fix dependency (#527)

modelscope/models/multi_modal/efficient_diffusion_tuning/efficient_stable_diffusion.py

@@ -13,7 +13,6 @@ from diffusers import (AutoencoderKL, DDPMScheduler, DiffusionPipeline,
                        utils)
 from diffusers.models import attention
 from diffusers.utils import deprecation_utils
-from swift import AdapterConfig, LoRAConfig, PromptConfig, Swift
 from transformers import CLIPTextModel, CLIPTokenizer
 
 from modelscope import snapshot_download
@@ -26,6 +25,7 @@ from modelscope.outputs import OutputKeys
 from modelscope.utils.checkpoint import save_checkpoint, save_configuration
 from modelscope.utils.config import Config
 from modelscope.utils.constant import ModelFile, Tasks
+from modelscope.utils.import_utils import is_swift_available
 from .control_sd_lora import ControlLoRATuner
 
 utils.deprecate = lambda *arg, **kwargs: None
@@ -34,6 +34,9 @@ attention.deprecate = lambda *arg, **kwargs: None
 
 __tuner_MAP__ = {'lora': LoRATuner, 'control_lora': ControlLoRATuner}
 
+if is_swift_available():
+    from swift import AdapterConfig, LoRAConfig, PromptConfig, Swift
+
 
 @MODELS.register_module(
     Tasks.efficient_diffusion_tuning,
@@ -110,6 +113,10 @@ class EfficientStableDiffusion(TorchModel):
         self.tuner_name = tuner_name
 
         if tuner_name == 'swift-lora':
+            if not is_swift_available():
+                raise ValueError(
+                    'Please install swift by `pip install ms-swift` to use swift tuners.'
+                )
             rank = tuner_config[
                 'rank'] if tuner_config and 'rank' in tuner_config else 4
             lora_config = LoRAConfig(
@@ -119,6 +126,10 @@ class EfficientStableDiffusion(TorchModel):
                 use_merged_linear=False)
             self.unet = Swift.prepare_model(self.unet, lora_config)
         elif tuner_name == 'swift-adapter':
+            if not is_swift_available():
+                raise ValueError(
+                    'Please install swift by `pip install ms-swift` to use swift tuners.'
+                )
             adapter_length = tuner_config[
                 'adapter_length'] if tuner_config and 'adapter_length' in tuner_config else 10
             adapter_config = AdapterConfig(
@@ -128,6 +139,10 @@ class EfficientStableDiffusion(TorchModel):
                 adapter_length=adapter_length)
             self.unet = Swift.prepare_model(self.unet, adapter_config)
         elif tuner_name == 'swift-prompt':
+            if not is_swift_available():
+                raise ValueError(
+                    'Please install swift by `pip install ms-swift` to use swift tuners.'
+                )
             prompt_length = tuner_config[
                 'prompt_length'] if tuner_config and 'prompt_length' in tuner_config else 10
             prompt_config = PromptConfig(

modelscope/utils/error.py

@@ -174,3 +174,9 @@ XFORMERS_IMPORT_ERROR = """
 {0} requires the timm library but it was not found in your environment. You can install it with pip:
 `pip install xformers>=0.0.17`
 """
+
+# docstyle-ignore
+SWIFT_IMPORT_ERROR = """
+{0} requires the ms-swift library but it was not found in your environment. You can install it with pip:
+`pip install ms-swift -U`
+"""

modelscope/utils/import_utils.py

@@ -310,6 +310,7 @@ REQUIREMENTS_MAAPING = OrderedDict([
     ('open_clip', (is_package_available('open_clip'), OPENCLIP_IMPORT_ERROR)),
     ('taming', (is_package_available('taming'), TAMING_IMPORT_ERROR)),
     ('xformers', (is_package_available('xformers'), XFORMERS_IMPORT_ERROR)),
+    ('swift', (is_package_available('swift'), SWIFT_IMPORT_ERROR)),
 ])
 
 SYSTEM_PACKAGE = set(['os', 'sys', 'typing'])

requirements/framework.txt

@@ -4,7 +4,6 @@ datasets>=2.8.0,<=2.13.0
 einops
 filelock>=3.3.0
 gast>=0.2.2
-ms-swift
 numpy
 oss2
 pandas

tests/pipelines/test_efficient_diffusion_tuning.py

@@ -1,8 +1,8 @@
 # Copyright 2022-2023 The Alibaba Fundamental Vision Team Authors. All rights reserved.
+import os
 import unittest
 
 from modelscope.models import Model
-from modelscope.models.multi_modal import EfficientStableDiffusion
 from modelscope.pipelines import pipeline
 from modelscope.utils.constant import Tasks
 from modelscope.utils.test_utils import test_level
@@ -11,6 +11,7 @@ from modelscope.utils.test_utils import test_level
 class EfficientDiffusionTuningTest(unittest.TestCase):
 
     def setUp(self) -> None:
+        os.system('pip install ms-swift -U')
         self.task = Tasks.efficient_diffusion_tuning
 
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
@@ -28,6 +29,7 @@ class EfficientDiffusionTuningTest(unittest.TestCase):
         model_id = 'damo/multi-modal_efficient-diffusion-tuning-lora'
         model_revision = 'v1.0.2'
         model = Model.from_pretrained(model_id, model_revision=model_revision)
+        from modelscope.models.multi_modal import EfficientStableDiffusion
         self.assertTrue(model.__class__ == EfficientStableDiffusion)
 
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
@@ -52,6 +54,7 @@ class EfficientDiffusionTuningTest(unittest.TestCase):
         model_id = 'damo/multi-modal_efficient-diffusion-tuning-control-lora'
         model_revision = 'v1.0.2'
         model = Model.from_pretrained(model_id, model_revision=model_revision)
+        from modelscope.models.multi_modal import EfficientStableDiffusion
         self.assertTrue(model.__class__ == EfficientStableDiffusion)
 
 

tests/pipelines/test_efficient_diffusion_tuning_swift.py

@@ -1,11 +1,11 @@
 # Copyright 2022-2023 The Alibaba Fundamental Vision Team Authors. All rights reserved.
+import os
 import tempfile
 import unittest
 
 import cv2
 
 from modelscope.models import Model
-from modelscope.models.multi_modal import EfficientStableDiffusion
 from modelscope.pipelines import pipeline
 from modelscope.utils.constant import Tasks
 from modelscope.utils.test_utils import test_level
@@ -14,6 +14,7 @@ from modelscope.utils.test_utils import test_level
 class EfficientDiffusionTuningTestSwift(unittest.TestCase):
 
     def setUp(self) -> None:
+        os.system('pip install ms-swift -U')
         self.task = Tasks.efficient_diffusion_tuning
 
     @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
@@ -39,6 +40,7 @@ class EfficientDiffusionTuningTestSwift(unittest.TestCase):
         model_id = 'damo/multi-modal_efficient-diffusion-tuning-swift-lora'
         model_revision = 'v1.0.2'
         model = Model.from_pretrained(model_id, model_revision=model_revision)
+        from modelscope.models.multi_modal import EfficientStableDiffusion
         self.assertTrue(model.__class__ == EfficientStableDiffusion)
 
     @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
@@ -64,6 +66,7 @@ class EfficientDiffusionTuningTestSwift(unittest.TestCase):
         model_id = 'damo/multi-modal_efficient-diffusion-tuning-swift-adapter'
         model_revision = 'v1.0.2'
         model = Model.from_pretrained(model_id, model_revision=model_revision)
+        from modelscope.models.multi_modal import EfficientStableDiffusion
         self.assertTrue(model.__class__ == EfficientStableDiffusion)
 
     @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
@@ -89,6 +92,7 @@ class EfficientDiffusionTuningTestSwift(unittest.TestCase):
         model_id = 'damo/multi-modal_efficient-diffusion-tuning-swift-prompt'
         model_revision = 'v1.0.2'
         model = Model.from_pretrained(model_id, model_revision=model_revision)
+        from modelscope.models.multi_modal import EfficientStableDiffusion
         self.assertTrue(model.__class__ == EfficientStableDiffusion)