use local vars

1 files changed, 6 insertions, 6 deletions

diff --git a/modules/processing.py b/modules/processing.py
index c16404f4..7043477f 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -722,20 +722,20 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         all_subseeds = p.all_subseeds[:]

 

         # apply changes to generation data

-        all_prompts[n * p.batch_size:(n + 1) * p.batch_size] = p.prompts

-        all_seeds[n * p.batch_size:(n + 1) * p.batch_size] = p.seeds

-        all_subseeds[n * p.batch_size:(n + 1) * p.batch_size] = p.subseeds

+        all_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.prompts

+        all_seeds[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.seeds

+        all_subseeds[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.subseeds

 

         # update p.all_negative_prompts in case extensions changed the size of the batch

         # create_infotext below uses it

-        old_negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]

-        p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size] = p.negative_prompts

+        old_negative_prompts = p.all_negative_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size]

+        p.all_negative_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.negative_prompts

 

         try:

             return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch, use_main_prompt)

         finally:

             # restore p.all_negative_prompts in case extensions changed the size of the batch

-            p.all_negative_prompts[n * p.batch_size:n * p.batch_size + len(p.negative_prompts)] = old_negative_prompts

+            p.all_negative_prompts[iteration * p.batch_size:iteration * p.batch_size + len(p.negative_prompts)] = old_negative_prompts

 

     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:

         model_hijack.embedding_db.load_textual_inversion_embeddings()