update

lama_cleaner/model/base.py

@@ -279,15 +279,12 @@ class DiffusionInpaintModel(InpaintModel):
         """
         # boxes = boxes_from_mask(mask)
         if config.use_croper:
-            if config.croper_is_outpainting:
-                inpaint_result = self._do_outpainting(image, config)
-            else:
-                crop_img, crop_mask, (l, t, r, b) = self._apply_cropper(
-                    image, mask, config
-                )
-                crop_image = self._scaled_pad_forward(crop_img, crop_mask, config)
-                inpaint_result = image[:, :, ::-1]
-                inpaint_result[t:b, l:r, :] = crop_image
+            crop_img, crop_mask, (l, t, r, b) = self._apply_cropper(image, mask, config)
+            crop_image = self._scaled_pad_forward(crop_img, crop_mask, config)
+            inpaint_result = image[:, :, ::-1]
+            inpaint_result[t:b, l:r, :] = crop_image
+        elif config.use_extender:
+            inpaint_result = self._do_outpainting(image, config)
         else:
             inpaint_result = self._scaled_pad_forward(image, mask, config)
 
@@ -297,10 +294,10 @@ class DiffusionInpaintModel(InpaintModel):
         # cropper 和 image 在同一个坐标系下，croper_x/y 可能为负数
         # 从 image 中 crop 出 outpainting 区域
         image_h, image_w = image.shape[:2]
-        cropper_l = config.croper_x
-        cropper_t = config.croper_y
-        cropper_r = config.croper_x + config.croper_width
-        cropper_b = config.croper_y + config.croper_height
+        cropper_l = config.extender_x
+        cropper_t = config.extender_y
+        cropper_r = config.extender_x + config.extender_width
+        cropper_b = config.extender_y + config.extender_height
         image_l = 0
         image_t = 0
         image_r = image_w
@@ -356,8 +353,8 @@ class DiffusionInpaintModel(InpaintModel):
         )[:, :, ::-1]
 
         # 把 cropped_result_image 贴到 outpainting_image 上，这一步不需要 blend
-        paste_t = 0 if config.croper_y < 0 else config.croper_y
-        paste_l = 0 if config.croper_x < 0 else config.croper_x
+        paste_t = 0 if config.extender_y < 0 else config.extender_y
+        paste_l = 0 if config.extender_x < 0 else config.extender_x
 
         outpainting_image[
             paste_t : paste_t + expanded_cropped_result_image.shape[0],
@@ -397,8 +394,6 @@ class DiffusionInpaintModel(InpaintModel):
     def set_scheduler(self, config: Config):
         scheduler_config = self.model.scheduler.config
         sd_sampler = config.sd_sampler
-        if config.sd_lcm_lora:
-            sd_sampler = SDSampler.lcm
         scheduler = get_scheduler(sd_sampler, scheduler_config)
         self.model.scheduler = scheduler
 

lama_cleaner/model/controlnet.py

@@ -31,6 +31,20 @@ class ControlNet(DiffusionInpaintModel):
     pad_mod = 8
     min_size = 512
 
+    @property
+    def lcm_lora_id(self):
+        if self.model_info.model_type in [
+            ModelType.DIFFUSERS_SD,
+            ModelType.DIFFUSERS_SD_INPAINT,
+        ]:
+            return "latent-consistency/lcm-lora-sdv1-5"
+        if self.model_info.model_type in [
+            ModelType.DIFFUSERS_SDXL,
+            ModelType.DIFFUSERS_SDXL_INPAINT,
+        ]:
+            return "latent-consistency/lcm-lora-sdxl"
+        raise NotImplementedError(f"Unsupported controlnet lcm model {self.model_info}")
+
     def init_model(self, device: torch.device, **kwargs):
         fp16 = not kwargs.get("no_half", False)
         model_info: ModelInfo = kwargs["model_info"]
@@ -72,7 +86,7 @@ class ControlNet(DiffusionInpaintModel):
             )
 
         controlnet = ControlNetModel.from_pretrained(
-            sd_controlnet_method, torch_dtype=torch_dtype
+            sd_controlnet_method, torch_dtype=torch_dtype, resume_download=True
         )
         if model_info.is_single_file_diffusers:
             if self.model_info.model_type == ModelType.DIFFUSERS_SD:
@@ -81,7 +95,7 @@ class ControlNet(DiffusionInpaintModel):
                 model_kwargs["num_in_channels"] = 9
 
             self.model = PipeClass.from_single_file(
-                model_info.path, controlnet=controlnet
+                model_info.path, controlnet=controlnet, **model_kwargs
             ).to(torch_dtype)
         else:
             self.model = PipeClass.from_pretrained(

lama_cleaner/model/sdxl.py

@@ -39,7 +39,7 @@ class SDXL(DiffusionInpaintModel):
             )
         else:
             vae = AutoencoderKL.from_pretrained(
-                "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16
+                "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch_dtype
             )
             self.model = StableDiffusionXLInpaintPipeline.from_pretrained(
                 self.model_id_or_path,

lama_cleaner/model/utils.py

@@ -16,6 +16,7 @@ from diffusers import (
     EulerAncestralDiscreteScheduler,
     DPMSolverMultistepScheduler,
     UniPCMultistepScheduler,
+    LCMScheduler
 )
 
 from lama_cleaner.schema import SDSampler
@@ -939,5 +940,7 @@ def get_scheduler(sd_sampler, scheduler_config):
         return DPMSolverMultistepScheduler.from_config(scheduler_config)
     elif sd_sampler == SDSampler.uni_pc:
         return UniPCMultistepScheduler.from_config(scheduler_config)
+    elif sd_sampler == SDSampler.lcm:
+        return LCMScheduler.from_config(scheduler_config)
     else:
         raise ValueError(sd_sampler)