added original negative prompt to img2img alt

scripts/img2imgalt.py

@@ -59,7 +59,7 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
     return x / x.std()
 
 
-Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt"])
+Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt", "original_negative_prompt"])
 
 
 class Script(scripts.Script):
@@ -74,19 +74,20 @@ class Script(scripts.Script):
 
     def ui(self, is_img2img):
         original_prompt = gr.Textbox(label="Original prompt", lines=1)
+        original_negative_prompt = gr.Textbox(label="Original negative prompt", lines=1)
         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)
         randomness = gr.Slider(label="randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0)
-        return [original_prompt, cfg, st, randomness]
+        return [original_prompt, original_negative_prompt, cfg, st, randomness]
 
-    def run(self, p, original_prompt, cfg, st, randomness):
+    def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness):
         p.batch_size = 1
         p.batch_count = 1
 
         def sample_extra(x, conditioning, unconditional_conditioning):
             lat = (p.init_latent.cpu().numpy() * 10).astype(int)
 
-            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_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st and self.cache.original_prompt == original_prompt and self.cache.original_negative_prompt == original_negative_prompt
             same_everything = same_params and self.cache.latent.shape == lat.shape and np.abs(self.cache.latent-lat).sum() < 100
 
             if same_everything:
@@ -94,9 +95,9 @@ class Script(scripts.Script):
             else:
                 shared.state.job_count += 1
                 cond = p.sd_model.get_learned_conditioning(p.batch_size * [original_prompt])
-                uncond = p.sd_model.get_learned_conditioning(p.batch_size * [""])
+                uncond = p.sd_model.get_learned_conditioning(p.batch_size * [original_negative_prompt])
                 rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
-                self.cache = Cached(rec_noise, cfg, st, lat, original_prompt)
+                self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt)
 
             rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], [p.seed + x + 1 for x in range(p.init_latent.shape[0])])