Fix LongCat LoRA load/unload and add regression test

src/diffusers/pipelines/longcat_image/pipeline_longcat_image.py

@@ -20,7 +20,7 @@
 from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer, Qwen2VLProcessor
 
 from ...image_processor import VaeImageProcessor
-from ...loaders import FromSingleFileMixin
+from ...loaders import FluxLoraLoaderMixin, FromSingleFileMixin
 from ...models.autoencoders import AutoencoderKL
 from ...models.transformers import LongCatImageTransformer2DModel
 from ...pipelines.pipeline_utils import DiffusionPipeline
@@ -202,7 +202,7 @@ def retrieve_timesteps(
     return timesteps, num_inference_steps
 
 
-class LongCatImagePipeline(DiffusionPipeline, FromSingleFileMixin):
+class LongCatImagePipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFileMixin):
     r"""
     The pipeline for text-to-image generation.
     """

src/diffusers/pipelines/longcat_image/pipeline_longcat_image_edit.py

@@ -22,7 +22,7 @@
 from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer, Qwen2VLProcessor
 
 from ...image_processor import VaeImageProcessor
-from ...loaders import FromSingleFileMixin
+from ...loaders import FluxLoraLoaderMixin, FromSingleFileMixin
 from ...models.autoencoders import AutoencoderKL
 from ...models.transformers import LongCatImageTransformer2DModel
 from ...pipelines.pipeline_utils import DiffusionPipeline
@@ -228,7 +228,7 @@ def calculate_dimensions(target_area, ratio):
     return width, height
 
 
-class LongCatImageEditPipeline(DiffusionPipeline, FromSingleFileMixin):
+class LongCatImageEditPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFileMixin):
     r"""
     The LongCat-Image-Edit pipeline for image editing.
     """

tests/pipelines/longcat_image/test_longcat_lora.py

@@ -0,0 +1,107 @@
+# Copyright 2025 The HuggingFace Team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+import torch
+
+from diffusers import LongCatImagePipeline
+
+from ...testing_utils import enable_full_determinism, require_accelerate, require_torch_gpu, slow
+
+
+enable_full_determinism()
+
+
+def _pil_to_np01(img):
+    """PIL -> float32 in [0, 1], shape (H, W, 3)."""
+    arr = np.asarray(img).astype(np.float32) / 255.0
+    if arr.ndim == 3 and arr.shape[-1] > 3:
+        arr = arr[..., :3]
+    return arr
+
+
+class LongCatImagePipelineLoRATests(unittest.TestCase):
+    @slow
+    @require_torch_gpu
+    @require_accelerate
+    def test_lora_load_changes_output_and_unload_restores(self):
+        """
+        1) Generate baseline image
+        2) Load LoRA -> output should change
+        3) Unload LoRA -> output should return close to baseline
+        """
+        model_id = "meituan-longcat/LongCat-Image"
+        lora_repo = "lrzjason/LongCatEmojiTest"
+        weight_name = "longcat_image-9-450.safetensors"
+        adapter_name = "emoji"
+
+        pipe = LongCatImagePipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
+        pipe.enable_model_cpu_offload()
+        pipe.set_progress_bar_config(disable=True)
+
+        prompt = "a 3d anime character, cute emoji style, studio lighting"
+
+        common_kwargs = {
+            "height": 768,
+            "width": 1344,
+            "guidance_scale": 4.0,
+            "num_inference_steps": 8,
+            "num_images_per_prompt": 1,
+            "output_type": "pil",
+        }
+
+        # 1) Baseline (no LoRA)
+        g0 = torch.Generator(device="cpu").manual_seed(123)
+        base_img = pipe(prompt, generator=g0, **common_kwargs).images[0]
+
+        # 2) Load LoRA
+        pipe.load_lora_weights(
+            lora_repo,
+            weight_name=weight_name,
+            adapter_name=adapter_name,
+        )
+
+        g1 = torch.Generator(device="cpu").manual_seed(123)
+        lora_img = pipe(prompt, generator=g1, **common_kwargs).images[0]
+
+        # 3) Unload LoRA
+        pipe.unload_lora_weights()
+
+        g2 = torch.Generator(device="cpu").manual_seed(123)
+        after_img = pipe(prompt, generator=g2, **common_kwargs).images[0]
+
+        base = _pil_to_np01(base_img)
+        lora = _pil_to_np01(lora_img)
+        after = _pil_to_np01(after_img)
+
+        diff_lora = float(np.mean(np.abs(base - lora)))
+        diff_after = float(np.mean(np.abs(base - after)))
+
+        self.assertGreater(
+            diff_lora,
+            1e-4,
+            msg=f"LoRA didn't change output enough (mean|base-lora|={diff_lora}).",
+        )
+
+        # After unload, output should be substantially closer to base than the LoRA output.
+        self.assertLess(
+            diff_after,
+            diff_lora * 0.5,
+            msg=(
+                "Unloading LoRA didn't restore base behavior enough "
+                f"(mean|base-after|={diff_after}, mean|base-lora|={diff_lora})."
+            ),
+        )