add pix2pix

lama_cleaner/model/pix2pix.py

@@ -0,0 +1,83 @@
+import PIL.Image
+import cv2
+import torch
+from loguru import logger
+
+from lama_cleaner.model.base import DiffusionInpaintModel
+from lama_cleaner.model.utils import set_seed
+from lama_cleaner.schema import Config
+
+
+class Pix2Pix(DiffusionInpaintModel):
+    pad_mod = 8
+    min_size = 512
+
+    def init_model(self, device: torch.device, **kwargs):
+        from diffusers import StableDiffusionInstructPix2PixPipeline
+        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 = StableDiffusionInstructPix2PixPipeline.from_pretrained(
+            "timbrooks/instruct-pix2pix",
+            revision="fp16" if use_gpu and fp16 else "main",
+            torch_dtype=torch_dtype,
+            **model_kwargs
+        )
+
+        self.model.enable_attention_slicing()
+        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)
+
+    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
+        edit = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1.5, guidance_scale=7).images[0]
+        """
+        set_seed(config.sd_seed)
+
+        output = self.model(
+            image=PIL.Image.fromarray(image),
+            prompt=config.prompt,
+            negative_prompt=config.negative_prompt,
+            num_inference_steps=config.p2p_steps,
+            image_guidance_scale=config.p2p_image_guidance_scale,
+            guidance_scale=config.p2p_guidance_scale,
+            output_type="np.array",
+        ).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