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Merge pull request #7789 from space-nuko/extra-networks-per-batch

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Apply extra networks per-batch instead of per-session (fixes wildcards)
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AUTOMATIC1111 2023-02-19 08:44:40 +03:00 committed by GitHub
commit b66b6829aa
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1 changed files with 5 additions and 6 deletions
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11
modules/processing.py
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@ -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: if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
model_hijack.embedding_db.load_textual_inversion_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: if p.scripts is not None:
p.scripts.process(p) p.scripts.process(p)
@ -582,9 +580,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN": if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
sd_vae_approx.model() 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: with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
processed = Processed(p, [], p.seed, "") processed = Processed(p, [], p.seed, "")
file.write(processed.infotext(p, 0)) file.write(processed.infotext(p, 0))
@ -609,7 +604,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if len(prompts) == 0: if len(prompts) == 0:
break 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: 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=prompts, seeds=seeds, subseeds=subseeds)