Improve loading ckpt

src/diffusers/loaders.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
+import io
+import requests
 import warnings
 from collections import defaultdict
 from pathlib import Path
@@ -1432,38 +1434,62 @@ class FromCkptMixin:
         else:
             raise ValueError(f"Unhandled pipeline class: {pipeline_name}")
 
-        # remove huggingface url
-        for prefix in ["https://huggingface.co/", "huggingface.co/", "hf.co/", "https://hf.co/"]:
-            if pretrained_model_link_or_path.startswith(prefix):
-                pretrained_model_link_or_path = pretrained_model_link_or_path[len(prefix) :]
-
         # Code based on diffusers.pipelines.pipeline_utils.DiffusionPipeline.from_pretrained
-        ckpt_path = Path(pretrained_model_link_or_path)
+        if Path(pretrained_model_link_or_path).is_file():
-        if not ckpt_path.is_file():
+            pretrained_model_path_or_dict = pretrained_model_link_or_path
-            # get repo_id and (potentially nested) file path of ckpt in repo
+        elif not Path(pretrained_model_link_or_path).is_file():
-            repo_id = "/".join(ckpt_path.parts[:2])
+            is_hf = False
-            file_path = "/".join(ckpt_path.parts[2:])
+            is_civit_ai = False
+            for prefix in ["https://huggingface.co/", "huggingface.co/", "hf.co/", "https://hf.co/"]:
+                if pretrained_model_link_or_path.startswith(prefix):
+                    pretrained_model_link_or_path = pretrained_model_link_or_path[len(prefix) :]
+                    is_hf = True
 
-            if file_path.startswith("blob/"):
+            for prefix in ["https://civitai.com/", "civitai.com"]:
-                file_path = file_path[len("blob/") :]
+                if pretrained_model_link_or_path.startswith(prefix):
+                    if "api" not in pretrained_model_link_or_path:
+                        raise ValueError(f"{pretrained_model_link_or_path} is not a valid Civitai link. Make sure to provide a link in the form: https://civitai.com/api/models/<num>")
+                    is_civit_ai = True
 
-            if file_path.startswith("main/"):
+            if is_hf:
-                file_path = file_path[len("main/") :]
+                # get repo_id and (potentially nested) file path of ckpt in repo
+                repo_id = "/".join(pretrained_model_link_or_path.parts[:2])
+                file_path = "/".join(pretrained_model_link_or_path.parts[2:])
 
-            pretrained_model_link_or_path = hf_hub_download(
+                if file_path.startswith("blob/"):
-                repo_id,
+                    file_path = file_path[len("blob/") :]
-                filename=file_path,
+
-                cache_dir=cache_dir,
+                if file_path.startswith("main/"):
-                resume_download=resume_download,
+                    file_path = file_path[len("main/") :]
-                proxies=proxies,
+
-                local_files_only=local_files_only,
+                pretrained_model_path_or_dict = hf_hub_download(
-                use_auth_token=use_auth_token,
+                    repo_id,
-                revision=revision,
+                    filename=file_path,
-                force_download=force_download,
+                    cache_dir=cache_dir,
-            )
+                    resume_download=resume_download,
+                    proxies=proxies,
+                    local_files_only=local_files_only,
+                    use_auth_token=use_auth_token,
+                    revision=revision,
+                    force_download=force_download,
+                )
+                pretrained_model_path_or_dict = pretrained_model_link_or_path
+            elif is_civit_ai:
+                response = requests.get(pretrained_model_link_or_path)
+                checkpoint_bytes = response.content
+
+                # Create an in-memory byte stream using io.BytesIO()
+                buffer = io.BytesIO(checkpoint_bytes)
+
+                try:
+                    pretrained_model_path_or_dict = safetensors.torch.load(buffer)
+                except IOError as e:
+                    pass
+
+                pretrained_model_path_or_dict = torch.load(buffer)
 
         pipe = download_from_original_stable_diffusion_ckpt(
-            pretrained_model_link_or_path,
+            pretrained_model_path_or_dict,
             pipeline_class=cls,
             model_type=model_type,
             stable_unclip=stable_unclip,