From 704196b0701982b295073cec1a5f3a47d568cfec Mon Sep 17 00:00:00 2001
From: ssit <ssit5568@gmail.com>
Date: Sat, 21 Oct 2023 18:04:58 -0400
Subject: [PATCH] Change match to ifs for older python combatibility

Co-Authored-By: shiertier <90256303+shiertier@users.noreply.github.com>
---
 restart_sampling.py | 58 ++++++++++++++++++++++-----------------------
 1 file changed, 28 insertions(+), 30 deletions(-)

diff --git a/restart_sampling.py b/restart_sampling.py
index af6f36b..994152b 100644
--- a/restart_sampling.py
+++ b/restart_sampling.py
@@ -89,11 +89,10 @@ def restart_sampling(model, seed, steps, cfg, sampler_name, scheduler, positive,
     _restart_scheduler = restart_scheduler
     _restart_segments = prepare_restart_segments(restart_info)
 
-    match sampler_name:
-        case "ddim":
-            sampler_wrapper = DDIMWrapper()
-        case _:
-            sampler_wrapper = KSamplerRestartWrapper(sampler_name)
+    if sampler_name == "ddim":
+        sampler_wrapper = DDIMWrapper()
+    else:
+        sampler_wrapper = KSamplerRestartWrapper(sampler_name)
 
     # Add the additional steps to the progress bar
     pbar_update_absolute = ProgressBar.update_absolute
@@ -127,31 +126,30 @@ class OneStepSampler:
         self.denoise = denoise
 
         # Get the sampler function
-        match sampler:
-            case "ddim":
-                sampler = DDIMSampler(self.model, device=self.device)
-                sampler.make_schedule_timesteps(ddim_timesteps=timesteps, verbose=False)
-                z_enc = sampler.stochastic_encode(latent_image, torch.tensor(
-                    [len(timesteps) - 1] * noise.shape[0]).to(self.device), noise=noise, max_denoise=max_denoise)
-                samples, _ = sampler.sample_custom(ddim_timesteps=timesteps,
-                                                   conditioning=positive,
-                                                   batch_size=noise.shape[0],
-                                                   shape=noise.shape[1:],
-                                                   verbose=False,
-                                                   unconditional_guidance_scale=cfg,
-                                                   unconditional_conditioning=negative,
-                                                   eta=0.0,
-                                                   x_T=z_enc,
-                                                   x0=latent_image,
-                                                   img_callback=ddim_callback,
-                                                   denoise_function=sampling_function,
-                                                   extra_args=extra_args,
-                                                   mask=noise_mask,
-                                                   to_zero=sigmas[-1] == 0,
-                                                   end_step=sigmas.shape[0] - 1,
-                                                   disable_pbar=disable_pbar)
-            case _:
-                sample = getattr(k_diffusion_sampling, f"sample_{sampler}")
+        if sampler == "ddim":
+            sampler = DDIMSampler(self.model, device=self.device)
+            sampler.make_schedule_timesteps(ddim_timesteps=timesteps, verbose=False)
+            z_enc = sampler.stochastic_encode(latent_image, torch.tensor(
+                [len(timesteps) - 1] * noise.shape[0]).to(self.device), noise=noise, max_denoise=max_denoise)
+            samples, _ = sampler.sample_custom(ddim_timesteps=timesteps,
+                                                conditioning=positive,
+                                                batch_size=noise.shape[0],
+                                                shape=noise.shape[1:],
+                                                verbose=False,
+                                                unconditional_guidance_scale=cfg,
+                                                unconditional_conditioning=negative,
+                                                eta=0.0,
+                                                x_T=z_enc,
+                                                x0=latent_image,
+                                                img_callback=ddim_callback,
+                                                denoise_function=sampling_function,
+                                                extra_args=extra_args,
+                                                mask=noise_mask,
+                                                to_zero=sigmas[-1] == 0,
+                                                end_step=sigmas.shape[0] - 1,
+                                                disable_pbar=disable_pbar)
+        else:
+            sample = getattr(k_diffusion_sampling, f"sample_{sampler}")
 
 
 class RestartWrapper: