130 lines
4.3 KiB
Python
130 lines
4.3 KiB
Python
import PIL.Image
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import cv2
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import torch
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from loguru import logger
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from .base import DiffusionInpaintModel
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from .helper.cpu_text_encoder import CPUTextEncoderWrapper
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from .original_sd_configs import get_config_files
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from .utils import (
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handle_from_pretrained_exceptions,
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get_torch_dtype,
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enable_low_mem,
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is_local_files_only,
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)
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from iopaint.schema import InpaintRequest, ModelType
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class SD(DiffusionInpaintModel):
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pad_mod = 8
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min_size = 512
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lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
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def init_model(self, device: torch.device, **kwargs):
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from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
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use_gpu, torch_dtype = get_torch_dtype(device, kwargs.get("no_half", False))
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model_kwargs = {
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**kwargs.get("pipe_components", {}),
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"local_files_only": is_local_files_only(**kwargs),
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}
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disable_nsfw_checker = kwargs["disable_nsfw"] or kwargs.get(
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"cpu_offload", False
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)
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if disable_nsfw_checker:
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logger.info("Disable Stable Diffusion Model NSFW checker")
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model_kwargs.update(
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dict(
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safety_checker=None,
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feature_extractor=None,
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requires_safety_checker=False,
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)
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)
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if self.model_info.is_single_file_diffusers:
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if self.model_info.model_type == ModelType.DIFFUSERS_SD:
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model_kwargs["num_in_channels"] = 4
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else:
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model_kwargs["num_in_channels"] = 9
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self.model = StableDiffusionInpaintPipeline.from_single_file(
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self.model_id_or_path,
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torch_dtype=torch_dtype,
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load_safety_checker=not disable_nsfw_checker,
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config_files=get_config_files(),
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**model_kwargs,
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)
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else:
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self.model = handle_from_pretrained_exceptions(
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StableDiffusionInpaintPipeline.from_pretrained,
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pretrained_model_name_or_path=self.model_id_or_path,
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variant="fp16",
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torch_dtype=torch_dtype,
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**model_kwargs,
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)
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enable_low_mem(self.model, kwargs.get("low_mem", False))
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if kwargs.get("cpu_offload", False) and use_gpu:
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logger.info("Enable sequential cpu offload")
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self.model.enable_sequential_cpu_offload(gpu_id=0)
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else:
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self.model = self.model.to(device)
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if kwargs["sd_cpu_textencoder"]:
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logger.info("Run Stable Diffusion TextEncoder on CPU")
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self.model.text_encoder = CPUTextEncoderWrapper(
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self.model.text_encoder, torch_dtype
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)
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self.callback = kwargs.pop("callback", None)
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def forward(self, image, mask, config: InpaintRequest):
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"""Input image and output image have same size
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image: [H, W, C] RGB
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mask: [H, W, 1] 255 means area to repaint
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return: BGR IMAGE
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"""
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self.set_scheduler(config)
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img_h, img_w = image.shape[:2]
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output = self.model(
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image=PIL.Image.fromarray(image),
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prompt=config.prompt,
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negative_prompt=config.negative_prompt,
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mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"),
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num_inference_steps=config.sd_steps,
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strength=config.sd_strength,
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guidance_scale=config.sd_guidance_scale,
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output_type="np",
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callback_on_step_end=self.callback,
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height=img_h,
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width=img_w,
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generator=torch.manual_seed(config.sd_seed),
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).images[0]
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output = (output * 255).round().astype("uint8")
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output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
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return output
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class SD15(SD):
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name = "runwayml/stable-diffusion-inpainting"
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model_id_or_path = "runwayml/stable-diffusion-inpainting"
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class Anything4(SD):
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name = "Sanster/anything-4.0-inpainting"
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model_id_or_path = "Sanster/anything-4.0-inpainting"
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class RealisticVision14(SD):
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name = "Sanster/Realistic_Vision_V1.4-inpainting"
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model_id_or_path = "Sanster/Realistic_Vision_V1.4-inpainting"
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class SD2(SD):
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name = "stabilityai/stable-diffusion-2-inpainting"
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model_id_or_path = "stabilityai/stable-diffusion-2-inpainting"
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