update

Author: DN6

src/diffusers/loaders/single_file.py

@@ -43,14 +43,13 @@ def build_sub_model_components(
     checkpoint,
     local_files_only=False,
     load_safety_checker=False,
-    **kwargs,
+    model_type=None,
+    image_size=None,
+    **kwargs
 ):
     if component_name in pipeline_components:
         return {}
 
-    model_type = kwargs.pop("model_type", None)
-    image_size = kwargs.pop("image_size", None)
-
     if component_name == "unet":
         num_in_channels = kwargs.pop("num_in_channels", None)
         unet_components = create_diffusers_unet_model_from_ldm(
@@ -112,10 +111,9 @@ def build_sub_model_components(
 def set_additional_components(
     pipeline_class_name,
     original_config,
-    **kwargs,
+    model_type=None,
 ):
     components = {}
-    model_type = kwargs.get("model_type", None)
     if pipeline_class_name in REFINER_PIPELINES:
         model_type = infer_model_type(original_config, model_type=model_type)
         is_refiner = model_type == "SDXL-Refiner"
@@ -235,6 +233,9 @@ def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
         passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
         passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
 
+        model_type = kwargs.pop("model_type", None)
+        image_size = kwargs.pop("image_size", None)
+
         init_kwargs = {}
         for name in expected_modules:
             if name in passed_class_obj:
@@ -247,13 +248,15 @@ def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
                     original_config,
                     checkpoint,
                     pretrained_model_link_or_path,
+                    model_type=model_type,
+                    image_size=image_size,
                     **kwargs,
                 )
                 if not components:
                     continue
                 init_kwargs.update(components)
 
-        additional_components = set_additional_components(class_name, original_config, **kwargs)
+        additional_components = set_additional_components(class_name, original_config, model_type=model_type)
         if additional_components:
             init_kwargs.update(additional_components)
 

src/diffusers/loaders/single_file_utils.py

@@ -191,14 +191,14 @@
 ]
 
 
-VALID_URL_PREFIXES = ["https://huggingface.co/", "huggingface.co/", "hf.co/", "https://hf.co/"]
+VALID_HF_URL_PREFIXES = ["https://huggingface.co/", "huggingface.co/", "hf.co/", "https://hf.co/"]
 
 
 def _extract_repo_id_and_weights_name(pretrained_model_name_or_path):
     pattern = r"([^/]+)/([^/]+)/(?:blob/main/)?(.+)"
     weights_name = None
     repo_id = (None,)
-    for prefix in VALID_URL_PREFIXES:
+    for prefix in VALID_HF_URL_PREFIXES:
         pretrained_model_name_or_path = pretrained_model_name_or_path.replace(prefix, "")
     match = re.match(pattern, pretrained_model_name_or_path)
     if not match:

src/diffusers/models/unet_3d_condition.py

@@ -533,7 +533,7 @@ def forward(
 
         Args:
             sample (`torch.FloatTensor`):
-                The noisy input tensor with the following shape `(batch, num_frames, channel, height, width`.
+                The noisy input tensor with the following shape `(batch, channel, num_frames, height, width)`.
             timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
             encoder_hidden_states (`torch.FloatTensor`):
                 The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py

@@ -753,14 +753,14 @@ def test_download_local(self):
     def test_download_ckpt_diff_format_is_same(self):
         ckpt_path = "https://huggingface.co/runwayml/stable-diffusion-inpainting/blob/main/sd-v1-5-inpainting.ckpt"
 
-        pipe = StableDiffusionInpaintPipeline.from_single_file(ckpt_path)
-        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
-        pipe.unet.set_attn_processor(AttnProcessor())
-        pipe.to("cuda")
+        sf_pipe = StableDiffusionInpaintPipeline.from_single_file(ckpt_path)
+        sf_pipe.scheduler = DDIMScheduler.from_config(sf_pipe.scheduler.config)
+        sf_pipe.unet.set_attn_processor(AttnProcessor())
+        sf_pipe.to("cuda")
 
         inputs = self.get_inputs(torch_device)
         inputs["num_inference_steps"] = 5
-        image_ckpt = pipe(**inputs).images[0]
+        image_ckpt = sf_pipe(**inputs).images[0]
 
         pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
         pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)