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infer.py

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+import os
+import argparse
+import torch
+from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline, AutoPipelineForText2Image, LCMScheduler
+
+from optimum.intel.openvino import OVLatentConsistencyModelPipeline, OVWeightQuantizationConfig
+import time
+import datasets
+
+prompt = "sailing ship in storm by Rembrandt"    
+#model_id = "./LCM_Dreamshaper_v7-ov-int8"
+model_id = "rippertnt/canvers-dream-v1.0.0-lcm-ov-int8"
+#quantization_config = OVWeightQuantizationConfig(bits=8, dataset="conceptual_captions")
+pipeline = OVLatentConsistencyModelPipeline.from_pretrained(model_id, bits=8)#,quantization_config=quantization_config)
+start = time.time()
+print("inference")
+image = pipeline(prompt, num_inference_steps=4, guidance_scale=8.0, height=512, width=512).images[0]
+
+print(time.time() - start )
+image.save("test2.png")
+

int.py

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+import os
+import argparse
+import torch
+from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline, AutoPipelineForText2Image, LCMScheduler
+
+from optimum.intel.openvino import OVLatentConsistencyModelPipeline, OVWeightQuantizationConfig
+import time
+import datasets
+
+prompt = "sailing ship in storm by Rembrandt"    
+model_id = "./LCM_Dreamshaper_v7-ov"
+quantization_config = OVWeightQuantizationConfig(bits=4, dataset="conceptual_captions")
+pipeline = OVLatentConsistencyModelPipeline.from_pretrained(model_id,quantization_config=quantization_config)
+start = time.time()
+print("inference")
+image = pipeline(prompt, num_inference_steps=4, guidance_scale=8.0, height=512, width=512).images[0]
+
+print(time.time() - start )
+image.save("test.png")
+pipeline.save_pretrained("./LCM_Dreamshaper_v7-ov-int4")
+# Don't forget to save the exported model
+

lcm.py

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+import torch
+from diffusers import StableDiffusionPipeline, LCMScheduler
+
+model_id = "circulus/canvers-real-v3.8.1"
+
+pipeline = StableDiffusionPipeline.from_pretrained(model_id, low_cpu_mem_usage=False)
+pipeline.scheduler = LCMScheduler.from_config(pipeline.scheduler.config)
+pipeline.load_lora_weights("latent-consistency/lcm-lora-sdv1-5")
+pipeline.fuse_lora(lora_scale=1.0)
+#pipeline = pipeline.to(dtype=torch.float16)
+pipeline.save_pretrained(f"{model_id}-lcm")

ov.py

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+from optimum.intel.openvino import OVLatentConsistencyModelPipeline
+import time
+import datasets
+
+
+model_id = "./circulus/canvers-real-v3.8.1-lcm"
+
+prompt = "sailing ship in storm by Rembrandt"    
+pipeline = OVLatentConsistencyModelPipeline.from_pretrained(model_id, export=True)
+start = time.time()
+print("inference")
+image = pipeline(prompt, num_inference_steps=4, guidance_scale=8.0, height=512, width=512).images[0]
+#image.save("test.jpg")
+print(time.time() - start )
+
+# Don't forget to save the exported model
+pipeline.save_pretrained("./circulus/canvers-real-v3.8.1-lcm-ov")