add download command

lama_cleaner/model/instruct_pix2pix.py

@@ -4,7 +4,6 @@ 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
 
 
@@ -15,18 +14,21 @@ class InstructPix2Pix(DiffusionInpaintModel):
 
     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', False)}
-        if kwargs['disable_nsfw'] or kwargs.get('cpu_offload', False):
+        fp16 = not kwargs.get("no_half", False)
+
+        model_kwargs = {"local_files_only": kwargs.get("local_files_only", False)}
+        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
-            ))
+            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()
+        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",
@@ -36,15 +38,23 @@ class InstructPix2Pix(DiffusionInpaintModel):
         )
 
         self.model.enable_attention_slicing()
-        if kwargs.get('enable_xformers', False):
+        if kwargs.get("enable_xformers", False):
             self.model.enable_xformers_memory_efficient_attention()
 
-        if kwargs.get('cpu_offload', False) and use_gpu:
+        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)
 
+    @staticmethod
+    def download():
+        from diffusers import StableDiffusionInstructPix2PixPipeline
+
+        StableDiffusionInstructPix2PixPipeline.from_pretrained(
+            "timbrooks/instruct-pix2pix", revision="fp16"
+        )
+
     def forward(self, image, mask, config: Config):
         """Input image and output image have same size
         image: [H, W, C] RGB
@@ -60,7 +70,7 @@ class InstructPix2Pix(DiffusionInpaintModel):
             image_guidance_scale=config.p2p_image_guidance_scale,
             guidance_scale=config.p2p_guidance_scale,
             output_type="np",
-            generator=torch.manual_seed(config.sd_seed)
+            generator=torch.manual_seed(config.sd_seed),
         ).images[0]
 
         output = (output * 255).round().astype("uint8")