Merge branch 'main' into fixinf

demo/mmagic_inference_demo.py

@@ -43,6 +43,11 @@ def parse_args():
         type=int,
         default=None,
         help='Pretrained mmagic algorithm setting')
+    parser.add_argument(
+        '--config-name',
+        type=str,
+        default=None,
+        help='Pretrained mmagic algorithm config name')
     parser.add_argument(
         '--model-config',
         type=str,

demo/mmagic_inference_tutorial.ipynb

@@ -404,9 +404,13 @@
     "\n",
     "There are some different configs and checkpoints for one model.\n",
     "\n",
+    "You could configure different settings by passing 'model_setting' to 'MMagicInferencer'. Every model's default setting is 0.\n",
+    "\n",
     "Take conditional GAN model 'biggan' as an example. We have pretrained model for Cifar and Imagenet, and all pretrained models of 'biggan' are listed in its [metafile.yaml](../configs/biggan/metafile.yml)\n",
     "\n",
-    "You could configure different settings by passing 'model_setting' to 'MMagicInferencer'. Every model's default setting is 0."
+    "There are six settings in this metafile. If you choose setting 1, then the config 'configs/biggan/biggan_ajbrock-sn_8xb32-1500kiters_imagenet1k-128x128.py' will be used. If 'model_setting' is not passed to 'MMagicInferencer', the config ‘configs/biggan/biggan_2xb25-500kiters_cifar10-32x32.py’ will be used by default.\n",
+    "\n",
+    "And you could also use 'config_name' to replace 'model_setting'. For example, you can init a MMagicInferencer with 'MMagicInferencer('biggan', config_name='biggan_2xb25-500kiters_cifar10-32x32')', which is the same with 'MMagicInferencer('biggan', model_setting=0)'."
    ]
   },
   {

mmagic/apis/inferencers/diffusers_pipeline_inferencer.py

@@ -22,7 +22,7 @@ class DiffusersPipelineInferencer(BaseMMagicInferencer):
         postprocess=[])
 
     def preprocess(self,
-                   text: InputsType,
+                   text: InputsType = None,
                    negative_prompt: InputsType = None,
                    num_inference_steps: int = 20,
                    height=None,
@@ -37,7 +37,8 @@ def preprocess(self,
             result(Dict): Results of preprocess.
         """
         result = self.extra_parameters
-        result['prompt'] = text
+        if text:
+            result['prompt'] = text
         if negative_prompt:
             result['negative_prompt'] = negative_prompt
         if num_inference_steps:

mmagic/apis/mmagic_inferencer.py

@@ -130,6 +130,7 @@ class MMagicInferencer:
     def __init__(self,
                  model_name: str = None,
                  model_setting: int = None,
+                 config_name: int = None,
                  model_config: str = None,
                  model_ckpt: str = None,
                  device: torch.device = None,
@@ -140,14 +141,15 @@ def __init__(self,
         MMagicInferencer.init_inference_supported_models_cfg()
         inferencer_kwargs = {}
         inferencer_kwargs.update(
-            self._get_inferencer_kwargs(model_name, model_setting,
+            self._get_inferencer_kwargs(model_name, model_setting, config_name,
                                         model_config, model_ckpt,
                                         extra_parameters))
         self.inferencer = Inferencers(
             device=device, seed=seed, **inferencer_kwargs)
 
     def _get_inferencer_kwargs(self, model_name: Optional[str],
                                model_setting: Optional[int],
+                               config_name: Optional[int],
                                model_config: Optional[str],
                                model_ckpt: Optional[str],
                                extra_parameters: Optional[Dict]) -> Dict:
@@ -161,6 +163,11 @@ def _get_inferencer_kwargs(self, model_name: Optional[str],
             if model_setting:
                 setting_to_use = model_setting
             config_dir = cfgs['settings'][setting_to_use]['Config']
+            if config_name:
+                for setting in cfgs['settings']:
+                    if setting['Name'] == config_name:
+                        config_dir = setting['Config']
+                        break
             config_dir = config_dir[config_dir.find('configs'):]
             if osp.exists(
                     osp.join(osp.dirname(__file__), '..', '..', config_dir)):

tests/test_apis/test_inferencers/test_diffusers_pipeline_inferencer.py

@@ -2,7 +2,6 @@
 import platform
 
 import pytest
-import torch
 from mmengine.utils import digit_version
 from mmengine.utils.dl_utils import TORCH_VERSION
 
@@ -21,29 +20,11 @@
 def test_diffusers_pipeline_inferencer():
     cfg = dict(
         model=dict(
-            type='DiffusionPipeline',
-            from_pretrained='runwayml/stable-diffusion-v1-5'))
+            type='DiffusionPipeline', from_pretrained='google/ddpm-cat-256'))
 
     inferencer_instance = DiffusersPipelineInferencer(cfg, None)
-
-    def mock_encode_prompt(prompt, do_classifier_free_guidance,
-                           num_images_per_prompt, *args, **kwargs):
-        batch_size = len(prompt) if isinstance(prompt, list) else 1
-        batch_size *= num_images_per_prompt
-        if do_classifier_free_guidance:
-            batch_size *= 2
-        return torch.randn(batch_size, 5, 16)  # 2 for cfg
-
-    inferencer_instance.model._encode_prompt = mock_encode_prompt
-
-    text_prompts = 'Japanese anime style, girl'
-    negative_prompt = 'bad face, bad hands'
-    result = inferencer_instance(
-        text=text_prompts,
-        negative_prompt=negative_prompt,
-        height=64,
-        width=64)
-    assert result[1][0].size == (64, 64)
+    result = inferencer_instance()
+    assert result[1][0].size == (256, 256)
 
 
 def teardown_module():