[to #42322933] Update cv-person-image-cartoon-pipeline to maas lib

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9268865

modelscope/models/base.py

@@ -69,7 +69,7 @@ class Model(ABC):
         model_cfg.model_dir = local_model_dir
 
         for k, v in kwargs.items():
-            model_cfg.k = v
+            model_cfg[k] = v
         model = build_model(model_cfg, task_name)
 
         # dynamically add pipeline info to model for pipeline inference

modelscope/models/multi_modal/imagen/imagen_model.py

@@ -215,8 +215,9 @@ class ImagenForTextToImageSynthesis(Model):
             eta=input.get('generator_ddim_eta', 0.0))
 
         # upsampling (64->256)
-        img = F.interpolate(
-            img, scale_factor=4.0, mode='bilinear', align_corners=False)
+        if not input.get('debug', False):
+            img = F.interpolate(
+                img, scale_factor=4.0, mode='bilinear', align_corners=False)
         img = self.diffusion_imagen_upsampler_256.ddim_sample_loop(
             noise=torch.randn_like(img),
             model=self.unet_imagen_upsampler_256,
@@ -233,14 +234,15 @@ class ImagenForTextToImageSynthesis(Model):
                 'context': torch.zeros_like(context),
                 'mask': torch.zeros_like(attention_mask)
             }],
-            percentile=input.get('generator_percentile', 0.995),
-            guide_scale=input.get('generator_guide_scale', 5.0),
-            ddim_timesteps=input.get('generator_ddim_timesteps', 50),
-            eta=input.get('generator_ddim_eta', 0.0))
+            percentile=input.get('upsampler_256_percentile', 0.995),
+            guide_scale=input.get('upsampler_256_guide_scale', 5.0),
+            ddim_timesteps=input.get('upsampler_256_ddim_timesteps', 50),
+            eta=input.get('upsampler_256_ddim_eta', 0.0))
 
         # upsampling (256->1024)
-        img = F.interpolate(
-            img, scale_factor=4.0, mode='bilinear', align_corners=False)
+        if not input.get('debug', False):
+            img = F.interpolate(
+                img, scale_factor=4.0, mode='bilinear', align_corners=False)
         img = self.diffusion_upsampler_1024.ddim_sample_loop(
             noise=torch.randn_like(img),
             model=self.unet_upsampler_1024,

modelscope/pipelines/multi_modal/text_to_image_synthesis_pipeline.py

@@ -1,5 +1,7 @@
 from typing import Any, Dict
 
+import torch
+
 from modelscope.metainfo import Pipelines
 from modelscope.pipelines.base import Input
 from modelscope.utils.constant import Tasks
@@ -16,16 +18,17 @@ logger = get_logger()
     module_name=Pipelines.text_to_image_synthesis)
 class TextToImageSynthesisPipeline(Pipeline):
 
-    def __init__(self, model: str, device_id: int = -1):
+    def __init__(self, model: str, **kwargs):
+        device_id = 0 if torch.cuda.is_available() else -1
         if isinstance(model, str):
-            pipe_model = Model.from_pretrained(model)
+            pipe_model = Model.from_pretrained(model, device_id=device_id)
         elif isinstance(model, Model):
             pipe_model = model
         else:
             raise NotImplementedError(
                 f'expecting a Model instance or str, but get {type(model)}.')
 
-        super().__init__(model=pipe_model)
+        super().__init__(model=pipe_model, **kwargs)
 
     def preprocess(self, input: Input) -> Dict[str, Any]:
         return input

tests/pipelines/test_text_to_image_synthesis.py

@@ -13,9 +13,15 @@ from modelscope.utils.test_utils import test_level
 
 class TextToImageSynthesisTest(unittest.TestCase):
     model_id = 'damo/cv_imagen_text-to-image-synthesis_tiny'
-    test_text = {'text': '宇航员'}
-
-    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
+    test_text = {
+        'text': '宇航员',
+        'generator_ddim_timesteps': 2,
+        'upsampler_256_ddim_timesteps': 2,
+        'upsampler_1024_ddim_timesteps': 2,
+        'debug': True
+    }
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_run_with_model_from_modelhub(self):
         model = Model.from_pretrained(self.model_id)
         pipe_line_text_to_image_synthesis = pipeline(
@@ -24,7 +30,7 @@ class TextToImageSynthesisTest(unittest.TestCase):
             self.test_text)[OutputKeys.OUTPUT_IMG]
         print(np.sum(np.abs(img)))
 
-    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
+    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
     def test_run_with_model_name(self):
         pipe_line_text_to_image_synthesis = pipeline(
             task=Tasks.text_to_image_synthesis, model=self.model_id)