="CPU", ov_config=None, use_hf=False, u
model = OVModelForFeatureExtraction.from_pretrained(
model_id, device=device, ov_config=ov_config, safety_checker=None,
)
except ValueError as e:
logger.error("Failed to load embedding pipeline. Details:\n", e)
except ValueError:
model = OVModelForFeatureExtraction.from_pretrained(
model_id,
trust_remote_code=True,
Expand Down Expand Up @@ -596,8 +600,7 @@ def load_reranking_model(model_id, device="CPU", ov_config=None, use_hf=False, u
model = model_cls.from_pretrained(
model_id, device=device, ov_config=ov_config, safety_checker=None,
)
except ValueError as e:
logger.error("Failed to load reranking pipeline, an attempt will be made again with updated parameters. Details:\n", e)
except ValueError:
model = model_cls.from_pretrained(
model_id,
trust_remote_code=True,
Expand Down Expand Up @@ -631,9 +634,9 @@ def load_model(
elif model_type == "visual-text":
return load_visual_text_model(model_id, device, ov_options, use_hf, use_genai, **kwargs)
elif model_type == "image-to-image":
return load_imagetext2image_model(model_id, device, ov_options, use_hf, use_genai)
return load_imagetext2image_model(model_id, device, ov_options, use_hf, use_genai, **kwargs)
elif model_type == "image-inpainting":
return load_inpainting_model(model_id, device, ov_options, use_hf, use_genai)
return load_inpainting_model(model_id, device, ov_options, use_hf, use_genai, **kwargs)
elif model_type == "text-embedding":
return load_embedding_model(model_id, device, ov_options, use_hf, use_genai, **kwargs)
elif model_type == "text-reranking":
Expand Down
10 changes: 5 additions & 5 deletions tools/who_what_benchmark/whowhatbench/wwb.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -711,7 +711,6 @@ def main():
kwargs["cb_config"] = read_cb_config(args.cb_config)
if args.from_onnx:
kwargs["from_onnx"] = args.from_onnx
kwargs["use_cache"] = False
if args.gguf_file:
kwargs["gguf_file"] = args.gguf_file
if args.adapters is not None:
Expand All @@ -720,10 +719,11 @@ def main():
kwargs["alphas"] = args.alphas
else:
kwargs["alphas"] = [1.0] * len(args.adapters)
kwargs["empty_adapters"] = args.empty_adapters
kwargs["embeds_pooling"] = args.embeds_pooling_type
kwargs["embeds_normalize"] = args.embeds_normalize
kwargs["embeds_padding_side"] = args.embeds_padding_side
if args.model_type == "text-embedding":
kwargs["empty_adapters"] = args.empty_adapters
kwargs["embeds_pooling"] = args.embeds_pooling_type
kwargs["embeds_normalize"] = args.embeds_normalize
kwargs["embeds_padding_side"] = args.embeds_padding_side

if args.gt_data and os.path.exists(args.gt_data):
evaluator = create_evaluator(None, args)
Expand Down
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