ControlNet+Adapter pipeline, and ControlNet+Adapter+Inpaint pipeline (#5869)

* ControlNet+Adapter pipeline, and +Inpaint pipeline


---------
Co-authored-by: andres <andres@hax.ai>

examples/community/README.md

@@ -2343,3 +2343,141 @@ images = pipe(
 
 assert len(images) == (len(prompts) - 1) * num_interpolation_steps
 ```
+
+### ControlNet + T2I Adapter Pipeline
+This pipelines combines both ControlNet and T2IAdapter into a single pipeline, where the forward pass is executed once. 
+It receives `control_image` and `adapter_image`, as well as `controlnet_conditioning_scale` and `adapter_conditioning_scale`, for the ControlNet and Adapter modules, respectively. Whenever `adapter_conditioning_scale = 0` or `controlnet_conditioning_scale = 0`, it will act as a full ControlNet module or as a full T2IAdapter module, respectively. 
+
+```py
+import cv2
+import numpy as np
+import torch
+from controlnet_aux.midas import MidasDetector
+from PIL import Image
+
+from diffusers import AutoencoderKL, ControlNetModel, MultiAdapter, T2IAdapter
+from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
+from diffusers.utils import load_image
+from examples.community.pipeline_stable_diffusion_xl_controlnet_adapter import (
+    StableDiffusionXLControlNetAdapterPipeline,
+)
+
+controlnet_depth = ControlNetModel.from_pretrained(
+    "diffusers/controlnet-depth-sdxl-1.0",
+    torch_dtype=torch.float16,
+    variant="fp16",
+    use_safetensors=True
+)
+adapter_depth = T2IAdapter.from_pretrained(
+  "TencentARC/t2i-adapter-depth-midas-sdxl-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+)
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16, use_safetensors=True)
+
+pipe = StableDiffusionXLControlNetAdapterPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    controlnet=controlnet_depth,
+    adapter=adapter_depth,
+    vae=vae,
+    variant="fp16",
+    use_safetensors=True,
+    torch_dtype=torch.float16,
+)
+pipe = pipe.to("cuda")
+pipe.enable_xformers_memory_efficient_attention()
+# pipe.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
+midas_depth = MidasDetector.from_pretrained(
+  "valhalla/t2iadapter-aux-models", filename="dpt_large_384.pt", model_type="dpt_large"
+).to("cuda")
+
+prompt = "a tiger sitting on a park bench"
+img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
+
+image = load_image(img_url).resize((1024, 1024))
+
+depth_image = midas_depth(
+  image, detect_resolution=512, image_resolution=1024
+)
+
+strength = 0.5
+
+images = pipe(
+    prompt,
+    control_image=depth_image,
+    adapter_image=depth_image,
+    num_inference_steps=30,
+    controlnet_conditioning_scale=strength,
+    adapter_conditioning_scale=strength,
+).images
+images[0].save("controlnet_and_adapter.png")
+
+```
+
+### ControlNet + T2I Adapter + Inpainting Pipeline
+```py
+import cv2
+import numpy as np
+import torch
+from controlnet_aux.midas import MidasDetector
+from PIL import Image
+
+from diffusers import AutoencoderKL, ControlNetModel, MultiAdapter, T2IAdapter
+from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
+from diffusers.utils import load_image
+from examples.community.pipeline_stable_diffusion_xl_controlnet_adapter_inpaint import (
+    StableDiffusionXLControlNetAdapterInpaintPipeline,
+)
+
+controlnet_depth = ControlNetModel.from_pretrained(
+    "diffusers/controlnet-depth-sdxl-1.0",
+    torch_dtype=torch.float16,
+    variant="fp16",
+    use_safetensors=True
+)
+adapter_depth = T2IAdapter.from_pretrained(
+  "TencentARC/t2i-adapter-depth-midas-sdxl-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+)
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16, use_safetensors=True)
+
+pipe = StableDiffusionXLControlNetAdapterInpaintPipeline.from_pretrained(
+    "diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
+    controlnet=controlnet_depth,
+    adapter=adapter_depth,
+    vae=vae,
+    variant="fp16",
+    use_safetensors=True,
+    torch_dtype=torch.float16,
+)
+pipe = pipe.to("cuda")
+pipe.enable_xformers_memory_efficient_attention()
+# pipe.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
+midas_depth = MidasDetector.from_pretrained(
+  "valhalla/t2iadapter-aux-models", filename="dpt_large_384.pt", model_type="dpt_large"
+).to("cuda")
+
+prompt = "a tiger sitting on a park bench"
+img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
+mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
+
+image = load_image(img_url).resize((1024, 1024))
+mask_image = load_image(mask_url).resize((1024, 1024))
+
+depth_image = midas_depth(
+  image, detect_resolution=512, image_resolution=1024
+)
+
+strength = 0.4
+
+images = pipe(
+    prompt,
+    image=image,
+    mask_image=mask_image,
+    control_image=depth_image,
+    adapter_image=depth_image,
+    num_inference_steps=30,
+    controlnet_conditioning_scale=strength,
+    adapter_conditioning_scale=strength,
+    strength=0.7,
+).images
+images[0].save("controlnet_and_adapter_inpaint.png")
+
+```
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