wip

lama_cleaner/model/pipeline/pipeline_stable_diffusion_controlnet_inpaint.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import gc
 from typing import Union, List, Optional, Callable, Dict, Any
 
 # Copy from https://github.com/mikonvergence/ControlNetInpaint/blob/main/src/pipeline_stable_diffusion_controlnet_inpaint.py
@@ -217,6 +218,38 @@ class StableDiffusionControlNetInpaintPipeline(StableDiffusionControlNetPipeline
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
 
+    @classmethod
+    def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
+        from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
+            download_from_original_stable_diffusion_ckpt,
+        )
+
+        controlnet = kwargs.pop("controlnet", None)
+
+        pipe = download_from_original_stable_diffusion_ckpt(
+            pretrained_model_link_or_path,
+            num_in_channels=9,
+            from_safetensors=pretrained_model_link_or_path.endswith("safetensors"),
+            device="cpu",
+            load_safety_checker=False,
+        )
+
+        inpaint_pipe = cls(
+            vae=pipe.vae,
+            text_encoder=pipe.text_encoder,
+            tokenizer=pipe.tokenizer,
+            unet=pipe.unet,
+            controlnet=controlnet,
+            scheduler=pipe.scheduler,
+            safety_checker=None,
+            feature_extractor=None,
+            requires_safety_checker=False,
+        )
+
+        del pipe
+        gc.collect()
+        return inpaint_pipe
+
     def prepare_mask_latents(
         self,
         mask,