From 65aee8cf7bd7262c058182a37d0def62f767d57c Mon Sep 17 00:00:00 2001
From: ljleb <lebel.louisjacob@gmail.com>
Date: Wed, 2 Aug 2023 15:54:41 -0400
Subject: [PATCH] refact

---
 modules/processing.py | 26 ++++++++++++--------------
 modules/scripts.py    |  1 -
 2 files changed, 12 insertions(+), 15 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index c65f16a5d..a313c89d7 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -648,20 +648,20 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if shared.state.interrupted:
                 shared.log.debug(f'Process interrupted: {n}/{p.n_iter}')
                 break
-            prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
-            negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
-            seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
-            subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
+            p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+            p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+            p.seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
+            p.subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
             if p.scripts is not None:
-                p.scripts.before_process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
-            if len(prompts) == 0:
+                p.scripts.before_process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
+            if len(p.prompts) == 0:
                 break
-            prompts, extra_network_data = extra_networks.parse_prompts(prompts)
+            p.prompts, extra_network_data = extra_networks.parse_prompts(p.prompts)
             if not p.disable_extra_networks:
                 with devices.autocast():
                     extra_networks.activate(p, extra_network_data)
             if p.scripts is not None:
-                p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
+                p.scripts.process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
             if n == 0:
                 with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
                     processed = Processed(p, [], p.seed, "")
@@ -673,13 +673,13 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
 
             if shared.backend == shared.Backend.ORIGINAL:
-                uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps * step_multiplier, cached_uc)
-                c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps * step_multiplier, cached_c)
+                uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, p.negative_prompts, p.steps * step_multiplier, cached_uc)
+                c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, p.prompts, p.steps * step_multiplier, cached_c)
                 if len(model_hijack.comments) > 0:
                     for comment in model_hijack.comments:
                         comments[comment] = 1
                 with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
-                    samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
+                    samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
                 x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
                 try:
                     for x in x_samples_ddim:
@@ -701,7 +701,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
             elif shared.backend == shared.Backend.DIFFUSERS:
                 from modules.processing_diffusers import process_diffusers
-                x_samples_ddim = process_diffusers(p, seeds, prompts, negative_prompts)
+                x_samples_ddim = process_diffusers(p, p.seeds, p.prompts, p.negative_prompts)
 
             else:
                 raise ValueError(f"Unknown backend {shared.backend}")
@@ -711,8 +711,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 devices.torch_gc()
             if p.scripts is not None:
                 p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
-            p.seeds = seeds
-            p.subseeds = subseeds
             if p.scripts is not None:
                 p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
                 p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
diff --git a/modules/scripts.py b/modules/scripts.py
index 8efc44018..bfd04c30e 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -124,7 +124,6 @@ class Script:
           - p.negative_prompts
           - p.seeds
           - p.subseeds
-
         **kwargs will have same items as process_batch, and also:
           - batch_number - index of current batch, from 0 to number of batches-1
         """