update

lama_cleaner/model/controlnet.py

@@ -12,8 +12,9 @@ from lama_cleaner.model.utils import torch_gc, get_scheduler
 from lama_cleaner.schema import Config
 
 
-class CPUTextEncoderWrapper:
+class CPUTextEncoderWrapper(torch.nn.Module):
     def __init__(self, text_encoder, torch_dtype):
+        super().__init__()
         self.config = text_encoder.config
         self.text_encoder = text_encoder.to(torch.device("cpu"), non_blocking=True)
         self.text_encoder = self.text_encoder.to(torch.float32, non_blocking=True)

lama_cleaner/model/pipeline/pipeline_stable_diffusion_controlnet_inpaint.py

@@ -285,6 +285,28 @@ class StableDiffusionControlNetInpaintPipeline(StableDiffusionControlNetPipeline
         masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
         return mask, masked_image_latents
 
+    def _default_height_width(self, height, width, image):
+        if isinstance(image, list):
+            image = image[0]
+
+        if height is None:
+            if isinstance(image, PIL.Image.Image):
+                height = image.height
+            elif isinstance(image, torch.Tensor):
+                height = image.shape[3]
+
+            height = (height // 8) * 8  # round down to nearest multiple of 8
+
+        if width is None:
+            if isinstance(image, PIL.Image.Image):
+                width = image.width
+            elif isinstance(image, torch.Tensor):
+                width = image.shape[2]
+
+            width = (width // 8) * 8  # round down to nearest multiple of 8
+
+        return height, width
+
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
     def __call__(
@@ -402,14 +424,11 @@ class StableDiffusionControlNetInpaintPipeline(StableDiffusionControlNetPipeline
 
         # 1. Check inputs. Raise error if not correct
         self.check_inputs(
-            prompt,
-            control_image,
-            height,
-            width,
-            callback_steps,
-            negative_prompt,
-            prompt_embeds,
-            negative_prompt_embeds,
+            prompt=prompt,
+            image=control_image,
+            callback_steps=callback_steps,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
         )
 
         # 2. Define call parameters

lama_cleaner/model/sd.py

@@ -7,12 +7,13 @@ import torch
 from loguru import logger
 
 from lama_cleaner.model.base import DiffusionInpaintModel
-from lama_cleaner.model.utils import torch_gc, get_scheduler
-from lama_cleaner.schema import Config, SDSampler
+from lama_cleaner.model.utils import torch_gc
+from lama_cleaner.schema import Config
 
 
-class CPUTextEncoderWrapper:
+class CPUTextEncoderWrapper(torch.nn.Module):
     def __init__(self, text_encoder, torch_dtype):
+        super().__init__()
         self.config = text_encoder.config
         self.text_encoder = text_encoder.to(torch.device("cpu"), non_blocking=True)
         self.text_encoder = self.text_encoder.to(torch.float32, non_blocking=True)