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import PIL.Image
import cv2
import torch
from loguru import logger
from lama_cleaner.model.base import DiffusionInpaintModel
from lama_cleaner.model.utils import set_seed
from lama_cleaner.schema import Config
class InstructPix2Pix(DiffusionInpaintModel):
name = "instruct_pix2pix"
pad_mod = 8
min_size = 512
def init_model(self, device: torch.device, **kwargs):
from diffusers import StableDiffusionInstructPix2PixPipeline
fp16 = not kwargs.get('no_half', False)
model_kwargs = {"local_files_only": kwargs.get('local_files_only', False)}
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
))
use_gpu = device == torch.device('cuda') and torch.cuda.is_available()
torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
self.model = StableDiffusionInstructPix2PixPipeline.from_pretrained(
"timbrooks/instruct-pix2pix",
revision="fp16" if use_gpu and fp16 else "main",
torch_dtype=torch_dtype,
**model_kwargs
)
self.model.enable_attention_slicing()
if kwargs.get('enable_xformers', False):
self.model.enable_xformers_memory_efficient_attention()
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: Config):
"""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.p2p_steps,
image_guidance_scale=config.p2p_image_guidance_scale,
guidance_scale=config.p2p_guidance_scale,
output_type="np.array",
generator=torch.manual_seed(config.sd_seed)
).images[0]
output = (output * 255).round().astype("uint8")
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
return output
#
# def forward_post_process(self, result, image, mask, config):
# if config.sd_match_histograms:
# result = self._match_histograms(result, image[:, :, ::-1], mask)
#
# if config.sd_mask_blur != 0:
# k = 2 * config.sd_mask_blur + 1
# mask = cv2.GaussianBlur(mask, (k, k), 0)
# return result, image, mask
@staticmethod
def is_downloaded() -> bool:
# model will be downloaded when app start, and can't switch in frontend settings
return True
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