[Enhance] support controlnet inferencer

configs/controlnet/README.md

@@ -26,10 +26,10 @@ This model has several weights including vae, unet and clip. You should download
 
 |                      Model                      | Dataset |                                             Download                                             |
 | :---------------------------------------------: | :-----: | :----------------------------------------------------------------------------------------------: |
+| [ControlNet-Demo](./controlnet-1xb1-fill50k.py) |    -    |                                                -                                                 |
 |    [ControlNet-Canny](./controlnet-canny.py)    |    -    |  [model](https://huggingface.co/lllyasviel/ControlNet/blob/main/models/control_sd15_canny.pth)   |
-| [ControlNet-Segmentation](./controlnet-seg.py)  |    -    |   [model](https://huggingface.co/lllyasviel/ControlNet/blob/main/models/control_sd15_seg.pth)    |
 |     [ControlNet-Pose](./controlnet-pose.py)     |    -    | [model](https://huggingface.co/lllyasviel/ControlNet/blob/main/models/control_sd15_openpose.pth) |
-| [ControlNet-Demo](./controlnet-1xb1-fill50k.py) |    -    |                                                -                                                 |
+| [ControlNet-Segmentation](./controlnet-seg.py)  |    -    |   [model](https://huggingface.co/lllyasviel/ControlNet/blob/main/models/control_sd15_seg.pth)    |
 
 Noted that, [ControlNet-Demo](./controlnet-1xb1-demo_dataset.py) is a demo config to train ControlNet with toy dataset named Fill50K.
 
@@ -159,6 +159,35 @@ for idx, control in enumerate(controls):
 </thead>
 </table>
 
+### Using MMInferencer
+
+You can only use several lines of codes to play controlnet by MMagic!
+
+```python
+from mmagic.apis import MMagicInferencer
+
+# controlnet-canny
+controlnet_canny_inferencer = MMagicInferencer(model_name='controlnet', model_setting=1)
+text_prompts = 'Room with blue walls and a yellow ceiling.'
+control = 'https://user-images.githubusercontent.com/28132635/230297033-4f5c32df-365c-4cf4-8e4f-1b76a4cbb0b7.png'
+result_out_dir = 'controlnet_canny_res.png'
+controlnet_canny_inferencer.infer(text=text_prompts, control=control, result_out_dir=result_out_dir)
+
+# controlnet-pose
+controlnet_pose_inferencer = MMagicInferencer(model_name='controlnet', model_setting=2)
+text_prompts = 'masterpiece, best quality, sky, black hair, skirt, sailor collar, looking at viewer, short hair, building, bangs, neckerchief, long sleeves, cloudy sky, power lines, shirt, cityscape, pleated skirt, scenery, blunt bangs, city, night, black sailor collar, closed mouth'
+control = 'https://user-images.githubusercontent.com/28132635/230380893-2eae68af-d610-4f7f-aa68-c2f22c2abf7e.png'
+result_out_dir = 'controlnet_pose_res.png'
+controlnet_pose_inferencer.infer(text=text_prompts, control=control, result_out_dir=result_out_dir)
+
+# controlnet-seg
+controlnet_seg_inferencer = MMagicInferencer(model_name='controlnet', model_setting=3)
+text_prompts = 'black house, blue sky'
+control = 'https://github-production-user-asset-6210df.s3.amazonaws.com/49083766/243599897-553a4c46-c61d-46df-b820-59a49aaf6678.png'
+result_out_dir = 'controlnet_seg_res.png'
+controlnet_seg_inferencer.infer(text=text_prompts, control=control, result_out_dir=result_out_dir)
+```
+
 ## Train your own ControlNet!
 
 You can start training your own ControlNet with the toy dataset [Fill50K](https://huggingface.co/lllyasviel/ControlNet/blob/main/training/fill50k.zip) with the following command:

configs/controlnet/controlnet-pose.py

@@ -6,12 +6,12 @@
     type='ControlStableDiffusion',
     vae=dict(
         type='AutoencoderKL',
-        from_pretrained=stable_diffusion_v15_url,
+        from_pretrained='gsdf/Counterfeit-V2.5',
         subfolder='vae'),
     unet=dict(
         type='UNet2DConditionModel',
         subfolder='unet',
-        from_pretrained=stable_diffusion_v15_url),
+        from_pretrained='gsdf/Counterfeit-V2.5'),
     text_encoder=dict(
         type='ClipWrapper',
         clip_type='huggingface',
@@ -29,4 +29,4 @@
         from_pretrained=stable_diffusion_v15_url,
         subfolder='scheduler'),
     data_preprocessor=dict(type='DataPreprocessor'),
-    init_cfg=dict(type='init_from_unet'))
+    init_cfg=dict(type='convert_from_unet'))

configs/controlnet/metafile.yml

@@ -8,22 +8,21 @@ Collections:
   - text2image
   Year: 2023
 Models:
-- Config: configs/controlnet/controlnet-canny.py
+- Config: configs/controlnet/controlnet-1xb1-fill50k.py
   In Collection: Control Net
-  Name: controlnet-canny
+  Name: controlnet-1xb1-fill50k
   Results:
   - Dataset: '-'
     Metrics: {}
     Task: Text2Image
-  Weights: https://huggingface.co/lllyasviel/ControlNet/blob/main/models/control_sd15_canny.pth
-- Config: configs/controlnet/controlnet-seg.py
+- Config: configs/controlnet/controlnet-canny.py
   In Collection: Control Net
-  Name: controlnet-seg
+  Name: controlnet-canny
   Results:
   - Dataset: '-'
     Metrics: {}
     Task: Text2Image
-  Weights: https://huggingface.co/lllyasviel/ControlNet/blob/main/models/control_sd15_seg.pth
+  Weights: https://huggingface.co/lllyasviel/ControlNet/blob/main/models/control_sd15_canny.pth
 - Config: configs/controlnet/controlnet-pose.py
   In Collection: Control Net
   Name: controlnet-pose
@@ -32,10 +31,11 @@ Models:
     Metrics: {}
     Task: Text2Image
   Weights: https://huggingface.co/lllyasviel/ControlNet/blob/main/models/control_sd15_openpose.pth
-- Config: configs/controlnet/controlnet-1xb1-fill50k.py
+- Config: configs/controlnet/controlnet-seg.py
   In Collection: Control Net
-  Name: controlnet-1xb1-fill50k
+  Name: controlnet-seg
   Results:
   - Dataset: '-'
     Metrics: {}
     Task: Text2Image
+  Weights: https://huggingface.co/lllyasviel/ControlNet/blob/main/models/control_sd15_seg.pth

demo/README.md

@@ -211,13 +211,48 @@ python mmagic_inference_demo.py \
 
 #### 2.2.9 Text-to-Image
 
+stable diffusion
+
 ```shell
 python mmagic_inference_demo.py \
         --model-name stable_diffusion \
         --text "A panda is having dinner at KFC" \
         --result-out-dir ../resources/output/text2image/demo_text2image_stable_diffusion_res.png
 ```
 
+controlnet-canny
+
+```shell
+python mmagic_inference_demo.py \
+        --model-name controlnet \
+        --model-setting 1 \
+        --text "Room with blue walls and a yellow ceiling." \
+        --control 'https://user-images.githubusercontent.com/28132635/230297033-4f5c32df-365c-4cf4-8e4f-1b76a4cbb0b7.png' \
+        --result-out-dir demo_text2image_controlnet_canny_res.png
+```
+
+controlnet-pose
+
+```shell
+python mmagic_inference_demo.py \
+        --model-name controlnet \
+        --model-setting 2 \
+        --text "masterpiece, best quality, sky, black hair, skirt, sailor collar, looking at viewer, short hair, building, bangs, neckerchief, long sleeves, cloudy sky, power lines, shirt, cityscape, pleated skirt, scenery, blunt bangs, city, night, black sailor collar, closed mouth" \
+        --control 'https://user-images.githubusercontent.com/28132635/230380893-2eae68af-d610-4f7f-aa68-c2f22c2abf7e.png' \
+        --result-out-dir demo_text2image_controlnet_pose_res.png
+```
+
+controlnet-seg
+
+```shell
+python mmagic_inference_demo.py \
+        --model-name controlnet \
+        --model-setting 3 \
+        --text "black house, blue sky" \
+        --control 'https://github-production-user-asset-6210df.s3.amazonaws.com/49083766/243599897-553a4c46-c61d-46df-b820-59a49aaf6678.png' \
+        --result-out-dir demo_text2image_controlnet_seg_res.png
+```
+
 #### 2.2.10 3D-aware Generation
 
 ```shell

mmagic/apis/inferencers/base_mmagic_inferencer.py

@@ -69,6 +69,9 @@ def _init_model(self, cfg: Union[ConfigType, str], ckpt: Optional[str],
         model = MODELS.build(cfg.model)
         if ckpt is not None and ckpt != '':
             ckpt = load_checkpoint(model, ckpt, map_location='cpu')
+        if cfg.model.get(
+                'init_cfg') and cfg.model.init_cfg.type == 'convert_from_unet':
+            model.init_weights()
         model.cfg = cfg
         model.to(device)
         model.eval()

mmagic/apis/inferencers/text2image_inferencer.py

@@ -2,9 +2,10 @@
 import os
 from typing import Dict, List
 
+import mmcv
 import numpy as np
 from mmengine import mkdir_or_exist
-from PIL.Image import Image
+from PIL.Image import Image, fromarray
 from torchvision.utils import save_image
 
 from .base_mmagic_inferencer import BaseMMagicInferencer, InputsType, PredType
@@ -14,14 +15,14 @@ class Text2ImageInferencer(BaseMMagicInferencer):
     """inferencer that predicts with text2image models."""
 
     func_kwargs = dict(
-        preprocess=['text'],
+        preprocess=['text', 'control'],
         forward=[],
         visualize=['result_out_dir'],
         postprocess=[])
 
     extra_parameters = dict(height=None, width=None, seed=1)
 
-    def preprocess(self, text: InputsType) -> Dict:
+    def preprocess(self, text: InputsType, control: str = None) -> Dict:
         """Process the inputs into a model-feedable format.
 
         Args:
@@ -36,6 +37,12 @@ def preprocess(self, text: InputsType) -> Dict:
         else:
             result['prompt'] = text
 
+        if control:
+            control_img = mmcv.imread(control)
+            control_img = fromarray(control_img)
+            result['control'] = control_img
+            result.pop('seed', None)
+
         return result
 
     def forward(self, inputs: InputsType) -> PredType:

mmagic/apis/mmagic_inferencer.py

@@ -106,6 +106,7 @@ class MMagicInferencer:
         'restormer',
 
         # text2image models
+        'controlnet',
         'disco_diffusion',
         'stable_diffusion',
 
@@ -163,6 +164,8 @@ def _get_inferencer_kwargs(self, model_name: Optional[str],
                     osp.dirname(__file__), '..', '.mim', config_dir)
             if 'Weights' in cfgs['settings'][setting_to_use].keys():
                 kwargs['ckpt'] = cfgs['settings'][setting_to_use]['Weights']
+                if model_name == 'controlnet':
+                    kwargs['ckpt'] = None
 
         if model_config is not None:
             if kwargs.get('config', None) is not None: