i

modules/core.py

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