65 lines
2.4 KiB
Python
65 lines
2.4 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 iopaint.const import INSTRUCT_PIX2PIX_NAME
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from .base import DiffusionInpaintModel
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from iopaint.schema import InpaintRequest
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from .utils import get_torch_dtype, enable_low_mem, is_local_files_only
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class InstructPix2Pix(DiffusionInpaintModel):
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name = INSTRUCT_PIX2PIX_NAME
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pad_mod = 8
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min_size = 512
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def init_model(self, device: torch.device, **kwargs):
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from diffusers import StableDiffusionInstructPix2PixPipeline
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use_gpu, torch_dtype = get_torch_dtype(device, kwargs.get("no_half", False))
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model_kwargs = {"local_files_only": is_local_files_only(**kwargs)}
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if kwargs["disable_nsfw"] or kwargs.get("cpu_offload", False):
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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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self.model = StableDiffusionInstructPix2PixPipeline.from_pretrained(
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self.name, variant="fp16", torch_dtype=torch_dtype, **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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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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edit = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1.5, guidance_scale=7).images[0]
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"""
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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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num_inference_steps=config.sd_steps,
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image_guidance_scale=config.p2p_image_guidance_scale,
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guidance_scale=config.sd_guidance_scale,
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output_type="np",
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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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