Merge branch 'master' into weighted-learning

1 files changed, 14 insertions, 10 deletions

diff --git a/modules/processing.py b/modules/processing.py
index e1b53ac0..2009d3bf 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -543,8 +543,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:

         model_hijack.embedding_db.load_textual_inversion_embeddings()

 

-    _, extra_network_data = extra_networks.parse_prompts(p.all_prompts[0:1])

-

     if p.scripts is not None:

         p.scripts.process(p)

 

@@ -582,13 +580,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":

                 sd_vae_approx.model()

 

-            if not p.disable_extra_networks:

-                extra_networks.activate(p, extra_network_data)

-

-        with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:

-            processed = Processed(p, [], p.seed, "")

-            file.write(processed.infotext(p, 0))

-

         if state.job_count == -1:

             state.job_count = p.n_iter

 

@@ -609,11 +600,24 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if len(prompts) == 0:

                 break

 

-            prompts, _ = extra_networks.parse_prompts(prompts)

+            prompts, extra_network_data = extra_networks.parse_prompts(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)

 

+            # params.txt should be saved after scripts.process_batch, since the

+            # infotext could be modified by that callback

+            # Example: a wildcard processed by process_batch sets an extra model

+            # strength, which is saved as "Model Strength: 1.0" in the infotext

+            if n == 0:

+                with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:

+                    processed = Processed(p, [], p.seed, "")

+                    file.write(processed.infotext(p, 0))

+

             uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc)

             c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c)