109 lines
4.1 KiB
Python
109 lines
4.1 KiB
Python
import cv2
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import os
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import torch
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from modules.paths import models_path
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from ldm_patched.modules import model_management
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from ldm_patched.modules.model_patcher import ModelPatcher
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from modules_forge.forge_util import resize_image_with_pad
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controlnet_dir = os.path.join(models_path, 'ControlNet')
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os.makedirs(controlnet_dir, exist_ok=True)
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preprocessor_dir = os.path.join(models_path, 'ControlNetPreprocessor')
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os.makedirs(preprocessor_dir, exist_ok=True)
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shared_preprocessors = {}
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def add_preprocessor(preprocessor):
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global shared_preprocessors
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p = preprocessor
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shared_preprocessors[p.name] = p
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return
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class PreprocessorParameter:
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def __init__(self, minimum=0.0, maximum=1.0, step=0.01, label='Parameter 1', value=0.5, visible=False, **kwargs):
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self.gradio_update_kwargs = dict(
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minimum=minimum, maximum=maximum, step=step, label=label, value=value, visible=visible, **kwargs
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)
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class Preprocessor:
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def __init__(self):
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self.name = 'PreprocessorBase'
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self.tags = []
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self.slider_resolution = PreprocessorParameter(label='Resolution', minimum=128, maximum=2048, value=512, step=8, visible=True)
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self.slider_1 = PreprocessorParameter()
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self.slider_2 = PreprocessorParameter()
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self.slider_3 = PreprocessorParameter()
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self.model_patcher: ModelPatcher = None
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self.show_control_mode = True
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self.do_not_need_model = False
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self.sorting_priority = 0.0 # higher goes to top in the list
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def setup_model_patcher(self, model, load_device=None, offload_device=None, dtype=torch.float32, **kwargs):
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if load_device is None:
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load_device = model_management.get_torch_device()
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if offload_device is None:
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offload_device = torch.device('cpu')
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if not model_management.should_use_fp16(load_device):
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dtype = torch.float32
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model.eval()
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model = model.to(device=offload_device, dtype=dtype)
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self.model_patcher = ModelPatcher(model=model, load_device=load_device, offload_device=offload_device, **kwargs)
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self.model_patcher.dtype = dtype
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return self.model_patcher
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def move_all_model_patchers_to_gpu(self):
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model_management.load_models_gpu([self.model_patcher])
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return
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def send_tensor_to_model_device(self, x):
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return x.to(device=self.model_patcher.current_device, dtype=self.model_patcher.dtype)
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def lazy_memory_management(self, model):
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# This is a lazy method to just free some memory
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# so that we can still use old codes to manage memory in a bad way
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# Ideally this should all be removed and all memory should be managed by model patcher.
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# But the workload is too big, so we just use a quick method to manage in dirty way.
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required_memory = model_management.module_size(model) + model_management.minimum_inference_memory()
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model_management.free_memory(required_memory, device=model_management.get_torch_device())
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return
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def process_before_every_sampling(self, process, cnet):
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return
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def __call__(self, input_image, resolution, slider_1=None, slider_2=None, slider_3=None, **kwargs):
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return input_image
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class PreprocessorNone(Preprocessor):
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def __init__(self):
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super().__init__()
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self.name = 'None'
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class PreprocessorCanny(Preprocessor):
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def __init__(self):
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super().__init__()
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self.name = 'canny'
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self.tags = ['Canny']
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self.slider_1 = PreprocessorParameter(minimum=0, maximum=256, step=1, value=100, label='Low Threshold', visible=True)
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self.slider_2 = PreprocessorParameter(minimum=0, maximum=256, step=1, value=200, label='High Threshold', visible=True)
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def __call__(self, input_image, resolution, slider_1=None, slider_2=None, slider_3=None, **kwargs):
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input_image, remove_pad = resize_image_with_pad(input_image, resolution)
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canny_image = cv2.cvtColor(cv2.Canny(input_image, int(slider_1), int(slider_2)), cv2.COLOR_GRAY2RGB)
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return remove_pad(canny_image)
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add_preprocessor(PreprocessorNone())
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add_preprocessor(PreprocessorCanny())
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