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import torch
from huggingface_hub import hf_hub_download
from iopaint.const import ANYTEXT_NAME
from iopaint.model.anytext.anytext_pipeline import AnyTextPipeline
from iopaint.model.base import DiffusionInpaintModel
from iopaint.model.utils import get_torch_dtype, is_local_files_only
from iopaint.schema import InpaintRequest
class AnyText(DiffusionInpaintModel):
name = ANYTEXT_NAME
pad_mod = 64
is_erase_model = False
@staticmethod
def download(local_files_only=False):
hf_hub_download(
repo_id=ANYTEXT_NAME,
filename="model_index.json",
local_files_only=local_files_only,
)
ckpt_path = hf_hub_download(
repo_id=ANYTEXT_NAME,
filename="pytorch_model.fp16.safetensors",
local_files_only=local_files_only,
)
font_path = hf_hub_download(
repo_id=ANYTEXT_NAME,
filename="SourceHanSansSC-Medium.otf",
local_files_only=local_files_only,
)
return ckpt_path, font_path
def init_model(self, device, **kwargs):
local_files_only = is_local_files_only(**kwargs)
ckpt_path, font_path = self.download(local_files_only)
use_gpu, torch_dtype = get_torch_dtype(device, kwargs.get("no_half", False))
self.model = AnyTextPipeline(
ckpt_path=ckpt_path,
font_path=font_path,
device=device,
use_fp16=torch_dtype == torch.float16,
)
self.callback = kwargs.pop("callback", None)
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 inpainting
return: BGR IMAGE
"""
height, width = image.shape[:2]
mask = mask.astype("float32") / 255.0
masked_image = image * (1 - mask)
# list of rgb ndarray
results, rtn_code, rtn_warning = self.model(
image=image,
masked_image=masked_image,
prompt=config.prompt,
negative_prompt=config.negative_prompt,
num_inference_steps=config.sd_steps,
strength=config.sd_strength,
guidance_scale=config.sd_guidance_scale,
height=height,
width=width,
seed=config.sd_seed,
sort_priority="y",
callback=self.callback
)
inpainted_rgb_image = results[0][..., ::-1]
return inpainted_rgb_image
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