add controlnet inpainting

lama_cleaner/model/controlnet.py

@@ -0,0 +1,157 @@
+import PIL.Image
+import cv2
+import numpy as np
+import torch
+from diffusers import (
+    ControlNetModel,
+)
+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 CPUTextEncoderWrapper:
+    def __init__(self, text_encoder, torch_dtype):
+        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)
+        self.torch_dtype = torch_dtype
+        del text_encoder
+        torch_gc()
+
+    def __call__(self, x, **kwargs):
+        input_device = x.device
+        return [
+            self.text_encoder(x.to(self.text_encoder.device), **kwargs)[0]
+            .to(input_device)
+            .to(self.torch_dtype)
+        ]
+
+    @property
+    def dtype(self):
+        return self.torch_dtype
+
+
+NAMES_MAP = {
+    "sd1.5": "runwayml/stable-diffusion-inpainting",
+    "anything4": "Sanster/anything-4.0-inpainting",
+    "realisticVision1.4": "Sanster/Realistic_Vision_V1.4-inpainting",
+}
+
+
+class ControlNet(DiffusionInpaintModel):
+    name = "controlnet"
+    pad_mod = 8
+    min_size = 512
+
+    def init_model(self, device: torch.device, **kwargs):
+        from .pipeline import StableDiffusionControlNetInpaintPipeline
+
+        model_id = NAMES_MAP[kwargs["name"]]
+        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
+
+        controlnet = ControlNetModel.from_pretrained(
+            f"lllyasviel/sd-controlnet-canny", torch_dtype=torch_dtype
+        )
+        self.model = StableDiffusionControlNetInpaintPipeline.from_pretrained(
+            model_id,
+            controlnet=controlnet,
+            revision="fp16" if use_gpu and fp16 else "main",
+            torch_dtype=torch_dtype,
+            **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.info("Run Stable Diffusion TextEncoder on CPU")
+                self.model.text_encoder = CPUTextEncoderWrapper(
+                    self.model.text_encoder, torch_dtype
+                )
+
+        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]
+
+        canny_image = cv2.Canny(image, 100, 200)
+        canny_image = canny_image[:, :, None]
+        canny_image = np.concatenate([canny_image, canny_image, canny_image], axis=2)
+        canny_image = PIL.Image.fromarray(canny_image)
+        mask_image = PIL.Image.fromarray(mask[:, :, -1], mode="L")
+        image = PIL.Image.fromarray(image)
+
+        output = self.model(
+            image=image,
+            control_image=canny_image,
+            prompt=config.prompt,
+            negative_prompt=config.negative_prompt,
+            mask_image=mask_image,
+            num_inference_steps=config.sd_steps,
+            guidance_scale=config.sd_guidance_scale,
+            output_type="np.array",
+            callback=self.callback,
+            height=img_h,
+            width=img_w,
+            generator=torch.manual_seed(config.sd_seed),
+            controlnet_conditioning_scale=config.controlnet_conditioning_scale,
+        ).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