import torch
from diffusers.pipelines.stable_diffusion import safety_checker
from diffusers import StableDiffusionPipeline, StableDiffusionInpaintPipeline
from compel import Compel
from PIL import Image, ImageOps
from diffusers import DPMSolverMultistepScheduler
# override
def sc(self, clip_input, images): return images, [False for i in images]
safety_checker.StableDiffusionSafetyChecker.forward = sc
pipeline = StableDiffusionInpaintPipeline.from_single_file('./majicmixRealistic_v7-inpainting.safetensors', torch_dtype = torch.float16).to('cuda')
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, algorithm_type = 'sde-dpmsolver++', use_karras_sigmas = True)
compel = Compel(tokenizer = pipeline.tokenizer, text_encoder = pipeline.text_encoder)
pipeline.enable_freeu(s1 = 0.9, s2 = 0.2, b1 = 1.2, b2 = 1.4)
prompt = 'photo++ of Luxury Loft: Sophisticated, upscale, modern interior, spacious layout, sleek design, floor-to-ceiling windows, minimalist decor, capturing a luxurious ambiance, crispy'
negative_prompt = 'ugly++, cartoon, animi, blurry++'
image = Image.open('./img.png')
mask = ImageOps.invert(Image.open('./img_mask.png'))
prompt_embeds = compel.build_conditioning_tensor(prompt)
negative_prompt_embeds = compel.build_conditioning_tensor(negative_prompt)
image = pipeline(
image = image,
mask_image = mask,
prompt_embeds = prompt_embeds,
negative_prompt_embeds = negative_prompt_embeds,
width = 600,
height = 960,
num_inference_steps = 70,
guidance_scale = 5,
clip_skip = 2,
strength = 1,
).images[0]
display(image)
