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@ -15,6 +15,7 @@ from nodes import VAEDecode, EmptyLatentImage, CLIPTextEncode
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from comfy.sample import prepare_mask, broadcast_cond, load_additional_models, cleanup_additional_models
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from modules.samplers_advanced import KSampler, KSamplerWithRefiner
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from modules.adm_patch import patch_negative_adm
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from modules.cv2win32 import show_preview
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patch_negative_adm()
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@ -22,8 +23,6 @@ opCLIPTextEncode = CLIPTextEncode()
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opEmptyLatentImage = EmptyLatentImage()
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opVAEDecode = VAEDecode()
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cv2_is_top = False
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class StableDiffusionModel:
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def __init__(self, unet, vae, clip, clip_vision):
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@ -82,26 +81,13 @@ def get_previewer(device, latent_format):
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x_sample = einops.rearrange(x_sample, 'b c h w -> b h w c')
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x_sample = x_sample.cpu().numpy()[..., ::-1].copy().clip(0, 255).astype(np.uint8)
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for i, s in enumerate(x_sample):
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flag = f'OpenCV Diffusion Preview {i}'
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cv2.imshow(flag, s)
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cv2.setWindowTitle(flag, f'Preview Image {i} [{step}/{total_steps}]')
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if not cv2_is_top:
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cv2.setWindowProperty(flag, cv2.WND_PROP_TOPMOST, 1)
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cv2_is_top = True
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else:
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cv2.setWindowProperty(flag, cv2.WND_PROP_TOPMOST, 0)
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cv2.waitKey(1)
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show_preview(f'OpenCV Diffusion Preview {i}', s, title=f'Preview Image {i} [{step}/{total_steps}]')
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taesd.preview = preview_function
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return taesd
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def close_all_preview():
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cv2_is_top = False
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cv2.destroyAllWindows()
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@torch.no_grad()
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def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sampler_name='dpmpp_2m_sde_gpu',
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scheduler='karras', denoise=1.0, disable_noise=False, start_step=None, last_step=None,
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@ -0,0 +1,13 @@
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import cv2
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def show_preview(flag, img, title=None):
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if title is None:
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title = flag
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cv2.imshow(flag, img)
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cv2.setWindowTitle(flag, title)
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cv2.waitKey(1)
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def close_all_preview():
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cv2.destroyAllWindows()
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@ -3,6 +3,7 @@ import os
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import torch
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from modules.path import modelfile_path, lorafile_path
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from modules.cv2win32 import close_all_preview
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xl_base_filename = os.path.join(modelfile_path, 'sd_xl_base_1.0.safetensors')
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@ -43,6 +44,6 @@ def process(positive_prompt, negative_prompt, width=1024, height=1024, batch_siz
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images = core.image_to_numpy(decoded_latent)
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core.close_all_preview()
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close_all_preview()
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return images
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