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import PIL.Image
import cv2
import torch
from loguru import logger
from iopaint.const import INSTRUCT_PIX2PIX_NAME
from .base import DiffusionInpaintModel
from iopaint.schema import InpaintRequest
from .utils import get_torch_dtype, enable_low_mem, is_local_files_only
class InstructPix2Pix(DiffusionInpaintModel):
name = INSTRUCT_PIX2PIX_NAME
pad_mod = 8
min_size = 512
def init_model(self, device: torch.device, **kwargs):
from diffusers import StableDiffusionInstructPix2PixPipeline
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,
)
)
self.model = StableDiffusionInstructPix2PixPipeline.from_pretrained(
self.name, variant="fp16", torch_dtype=torch_dtype, **model_kwargs
)
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)
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
edit = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1.5, guidance_scale=7).images[0]
"""
output = self.model(
image=PIL.Image.fromarray(image),
prompt=config.prompt,
negative_prompt=config.negative_prompt,
num_inference_steps=config.sd_steps,
image_guidance_scale=config.p2p_image_guidance_scale,
guidance_scale=config.sd_guidance_scale,
output_type="np",
generator=torch.manual_seed(config.sd_seed),
).images[0]
output = (output * 255).round().astype("uint8")
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
return output
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