compatibility with diffusers 0.31.0

Author: eaidova

optimum/exporters/openvino/model_configs.py

@@ -55,7 +55,7 @@
 )
 from optimum.utils.normalized_config import NormalizedConfig, NormalizedTextConfig, NormalizedVisionConfig
 
-from ...intel.utils.import_utils import _transformers_version, is_transformers_version
+from ...intel.utils.import_utils import _transformers_version, is_diffusers_version, is_transformers_version
 from .model_patcher import (
     AquilaModelPatcher,
     ArcticModelPatcher,
@@ -1681,7 +1681,9 @@ def generate(self, input_name: str, framework: str = "pt", int_dtype: str = "int
             img_ids_height = self.height // 2
             img_ids_width = self.width // 2
             return self.random_int_tensor(
-                [self.batch_size, img_ids_height * img_ids_width, 3],
+                [self.batch_size, img_ids_height * img_ids_width, 3]
+                if is_diffusers_version("<", "0.31.0")
+                else [img_ids_height * img_ids_width, 3],
                 min_value=0,
                 max_value=min(img_ids_height, img_ids_width),
                 framework=framework,
@@ -1704,7 +1706,11 @@ def generate(self, input_name: str, framework: str = "pt", int_dtype: str = "int
         if input_name == "txt_ids":
             import torch
 
-            shape = [self.batch_size, self.sequence_length, 3]
+            shape = (
+                [self.batch_size, self.sequence_length, 3]
+                if is_diffusers_version("<", "0.31.0")
+                else [self.sequence_length, 3]
+            )
             dtype = DTYPE_MAPPER.pt(float_dtype)
             return torch.full(shape, 0, dtype=dtype)
         return super().generate(input_name, framework, int_dtype, float_dtype)
@@ -1724,8 +1730,14 @@ def inputs(self):
         common_inputs = super().inputs
         common_inputs.pop("sample", None)
         common_inputs["hidden_states"] = {0: "batch_size", 1: "packed_height_width"}
-        common_inputs["txt_ids"] = {0: "batch_size", 1: "sequence_length"}
-        common_inputs["img_ids"] = {0: "batch_size", 1: "packed_height_width"}
+        common_inputs["txt_ids"] = (
+            {0: "batch_size", 1: "sequence_length"} if is_diffusers_version("<", "0.31.0") else {0: "sequence_length"}
+        )
+        common_inputs["img_ids"] = (
+            {0: "batch_size", 1: "packed_height_width"}
+            if is_diffusers_version("<", "0.31.0")
+            else {0: "packed_height_width"}
+        )
         if getattr(self._normalized_config, "guidance_embeds", False):
             common_inputs["guidance"] = {0: "batch_size"}
         return common_inputs

optimum/exporters/openvino/model_patcher.py

@@ -29,6 +29,7 @@
     _openvino_version,
     _torch_version,
     _transformers_version,
+    is_diffusers_version,
     is_openvino_version,
     is_torch_version,
     is_transformers_version,
@@ -2734,10 +2735,11 @@ def rope(pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor:
 class FluxTransfromerModelPatcher(ModelPatcher):
     def __enter__(self):
         super().__enter__()
-        self._model.pos_embed._orig_forward = self._model.pos_embed.forward
-        self._model.pos_embed.forward = types.MethodType(_embednb_forward, self._model.pos_embed)
+        if is_diffusers_version("<", "0.31.0"):
+            self._model.pos_embed._orig_forward = self._model.pos_embed.forward
+            self._model.pos_embed.forward = types.MethodType(_embednb_forward, self._model.pos_embed)
 
     def __exit__(self, exc_type, exc_value, traceback):
         super().__exit__(exc_type, exc_value, traceback)
-
-        self._model.pos_embed.forward = self._model.pos_embed._orig_forward
+        if hasattr(self._model.pos_embed, "_orig_forward"):
+            self._model.pos_embed.forward = self._model.pos_embed._orig_forward

optimum/intel/openvino/modeling_diffusion.py

@@ -681,9 +681,13 @@ def _reshape_transformer(
             elif inputs.get_any_name() == "pooled_projections":
                 shapes[inputs] = [batch_size, self.transformer.config["pooled_projection_dim"]]
             elif inputs.get_any_name() == "img_ids":
-                shapes[inputs] = [batch_size, packed_height_width, 3]
+                shapes[inputs] = (
+                    [batch_size, packed_height_width, 3]
+                    if is_diffusers_version("<", "0.31.0")
+                    else [packed_height_width, 3]
+                )
             elif inputs.get_any_name() == "txt_ids":
-                shapes[inputs] = [batch_size, -1, 3]
+                shapes[inputs] = [batch_size, -1, 3] if is_diffusers_version("<", "0.31.0") else [-1, 3]
             else:
                 shapes[inputs][0] = batch_size
                 shapes[inputs][1] = -1  # text_encoder_3 may have vary input length