fix z-image non-diffusers lora loading.

* update

* update

src/diffusers/loaders/lora_conversion_utils.py

@@ -2538,8 +2538,12 @@ def _convert_non_diffusers_z_image_lora_to_diffusers(state_dict):
 
     def get_alpha_scales(down_weight, alpha_key):
         rank = down_weight.shape[0]
-        alpha = state_dict.pop(alpha_key).item()
-        scale = alpha / rank  # LoRA is scaled by 'alpha / rank' in forward pass, so we need to scale it back here
+        alpha_tensor = state_dict.pop(alpha_key, None)
+        if alpha_tensor is None:
+            return 1.0, 1.0
+        scale = (
+            alpha_tensor.item() / rank
+        )  # LoRA is scaled by 'alpha / rank' in forward pass, so we need to scale it back here
         scale_down = scale
         scale_up = 1.0
         while scale_down * 2 < scale_up:

src/diffusers/quantizers/quantization_config.py

@@ -36,7 +36,7 @@ from typing import Any, Callable
 
 from packaging import version
 
-from ..utils import is_torch_available, is_torchao_available, is_torchao_version, logging
+from ..utils import deprecate, is_torch_available, is_torchao_available, is_torchao_version, logging
 
 
 if is_torch_available():
@@ -844,6 +844,8 @@ class QuantoConfig(QuantizationConfigMixin):
         modules_to_not_convert: list[str] | None = None,
         **kwargs,
     ):
+        deprecation_message = "`QuantoConfig` is deprecated and will be removed in version 1.0.0."
+        deprecate("QuantoConfig", "1.0.0", deprecation_message)
         self.quant_method = QuantizationMethod.QUANTO
         self.weights_dtype = weights_dtype
         self.modules_to_not_convert = modules_to_not_convert

src/diffusers/quantizers/quanto/quanto_quantizer.py

@@ -3,6 +3,7 @@ from typing import TYPE_CHECKING, Any
 from diffusers.utils.import_utils import is_optimum_quanto_version
 
 from ...utils import (
+    deprecate,
     get_module_from_name,
     is_accelerate_available,
     is_accelerate_version,
@@ -42,6 +43,9 @@ class QuantoQuantizer(DiffusersQuantizer):
         super().__init__(quantization_config, **kwargs)
 
     def validate_environment(self, *args, **kwargs):
+        deprecation_message = "The Quanto quantizer is deprecated and will be removed in version 1.0.0."
+        deprecate("QuantoQuantizer", "1.0.0", deprecation_message)
+
         if not is_optimum_quanto_available():
             raise ImportError(
                 "Loading an optimum-quanto quantized model requires optimum-quanto library (`pip install optimum-quanto`)"

tests/modular_pipelines/test_modular_pipelines_common.py

@@ -5,7 +5,6 @@ from typing import Callable
 
 import pytest
 import torch
-from huggingface_hub import hf_hub_download
 
 import diffusers
 from diffusers import AutoModel, ComponentsManager, ModularPipeline, ModularPipelineBlocks
@@ -33,33 +32,6 @@ from ..testing_utils import (
 )
 
 
-def _get_specified_components(path_or_repo_id, cache_dir=None):
-    if os.path.isdir(path_or_repo_id):
-        config_path = os.path.join(path_or_repo_id, "modular_model_index.json")
-    else:
-        try:
-            config_path = hf_hub_download(
-                repo_id=path_or_repo_id,
-                filename="modular_model_index.json",
-                local_dir=cache_dir,
-            )
-        except Exception:
-            return None
-
-    with open(config_path) as f:
-        config = json.load(f)
-
-    components = set()
-    for k, v in config.items():
-        if isinstance(v, (str, int, float, bool)):
-            continue
-        for entry in v:
-            if isinstance(entry, dict) and (entry.get("repo") or entry.get("pretrained_model_name_or_path")):
-                components.add(k)
-                break
-    return components
-
-
 class ModularPipelineTesterMixin:
     """
     It provides a set of common tests for each modular pipeline,
@@ -388,39 +360,6 @@ class ModularPipelineTesterMixin:
 
         assert torch.abs(image_slices[0] - image_slices[1]).max() < 1e-3
 
-    def test_load_expected_components_from_pretrained(self, tmp_path):
-        pipe = self.get_pipeline()
-        expected = _get_specified_components(self.pretrained_model_name_or_path, cache_dir=tmp_path)
-        if not expected:
-            pytest.skip("Skipping test as we couldn't fetch the expected components.")
-
-        actual = {
-            name
-            for name in pipe.components
-            if getattr(pipe, name, None) is not None
-            and getattr(getattr(pipe, name), "_diffusers_load_id", None) not in (None, "null")
-        }
-        assert expected == actual, f"Component mismatch: missing={expected - actual}, unexpected={actual - expected}"
-
-    def test_load_expected_components_from_save_pretrained(self, tmp_path):
-        pipe = self.get_pipeline()
-        save_dir = str(tmp_path / "saved-pipeline")
-        pipe.save_pretrained(save_dir)
-
-        expected = _get_specified_components(save_dir)
-        loaded_pipe = ModularPipeline.from_pretrained(save_dir)
-        loaded_pipe.load_components(torch_dtype=torch.float32)
-
-        actual = {
-            name
-            for name in loaded_pipe.components
-            if getattr(loaded_pipe, name, None) is not None
-            and getattr(getattr(loaded_pipe, name), "_diffusers_load_id", None) not in (None, "null")
-        }
-        assert expected == actual, (
-            f"Component mismatch after save/load: missing={expected - actual}, unexpected={actual - expected}"
-        )
-
     def test_modular_index_consistency(self, tmp_path):
         pipe = self.get_pipeline()
         components_spec = pipe._component_specs