Merge branch 'main' into fix-timeout-constant-notify

relax tolerance for unclip further.

scripts/convert_vae_pt_to_diffusers.py

@@ -53,7 +53,12 @@ def custom_convert_ldm_vae_checkpoint(checkpoint, config):
     }
 
     for i in range(num_down_blocks):
-        resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
+        resnets = [
+            key
+            for key in down_blocks[i]
+            if f"down.{i}" in key and f"down.{i}.downsample" not in key and "attn" not in key
+        ]
+        attentions = [key for key in down_blocks[i] if f"down.{i}.attn" in key]
 
         if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
             new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
@@ -67,6 +72,10 @@ def custom_convert_ldm_vae_checkpoint(checkpoint, config):
         meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
         assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
 
+        paths = renew_vae_attention_paths(attentions)
+        meta_path = {"old": f"down.{i}.attn", "new": f"down_blocks.{i}.attentions"}
+        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+
     mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
     num_mid_res_blocks = 2
     for i in range(1, num_mid_res_blocks + 1):
@@ -85,8 +94,11 @@ def custom_convert_ldm_vae_checkpoint(checkpoint, config):
     for i in range(num_up_blocks):
         block_id = num_up_blocks - 1 - i
         resnets = [
-            key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
+            key
+            for key in up_blocks[block_id]
+            if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key and "attn" not in key
         ]
+        attentions = [key for key in up_blocks[block_id] if f"up.{block_id}.attn" in key]
 
         if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
             new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
@@ -100,6 +112,10 @@ def custom_convert_ldm_vae_checkpoint(checkpoint, config):
         meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
         assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
 
+        paths = renew_vae_attention_paths(attentions)
+        meta_path = {"old": f"up.{block_id}.attn", "new": f"up_blocks.{i}.attentions"}
+        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+
     mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
     num_mid_res_blocks = 2
     for i in range(1, num_mid_res_blocks + 1):

src/diffusers/loaders/lora_conversion_utils.py

@@ -1608,3 +1608,64 @@ def _convert_non_diffusers_wan_lora_to_diffusers(state_dict):
         converted_state_dict[f"transformer.{key}"] = converted_state_dict.pop(key)
 
     return converted_state_dict
+
+
+def _convert_musubi_wan_lora_to_diffusers(state_dict):
+    # https://github.com/kohya-ss/musubi-tuner
+    converted_state_dict = {}
+    original_state_dict = {k[len("lora_unet_") :]: v for k, v in state_dict.items()}
+
+    num_blocks = len({k.split("blocks_")[1].split("_")[0] for k in original_state_dict})
+    is_i2v_lora = any("k_img" in k for k in original_state_dict) and any("v_img" in k for k in original_state_dict)
+
+    def get_alpha_scales(down_weight, key):
+        rank = down_weight.shape[0]
+        alpha = original_state_dict.pop(key + ".alpha").item()
+        scale = alpha / 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:
+            scale_down *= 2
+            scale_up /= 2
+        return scale_down, scale_up
+
+    for i in range(num_blocks):
+        # Self-attention
+        for o, c in zip(["q", "k", "v", "o"], ["to_q", "to_k", "to_v", "to_out.0"]):
+            down_weight = original_state_dict.pop(f"blocks_{i}_self_attn_{o}.lora_down.weight")
+            up_weight = original_state_dict.pop(f"blocks_{i}_self_attn_{o}.lora_up.weight")
+            scale_down, scale_up = get_alpha_scales(down_weight, f"blocks_{i}_self_attn_{o}")
+            converted_state_dict[f"blocks.{i}.attn1.{c}.lora_A.weight"] = down_weight * scale_down
+            converted_state_dict[f"blocks.{i}.attn1.{c}.lora_B.weight"] = up_weight * scale_up
+
+        # Cross-attention
+        for o, c in zip(["q", "k", "v", "o"], ["to_q", "to_k", "to_v", "to_out.0"]):
+            down_weight = original_state_dict.pop(f"blocks_{i}_cross_attn_{o}.lora_down.weight")
+            up_weight = original_state_dict.pop(f"blocks_{i}_cross_attn_{o}.lora_up.weight")
+            scale_down, scale_up = get_alpha_scales(down_weight, f"blocks_{i}_cross_attn_{o}")
+            converted_state_dict[f"blocks.{i}.attn2.{c}.lora_A.weight"] = down_weight * scale_down
+            converted_state_dict[f"blocks.{i}.attn2.{c}.lora_B.weight"] = up_weight * scale_up
+
+        if is_i2v_lora:
+            for o, c in zip(["k_img", "v_img"], ["add_k_proj", "add_v_proj"]):
+                down_weight = original_state_dict.pop(f"blocks_{i}_cross_attn_{o}.lora_down.weight")
+                up_weight = original_state_dict.pop(f"blocks_{i}_cross_attn_{o}.lora_up.weight")
+                scale_down, scale_up = get_alpha_scales(down_weight, f"blocks_{i}_cross_attn_{o}")
+                converted_state_dict[f"blocks.{i}.attn2.{c}.lora_A.weight"] = down_weight * scale_down
+                converted_state_dict[f"blocks.{i}.attn2.{c}.lora_B.weight"] = up_weight * scale_up
+
+        # FFN
+        for o, c in zip(["ffn_0", "ffn_2"], ["net.0.proj", "net.2"]):
+            down_weight = original_state_dict.pop(f"blocks_{i}_{o}.lora_down.weight")
+            up_weight = original_state_dict.pop(f"blocks_{i}_{o}.lora_up.weight")
+            scale_down, scale_up = get_alpha_scales(down_weight, f"blocks_{i}_{o}")
+            converted_state_dict[f"blocks.{i}.ffn.{c}.lora_A.weight"] = down_weight * scale_down
+            converted_state_dict[f"blocks.{i}.ffn.{c}.lora_B.weight"] = up_weight * scale_up
+
+    if len(original_state_dict) > 0:
+        raise ValueError(f"`state_dict` should be empty at this point but has {original_state_dict.keys()=}")
+
+    for key in list(converted_state_dict.keys()):
+        converted_state_dict[f"transformer.{key}"] = converted_state_dict.pop(key)
+
+    return converted_state_dict

src/diffusers/loaders/lora_pipeline.py

@@ -42,6 +42,7 @@ from .lora_conversion_utils import (
     _convert_bfl_flux_control_lora_to_diffusers,
     _convert_hunyuan_video_lora_to_diffusers,
     _convert_kohya_flux_lora_to_diffusers,
+    _convert_musubi_wan_lora_to_diffusers,
     _convert_non_diffusers_lora_to_diffusers,
     _convert_non_diffusers_lumina2_lora_to_diffusers,
     _convert_non_diffusers_wan_lora_to_diffusers,
@@ -4794,6 +4795,8 @@ class WanLoraLoaderMixin(LoraBaseMixin):
         )
         if any(k.startswith("diffusion_model.") for k in state_dict):
             state_dict = _convert_non_diffusers_wan_lora_to_diffusers(state_dict)
+        elif any(k.startswith("lora_unet_") for k in state_dict):
+            state_dict = _convert_musubi_wan_lora_to_diffusers(state_dict)
 
         is_dora_scale_present = any("dora_scale" in k for k in state_dict)
         if is_dora_scale_present:

src/diffusers/loaders/single_file_utils.py

@@ -177,6 +177,7 @@ DIFFUSERS_DEFAULT_PIPELINE_PATHS = {
     "flux-schnell": {"pretrained_model_name_or_path": "black-forest-labs/FLUX.1-schnell"},
     "ltx-video": {"pretrained_model_name_or_path": "diffusers/LTX-Video-0.9.0"},
     "ltx-video-0.9.1": {"pretrained_model_name_or_path": "diffusers/LTX-Video-0.9.1"},
+    "ltx-video-0.9.5": {"pretrained_model_name_or_path": "Lightricks/LTX-Video-0.9.5"},
     "autoencoder-dc-f128c512": {"pretrained_model_name_or_path": "mit-han-lab/dc-ae-f128c512-mix-1.0-diffusers"},
     "autoencoder-dc-f64c128": {"pretrained_model_name_or_path": "mit-han-lab/dc-ae-f64c128-mix-1.0-diffusers"},
     "autoencoder-dc-f32c32": {"pretrained_model_name_or_path": "mit-han-lab/dc-ae-f32c32-mix-1.0-diffusers"},
@@ -638,7 +639,9 @@ def infer_diffusers_model_type(checkpoint):
             model_type = "flux-schnell"
 
     elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["ltx-video"]):
-        if "vae.decoder.last_time_embedder.timestep_embedder.linear_1.weight" in checkpoint:
+        if checkpoint["vae.encoder.conv_out.conv.weight"].shape[1] == 2048:
+            model_type = "ltx-video-0.9.5"
+        elif "vae.decoder.last_time_embedder.timestep_embedder.linear_1.weight" in checkpoint:
             model_type = "ltx-video-0.9.1"
         else:
             model_type = "ltx-video"
@@ -2403,13 +2406,41 @@ def convert_ltx_vae_checkpoint_to_diffusers(checkpoint, **kwargs):
         "last_scale_shift_table": "scale_shift_table",
     }
 
+    VAE_095_RENAME_DICT = {
+        # decoder
+        "up_blocks.0": "mid_block",
+        "up_blocks.1": "up_blocks.0.upsamplers.0",
+        "up_blocks.2": "up_blocks.0",
+        "up_blocks.3": "up_blocks.1.upsamplers.0",
+        "up_blocks.4": "up_blocks.1",
+        "up_blocks.5": "up_blocks.2.upsamplers.0",
+        "up_blocks.6": "up_blocks.2",
+        "up_blocks.7": "up_blocks.3.upsamplers.0",
+        "up_blocks.8": "up_blocks.3",
+        # encoder
+        "down_blocks.0": "down_blocks.0",
+        "down_blocks.1": "down_blocks.0.downsamplers.0",
+        "down_blocks.2": "down_blocks.1",
+        "down_blocks.3": "down_blocks.1.downsamplers.0",
+        "down_blocks.4": "down_blocks.2",
+        "down_blocks.5": "down_blocks.2.downsamplers.0",
+        "down_blocks.6": "down_blocks.3",
+        "down_blocks.7": "down_blocks.3.downsamplers.0",
+        "down_blocks.8": "mid_block",
+        # common
+        "last_time_embedder": "time_embedder",
+        "last_scale_shift_table": "scale_shift_table",
+    }
+
     VAE_SPECIAL_KEYS_REMAP = {
         "per_channel_statistics.channel": remove_keys_,
         "per_channel_statistics.mean-of-means": remove_keys_,
         "per_channel_statistics.mean-of-stds": remove_keys_,
     }
 
-    if "vae.decoder.last_time_embedder.timestep_embedder.linear_1.weight" in converted_state_dict:
+    if converted_state_dict["vae.encoder.conv_out.conv.weight"].shape[1] == 2048:
+        VAE_KEYS_RENAME_DICT.update(VAE_095_RENAME_DICT)
+    elif "vae.decoder.last_time_embedder.timestep_embedder.linear_1.weight" in converted_state_dict:
         VAE_KEYS_RENAME_DICT.update(VAE_091_RENAME_DICT)
 
     for key in list(converted_state_dict.keys()):

src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py

@@ -350,8 +350,14 @@ def create_vae_diffusers_config(original_config, image_size: int):
     _ = original_config["model"]["params"]["first_stage_config"]["params"]["embed_dim"]
 
     block_out_channels = [vae_params["ch"] * mult for mult in vae_params["ch_mult"]]
-    down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)
-    up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)
+    down_block_types = [
+        "DownEncoderBlock2D" if image_size // 2**i not in vae_params["attn_resolutions"] else "AttnDownEncoderBlock2D"
+        for i, _ in enumerate(block_out_channels)
+    ]
+    up_block_types = [
+        "UpDecoderBlock2D" if image_size // 2**i not in vae_params["attn_resolutions"] else "AttnUpDecoderBlock2D"
+        for i, _ in enumerate(block_out_channels)
+    ][::-1]
 
     config = {
         "sample_size": image_size,

src/diffusers/pipelines/wan/pipeline_wan.py

@@ -15,7 +15,6 @@
 import html
 from typing import Any, Callable, Dict, List, Optional, Union
 
-import ftfy
 import regex as re
 import torch
 from transformers import AutoTokenizer, UMT5EncoderModel
@@ -24,7 +23,7 @@ from ...callbacks import MultiPipelineCallbacks, PipelineCallback
 from ...loaders import WanLoraLoaderMixin
 from ...models import AutoencoderKLWan, WanTransformer3DModel
 from ...schedulers import FlowMatchEulerDiscreteScheduler
-from ...utils import is_torch_xla_available, logging, replace_example_docstring
+from ...utils import is_ftfy_available, is_torch_xla_available, logging, replace_example_docstring
 from ...utils.torch_utils import randn_tensor
 from ...video_processor import VideoProcessor
 from ..pipeline_utils import DiffusionPipeline
@@ -40,6 +39,9 @@ else:
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
+if is_ftfy_available():
+    import ftfy
+
 
 EXAMPLE_DOC_STRING = """
     Examples:

src/diffusers/pipelines/wan/pipeline_wan_i2v.py

@@ -15,7 +15,6 @@
 import html
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
-import ftfy
 import PIL
 import regex as re
 import torch
@@ -26,7 +25,7 @@ from ...image_processor import PipelineImageInput
 from ...loaders import WanLoraLoaderMixin
 from ...models import AutoencoderKLWan, WanTransformer3DModel
 from ...schedulers import FlowMatchEulerDiscreteScheduler
-from ...utils import is_torch_xla_available, logging, replace_example_docstring
+from ...utils import is_ftfy_available, is_torch_xla_available, logging, replace_example_docstring
 from ...utils.torch_utils import randn_tensor
 from ...video_processor import VideoProcessor
 from ..pipeline_utils import DiffusionPipeline
@@ -42,6 +41,9 @@ else:
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
+if is_ftfy_available():
+    import ftfy
+
 EXAMPLE_DOC_STRING = """
     Examples:
         ```python

src/diffusers/pipelines/wan/pipeline_wan_video2video.py

@@ -16,7 +16,6 @@ import html
 import inspect
 from typing import Any, Callable, Dict, List, Optional, Union
 
-import ftfy
 import regex as re
 import torch
 from PIL import Image
@@ -26,7 +25,7 @@ from ...callbacks import MultiPipelineCallbacks, PipelineCallback
 from ...loaders import WanLoraLoaderMixin
 from ...models import AutoencoderKLWan, WanTransformer3DModel
 from ...schedulers import FlowMatchEulerDiscreteScheduler
-from ...utils import is_torch_xla_available, logging, replace_example_docstring
+from ...utils import is_ftfy_available, is_torch_xla_available, logging, replace_example_docstring
 from ...utils.torch_utils import randn_tensor
 from ...video_processor import VideoProcessor
 from ..pipeline_utils import DiffusionPipeline
@@ -42,6 +41,9 @@ else:
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
+if is_ftfy_available():
+    import ftfy
+
 
 EXAMPLE_DOC_STRING = """
     Examples:

src/diffusers/utils/import_utils.py

@@ -101,18 +101,20 @@ _onnx_available = importlib.util.find_spec("onnxruntime") is not None
 if _onnx_available:
     candidates = (
         "onnxruntime",
+        "onnxruntime-cann",
+        "onnxruntime-directml",
+        "ort_nightly_directml",
         "onnxruntime-gpu",
         "ort_nightly_gpu",
-        "onnxruntime-directml",
-        "onnxruntime-openvino",
-        "ort_nightly_directml",
-        "onnxruntime-rocm",
         "onnxruntime-migraphx",
+        "onnxruntime-openvino",
+        "onnxruntime-qnn",
+        "onnxruntime-rocm",
         "onnxruntime-training",
         "onnxruntime-vitisai",
     )
     _onnxruntime_version = None
-    # For the metadata, we have to look for both onnxruntime and onnxruntime-gpu
+    # For the metadata, we have to look for both onnxruntime and onnxruntime-x
     for pkg in candidates:
         try:
             _onnxruntime_version = importlib_metadata.version(pkg)

tests/lora/test_lora_layers_sd.py

@@ -33,6 +33,7 @@ from diffusers import (
 )
 from diffusers.utils.import_utils import is_accelerate_available
 from diffusers.utils.testing_utils import (
+    Expectations,
     backend_empty_cache,
     load_image,
     nightly,
@@ -455,11 +456,54 @@ class LoraIntegrationTests(unittest.TestCase):
 
         images = pipe("A pokemon with blue eyes.", output_type="np", generator=generator, num_inference_steps=2).images
 
-        images = images[0, -3:, -3:, -1].flatten()
+        image_slice = images[0, -3:, -3:, -1].flatten()
 
-        expected = np.array([0.7406, 0.699, 0.5963, 0.7493, 0.7045, 0.6096, 0.6886, 0.6388, 0.583])
+        expected_slices = Expectations(
+            {
+                ("xpu", 3): np.array(
+                    [
+                        0.6544,
+                        0.6127,
+                        0.5397,
+                        0.6845,
+                        0.6047,
+                        0.5469,
+                        0.6349,
+                        0.5906,
+                        0.5382,
+                    ]
+                ),
+                ("cuda", 7): np.array(
+                    [
+                        0.7406,
+                        0.699,
+                        0.5963,
+                        0.7493,
+                        0.7045,
+                        0.6096,
+                        0.6886,
+                        0.6388,
+                        0.583,
+                    ]
+                ),
+                ("cuda", 8): np.array(
+                    [
+                        0.6542,
+                        0.61253,
+                        0.5396,
+                        0.6843,
+                        0.6044,
+                        0.5468,
+                        0.6349,
+                        0.5905,
+                        0.5381,
+                    ]
+                ),
+            }
+        )
+        expected_slice = expected_slices.get_expectation()
 
-        max_diff = numpy_cosine_similarity_distance(expected, images)
+        max_diff = numpy_cosine_similarity_distance(expected_slice, image_slice)
         assert max_diff < 1e-4
 
         pipe.unload_lora_weights()

tests/lora/utils.py

@@ -260,6 +260,31 @@ class PeftLoraLoaderMixinTests:
 
         return modules_to_save
 
+    def check_if_adapters_added_correctly(
+        self, pipe, text_lora_config=None, denoiser_lora_config=None, adapter_name="default"
+    ):
+        if text_lora_config is not None:
+            if "text_encoder" in self.pipeline_class._lora_loadable_modules:
+                pipe.text_encoder.add_adapter(text_lora_config, adapter_name=adapter_name)
+                self.assertTrue(
+                    check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
+                )
+
+        if denoiser_lora_config is not None:
+            denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
+            denoiser.add_adapter(denoiser_lora_config, adapter_name=adapter_name)
+            self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
+        else:
+            denoiser = None
+
+        if text_lora_config is not None and self.has_two_text_encoders or self.has_three_text_encoders:
+            if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
+                pipe.text_encoder_2.add_adapter(text_lora_config, adapter_name=adapter_name)
+                self.assertTrue(
+                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
+                )
+        return pipe, denoiser
+
     def test_simple_inference(self):
         """
         Tests a simple inference and makes sure it works as expected
@@ -289,16 +314,7 @@ class PeftLoraLoaderMixinTests:
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
             self.assertTrue(output_no_lora.shape == self.output_shape)
 
-            pipe.text_encoder.add_adapter(text_lora_config)
-            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-
-            if self.has_two_text_encoders or self.has_three_text_encoders:
-                lora_loadable_components = self.pipeline_class._lora_loadable_modules
-                if "text_encoder_2" in lora_loadable_components:
-                    pipe.text_encoder_2.add_adapter(text_lora_config)
-                    self.assertTrue(
-                        check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
-                    )
+            pipe, _ = self.check_if_adapters_added_correctly(pipe, text_lora_config, denoiser_lora_config=None)
 
             output_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
             self.assertTrue(
@@ -381,22 +397,7 @@ class PeftLoraLoaderMixinTests:
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
             self.assertTrue(output_no_lora.shape == self.output_shape)
 
-            if "text_encoder" in self.pipeline_class._lora_loadable_modules:
-                pipe.text_encoder.add_adapter(text_lora_config)
-                self.assertTrue(
-                    check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
-                )
-
-            denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
-            denoiser.add_adapter(denoiser_lora_config)
-            self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
-
-            if self.has_two_text_encoders or self.has_three_text_encoders:
-                if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
-                    pipe.text_encoder_2.add_adapter(text_lora_config)
-                    self.assertTrue(
-                        check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
-                    )
+            pipe, _ = self.check_if_adapters_added_correctly(pipe, text_lora_config, denoiser_lora_config)
 
             images_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
 
@@ -459,16 +460,7 @@ class PeftLoraLoaderMixinTests:
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
             self.assertTrue(output_no_lora.shape == self.output_shape)
 
-            pipe.text_encoder.add_adapter(text_lora_config)
-            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-
-            if self.has_two_text_encoders or self.has_three_text_encoders:
-                lora_loadable_components = self.pipeline_class._lora_loadable_modules
-                if "text_encoder_2" in lora_loadable_components:
-                    pipe.text_encoder_2.add_adapter(text_lora_config)
-                    self.assertTrue(
-                        check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
-                    )
+            pipe, _ = self.check_if_adapters_added_correctly(pipe, text_lora_config, denoiser_lora_config=None)
 
             output_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
             self.assertTrue(
@@ -506,15 +498,7 @@ class PeftLoraLoaderMixinTests:
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
             self.assertTrue(output_no_lora.shape == self.output_shape)
 
-            pipe.text_encoder.add_adapter(text_lora_config)
-            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-
-            if self.has_two_text_encoders or self.has_three_text_encoders:
-                if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
-                    pipe.text_encoder_2.add_adapter(text_lora_config)
-                    self.assertTrue(
-                        check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
-                    )
+            pipe, _ = self.check_if_adapters_added_correctly(pipe, text_lora_config, denoiser_lora_config=None)
 
             pipe.fuse_lora()
             # Fusing should still keep the LoRA layers
@@ -546,19 +530,7 @@ class PeftLoraLoaderMixinTests:
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
             self.assertTrue(output_no_lora.shape == self.output_shape)
 
-            if "text_encoder" in self.pipeline_class._lora_loadable_modules:
-                pipe.text_encoder.add_adapter(text_lora_config)
-                self.assertTrue(
-                    check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
-                )
-
-            if self.has_two_text_encoders or self.has_three_text_encoders:
-                lora_loadable_components = self.pipeline_class._lora_loadable_modules
-                if "text_encoder_2" in lora_loadable_components:
-                    pipe.text_encoder_2.add_adapter(text_lora_config)
-                    self.assertTrue(
-                        check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
-                    )
+            pipe, _ = self.check_if_adapters_added_correctly(pipe, text_lora_config, denoiser_lora_config=None)
 
             pipe.unload_lora_weights()
             # unloading should remove the LoRA layers
@@ -593,18 +565,7 @@ class PeftLoraLoaderMixinTests:
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
             self.assertTrue(output_no_lora.shape == self.output_shape)
 
-            if "text_encoder" in self.pipeline_class._lora_loadable_modules:
-                pipe.text_encoder.add_adapter(text_lora_config)
-                self.assertTrue(
-                    check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
-                )
-
-            if self.has_two_text_encoders or self.has_three_text_encoders:
-                if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
-                    pipe.text_encoder_2.add_adapter(text_lora_config)
-                    self.assertTrue(
-                        check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
-                    )
+            pipe, _ = self.check_if_adapters_added_correctly(pipe, text_lora_config, denoiser_lora_config=None)
 
             images_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
 
@@ -655,22 +616,20 @@ class PeftLoraLoaderMixinTests:
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
             self.assertTrue(output_no_lora.shape == self.output_shape)
 
-            pipe.text_encoder.add_adapter(text_lora_config)
-            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-            # Gather the state dict for the PEFT model, excluding `layers.4`, to ensure `load_lora_into_text_encoder`
-            # supports missing layers (PR#8324).
-            state_dict = {
-                f"text_encoder.{module_name}": param
-                for module_name, param in get_peft_model_state_dict(pipe.text_encoder).items()
-                if "text_model.encoder.layers.4" not in module_name
-            }
+            pipe, _ = self.check_if_adapters_added_correctly(pipe, text_lora_config, denoiser_lora_config=None)
+
+            state_dict = {}
+            if "text_encoder" in self.pipeline_class._lora_loadable_modules:
+                # Gather the state dict for the PEFT model, excluding `layers.4`, to ensure `load_lora_into_text_encoder`
+                # supports missing layers (PR#8324).
+                state_dict = {
+                    f"text_encoder.{module_name}": param
+                    for module_name, param in get_peft_model_state_dict(pipe.text_encoder).items()
+                    if "text_model.encoder.layers.4" not in module_name
+                }
 
             if self.has_two_text_encoders or self.has_three_text_encoders:
                 if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
-                    pipe.text_encoder_2.add_adapter(text_lora_config)
-                    self.assertTrue(
-                        check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
-                    )
                     state_dict.update(
                         {
                             f"text_encoder_2.{module_name}": param
@@ -694,7 +653,7 @@ class PeftLoraLoaderMixinTests:
                 "Removing adapters should change the output",
             )
 
-    def test_simple_inference_save_pretrained(self):
+    def test_simple_inference_save_pretrained_with_text_lora(self):
         """
         Tests a simple usecase where users could use saving utilities for LoRA through save_pretrained
         """
@@ -708,16 +667,7 @@ class PeftLoraLoaderMixinTests:
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
             self.assertTrue(output_no_lora.shape == self.output_shape)
 
-            pipe.text_encoder.add_adapter(text_lora_config)
-            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-
-            if self.has_two_text_encoders or self.has_three_text_encoders:
-                if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
-                    pipe.text_encoder_2.add_adapter(text_lora_config)
-                    self.assertTrue(
-                        check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
-                    )
-
+            pipe, _ = self.check_if_adapters_added_correctly(pipe, text_lora_config, denoiser_lora_config=None)
             images_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
 
             with tempfile.TemporaryDirectory() as tmpdirname:
@@ -726,10 +676,11 @@ class PeftLoraLoaderMixinTests:
                 pipe_from_pretrained = self.pipeline_class.from_pretrained(tmpdirname)
                 pipe_from_pretrained.to(torch_device)
 
-            self.assertTrue(
-                check_if_lora_correctly_set(pipe_from_pretrained.text_encoder),
-                "Lora not correctly set in text encoder",
-            )
+            if "text_encoder" in self.pipeline_class._lora_loadable_modules:
+                self.assertTrue(
+                    check_if_lora_correctly_set(pipe_from_pretrained.text_encoder),
+                    "Lora not correctly set in text encoder",
+                )
 
             if self.has_two_text_encoders or self.has_three_text_encoders:
                 if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
@@ -759,22 +710,7 @@ class PeftLoraLoaderMixinTests:
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
             self.assertTrue(output_no_lora.shape == self.output_shape)
 
-            if "text_encoder" in self.pipeline_class._lora_loadable_modules:
-                pipe.text_encoder.add_adapter(text_lora_config)
-                self.assertTrue(
-                    check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
-                )
-
-            denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
-            denoiser.add_adapter(denoiser_lora_config)
-            self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
-
-            if self.has_two_text_encoders or self.has_three_text_encoders:
-                if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
-                    pipe.text_encoder_2.add_adapter(text_lora_config)
-                    self.assertTrue(
-                        check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
-                    )
+            pipe, _ = self.check_if_adapters_added_correctly(pipe, text_lora_config, denoiser_lora_config)
 
             images_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
 
@@ -820,22 +756,7 @@ class PeftLoraLoaderMixinTests:
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
             self.assertTrue(output_no_lora.shape == self.output_shape)
 
-            if "text_encoder" in self.pipeline_class._lora_loadable_modules:
-                pipe.text_encoder.add_adapter(text_lora_config)
-                self.assertTrue(
-                    check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
-                )
-
-            denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
-            denoiser.add_adapter(denoiser_lora_config)
-            self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
-
-            if self.has_two_text_encoders or self.has_three_text_encoders:
-                if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
-                    pipe.text_encoder_2.add_adapter(text_lora_config)
-                    self.assertTrue(
-                        check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
-                    )
+            pipe, _ = self.check_if_adapters_added_correctly(pipe, text_lora_config, denoiser_lora_config)
 
             output_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
             self.assertTrue(
@@ -879,22 +800,7 @@ class PeftLoraLoaderMixinTests:
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
             self.assertTrue(output_no_lora.shape == self.output_shape)
 
-            if "text_encoder" in self.pipeline_class._lora_loadable_modules:
-                pipe.text_encoder.add_adapter(text_lora_config)
-                self.assertTrue(
-                    check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
-                )
-
-            denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
-            denoiser.add_adapter(denoiser_lora_config)
-            self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
-
-            if self.has_two_text_encoders or self.has_three_text_encoders:
-                if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
-                    pipe.text_encoder_2.add_adapter(text_lora_config)
-                    self.assertTrue(
-                        check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
-                    )
+            pipe, denoiser = self.check_if_adapters_added_correctly(pipe, text_lora_config, denoiser_lora_config)
 
             pipe.fuse_lora(components=self.pipeline_class._lora_loadable_modules)
 
@@ -932,22 +838,7 @@ class PeftLoraLoaderMixinTests:
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
             self.assertTrue(output_no_lora.shape == self.output_shape)
 
-            if "text_encoder" in self.pipeline_class._lora_loadable_modules:
-                pipe.text_encoder.add_adapter(text_lora_config)
-                self.assertTrue(
-                    check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
-                )
-
-            denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
-            denoiser.add_adapter(denoiser_lora_config)
-            self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
-
-            if self.has_two_text_encoders or self.has_three_text_encoders:
-                if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
-                    pipe.text_encoder_2.add_adapter(text_lora_config)
-                    self.assertTrue(
-                        check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
-                    )
+            pipe, denoiser = self.check_if_adapters_added_correctly(pipe, text_lora_config, denoiser_lora_config)
 
             pipe.unload_lora_weights()
             # unloading should remove the LoRA layers
@@ -983,22 +874,7 @@ class PeftLoraLoaderMixinTests:
             pipe.set_progress_bar_config(disable=None)
             _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
-            if "text_encoder" in self.pipeline_class._lora_loadable_modules:
-                pipe.text_encoder.add_adapter(text_lora_config)
-                self.assertTrue(
-                    check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
-                )
-
-            denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
-            denoiser.add_adapter(denoiser_lora_config)
-            self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
-
-            if self.has_two_text_encoders or self.has_three_text_encoders:
-                if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
-                    pipe.text_encoder_2.add_adapter(text_lora_config)
-                    self.assertTrue(
-                        check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
-                    )
+            pipe, denoiser = self.check_if_adapters_added_correctly(pipe, text_lora_config, denoiser_lora_config)
 
             pipe.fuse_lora(components=self.pipeline_class._lora_loadable_modules)
             output_fused_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
@@ -1104,6 +980,8 @@ class PeftLoraLoaderMixinTests:
             )
 
     def test_wrong_adapter_name_raises_error(self):
+        adapter_name = "adapter-1"
+
         scheduler_cls = self.scheduler_classes[0]
         components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
         pipe = self.pipeline_class(**components)
@@ -1111,20 +989,9 @@ class PeftLoraLoaderMixinTests:
         pipe.set_progress_bar_config(disable=None)
         _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
-        if "text_encoder" in self.pipeline_class._lora_loadable_modules:
-            pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
-            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-
-        denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
-        denoiser.add_adapter(denoiser_lora_config, "adapter-1")
-        self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
-
-        if self.has_two_text_encoders or self.has_three_text_encoders:
-            if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
-                pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-1")
-                self.assertTrue(
-                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
-                )
+        pipe, _ = self.check_if_adapters_added_correctly(
+            pipe, text_lora_config, denoiser_lora_config, adapter_name=adapter_name
+        )
 
         with self.assertRaises(ValueError) as err_context:
             pipe.set_adapters("test")
@@ -1132,10 +999,11 @@ class PeftLoraLoaderMixinTests:
         self.assertTrue("not in the list of present adapters" in str(err_context.exception))
 
         # test this works.
-        pipe.set_adapters("adapter-1")
+        pipe.set_adapters(adapter_name)
         _ = pipe(**inputs, generator=torch.manual_seed(0))[0]
 
     def test_multiple_wrong_adapter_name_raises_error(self):
+        adapter_name = "adapter-1"
         scheduler_cls = self.scheduler_classes[0]
         components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
         pipe = self.pipeline_class(**components)
@@ -1143,33 +1011,22 @@ class PeftLoraLoaderMixinTests:
         pipe.set_progress_bar_config(disable=None)
         _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
-        if "text_encoder" in self.pipeline_class._lora_loadable_modules:
-            pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
-            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-
-        denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
-        denoiser.add_adapter(denoiser_lora_config, "adapter-1")
-        self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
-
-        if self.has_two_text_encoders or self.has_three_text_encoders:
-            if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
-                pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-1")
-                self.assertTrue(
-                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
-                )
+        pipe, _ = self.check_if_adapters_added_correctly(
+            pipe, text_lora_config, denoiser_lora_config, adapter_name=adapter_name
+        )
 
         scale_with_wrong_components = {"foo": 0.0, "bar": 0.0, "tik": 0.0}
         logger = logging.get_logger("diffusers.loaders.lora_base")
         logger.setLevel(30)
         with CaptureLogger(logger) as cap_logger:
-            pipe.set_adapters("adapter-1", adapter_weights=scale_with_wrong_components)
+            pipe.set_adapters(adapter_name, adapter_weights=scale_with_wrong_components)
 
         wrong_components = sorted(set(scale_with_wrong_components.keys()))
         msg = f"The following components in `adapter_weights` are not part of the pipeline: {wrong_components}. "
         self.assertTrue(msg in str(cap_logger.out))
 
         # test this works.
-        pipe.set_adapters("adapter-1")
+        pipe.set_adapters(adapter_name)
         _ = pipe(**inputs, generator=torch.manual_seed(0))[0]
 
     def test_simple_inference_with_text_denoiser_block_scale(self):
@@ -1804,20 +1661,7 @@ class PeftLoraLoaderMixinTests:
             output_no_dora_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
             self.assertTrue(output_no_dora_lora.shape == self.output_shape)
 
-            pipe.text_encoder.add_adapter(text_lora_config)
-            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-
-            denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
-            denoiser.add_adapter(denoiser_lora_config)
-            self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
-
-            if self.has_two_text_encoders or self.has_three_text_encoders:
-                lora_loadable_components = self.pipeline_class._lora_loadable_modules
-                if "text_encoder_2" in lora_loadable_components:
-                    pipe.text_encoder_2.add_adapter(text_lora_config)
-                    self.assertTrue(
-                        check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
-                    )
+            pipe, _ = self.check_if_adapters_added_correctly(pipe, text_lora_config, denoiser_lora_config)
 
             output_dora_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
 
@@ -1908,18 +1752,7 @@ class PeftLoraLoaderMixinTests:
             pipe.set_progress_bar_config(disable=None)
             _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
-            pipe.text_encoder.add_adapter(text_lora_config)
-            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
-
-            denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
-            denoiser.add_adapter(denoiser_lora_config)
-            self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
-
-            if self.has_two_text_encoders or self.has_three_text_encoders:
-                pipe.text_encoder_2.add_adapter(text_lora_config)
-                self.assertTrue(
-                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
-                )
+            pipe, _ = self.check_if_adapters_added_correctly(pipe, text_lora_config, denoiser_lora_config)
 
             pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
             pipe.text_encoder = torch.compile(pipe.text_encoder, mode="reduce-overhead", fullgraph=True)
@@ -2011,22 +1844,7 @@ class PeftLoraLoaderMixinTests:
             output_no_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
             self.assertTrue(output_no_lora.shape == self.output_shape)
 
-            if "text_encoder" in self.pipeline_class._lora_loadable_modules:
-                pipe.text_encoder.add_adapter(text_lora_config)
-                self.assertTrue(
-                    check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
-                )
-
-            denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
-            denoiser.add_adapter(denoiser_lora_config)
-            self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
-
-            if self.has_two_text_encoders or self.has_three_text_encoders:
-                if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
-                    pipe.text_encoder_2.add_adapter(text_lora_config)
-                    self.assertTrue(
-                        check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
-                    )
+            pipe, _ = self.check_if_adapters_added_correctly(pipe, text_lora_config, denoiser_lora_config)
 
             lora_scale = 0.5
             attention_kwargs = {attention_kwargs_name: {"scale": lora_scale}}
@@ -2211,22 +2029,7 @@ class PeftLoraLoaderMixinTests:
             pipe = pipe.to(torch_device, dtype=compute_dtype)
             pipe.set_progress_bar_config(disable=None)
 
-            if "text_encoder" in self.pipeline_class._lora_loadable_modules:
-                pipe.text_encoder.add_adapter(text_lora_config)
-                self.assertTrue(
-                    check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
-                )
-
-            denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
-            denoiser.add_adapter(denoiser_lora_config)
-            self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
-
-            if self.has_two_text_encoders or self.has_three_text_encoders:
-                if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
-                    pipe.text_encoder_2.add_adapter(text_lora_config)
-                    self.assertTrue(
-                        check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
-                    )
+            pipe, denoiser = self.check_if_adapters_added_correctly(pipe, text_lora_config, denoiser_lora_config)
 
             if storage_dtype is not None:
                 denoiser.enable_layerwise_casting(storage_dtype=storage_dtype, compute_dtype=compute_dtype)

tests/pipelines/i2vgen_xl/test_i2vgenxl.py

@@ -187,7 +187,7 @@ class I2VGenXLPipelineFastTests(SDFunctionTesterMixin, PipelineTesterMixin, unit
         super().test_sequential_cpu_offload_forward_pass(expected_max_diff=0.008)
 
     def test_dict_tuple_outputs_equivalent(self):
-        super().test_dict_tuple_outputs_equivalent(expected_max_difference=0.008)
+        super().test_dict_tuple_outputs_equivalent(expected_max_difference=0.009)
 
     def test_save_load_optional_components(self):
         super().test_save_load_optional_components(expected_max_difference=0.008)

tests/pipelines/ip_adapters/test_ip_adapter_stable_diffusion.py

@@ -34,6 +34,7 @@ from diffusers import (
 from diffusers.image_processor import IPAdapterMaskProcessor
 from diffusers.utils import load_image
 from diffusers.utils.testing_utils import (
+    Expectations,
     backend_empty_cache,
     enable_full_determinism,
     is_flaky,
@@ -664,7 +665,50 @@ class IPAdapterSDXLIntegrationTests(IPAdapterNightlyTestsMixin):
         images = pipeline(**inputs).images
         image_slice = images[0, :3, :3, -1].flatten()
 
-        expected_slice = np.array([0.2323, 0.1026, 0.1338, 0.0638, 0.0662, 0.0000, 0.0000, 0.0000, 0.0199])
+        expected_slices = Expectations(
+            {
+                ("xpu", 3): np.array(
+                    [
+                        0.2520,
+                        0.1050,
+                        0.1510,
+                        0.0997,
+                        0.0893,
+                        0.0019,
+                        0.0000,
+                        0.0000,
+                        0.0210,
+                    ]
+                ),
+                ("cuda", 7): np.array(
+                    [
+                        0.2323,
+                        0.1026,
+                        0.1338,
+                        0.0638,
+                        0.0662,
+                        0.0000,
+                        0.0000,
+                        0.0000,
+                        0.0199,
+                    ]
+                ),
+                ("cuda", 8): np.array(
+                    [
+                        0.2518,
+                        0.1059,
+                        0.1553,
+                        0.0977,
+                        0.0852,
+                        0.0000,
+                        0.0000,
+                        0.0000,
+                        0.0220,
+                    ]
+                ),
+            }
+        )
+        expected_slice = expected_slices.get_expectation()
 
         max_diff = numpy_cosine_similarity_distance(image_slice, expected_slice)
         assert max_diff < 5e-4

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py

@@ -37,6 +37,7 @@ from diffusers import (
     UNet2DConditionModel,
 )
 from diffusers.utils.testing_utils import (
+    Expectations,
     backend_empty_cache,
     backend_max_memory_allocated,
     backend_reset_max_memory_allocated,
@@ -866,7 +867,37 @@ class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.Te
         image_slice = image[0, 253:256, 253:256, -1].flatten()
 
         assert image.shape == (1, 512, 512, 3)
-        expected_slice = np.array([0.1343, 0.1406, 0.1440, 0.1504, 0.1729, 0.0989, 0.1807, 0.2822, 0.1179])
+        expected_slices = Expectations(
+            {
+                ("xpu", 3): np.array(
+                    [
+                        0.2063,
+                        0.1731,
+                        0.1553,
+                        0.1741,
+                        0.1772,
+                        0.1077,
+                        0.2109,
+                        0.2407,
+                        0.1243,
+                    ]
+                ),
+                ("cuda", 7): np.array(
+                    [
+                        0.1343,
+                        0.1406,
+                        0.1440,
+                        0.1504,
+                        0.1729,
+                        0.0989,
+                        0.1807,
+                        0.2822,
+                        0.1179,
+                    ]
+                ),
+            }
+        )
+        expected_slice = expected_slices.get_expectation()
 
         assert np.abs(expected_slice - image_slice).max() < 5e-2
 

tests/pipelines/test_pipelines_common.py

@@ -1347,7 +1347,7 @@ class PipelineTesterMixin:
 
     @unittest.skipIf(torch_device not in ["cuda", "xpu"], reason="float16 requires CUDA or XPU")
     @require_accelerator
-    def test_float16_inference(self, expected_max_diff=6e-2):
+    def test_float16_inference(self, expected_max_diff=5e-2):
         components = self.get_dummy_components()
         pipe = self.pipeline_class(**components)
         for component in pipe.components.values():

tests/pipelines/unclip/test_unclip.py

@@ -381,7 +381,7 @@ class UnCLIPPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         ]
 
         self._test_inference_batch_single_identical(
-            additional_params_copy_to_batched_inputs=additional_params_copy_to_batched_inputs, expected_max_diff=5e-3
+            additional_params_copy_to_batched_inputs=additional_params_copy_to_batched_inputs, expected_max_diff=9.8e-3
         )
 
     def test_inference_batch_consistent(self):

utils/notify_slack_about_release.py

@@ -17,8 +17,6 @@ import os
 
 import requests
 
-from ..src.diffusers.utils.constants import DIFFUSERS_REQUEST_TIMEOUT
-
 
 # Configuration
 LIBRARY_NAME = "diffusers"
@@ -28,7 +26,7 @@ SLACK_WEBHOOK_URL = os.getenv("SLACK_WEBHOOK_URL")
 
 def check_pypi_for_latest_release(library_name):
     """Check PyPI for the latest release of the library."""
-    response = requests.get(f"https://pypi.org/pypi/{library_name}/json", timeout=DIFFUSERS_REQUEST_TIMEOUT)
+    response = requests.get(f"https://pypi.org/pypi/{library_name}/json", timeout=60)
     if response.status_code == 200:
         data = response.json()
         return data["info"]["version"]
@@ -40,7 +38,7 @@ def check_pypi_for_latest_release(library_name):
 def get_github_release_info(github_repo):
     """Fetch the latest release info from GitHub."""
     url = f"https://api.github.com/repos/{github_repo}/releases/latest"
-    response = requests.get(url, timeout=DIFFUSERS_REQUEST_TIMEOUT)
+    response = requests.get(url, timeout=60)
 
     if response.status_code == 200:
         data = response.json()