[LoRA] deprecate certain lora methods from the old backend. (#6889)

* deprecate certain lora methods from the old backend.

* uncomment necessary things.

* safe remove old lora backend 👋

.github/workflows/pr_tests.yml

@@ -34,11 +34,6 @@ jobs:
             runner: docker-cpu
             image: diffusers/diffusers-pytorch-cpu
             report: torch_cpu_models_schedulers
-          - name: LoRA
-            framework: lora
-            runner: docker-cpu
-            image: diffusers/diffusers-pytorch-cpu
-            report: torch_cpu_lora
           - name: Fast Flax CPU tests
             framework: flax
             runner: docker-cpu
@@ -94,14 +89,6 @@ jobs:
           --make-reports=tests_${{ matrix.config.report }} \
           tests/models tests/schedulers tests/others
 
-    - name: Run fast PyTorch LoRA CPU tests
-      if: ${{ matrix.config.framework == 'lora' }}
-      run: |
-        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
-          -s -v -k "not Flax and not Onnx and not Dependency" \
-          --make-reports=tests_${{ matrix.config.report }} \
-          tests/lora
-
     - name: Run fast Flax TPU tests
       if: ${{ matrix.config.framework == 'flax' }}
       run: |

src/diffusers/models/lora.py

@@ -27,7 +27,7 @@ import torch
 import torch.nn.functional as F
 from torch import nn
 
-from ..utils import logging
+from ..utils import deprecate, logging
 from ..utils.import_utils import is_transformers_available
 
 
@@ -82,6 +82,9 @@ def adjust_lora_scale_text_encoder(text_encoder, lora_scale: float = 1.0):
 
 class PatchedLoraProjection(torch.nn.Module):
     def __init__(self, regular_linear_layer, lora_scale=1, network_alpha=None, rank=4, dtype=None):
+        deprecation_message = "Use of `PatchedLoraProjection` is deprecated. Please switch to PEFT backend by installing PEFT: `pip install peft`."
+        deprecate("PatchedLoraProjection", "1.0.0", deprecation_message)
+
         super().__init__()
         from ..models.lora import LoRALinearLayer
 
@@ -293,10 +296,16 @@ class LoRACompatibleConv(nn.Conv2d):
     """
 
     def __init__(self, *args, lora_layer: Optional[LoRAConv2dLayer] = None, **kwargs):
+        deprecation_message = "Use of `LoRACompatibleConv` is deprecated. Please switch to PEFT backend by installing PEFT: `pip install peft`."
+        deprecate("LoRACompatibleConv", "1.0.0", deprecation_message)
+
         super().__init__(*args, **kwargs)
         self.lora_layer = lora_layer
 
     def set_lora_layer(self, lora_layer: Optional[LoRAConv2dLayer]):
+        deprecation_message = "Use of `set_lora_layer()` is deprecated. Please switch to PEFT backend by installing PEFT: `pip install peft`."
+        deprecate("set_lora_layer", "1.0.0", deprecation_message)
+
         self.lora_layer = lora_layer
 
     def _fuse_lora(self, lora_scale: float = 1.0, safe_fusing: bool = False):
@@ -371,10 +380,15 @@ class LoRACompatibleLinear(nn.Linear):
     """
 
     def __init__(self, *args, lora_layer: Optional[LoRALinearLayer] = None, **kwargs):
+        deprecation_message = "Use of `LoRACompatibleLinear` is deprecated. Please switch to PEFT backend by installing PEFT: `pip install peft`."
+        deprecate("LoRACompatibleLinear", "1.0.0", deprecation_message)
+
         super().__init__(*args, **kwargs)
         self.lora_layer = lora_layer
 
     def set_lora_layer(self, lora_layer: Optional[LoRALinearLayer]):
+        deprecation_message = "Use of `set_lora_layer()` is deprecated. Please switch to PEFT backend by installing PEFT: `pip install peft`."
+        deprecate("set_lora_layer", "1.0.0", deprecation_message)
         self.lora_layer = lora_layer
 
     def _fuse_lora(self, lora_scale: float = 1.0, safe_fusing: bool = False):

tests/lora/test_peft_lora_in_non_peft.py

-# coding=utf-8
-# Copyright 2024 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)