added original negative prompt to img2img alt

1 files changed, 7 insertions, 6 deletions

diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py
index dbda3255..7f1f53a7 100644
--- a/scripts/img2imgalt.py
+++ b/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])])