Enable Latent Consistency models OpenVINO export and inference

optimum/intel/__init__.py

@@ -62,6 +62,7 @@
         "OVStableDiffusionInpaintPipeline",
         "OVStableDiffusionXLPipeline",
         "OVStableDiffusionXLImg2ImgPipeline",
+        "OVLatentConsistencyModelPipeline",
     ]
 else:
     _import_structure["openvino"].extend(
@@ -71,6 +72,7 @@
             "OVStableDiffusionInpaintPipeline",
             "OVStableDiffusionXLPipeline",
             "OVStableDiffusionXLImg2ImgPipeline",
+            "OVLatentConsistencyModelPipeline",
         ]
     )
 
@@ -158,6 +160,7 @@
             raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
         from .utils.dummy_openvino_and_diffusers_objects import (
+            OVLatentConsistencyModelPipeline,
             OVStableDiffusionImg2ImgPipeline,
             OVStableDiffusionInpaintPipeline,
             OVStableDiffusionPipeline,
@@ -166,6 +169,7 @@
         )
     else:
         from .openvino import (
+            OVLatentConsistencyModelPipeline,
             OVStableDiffusionImg2ImgPipeline,
             OVStableDiffusionInpaintPipeline,
             OVStableDiffusionPipeline,

optimum/intel/openvino/__init__.py

@@ -51,6 +51,7 @@
 
 if is_diffusers_available():
     from .modeling_diffusion import (
+        OVLatentConsistencyModelPipeline,
         OVStableDiffusionImg2ImgPipeline,
         OVStableDiffusionInpaintPipeline,
         OVStableDiffusionPipeline,

optimum/intel/openvino/modeling_diffusion.py

@@ -37,6 +37,7 @@
 from openvino.runtime import Core
 from transformers import CLIPFeatureExtractor, CLIPTokenizer
 
+from optimum.pipelines.diffusers.pipeline_latent_consistency import LatentConsistencyPipelineMixin
 from optimum.pipelines.diffusers.pipeline_stable_diffusion import StableDiffusionPipelineMixin
 from optimum.pipelines.diffusers.pipeline_stable_diffusion_img2img import StableDiffusionImg2ImgPipelineMixin
 from optimum.pipelines.diffusers.pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipelineMixin
@@ -69,16 +70,16 @@ class OVStableDiffusionPipelineBase(OVBaseModel, OVTextualInversionLoaderMixin):
 
     def __init__(
         self,
-        vae_decoder: openvino.runtime.Model,
-        text_encoder: openvino.runtime.Model,
         unet: openvino.runtime.Model,
         config: Dict[str, Any],
-        tokenizer: "CLIPTokenizer",
         scheduler: Union["DDIMScheduler", "PNDMScheduler", "LMSDiscreteScheduler"],
-        feature_extractor: Optional["CLIPFeatureExtractor"] = None,
+        vae_decoder: Optional[openvino.runtime.Model] = None,
         vae_encoder: Optional[openvino.runtime.Model] = None,
+        text_encoder: Optional[openvino.runtime.Model] = None,
         text_encoder_2: Optional[openvino.runtime.Model] = None,
+        tokenizer: Optional["CLIPTokenizer"] = None,
         tokenizer_2: Optional["CLIPTokenizer"] = None,
+        feature_extractor: Optional["CLIPFeatureExtractor"] = None,
         device: str = "CPU",
         dynamic_shapes: bool = True,
         compile: bool = True,
@@ -270,20 +271,7 @@ def _from_pretrained(
         if model_save_dir is None:
             model_save_dir = new_model_save_dir
 
-        return cls(
-            vae_decoder=components["vae_decoder"],
-            text_encoder=components["text_encoder"],
-            unet=unet,
-            config=config,
-            tokenizer=kwargs.pop("tokenizer", None),
-            scheduler=kwargs.pop("scheduler"),
-            feature_extractor=kwargs.pop("feature_extractor", None),
-            vae_encoder=components["vae_encoder"],
-            text_encoder_2=components["text_encoder_2"],
-            tokenizer_2=kwargs.pop("tokenizer_2", None),
-            model_save_dir=model_save_dir,
-            **kwargs,
-        )
+        return cls(unet=unet, config=config, model_save_dir=model_save_dir, **components, **kwargs)
 
     @classmethod
     def _from_transformers(
@@ -295,10 +283,11 @@ def _from_transformers(
         force_download: bool = False,
         cache_dir: Optional[str] = None,
         local_files_only: bool = False,
-        tokenizer: "CLIPTokenizer" = None,
+        tokenizer: Optional["CLIPTokenizer"] = None,
         scheduler: Union["DDIMScheduler", "PNDMScheduler", "LMSDiscreteScheduler"] = None,
         feature_extractor: Optional["CLIPFeatureExtractor"] = None,
         load_in_8bit: bool = False,
+        tokenizer_2: Optional["CLIPTokenizer"] = None,
         **kwargs,
     ):
         save_dir = TemporaryDirectory()
@@ -329,6 +318,7 @@ def _from_transformers(
             local_files_only=local_files_only,
             model_save_dir=save_dir,
             tokenizer=tokenizer,
+            tokenizer_2=tokenizer_2,
             scheduler=scheduler,
             feature_extractor=feature_extractor,
             load_in_8bit=load_in_8bit,
@@ -377,8 +367,10 @@ def _reshape_unet(
         if batch_size == -1 or num_images_per_prompt == -1:
             batch_size = -1
         else:
+            batch_size *= num_images_per_prompt
             # The factor of 2 comes from the guidance scale > 1
-            batch_size = 2 * batch_size * num_images_per_prompt
+            if "timestep_cond" not in {inputs.get_any_name() for inputs in model.inputs}:
+                batch_size *= 2
 
         height = height // self.vae_scale_factor if height > 0 else height
         width = width // self.vae_scale_factor if width > 0 else width
@@ -402,6 +394,8 @@ def _reshape_unet(
                 shapes[inputs] = [batch_size, self.text_encoder_2.config["projection_dim"]]
             elif inputs.get_any_name() == "time_ids":
                 shapes[inputs] = [batch_size, inputs.get_partial_shape()[1]]
+            elif inputs.get_any_name() == "timestep_cond":
+                shapes[inputs] = [batch_size, self.unet.config["time_cond_proj_dim"]]
             else:
                 shapes[inputs][0] = batch_size
                 shapes[inputs][1] = tokenizer_max_length
@@ -585,6 +579,7 @@ def __call__(
         encoder_hidden_states: np.ndarray,
         text_embeds: Optional[np.ndarray] = None,
         time_ids: Optional[np.ndarray] = None,
+        timestep_cond: Optional[np.ndarray] = None,
     ):
         self._compile()
 
@@ -598,6 +593,8 @@ def __call__(
             inputs["text_embeds"] = text_embeds
         if time_ids is not None:
             inputs["time_ids"] = time_ids
+        if timestep_cond is not None:
+            inputs["timestep_cond"] = timestep_cond
 
         outputs = self.request(inputs, shared_memory=True)
         return list(outputs.values())
@@ -930,6 +927,61 @@ def __call__(
         )
 
 
+class OVLatentConsistencyModelPipeline(OVStableDiffusionPipelineBase, LatentConsistencyPipelineMixin):
+    def __call__(
+        self,
+        prompt: Optional[Union[str, List[str]]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 4,
+        original_inference_steps: int = None,
+        guidance_scale: float = 8.5,
+        num_images_per_prompt: int = 1,
+        **kwargs,
+    ):
+        height = height or self.unet.config["sample_size"] * self.vae_scale_factor
+        width = width or self.unet.config["sample_size"] * self.vae_scale_factor
+        _height = self.height
+        _width = self.width
+        expected_batch_size = self._batch_size
+
+        if _height != -1 and height != _height:
+            logger.warning(
+                f"`height` was set to {height} but the static model will output images of height {_height}."
+                "To fix the height, please reshape your model accordingly using the `.reshape()` method."
+            )
+            height = _height
+
+        if _width != -1 and width != _width:
+            logger.warning(
+                f"`width` was set to {width} but the static model will output images of width {_width}."
+                "To fix the width, please reshape your model accordingly using the `.reshape()` method."
+            )
+            width = _width
+
+        if expected_batch_size != -1:
+            if isinstance(prompt, str):
+                batch_size = 1
+            elif isinstance(prompt, list):
+                batch_size = len(prompt)
+            else:
+                batch_size = kwargs.get("prompt_embeds").shape[0]
+
+            _raise_invalid_batch_size(expected_batch_size, batch_size, num_images_per_prompt, guidance_scale=0.0)
+
+        return LatentConsistencyPipelineMixin.__call__(
+            self,
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            original_inference_steps=original_inference_steps,
+            guidance_scale=guidance_scale,
+            num_images_per_prompt=num_images_per_prompt,
+            **kwargs,
+        )
+
+
 def _raise_invalid_batch_size(
     expected_batch_size: int, batch_size: int, num_images_per_prompt: int, guidance_scale: float
 ):

optimum/intel/utils/dummy_openvino_and_diffusers_objects.py

@@ -68,3 +68,14 @@ def __init__(self, *args, **kwargs):
     @classmethod
     def from_pretrained(cls, *args, **kwargs):
         requires_backends(cls, ["openvino", "diffusers"])
+
+
+class OVLatentConsistencyModelPipeline(metaclass=DummyObject):
+    _backends = ["openvino", "diffusers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["openvino", "diffusers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["openvino", "diffusers"])

tests/openvino/test_stable_diffusion.py

@@ -28,10 +28,12 @@
 from diffusers.utils import load_image
 from diffusers.utils.testing_utils import floats_tensor
 from openvino.runtime.ie_api import CompiledModel
+from packaging.version import Version, parse
 from parameterized import parameterized
 from utils_tests import MODEL_NAMES, SEED
 
 from optimum.intel import (
+    OVLatentConsistencyModelPipeline,
     OVStableDiffusionImg2ImgPipeline,
     OVStableDiffusionInpaintPipeline,
     OVStableDiffusionPipeline,
@@ -50,6 +52,7 @@
     ORTStableDiffusionXLImg2ImgPipeline,
     ORTStableDiffusionXLPipeline,
 )
+from optimum.utils.import_utils import _diffusers_version
 
 
 def _generate_inputs(batch_size=1):
@@ -475,3 +478,68 @@ def generate_inputs(self, height=128, width=128, batch_size=1, input_type="np"):
         inputs["image"] = _create_image(height=height, width=width, batch_size=batch_size, input_type=input_type)
         inputs["strength"] = 0.75
         return inputs
+
+
+class OVLatentConsistencyModelPipelineTest(unittest.TestCase):
+    SUPPORTED_ARCHITECTURES = ("latent-consistency",)
+    MODEL_CLASS = OVLatentConsistencyModelPipeline
+    TASK = "text-to-image"
+
+    @parameterized.expand(SUPPORTED_ARCHITECTURES)
+    @unittest.skipIf(parse(_diffusers_version) <= Version("0.21.4"), "not supported with this diffusers version")
+    def test_compare_to_diffusers(self, model_arch: str):
+        ov_pipeline = self.MODEL_CLASS.from_pretrained(MODEL_NAMES[model_arch], export=True)
+        self.assertIsInstance(ov_pipeline.text_encoder, OVModelTextEncoder)
+        self.assertIsInstance(ov_pipeline.vae_encoder, OVModelVaeEncoder)
+        self.assertIsInstance(ov_pipeline.vae_decoder, OVModelVaeDecoder)
+        self.assertIsInstance(ov_pipeline.unet, OVModelUnet)
+        self.assertIsInstance(ov_pipeline.config, Dict)
+
+        from diffusers import LatentConsistencyModelPipeline
+
+        pipeline = LatentConsistencyModelPipeline.from_pretrained(MODEL_NAMES[model_arch])
+        batch_size, num_images_per_prompt, height, width = 2, 3, 64, 128
+        latents = ov_pipeline.prepare_latents(
+            batch_size * num_images_per_prompt,
+            ov_pipeline.unet.config["in_channels"],
+            height,
+            width,
+            dtype=np.float32,
+            generator=np.random.RandomState(0),
+        )
+
+        kwargs = {
+            "prompt": ["sailing ship in storm by Leonardo da Vinci"] * batch_size,
+            "num_inference_steps": 1,
+            "num_images_per_prompt": num_images_per_prompt,
+            "height": height,
+            "width": width,
+            "guidance_scale": 8.5,
+        }
+
+        for output_type in ["latent", "np"]:
+            ov_outputs = ov_pipeline(latents=latents, output_type=output_type, **kwargs).images
+            self.assertIsInstance(ov_outputs, np.ndarray)
+            with torch.no_grad():
+                outputs = pipeline(latents=torch.from_numpy(latents), output_type=output_type, **kwargs).images
+
+            # Compare model outputs
+            self.assertTrue(np.allclose(ov_outputs, outputs, atol=1e-4))
+        # Compare model devices
+        self.assertEqual(pipeline.device.type, ov_pipeline.device)
+
+    @parameterized.expand(SUPPORTED_ARCHITECTURES)
+    @unittest.skipIf(parse(_diffusers_version) <= Version("0.21.4"), "not supported with this diffusers version")
+    def test_num_images_per_prompt_static_model(self, model_arch: str):
+        model_id = MODEL_NAMES[model_arch]
+        pipeline = self.MODEL_CLASS.from_pretrained(model_id, export=True, compile=False, dynamic_shapes=False)
+        batch_size, num_images, height, width = 3, 4, 128, 64
+        pipeline.half()
+        pipeline.reshape(batch_size=batch_size, height=height, width=width, num_images_per_prompt=num_images)
+        self.assertFalse(pipeline.is_dynamic)
+        pipeline.compile()
+
+        for _height in [height, height + 16]:
+            inputs = _generate_inputs(batch_size)
+            outputs = pipeline(**inputs, num_images_per_prompt=num_images, height=_height, width=width).images
+            self.assertEqual(outputs.shape, (batch_size * num_images, height, width, 3))

tests/openvino/utils_tests.py

@@ -72,6 +72,7 @@
     "stable-diffusion": "hf-internal-testing/tiny-stable-diffusion-torch",
     "stable-diffusion-xl": "echarlaix/tiny-random-stable-diffusion-xl",
     "stable-diffusion-xl-refiner": "echarlaix/tiny-random-stable-diffusion-xl-refiner",
+    "latent-consistency": "echarlaix/tiny-random-latent-consistency",
     "sew": "hf-internal-testing/tiny-random-SEWModel",
     "sew_d": "hf-internal-testing/tiny-random-SEWDModel",
     "swin": "hf-internal-testing/tiny-random-SwinModel",