Taited/stable diffusion inpaint

configs/stable_diffusion/README.md

@@ -2,7 +2,7 @@
 
 > [Stable Diffusion](https://github.com/CompVis/stable-diffusion)
 
-> **Task**: Text2Image
+> **Task**: Text2Image, Inpainting
 
 <!-- [ALGORITHM] -->
 
@@ -45,6 +45,7 @@ Stable Diffusion is a latent diffusion model conditioned on the text embeddings
 | :----------------------------------------------------------------------------------: | :-----: | :------: |
 |          [stable_diffusion_v1.5](./stable-diffusion_ddim_denoisingunet.py)           |    -    |    -     |
 | [stable_diffusion_v1.5_tomesd](./stable-diffusion_ddim_denoisingunet-tomesd_5e-1.py) |    -    |    -     |
+|  [stable_diffusion_v1.5_inpaint](./stable-diffusion_ddim_denoisingunet-inpaint.py)   |    -    |    -     |
 
 We use stable diffusion v1.5 weights. This model has several weights including vae, unet and clip.
 
@@ -83,6 +84,40 @@ image = StableDiffuser.infer(prompt)['samples'][0]
 image.save('robot.png')
 ```
 
+To inpaint an image, you could run the following codes.
+
+```python
+import mmcv
+from mmengine import MODELS, Config
+from mmengine.registry import init_default_scope
+from PIL import Image
+
+init_default_scope('mmagic')
+
+config = 'configs/stable_diffusion/stable-diffusion_ddim_denoisingunet-inpaint.py'
+config = Config.fromfile(config).copy()
+# change the 'pretrained_model_path' if you have downloaded the weights manually
+# config.model.unet.from_pretrained = '/path/to/your/stable-diffusion-inpainting'
+# config.model.vae.from_pretrained = '/path/to/your/stable-diffusion-inpainting'
+
+StableDiffuser = MODELS.build(config.model)
+prompt = 'a mecha robot sitting on a bench'
+
+img_url = 'https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png'  # noqa
+mask_url = 'https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png'  # noqa
+
+image = Image.fromarray(mmcv.imread(img_url, channel_order='rgb'))
+mask = Image.fromarray(mmcv.imread(mask_url)).convert('L')
+StableDiffuser = StableDiffuser.to('cuda')
+
+image = StableDiffuser.infer(
+    prompt,
+    image,
+    mask
+)['samples'][0]
+image.save('inpaint.png')
+```
+
 ## Use ToMe to accelerate your stable diffusion model
 
 We support **[tomesd](https://github.com/dbolya/tomesd)** now! It is developed based on [ToMe](https://github.com/facebookresearch/ToMe), an efficient ViT speed-up tool based on token merging. To work on with **tomesd** in `mmagic`, you just need to add `tomesd_cfg` to `model` as shown in [stable_diffusion_v1.5_tomesd](stable-diffusion_ddim_denoisingunet-tomesd_5e-1.py). The only requirement is `torch >= 1.12.1` in order to properly support `torch.Tensor.scatter_reduce()` functionality. Please do check it before running the demo.

configs/stable_diffusion/metafile.yml

@@ -6,6 +6,7 @@ Collections:
   README: configs/stable_diffusion/README.md
   Task:
   - text2image
+  - inpainting
   Year: 2022
 Models:
 - Config: configs/stable_diffusion/stable-diffusion_ddim_denoisingunet.py
@@ -14,23 +15,30 @@ Models:
   Results:
   - Dataset: '-'
     Metrics: {}
-    Task: Text2Image
+    Task: Text2Image, Inpainting
 - Config: configs/stable_diffusion/stable-diffusion_ddim_denoisingunet-tomesd_5e-1.py
   In Collection: Stable Diffusion
   Name: stable-diffusion_ddim_denoisingunet-tomesd_5e-1
   Results:
   - Dataset: '-'
     Metrics: {}
-    Task: Text2Image
+    Task: Text2Image, Inpainting
   - Dataset: '-'
     Metrics:
       Size / Num images per prompt:
         PSNR: 512.0
         SSIM: 5.0
-    Task: Text2Image
+    Task: Text2Image, Inpainting
   - Dataset: '-'
     Metrics:
       Size / Num images per prompt:
         PSNR: 512.0
         SSIM: 5.0
-    Task: Text2Image
+    Task: Text2Image, Inpainting
+- Config: configs/stable_diffusion/stable-diffusion_ddim_denoisingunet-inpaint.py
+  In Collection: Stable Diffusion
+  Name: stable-diffusion_ddim_denoisingunet-inpaint
+  Results:
+  - Dataset: '-'
+    Metrics: {}
+    Task: Text2Image, Inpainting

configs/stable_diffusion/stable-diffusion_ddim_denoisingunet-inpaint.py

@@ -0,0 +1,34 @@
+# Use DiffuserWrapper!
+stable_diffusion_v15_url = 'runwayml/stable-diffusion-inpainting'
+unet = dict(
+    type='UNet2DConditionModel',
+    subfolder='unet',
+    from_pretrained=stable_diffusion_v15_url)
+vae = dict(
+    type='AutoencoderKL',
+    from_pretrained=stable_diffusion_v15_url,
+    subfolder='vae')
+
+diffusion_scheduler = dict(
+    type='EditDDIMScheduler',
+    variance_type='learned_range',
+    beta_end=0.012,
+    beta_schedule='scaled_linear',
+    beta_start=0.00085,
+    num_train_timesteps=1000,
+    set_alpha_to_one=False,
+    clip_sample=False)
+
+model = dict(
+    type='StableDiffusionInpaint',
+    unet=unet,
+    vae=vae,
+    enable_xformers=False,
+    text_encoder=dict(
+        type='ClipWrapper',
+        clip_type='huggingface',
+        pretrained_model_name_or_path=stable_diffusion_v15_url,
+        subfolder='text_encoder'),
+    tokenizer=stable_diffusion_v15_url,
+    scheduler=diffusion_scheduler,
+    test_scheduler=diffusion_scheduler)

mmagic/models/editors/__init__.py

@@ -54,7 +54,7 @@
 from .singan import SinGAN
 from .srcnn import SRCNNNet
 from .srgan import SRGAN, ModifiedVGG, MSRResNet
-from .stable_diffusion import StableDiffusion
+from .stable_diffusion import StableDiffusion, StableDiffusionInpaint
 from .stylegan1 import StyleGAN1
 from .stylegan2 import StyleGAN2
 from .stylegan3 import StyleGAN3, StyleGAN3Generator
@@ -92,5 +92,6 @@
     'NAFBaselineLocal', 'NAFNet', 'NAFNetLocal', 'DenoisingUnet',
     'ClipWrapper', 'EG3D', 'Restormer', 'SwinIRNet', 'StableDiffusion',
     'ControlStableDiffusion', 'DreamBooth', 'TextualInversion', 'DeblurGanV2',
-    'DeblurGanV2Generator', 'DeblurGanV2Discriminator'
+    'DeblurGanV2Generator', 'DeblurGanV2Discriminator',
+    'StableDiffusionInpaint'
 ]

mmagic/models/editors/ddpm/denoising_unet.py

@@ -759,6 +759,9 @@ class DenoisingUnet(BaseModule):
         image_size (int | list[int]): The size of image to denoise.
         in_channels (int, optional): The input channels of the input image.
             Defaults as ``3``.
+        out_channels (int, optional): The output channels of the output
+            prediction. Defaults as ``None`` for automaticaaly assigned by
+            ``var_mode``.
         base_channels (int, optional): The basic channel number of the
             generator. The other layers contain channels based on this number.
             Defaults to ``128``.
@@ -837,6 +840,7 @@ class DenoisingUnet(BaseModule):
     def __init__(self,
                  image_size,
                  in_channels=3,
+                 out_channels=None,
                  base_channels=128,
                  resblocks_per_downsample=3,
                  num_timesteps=1000,
@@ -886,8 +890,9 @@ def __init__(self,
         self.in_channels = in_channels
 
         # double output_channels to output mean and var at same time
-        out_channels = in_channels if 'FIXED' in self.var_mode.upper() \
-            else 2 * in_channels
+        if out_channels is None:
+            out_channels = in_channels if 'FIXED' in self.var_mode.upper() \
+                else 2 * in_channels
         self.out_channels = out_channels
 
         # check type of image_size

mmagic/models/editors/stable_diffusion/__init__.py

@@ -1,5 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .stable_diffusion import StableDiffusion
+from .stable_diffusion_inpaint import StableDiffusionInpaint
 from .vae import AutoencoderKL
 
-__all__ = ['StableDiffusion', 'AutoencoderKL']
+__all__ = ['StableDiffusion', 'AutoencoderKL', 'StableDiffusionInpaint']