fix denoising on controlnet

sampling.py

@@ -252,22 +252,17 @@ def denoise_controlnet(
     #init_latents = rearrange(init_latents, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
     if image2image_strength is not None and orig_image is not None:
 
-        t_idx = np.clip(
-            int((np.clip(image2image_strength, 0.0, 1.0)) * len(timesteps)), 0, 1
-        )
+        t_idx = int((1 - np.clip(image2image_strength, 0.0, 1.0)) * len(timesteps))
         t = timesteps[t_idx]
-        try:
-            timesteps = timesteps[t_idx:]
-        except:
-            pass
+        timesteps = timesteps[t_idx:]
         orig_image = rearrange(orig_image, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2).to(img.device, dtype = img.dtype)
         img = t * img + (1.0 - t) * orig_image
-    controlnet.to(img.device, dtype=img.dtype)
     img_ids=img_ids.to(img.device, dtype=img.dtype)
-    controlnet_cond=controlnet_cond.to(img.device, dtype=img.dtype)
     txt=txt.to(img.device, dtype=img.dtype)
     txt_ids=txt_ids.to(img.device, dtype=img.dtype)
     vec=vec.to(img.device, dtype=img.dtype)
+    controlnet.to(img.device, dtype=img.dtype)
+    controlnet_cond=controlnet_cond.to(img.device, dtype=img.dtype)
     if hasattr(model, "guidance_in"):
         guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
     else: