Add loading ckpt from file for SDXL controlNet (#4683)

* Add load ckpt from file for ControlNet SDXL

* Reformat code

* Resort imports

---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py

@@ -26,7 +26,7 @@ from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokeniz
 from diffusers.utils.import_utils import is_invisible_watermark_available
 
 from ...image_processor import PipelineImageInput, VaeImageProcessor
-from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
+from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
 from ...models.attention_processor import (
     AttnProcessor2_0,
@@ -102,7 +102,9 @@ EXAMPLE_DOC_STRING = """
 """
 
 
-class StableDiffusionXLControlNetPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
+class StableDiffusionXLControlNetPipeline(
+    DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
+):
     r"""
     Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet guidance.
 
@@ -112,6 +114,7 @@ class StableDiffusionXLControlNetPipeline(DiffusionPipeline, TextualInversionLoa
     In addition the pipeline inherits the following loading methods:
         - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]
         - *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`]
+        - *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`]
 
     Args:
         vae ([`AutoencoderKL`]):

src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py

@@ -1599,6 +1599,19 @@ def download_from_original_stable_diffusion_ckpt(
                     for param_name, param in converted_unet_checkpoint.items():
                         set_module_tensor_to_device(unet, param_name, "cpu", value=param)
 
+            if controlnet:
+                pipe = pipeline_class(
+                    vae=vae,
+                    text_encoder=text_encoder,
+                    tokenizer=tokenizer,
+                    text_encoder_2=text_encoder_2,
+                    tokenizer_2=tokenizer_2,
+                    unet=unet,
+                    controlnet=controlnet,
+                    scheduler=scheduler,
+                    force_zeros_for_empty_prompt=True,
+                )
+            else:
                 pipe = pipeline_class(
                     vae=vae,
                     text_encoder=text_encoder,