[LoRA] add: test to check if peft loras are loadable in non-peft envs. (#6400)

* add: test to check if peft loras are loadable in non-peft envs.

* add torch_device approrpiately.

* fix: get_dummy_inputs().

* test logits.

* rename

* debug

* debug

* fix: generator

* new assertion values after fixing the seed.

* shape

* remove print statements and settle this.

* to update values.

* change values when lora config is initialized under a fixed seed.

* update colab link

* update notebook link

* sanity restored by getting the exact same values without peft.

Author: sayakpaul

tests/lora/test_peft_lora_in_non_peft.py

@@ -0,0 +1,64 @@
+# coding=utf-8
+# Copyright 2023 HuggingFace Inc.
+#
+# 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 DiffusionPipeline
+from diffusers.utils.testing_utils import torch_device
+
+
+class PEFTLoRALoading(unittest.TestCase):
+    def get_dummy_inputs(self):
+        pipeline_inputs = {
+            "prompt": "A painting of a squirrel eating a burger",
+            "num_inference_steps": 2,
+            "guidance_scale": 6.0,
+            "output_type": "np",
+            "generator": torch.manual_seed(0),
+        }
+        return pipeline_inputs
+
+    def test_stable_diffusion_peft_lora_loading_in_non_peft(self):
+        sd_pipe = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-sd-pipe").to(torch_device)
+        # This LoRA was obtained using similarly as how it's done in the training scripts.
+        # For details on how the LoRA was obtained, refer to:
+        # https://hf.co/datasets/diffusers/notebooks/blob/main/check_logits_with_serialization_peft_lora.py
+        sd_pipe.load_lora_weights("hf-internal-testing/tiny-sd-lora-peft")
+
+        inputs = self.get_dummy_inputs()
+        outputs = sd_pipe(**inputs).images
+
+        predicted_slice = outputs[0, -3:, -3:, -1].flatten()
+        expected_slice = np.array([0.5396, 0.5707, 0.477, 0.4665, 0.5419, 0.4594, 0.4857, 0.4741, 0.4804])
+
+        self.assertTrue(outputs.shape == (1, 64, 64, 3))
+        assert np.allclose(expected_slice, predicted_slice, atol=1e-3, rtol=1e-3)
+
+    def test_stable_diffusion_xl_peft_lora_loading_in_non_peft(self):
+        sd_pipe = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-sdxl-pipe").to(torch_device)
+        # This LoRA was obtained using similarly as how it's done in the training scripts.
+        sd_pipe.load_lora_weights("hf-internal-testing/tiny-sdxl-lora-peft")
+
+        inputs = self.get_dummy_inputs()
+        outputs = sd_pipe(**inputs).images
+
+        predicted_slice = outputs[0, -3:, -3:, -1].flatten()
+        expected_slice = np.array([0.613, 0.5566, 0.54, 0.4162, 0.4042, 0.4596, 0.5374, 0.5286, 0.5038])
+
+        self.assertTrue(outputs.shape == (1, 64, 64, 3))
+        assert np.allclose(expected_slice, predicted_slice, atol=1e-3, rtol=1e-3)