Add quantize_int4.py

quantize_int4.py

@@ -0,0 +1,130 @@
+"""Qwen-Image-2.1 -> OpenVINO FP16 export + NNCF weight-only INT4 quantization.
+
+NOTE on versions (deviation from original pinned env, approved by user):
+  Qwen/Qwen-Image-2.1 requires QwenImage21Pipeline (added in diffusers
+  commit 6256aa766 "Add Qwen-Image 2.1 (#14804)", post-v0.40.0). Pinned
+  diffusers==0.37.1 cannot load it (AttributeError). Therefore:
+  - diffusers: 0.41.0.dev0 (git main, includes QwenImage21)
+  - transformers: 5.10.4 (pulled by optimum-intel git; supports hub 1.x)
+  - huggingface-hub: 1.33.0 (required by git diffusers >=1.31)
+  - tokenizers: 0.22.2, optimum: 2.3.0
+  - optimum-intel: 2.3.0.dev0+git (main, first version with QwenImage21 support)
+  - openvino==2026.4.0 nncf==3.4.0 torch pillow psutil unchanged
+  - transformers dependency table patched: hub cap <1.0 -> <2.0
+  - optimum-intel modeling_visual_language.py patched for transformers>=5
+    (VisionRotaryEmbedding alias + rot_pos_emb try/except, diffusion unused)
+
+FP16 export CLI (same pattern as task example):
+  optimum-cli export openvino -m Qwen/Qwen-Image-2.1 \
+    --task text-to-image --library diffusers --weight-format fp16 \
+    /home/user/app/qwen-image-2.1-ov-fp16
+
+INT4 quantization:
+  OVQuantizer + OVPipelineQuantizationConfig nested in OVConfig:
+  transformer + text_encoder (+ text_encoder_i2i, same Qwen3VL arch) with
+  OVWeightQuantizationConfig(bits=4, sym=False, group_size=128,
+    group_size_fallback="adjust", ratio=1.0),
+  rest default INT8. Passed as ov_config=OVConfig(quantization_config=...).
+"""
+import os
+import time
+import json
+
+FP16_DIR = "/home/user/app/qwen-image-2.1-ov-fp16"
+INT4_DIR = "/home/user/app/qwen-image-2.1-ov-int4"
+MODEL_ID = "Qwen/Qwen-Image-2.1"
+
+
+def export_fp16():
+    if os.path.exists(FP16_DIR) and os.listdir(FP16_DIR):
+        print(f"FP16 model already exists at {FP16_DIR}, skipping export...")
+        print("CLI: optimum-cli export openvino -m "
+              f"{MODEL_ID} --task text-to-image --library diffusers "
+              f"--weight-format fp16 {FP16_DIR}")
+        return FP16_DIR, 0.0
+    # Export is done via optimum-cli (see docstring); this fallback uses API.
+    from optimum.intel import OVDiffusionPipeline
+    print(f"Exporting {MODEL_ID} to FP16 OpenVINO...")
+    start = time.time()
+    pipeline = OVDiffusionPipeline.from_pretrained(
+        MODEL_ID,
+        export=True,
+        compile=False,
+        weight_format="fp16",
+        token=os.environ.get("HF_TOKEN"),
+    )
+    pipeline.save_pretrained(FP16_DIR)
+    dt = time.time() - start
+    print(f"FP16 export completed in {dt:.2f}s")
+    return FP16_DIR, dt
+
+
+def quantize_int4(fp16_dir):
+    from optimum.intel import OVDiffusionPipeline
+    from optimum.intel.openvino import (
+        OVQuantizer,
+        OVConfig,
+        OVPipelineQuantizationConfig,
+        OVWeightQuantizationConfig,
+    )
+
+    print(f"Quantizing {fp16_dir} to INT4...")
+    start = time.time()
+    pipeline = OVDiffusionPipeline.from_pretrained(fp16_dir, compile=False)
+    print("OV submodels:", pipeline._ov_model_names)
+    quantizer = OVQuantizer.from_pretrained(pipeline)
+
+    int4_cfg = OVWeightQuantizationConfig(
+        bits=4,
+        sym=False,
+        group_size=128,
+        group_size_fallback="adjust",
+        ratio=1.0,
+    )
+    int8_default = OVWeightQuantizationConfig()  # bits=8 default INT8
+
+    ov_config = OVConfig(
+        quantization_config=OVPipelineQuantizationConfig(
+            quantization_configs={
+                "transformer": int4_cfg,
+                "text_encoder": int4_cfg,
+                "text_encoder_i2i": int4_cfg,
+            },
+            default_config=int8_default,
+        )
+    )
+    quantizer.quantize(save_directory=INT4_DIR, ov_config=ov_config)
+    dt = time.time() - start
+    print(f"INT4 quantization completed in {dt:.2f}s")
+    return INT4_DIR, dt
+
+
+def main():
+    print("=" * 60)
+    print("Qwen-Image-2.1 OpenVINO INT4 Quantization")
+    print("=" * 60)
+    os.makedirs("/home/user/app/outputs", exist_ok=True)
+    fp16_dir, export_time = export_fp16()
+    int4_dir, quantize_time = quantize_int4(fp16_dir)
+    data = {
+        "model_id": MODEL_ID,
+        "fp16_export_dir": fp16_dir,
+        "int4_dir": int4_dir,
+        "export_time_seconds": export_time,
+        "quantize_time_seconds": quantize_time,
+        "total_time_seconds": export_time + quantize_time,
+        "quant_config": {
+            "transformer": "OVWeightQuantizationConfig(bits=4,sym=False,group_size=128,group_size_fallback=adjust,ratio=1.0)",
+            "text_encoder": "same INT4",
+            "text_encoder_i2i": "same INT4 (Qwen-Image-2.1 editing text encoder, same arch)",
+            "others": "default INT8 (OVWeightQuantizationConfig bits=8)",
+            "ov_config_class": "OVPipelineQuantizationConfig nested in OVConfig",
+        },
+    }
+    with open("/home/user/app/outputs/benchmark_quantization.json", "w") as f:
+        json.dump(data, f, indent=2)
+    print(f"\nDone. FP16: {fp16_dir} INT4: {int4_dir}")
+
+
+if __name__ == "__main__":
+    main()