Fix Callback Tensor Inputs of the SDXL Controlnet Inpaint and Img2img Pipelines are missing "controlnet_image". (huggingface#10880)

* Update pipeline_controlnet_inpaint_sd_xl.py

* Update pipeline_controlnet_sd_xl_img2img.py

* Update pipeline_controlnet_union_inpaint_sd_xl.py

* Update pipeline_controlnet_union_sd_xl_img2img.py

* Update pipeline_controlnet_inpaint_sd_xl.py

* Update pipeline_controlnet_sd_xl_img2img.py

* Update pipeline_controlnet_union_inpaint_sd_xl.py

* Update pipeline_controlnet_union_sd_xl_img2img.py

* Apply make style and make fix-copies fixes

* Update geodiff_molecule_conformation.ipynb

* Delete examples/research_projects/geodiff/geodiff_molecule_conformation.ipynb

* Delete examples/research_projects/gligen/demo.ipynb

* Create geodiff_molecule_conformation.ipynb

* Create demo.ipynb

* Update geodiff_molecule_conformation.ipynb

* Update geodiff_molecule_conformation.ipynb

* Delete examples/research_projects/geodiff/geodiff_molecule_conformation.ipynb

* Add files via upload

* Delete src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py

* Add files via upload

Author: CyberVy

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py

@@ -237,6 +237,7 @@ class StableDiffusionXLControlNetInpaintPipeline(
         "add_neg_time_ids",
         "mask",
         "masked_image_latents",
+        "control_image",
     ]
 
     def __init__(
@@ -743,15 +744,15 @@ def check_inputs(
         if padding_mask_crop is not None:
             if not isinstance(image, PIL.Image.Image):
                 raise ValueError(
-                    f"The image should be a PIL image when inpainting mask crop, but is of type" f" {type(image)}."
+                    f"The image should be a PIL image when inpainting mask crop, but is of type {type(image)}."
                 )
             if not isinstance(mask_image, PIL.Image.Image):
                 raise ValueError(
                     f"The mask image should be a PIL image when inpainting mask crop, but is of type"
                     f" {type(mask_image)}."
                 )
             if output_type != "pil":
-                raise ValueError(f"The output type should be PIL when inpainting mask crop, but is" f" {output_type}.")
+                raise ValueError(f"The output type should be PIL when inpainting mask crop, but is {output_type}.")
 
         if prompt_embeds is not None and pooled_prompt_embeds is None:
             raise ValueError(
@@ -1644,7 +1645,7 @@ def denoising_value_valid(dnv):
                     f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
                     f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
                     f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
-                    f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
+                    f" = {num_channels_latents + num_channels_masked_image + num_channels_mask}. Please verify the config of"
                     " `pipeline.unet` or your `mask_image` or `image` input."
                 )
         elif num_channels_unet != 4:
@@ -1835,6 +1836,7 @@ def denoising_value_valid(dnv):
                     latents = callback_outputs.pop("latents", latents)
                     prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
                     negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+                    control_image = callback_outputs.pop("control_image", control_image)
 
                 # call the callback, if provided
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py

@@ -242,6 +242,7 @@ class StableDiffusionXLControlNetImg2ImgPipeline(
         "add_time_ids",
         "negative_pooled_prompt_embeds",
         "add_neg_time_ids",
+        "control_image",
     ]
 
     def __init__(
@@ -1614,6 +1615,7 @@ def __call__(
                     )
                     add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
                     add_neg_time_ids = callback_outputs.pop("add_neg_time_ids", add_neg_time_ids)
+                    control_image = callback_outputs.pop("control_image", control_image)
 
                 # call the callback, if provided
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):

src/diffusers/pipelines/controlnet/pipeline_controlnet_union_inpaint_sd_xl.py

@@ -219,6 +219,7 @@ class StableDiffusionXLControlNetUnionInpaintPipeline(
         "add_time_ids",
         "mask",
         "masked_image_latents",
+        "control_image",
     ]
 
     def __init__(
@@ -726,15 +727,15 @@ def check_inputs(
         if padding_mask_crop is not None:
             if not isinstance(image, PIL.Image.Image):
                 raise ValueError(
-                    f"The image should be a PIL image when inpainting mask crop, but is of type" f" {type(image)}."
+                    f"The image should be a PIL image when inpainting mask crop, but is of type {type(image)}."
                 )
             if not isinstance(mask_image, PIL.Image.Image):
                 raise ValueError(
                     f"The mask image should be a PIL image when inpainting mask crop, but is of type"
                     f" {type(mask_image)}."
                 )
             if output_type != "pil":
-                raise ValueError(f"The output type should be PIL when inpainting mask crop, but is" f" {output_type}.")
+                raise ValueError(f"The output type should be PIL when inpainting mask crop, but is {output_type}.")
 
         if prompt_embeds is not None and pooled_prompt_embeds is None:
             raise ValueError(
@@ -1743,6 +1744,7 @@ def denoising_value_valid(dnv):
                     latents = callback_outputs.pop("latents", latents)
                     prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
                     negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+                    control_image = callback_outputs.pop("control_image", control_image)
 
                 # call the callback, if provided
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):

src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py

@@ -252,12 +252,7 @@ class StableDiffusionXLControlNetUnionImg2ImgPipeline(
         "feature_extractor",
         "image_encoder",
     ]
-    _callback_tensor_inputs = [
-        "latents",
-        "prompt_embeds",
-        "add_text_embeds",
-        "add_time_ids",
-    ]
+    _callback_tensor_inputs = ["latents", "prompt_embeds", "add_text_embeds", "add_time_ids", "control_image"]
 
     def __init__(
         self,
@@ -1562,6 +1557,7 @@ def __call__(
                     prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
                     add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
                     add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
+                    control_image = callback_outputs.pop("control_image", control_image)
 
                 # call the callback, if provided
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):