add sdxl

lama_cleaner/model/g_diffuser_bot.py

@@ -151,6 +151,7 @@ def expand_image(
         hard_mask[:, 0 : origin_w // 2] = 255
     if right != 0:
         hard_mask[:, origin_w // 2 :] = 255
+
     hard_mask = cv2.copyMakeBorder(
         hard_mask, top, bottom, left, right, cv2.BORDER_CONSTANT, value=255
     )
@@ -166,12 +167,12 @@ if __name__ == "__main__":
     init_image = cv2.imread(str(image_path))
     init_image, mask_image = expand_image(
         init_image,
-        200,
-        200,
-        0,
-        0,
-        60,
-        50,
+        top=100,
+        right=100,
+        bottom=100,
+        left=100,
+        softness=20,
+        space=20,
     )
     print(mask_image.dtype, mask_image.min(), mask_image.max())
     print(init_image.dtype, init_image.min(), init_image.max())

lama_cleaner/model/sdxl.py

@@ -0,0 +1,111 @@
+import PIL.Image
+import cv2
+import numpy as np
+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
+
+
+class SDXL(DiffusionInpaintModel):
+    name = "sdxl"
+    pad_mod = 8
+    min_size = 512
+
+    def init_model(self, device: torch.device, **kwargs):
+        from diffusers.pipelines import AutoPipelineForInpainting
+
+        fp16 = not kwargs.get("no_half", False)
+
+        model_kwargs = {
+            "local_files_only": kwargs.get("local_files_only", kwargs["sd_run_local"])
+        }
+        if kwargs["disable_nsfw"] or kwargs.get("cpu_offload", False):
+            logger.info("Disable Stable Diffusion Model NSFW checker")
+            model_kwargs.update(
+                dict(
+                    safety_checker=None,
+                    feature_extractor=None,
+                    requires_safety_checker=False,
+                )
+            )
+
+        use_gpu = device == torch.device("cuda") and torch.cuda.is_available()
+        torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
+
+        self.model = AutoPipelineForInpainting.from_pretrained(
+            "diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
+            revision="main",
+            torch_dtype=torch_dtype,
+            use_auth_token=kwargs["hf_access_token"],
+            **model_kwargs,
+        )
+
+        # https://huggingface.co/docs/diffusers/v0.7.0/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing
+        self.model.enable_attention_slicing()
+        # https://huggingface.co/docs/diffusers/v0.7.0/en/optimization/fp16#memory-efficient-attention
+        if kwargs.get("enable_xformers", False):
+            self.model.enable_xformers_memory_efficient_attention()
+
+        if kwargs.get("cpu_offload", False) and use_gpu:
+            logger.info("Enable sequential cpu offload")
+            self.model.enable_sequential_cpu_offload(gpu_id=0)
+        else:
+            self.model = self.model.to(device)
+            if kwargs["sd_cpu_textencoder"]:
+                logger.warning("Stable Diffusion XL not support run TextEncoder on CPU")
+
+        self.callback = kwargs.pop("callback", None)
+
+    def forward(self, image, mask, config: Config):
+        """Input image and output image have same size
+        image: [H, W, C] RGB
+        mask: [H, W, 1] 255 means area to repaint
+        return: BGR IMAGE
+        """
+
+        scheduler_config = self.model.scheduler.config
+        scheduler = get_scheduler(config.sd_sampler, scheduler_config)
+        self.model.scheduler = scheduler
+
+        if config.sd_mask_blur != 0:
+            k = 2 * config.sd_mask_blur + 1
+            mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]
+
+        img_h, img_w = image.shape[:2]
+
+        output = self.model(
+            image=PIL.Image.fromarray(image),
+            prompt=config.prompt,
+            negative_prompt=config.negative_prompt,
+            mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"),
+            num_inference_steps=config.sd_steps,
+            strength=0.999 if config.sd_strength == 1.0 else config.sd_strength,
+            guidance_scale=config.sd_guidance_scale,
+            output_type="np",
+            callback=self.callback,
+            height=img_h,
+            width=img_w,
+            generator=torch.manual_seed(config.sd_seed),
+            callback_steps=1
+        ).images[0]
+
+        output = (output * 255).round().astype("uint8")
+        output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
+        return output
+
+    def forward_post_process(self, result, image, mask, config):
+        if config.sd_match_histograms:
+            result = self._match_histograms(result, image[:, :, ::-1], mask)
+
+        if config.sd_mask_blur != 0:
+            k = 2 * config.sd_mask_blur + 1
+            mask = cv2.GaussianBlur(mask, (k, k), 0)
+        return result, image, mask
+
+    @staticmethod
+    def is_downloaded() -> bool:
+        # model will be downloaded when app start, and can't switch in frontend settings
+        return True