add models for T2I-Adapter-XL (#4696)

* T2I-Adapter-XL

* update

* update

* add pipeline

* modify pipeline

* modify pipeline

* modify pipeline

* modify pipeline

* modify pipeline

* modify modeling_text_unet

* fix styling.

* fix: copies.

* adapter settings

* new test case

* new test case

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging

* revert prints.

* new test case

* remove print

* org test case

* add test_pipeline

* styling.

* fix copies.

* modify test parameter

* style.

* add adapter-xl doc

* double quotes in docs

* Fix potential type mismatch

* style.

---------

Co-authored-by: sayakpaul <[email protected]>

Author: MC-E

docs/source/en/api/pipelines/stable_diffusion/adapter.md

@@ -29,10 +29,11 @@ This model was contributed by the community contributor [HimariO](https://github
 | Pipeline | Tasks | Demo
 |---|---|:---:|
 | [StableDiffusionAdapterPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_adapter.py) | *Text-to-Image Generation with T2I-Adapter Conditioning* | -
+| [StableDiffusionXLAdapterPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_xl_adapter.py) | *Text-to-Image Generation with T2I-Adapter Conditioning on StableDiffusion-XL* | -
 
-## Usage example
+## Usage example with the base model of StableDiffusion-1.4/1.5
 
-In the following we give a simple example of how to use a *T2IAdapter* checkpoint with Diffusers for inference.
+In the following we give a simple example of how to use a *T2IAdapter* checkpoint with Diffusers for inference based on StableDiffusion-1.4/1.5.
 All adapters use the same pipeline.
 
  1. Images are first converted into the appropriate *control image* format.
@@ -93,6 +94,62 @@ out_image = pipe(
 
 ![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_output.png)
 
+## Usage example with the base model of StableDiffusion-XL
+
+In the following we give a simple example of how to use a *T2IAdapter* checkpoint with Diffusers for inference based on StableDiffusion-XL.
+All adapters use the same pipeline.
+
+ 1. Images are first downloaded into the appropriate *control image* format.
+ 2. The *control image* and *prompt* are passed to the [`StableDiffusionXLAdapterPipeline`].
+
+Let's have a look at a simple example using the [Sketch Adapter](https://huggingface.co/Adapter/t2iadapter/tree/main/sketch_sdxl_1.0).
+
+```python
+from diffusers.utils import load_image
+
+sketch_image = load_image("https://huggingface.co/Adapter/t2iadapter/resolve/main/sketch.png").convert("L")
+```
+
+![img](https://huggingface.co/Adapter/t2iadapter/resolve/main/sketch.png)
+
+Then, create the adapter pipeline
+
+```py
+import torch
+from diffusers import (
+    T2IAdapter,
+    StableDiffusionXLAdapterPipeline,
+    DDPMScheduler
+)
+from diffusers.models.unet_2d_condition import UNet2DConditionModel
+
+model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+adapter = T2IAdapter.from_pretrained("Adapter/t2iadapter", subfolder="sketch_sdxl_1.0",torch_dtype=torch.float16, adapter_type="full_adapter_xl")
+scheduler = DDPMScheduler.from_pretrained(model_id, subfolder="scheduler")
+
+pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
+    model_id, adapter=adapter, safety_checker=None, torch_dtype=torch.float16, variant="fp16", scheduler=scheduler
+)
+
+pipe.to("cuda")
+```
+
+Finally, pass the prompt and control image to the pipeline
+
+```py
+# fix the random seed, so you will get the same result as the example
+generator = torch.Generator().manual_seed(42)
+
+sketch_image_out = pipe(
+    prompt="a photo of a dog in real world, high quality", 
+    negative_prompt="extra digit, fewer digits, cropped, worst quality, low quality", 
+    image=sketch_image, 
+    generator=generator, 
+    guidance_scale=7.5
+).images[0]
+```
+
+![img](https://huggingface.co/Adapter/t2iadapter/resolve/main/sketch_output.png)
 
 ## Available checkpoints
 
@@ -113,6 +170,9 @@ Non-diffusers checkpoints can be found under [TencentARC/T2I-Adapter](https://hu
 |[TencentARC/t2iadapter_depth_sd15v2](https://huggingface.co/TencentARC/t2iadapter_depth_sd15v2)||
 |[TencentARC/t2iadapter_sketch_sd15v2](https://huggingface.co/TencentARC/t2iadapter_sketch_sd15v2)||
 |[TencentARC/t2iadapter_zoedepth_sd15v1](https://huggingface.co/TencentARC/t2iadapter_zoedepth_sd15v1)||
+|[Adapter/t2iadapter, subfolder='sketch_sdxl_1.0'](https://huggingface.co/Adapter/t2iadapter/tree/main/sketch_sdxl_1.0)||
+|[Adapter/t2iadapter, subfolder='canny_sdxl_1.0'](https://huggingface.co/Adapter/t2iadapter/tree/main/canny_sdxl_1.0)||
+|[Adapter/t2iadapter, subfolder='openpose_sdxl_1.0'](https://huggingface.co/Adapter/t2iadapter/tree/main/openpose_sdxl_1.0)||
 
 ## Combining multiple adapters
 
@@ -185,3 +245,14 @@ However, T2I-Adapter performs slightly worse than ControlNet.
 	- disable_vae_slicing
 	- enable_xformers_memory_efficient_attention
 	- disable_xformers_memory_efficient_attention
+
+## StableDiffusionXLAdapterPipeline
+[[autodoc]] StableDiffusionXLAdapterPipeline
+	- all
+	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing
+	- enable_vae_slicing
+	- disable_vae_slicing
+	- enable_xformers_memory_efficient_attention
+	- disable_xformers_memory_efficient_attention

src/diffusers/__init__.py

@@ -191,6 +191,7 @@
         StableDiffusionPix2PixZeroPipeline,
         StableDiffusionSAGPipeline,
         StableDiffusionUpscalePipeline,
+        StableDiffusionXLAdapterPipeline,
         StableDiffusionXLControlNetImg2ImgPipeline,
         StableDiffusionXLControlNetPipeline,
         StableDiffusionXLImg2ImgPipeline,

src/diffusers/models/adapter.py

@@ -128,6 +128,8 @@ def __init__(
 
         if adapter_type == "full_adapter":
             self.adapter = FullAdapter(in_channels, channels, num_res_blocks, downscale_factor)
+        elif adapter_type == "full_adapter_xl":
+            self.adapter = FullAdapterXL(in_channels, channels, num_res_blocks, downscale_factor)
         elif adapter_type == "light_adapter":
             self.adapter = LightAdapter(in_channels, channels, num_res_blocks, downscale_factor)
         else:
@@ -184,6 +186,48 @@ def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
         return features
 
 
+class FullAdapterXL(nn.Module):
+    def __init__(
+        self,
+        in_channels: int = 3,
+        channels: List[int] = [320, 640, 1280, 1280],
+        num_res_blocks: int = 2,
+        downscale_factor: int = 16,
+    ):
+        super().__init__()
+
+        in_channels = in_channels * downscale_factor**2
+
+        self.unshuffle = nn.PixelUnshuffle(downscale_factor)
+        self.conv_in = nn.Conv2d(in_channels, channels[0], kernel_size=3, padding=1)
+
+        self.body = []
+        # blocks to extract XL features with dimensions of [320, 64, 64], [640, 64, 64], [1280, 32, 32], [1280, 32, 32]
+        for i in range(len(channels)):
+            if i == 1:
+                self.body.append(AdapterBlock(channels[i - 1], channels[i], num_res_blocks))
+            elif i == 2:
+                self.body.append(AdapterBlock(channels[i - 1], channels[i], num_res_blocks, down=True))
+            else:
+                self.body.append(AdapterBlock(channels[i], channels[i], num_res_blocks))
+
+        self.body = nn.ModuleList(self.body)
+        # XL has one fewer downsampling
+        self.total_downscale_factor = downscale_factor * 2 ** (len(channels) - 2)
+
+    def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
+        x = self.unshuffle(x)
+        x = self.conv_in(x)
+
+        features = []
+
+        for block in self.body:
+            x = block(x)
+            features.append(x)
+
+        return features
+
+
 class AdapterBlock(nn.Module):
     def __init__(self, in_channels, out_channels, num_res_blocks, down=False):
         super().__init__()

src/diffusers/models/unet_2d_condition.py

@@ -965,6 +965,13 @@ def forward(
                 cross_attention_kwargs=cross_attention_kwargs,
                 encoder_attention_mask=encoder_attention_mask,
             )
+            # To support T2I-Adapter-XL
+            if (
+                is_adapter
+                and len(down_block_additional_residuals) > 0
+                and sample.shape == down_block_additional_residuals[0].shape
+            ):
+                sample += down_block_additional_residuals.pop(0)
 
         if is_controlnet:
             sample = sample + mid_block_additional_residual

src/diffusers/pipelines/__init__.py

@@ -118,7 +118,7 @@
         StableDiffusionXLInstructPix2PixPipeline,
         StableDiffusionXLPipeline,
     )
-    from .t2i_adapter import StableDiffusionAdapterPipeline
+    from .t2i_adapter import StableDiffusionAdapterPipeline, StableDiffusionXLAdapterPipeline
     from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
     from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
     from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder

src/diffusers/pipelines/t2i_adapter/__init__.py

@@ -12,3 +12,4 @@
     from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
 else:
     from .pipeline_stable_diffusion_adapter import StableDiffusionAdapterPipeline
+    from .pipeline_stable_diffusion_xl_adapter import StableDiffusionXLAdapterPipeline