add powerpaint v2

iopaint/model/power_paint/power_paint_v2.py

@@ -0,0 +1,141 @@
+import PIL.Image
+import cv2
+import torch
+from loguru import logger
+from transformers import CLIPTextModel, CLIPTokenizer
+import numpy as np
+
+from ..base import DiffusionInpaintModel
+from ..helper.cpu_text_encoder import CPUTextEncoderWrapper
+from ..utils import (
+    get_torch_dtype,
+    enable_low_mem,
+    is_local_files_only,
+    handle_from_pretrained_exceptions,
+)
+from .powerpaint_tokenizer import task_to_prompt
+from iopaint.schema import InpaintRequest
+from .v2.BrushNet_CA import BrushNetModel
+from .v2.unet_2d_condition import UNet2DConditionModel
+
+
+class PowerPaintV2(DiffusionInpaintModel):
+    pad_mod = 8
+    min_size = 512
+    lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
+    hf_model_id = "Sanster/PowerPaint_v2"
+
+    def init_model(self, device: torch.device, **kwargs):
+        from .v2.pipeline_PowerPaint_Brushnet_CA import (
+            StableDiffusionPowerPaintBrushNetPipeline,
+        )
+        from .powerpaint_tokenizer import PowerPaintTokenizer
+
+        use_gpu, torch_dtype = get_torch_dtype(device, kwargs.get("no_half", False))
+        model_kwargs = {"local_files_only": is_local_files_only(**kwargs)}
+        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,
+                )
+            )
+
+        text_encoder_brushnet = CLIPTextModel.from_pretrained(
+            self.hf_model_id,
+            subfolder="text_encoder_brushnet",
+            variant="fp16",
+            torch_dtype=torch_dtype,
+            local_files_only=model_kwargs["local_files_only"],
+        )
+        unet = handle_from_pretrained_exceptions(
+            UNet2DConditionModel.from_pretrained,
+            pretrained_model_name_or_path=self.model_id_or_path,
+            subfolder="unet",
+            variant="fp16",
+            torch_dtype=torch_dtype,
+            local_files_only=model_kwargs["local_files_only"],
+        )
+        brushnet = BrushNetModel.from_pretrained(
+            self.hf_model_id,
+            subfolder="PowerPaint_Brushnet",
+            variant="fp16",
+            torch_dtype=torch_dtype,
+            local_files_only=model_kwargs["local_files_only"],
+        )
+        pipe = handle_from_pretrained_exceptions(
+            StableDiffusionPowerPaintBrushNetPipeline.from_pretrained,
+            pretrained_model_name_or_path=self.model_id_or_path,
+            torch_dtype=torch_dtype,
+            unet=unet,
+            brushnet=brushnet,
+            text_encoder_brushnet=text_encoder_brushnet,
+            variant="fp16",
+            **model_kwargs,
+        )
+        pipe.tokenizer = PowerPaintTokenizer(
+            CLIPTokenizer.from_pretrained(self.hf_model_id, subfolder="tokenizer")
+        )
+        self.model = pipe
+
+        enable_low_mem(self.model, kwargs.get("low_mem", False))
+
+        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: InpaintRequest):
+        """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
+        """
+        self.set_scheduler(config)
+
+        image = image * (1 - mask / 255.0)
+        img_h, img_w = image.shape[:2]
+
+        image = PIL.Image.fromarray(image.astype(np.uint8))
+        mask = PIL.Image.fromarray(mask[:, :, -1], mode="L").convert("RGB")
+
+        promptA, promptB, negative_promptA, negative_promptB = task_to_prompt(
+            config.powerpaint_task
+        )
+
+        output = self.model(
+            image=image,
+            mask=mask,
+            promptA=promptA,
+            promptB=promptB,
+            promptU=config.prompt,
+            tradoff=config.fitting_degree,
+            tradoff_nag=config.fitting_degree,
+            negative_promptA=negative_promptA,
+            negative_promptB=negative_promptB,
+            negative_promptU=config.negative_prompt,
+            num_inference_steps=config.sd_steps,
+            # strength=config.sd_strength,
+            brushnet_conditioning_scale=1.0,
+            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