Update app.py

app.py

@@ -16,8 +16,8 @@ def run(image, src_style, src_prompt, prompts, shared_score_shift, shared_score_
     image = image.resize((dim, dim))
     x0 = np.array(image)
     zts = inversion.ddim_inversion(pipeline, x0, src_prompt, num_inference_steps, 2)
+    offset = min(5, len(zts) - 1)
     prompts.insert(0, src_prompt)
-
     shared_score_shift = np.log(shared_score_shift)
     handler = sa_handler.Handler(pipeline)
     sa_args = sa_handler.StyleAlignedArgs(
@@ -25,12 +25,11 @@ def run(image, src_style, src_prompt, prompts, shared_score_shift, shared_score_
         adain_queries=True, adain_keys=True, adain_values=False,
         shared_score_shift=shared_score_shift, shared_score_scale=shared_score_scale,)
     handler.register(sa_args)
-
     for i in range(1, len(prompts)):
         prompts[i] = f'{prompts[i]}, {src_style}.'
-
-    zT, inversion_callback = inversion.make_inversion_callback(zts, offset=5)
+    zT, inversion_callback = inversion.make_inversion_callback(zts, offset=offset)
     g_cpu = torch.Generator(device='cpu')
+    
     if seed > 0:
         g_cpu.manual_seed(seed)
     latents = torch.randn(len(prompts), 4, d, d, device='cpu', generator=g_cpu, dtype=pipeline.unet.dtype,).to(device)