update

src/diffusers/loaders/single_file.py

@@ -169,10 +169,12 @@ class FromSingleFileMixin:
         load_safety_checker = kwargs.pop("load_safety_checker", True)
         prediction_type = kwargs.pop("prediction_type", None)
         text_encoder = kwargs.pop("text_encoder", None)
+        text_encoder_2 = kwargs.pop("text_encoder_2", None)
         vae = kwargs.pop("vae", None)
         controlnet = kwargs.pop("controlnet", None)
         adapter = kwargs.pop("adapter", None)
         tokenizer = kwargs.pop("tokenizer", None)
+        tokenizer_2 = kwargs.pop("tokenizer_2", None)
 
         torch_dtype = kwargs.pop("torch_dtype", None)
 
@@ -274,8 +276,10 @@ class FromSingleFileMixin:
             load_safety_checker=load_safety_checker,
             prediction_type=prediction_type,
             text_encoder=text_encoder,
+            text_encoder_2=text_encoder_2,
             vae=vae,
             tokenizer=tokenizer,
+            tokenizer_2=tokenizer_2,
             original_config_file=original_config_file,
             config_files=config_files,
             local_files_only=local_files_only,

src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py

@@ -1153,7 +1153,9 @@ def download_from_original_stable_diffusion_ckpt(
     vae_path=None,
     vae=None,
     text_encoder=None,
+    text_encoder_2=None,
     tokenizer=None,
+    tokenizer_2=None,
     config_files=None,
 ) -> DiffusionPipeline:
     """
@@ -1232,7 +1234,9 @@ def download_from_original_stable_diffusion_ckpt(
         StableDiffusionInpaintPipeline,
         StableDiffusionPipeline,
         StableDiffusionUpscalePipeline,
+        StableDiffusionXLControlNetInpaintPipeline,
         StableDiffusionXLImg2ImgPipeline,
+        StableDiffusionXLInpaintPipeline,
         StableDiffusionXLPipeline,
         StableUnCLIPImg2ImgPipeline,
         StableUnCLIPPipeline,
@@ -1339,7 +1343,11 @@ def download_from_original_stable_diffusion_ckpt(
         else:
             pipeline_class = StableDiffusionXLPipeline if model_type == "SDXL" else StableDiffusionXLImg2ImgPipeline
 
-    if num_in_channels is None and pipeline_class == StableDiffusionInpaintPipeline:
+    if num_in_channels is None and pipeline_class in [
+        StableDiffusionInpaintPipeline,
+        StableDiffusionXLInpaintPipeline,
+        StableDiffusionXLControlNetInpaintPipeline,
+    ]:
         num_in_channels = 9
     if num_in_channels is None and pipeline_class == StableDiffusionUpscalePipeline:
         num_in_channels = 7
@@ -1686,7 +1694,9 @@ def download_from_original_stable_diffusion_ckpt(
                 feature_extractor=feature_extractor,
             )
     elif model_type in ["SDXL", "SDXL-Refiner"]:
-        if model_type == "SDXL":
+        is_refiner = model_type == "SDXL-Refiner"
+
+        if (is_refiner is False) and (tokenizer is None):
             try:
                 tokenizer = CLIPTokenizer.from_pretrained(
                     "openai/clip-vit-large-patch14", local_files_only=local_files_only
@@ -1695,7 +1705,11 @@ def download_from_original_stable_diffusion_ckpt(
                 raise ValueError(
                     f"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'openai/clip-vit-large-patch14'."
                 )
+
+        if (is_refiner is False) and (text_encoder is None):
             text_encoder = convert_ldm_clip_checkpoint(checkpoint, local_files_only=local_files_only)
+
+        if tokenizer_2 is None:
             try:
                 tokenizer_2 = CLIPTokenizer.from_pretrained(
                     "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", pad_token="!", local_files_only=local_files_only
@@ -1705,95 +1719,66 @@ def download_from_original_stable_diffusion_ckpt(
                     f"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'laion/CLIP-ViT-bigG-14-laion2B-39B-b160k' with `pad_token` set to '!'."
                 )
 
+        if text_encoder_2 is None:
             config_name = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
             config_kwargs = {"projection_dim": 1280}
+            prefix = "conditioner.embedders.0.model." if is_refiner else "conditioner.embedders.1.model."
+
             text_encoder_2 = convert_open_clip_checkpoint(
                 checkpoint,
                 config_name,
-                prefix="conditioner.embedders.1.model.",
+                prefix=prefix,
                 has_projection=True,
                 local_files_only=local_files_only,
                 **config_kwargs,
             )
 
-            if is_accelerate_available():  # SBM Now move model to cpu.
+        if is_accelerate_available():  # SBM Now move model to cpu.
-                if model_type in ["SDXL", "SDXL-Refiner"]:
+            for param_name, param in converted_unet_checkpoint.items():
-                    for param_name, param in converted_unet_checkpoint.items():
+                set_module_tensor_to_device(unet, param_name, "cpu", value=param)
-                        set_module_tensor_to_device(unet, param_name, "cpu", value=param)
 
-            if controlnet:
+        if controlnet:
-                pipe = pipeline_class(
+            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,
-                )
-            elif adapter:
-                pipe = pipeline_class(
-                    vae=vae,
-                    text_encoder=text_encoder,
-                    tokenizer=tokenizer,
-                    text_encoder_2=text_encoder_2,
-                    tokenizer_2=tokenizer_2,
-                    unet=unet,
-                    adapter=adapter,
-                    scheduler=scheduler,
-                    force_zeros_for_empty_prompt=True,
-                )
-            else:
-                pipe = pipeline_class(
-                    vae=vae,
-                    text_encoder=text_encoder,
-                    tokenizer=tokenizer,
-                    text_encoder_2=text_encoder_2,
-                    tokenizer_2=tokenizer_2,
-                    unet=unet,
-                    scheduler=scheduler,
-                    force_zeros_for_empty_prompt=True,
-                )
-        else:
-            tokenizer = None
-            text_encoder = None
-            try:
-                tokenizer_2 = CLIPTokenizer.from_pretrained(
-                    "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", pad_token="!", local_files_only=local_files_only
-                )
-            except Exception:
-                raise ValueError(
-                    f"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'laion/CLIP-ViT-bigG-14-laion2B-39B-b160k' with `pad_token` set to '!'."
-                )
-            config_name = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
-            config_kwargs = {"projection_dim": 1280}
-            text_encoder_2 = convert_open_clip_checkpoint(
-                checkpoint,
-                config_name,
-                prefix="conditioner.embedders.0.model.",
-                has_projection=True,
-                local_files_only=local_files_only,
-                **config_kwargs,
-            )
-
-            if is_accelerate_available():  # SBM Now move model to cpu.
-                if model_type in ["SDXL", "SDXL-Refiner"]:
-                    for param_name, param in converted_unet_checkpoint.items():
-                        set_module_tensor_to_device(unet, param_name, "cpu", value=param)
-
-            pipe = StableDiffusionXLImg2ImgPipeline(
                 vae=vae,
                 text_encoder=text_encoder,
                 tokenizer=tokenizer,
                 text_encoder_2=text_encoder_2,
                 tokenizer_2=tokenizer_2,
                 unet=unet,
+                controlnet=controlnet,
                 scheduler=scheduler,
-                requires_aesthetics_score=True,
+                force_zeros_for_empty_prompt=True,
-                force_zeros_for_empty_prompt=False,
             )
+        elif adapter:
+            pipe = pipeline_class(
+                vae=vae,
+                text_encoder=text_encoder,
+                tokenizer=tokenizer,
+                text_encoder_2=text_encoder_2,
+                tokenizer_2=tokenizer_2,
+                unet=unet,
+                adapter=adapter,
+                scheduler=scheduler,
+                force_zeros_for_empty_prompt=True,
+            )
+
+        else:
+            pipeline_kwargs = {
+                "vae": vae,
+                "text_encoder": text_encoder,
+                "tokenizer": tokenizer,
+                "text_encoder_2": text_encoder_2,
+                "tokenizer_2": tokenizer_2,
+                "unet": unet,
+                "scheduler": scheduler,
+            }
+
+            if (pipeline_class == StableDiffusionXLImg2ImgPipeline) or (
+                pipeline_class == StableDiffusionXLInpaintPipeline
+            ):
+                pipeline_kwargs.update({"requires_aesthetics_score": is_refiner})
+
+            pipe = pipeline_class(**pipeline_kwargs)
     else:
         text_config = create_ldm_bert_config(original_config)
         text_model = convert_ldm_bert_checkpoint(checkpoint, text_config)