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import cv2
import torch
from modules_forge.shared import add_supported_preprocessor, preprocessor_dir
from ldm_patched.modules import model_management
from ldm_patched.modules.model_patcher import ModelPatcher
from modules_forge.forge_util import resize_image_with_pad
import ldm_patched.modules.clip_vision
from modules.modelloader import load_file_from_url
from modules_forge.forge_util import numpy_to_pytorch
class PreprocessorParameter:
def __init__(self, minimum=0.0, maximum=1.0, step=0.01, label='Parameter 1', value=0.5, visible=False, **kwargs):
self.gradio_update_kwargs = dict(
minimum=minimum, maximum=maximum, step=step, label=label, value=value, visible=visible, **kwargs
)
class Preprocessor:
def __init__(self):
self.name = 'PreprocessorBase'
self.tags = []
self.model_filename_filters = []
self.slider_resolution = PreprocessorParameter(label='Resolution', minimum=128, maximum=2048, value=512, step=8, visible=True)
self.slider_1 = PreprocessorParameter()
self.slider_2 = PreprocessorParameter()
self.slider_3 = PreprocessorParameter()
self.model_patcher: ModelPatcher = None
self.show_control_mode = True
self.do_not_need_model = False
self.sorting_priority = 0 # higher goes to top in the list
self.corp_image_with_a1111_mask_when_in_img2img_inpaint_tab = True
self.fill_mask_with_one_when_resize_and_fill = False
self.use_soft_projection_in_hr_fix = False
self.expand_mask_when_resize_and_fill = False
def setup_model_patcher(self, model, load_device=None, offload_device=None, dtype=torch.float32, **kwargs):
if load_device is None:
load_device = model_management.get_torch_device()
if offload_device is None:
offload_device = torch.device('cpu')
if not model_management.should_use_fp16(load_device):
dtype = torch.float32
model.eval()
model = model.to(device=offload_device, dtype=dtype)
self.model_patcher = ModelPatcher(model=model, load_device=load_device, offload_device=offload_device, **kwargs)
self.model_patcher.dtype = dtype
return self.model_patcher
def move_all_model_patchers_to_gpu(self):
model_management.load_models_gpu([self.model_patcher])
return
def send_tensor_to_model_device(self, x):
return x.to(device=self.model_patcher.current_device, dtype=self.model_patcher.dtype)
def process_after_running_preprocessors(self, process, params, *args, **kwargs):
return
def process_before_every_sampling(self, process, cond, mask, *args, **kwargs):
return cond, mask
def process_after_every_sampling(self, process, params, *args, **kwargs):
return
def __call__(self, input_image, resolution, slider_1=None, slider_2=None, slider_3=None, input_mask=None, **kwargs):
return input_image
class PreprocessorNone(Preprocessor):
def __init__(self):
super().__init__()
self.name = 'None'
self.sorting_priority = 10
class PreprocessorCanny(Preprocessor):
def __init__(self):
super().__init__()
self.name = 'canny'
self.tags = ['Canny']
self.model_filename_filters = ['canny']
self.slider_1 = PreprocessorParameter(minimum=0, maximum=256, step=1, value=100, label='Low Threshold', visible=True)
self.slider_2 = PreprocessorParameter(minimum=0, maximum=256, step=1, value=200, label='High Threshold', visible=True)
self.sorting_priority = 100
self.use_soft_projection_in_hr_fix = True
def __call__(self, input_image, resolution, slider_1=None, slider_2=None, slider_3=None, **kwargs):
input_image, remove_pad = resize_image_with_pad(input_image, resolution)
canny_image = cv2.cvtColor(cv2.Canny(input_image, int(slider_1), int(slider_2)), cv2.COLOR_GRAY2RGB)
return remove_pad(canny_image)
add_supported_preprocessor(PreprocessorNone())
add_supported_preprocessor(PreprocessorCanny())
class PreprocessorClipVision(Preprocessor):
global_cache = {}
def __init__(self, name, url, filename):
super().__init__()
self.name = name
self.url = url
self.filename = filename
self.slider_resolution = PreprocessorParameter(visible=False)
self.corp_image_with_a1111_mask_when_in_img2img_inpaint_tab = False
self.show_control_mode = False
self.sorting_priority = 1
self.clipvision = None
def load_clipvision(self):
if self.clipvision is not None:
return self.clipvision
ckpt_path = load_file_from_url(
url=self.url,
model_dir=preprocessor_dir,
file_name=self.filename
)
if ckpt_path in PreprocessorClipVision.global_cache:
self.clipvision = PreprocessorClipVision.global_cache[ckpt_path]
else:
self.clipvision = ldm_patched.modules.clip_vision.load(ckpt_path)
PreprocessorClipVision.global_cache[ckpt_path] = self.clipvision
return self.clipvision
@torch.no_grad()
def __call__(self, input_image, resolution, slider_1=None, slider_2=None, slider_3=None, **kwargs):
clipvision = self.load_clipvision()
return clipvision.encode_image(numpy_to_pytorch(input_image))
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