fix caching for img2imgalt

scripts/img2imgalt.py

@@ -1,3 +1,5 @@
+from collections import namedtuple
+
 import numpy as np
 from tqdm import trange
 
@@ -56,9 +58,14 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
 
     return x / x.std()
 
-cache = [None, None, None, None, None]
+
+Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt"])
+
 
 class Script(scripts.Script):
+    def __init__(self):
+        self.cache = None
+
     def title(self):
         return "img2img alternative test"
 
@@ -67,7 +74,7 @@ class Script(scripts.Script):
 
     def ui(self, is_img2img):
         original_prompt = gr.Textbox(label="Original prompt", lines=1)
-        cfg = gr.Slider(label="Decode CFG scale", minimum=0.1, maximum=3.0, step=0.1, value=1.0)
+        cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
         st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50)
 
         return [original_prompt, cfg, st]
@@ -77,19 +84,18 @@ class Script(scripts.Script):
         p.batch_count = 1
 
         def sample_extra(x, conditioning, unconditional_conditioning):
-            lat = tuple([int(x*10) for x in p.init_latent.cpu().numpy().flatten().tolist()])
+            lat = (p.init_latent.cpu().numpy() * 10).astype(int)
 
-            if cache[0] is not None and cache[1] == cfg and cache[2] == st and len(cache[3]) == len(lat) and sum(np.array(cache[3])-np.array(lat)) < 100 and cache[4] == original_prompt:
-                noise = cache[0]
+            same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st and self.cache.original_prompt == original_prompt
+            same_everything = same_params and self.cache.latent.shape == lat.shape and np.abs(self.cache.latent-lat).sum() < 100
+
+            if same_everything:
+                noise = self.cache.noise
             else:
                 shared.state.job_count += 1
                 cond = p.sd_model.get_learned_conditioning(p.batch_size * [original_prompt])
                 noise = find_noise_for_image(p, cond, unconditional_conditioning, cfg, st)
-                cache[0] = noise
-                cache[1] = cfg
-                cache[2] = st
-                cache[3] = lat
-                cache[4] = original_prompt
+                self.cache = Cached(noise, cfg, st, lat, original_prompt)
 
             sampler = samplers[p.sampler_index].constructor(p.sd_model)