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facehires support batch size&count, add override strength

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Vladimir Mandic
2024-03-14 11:40:58 -04:00
parent 5b4aca87d1
commit 940b6d02ba
8 changed files with 39 additions and 33 deletions
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modules/processing.py
+13 -13
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@@ -268,18 +268,19 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
extra_network_data = None
debug(f'Processing inner: args={vars(p)}')
for n in range(p.n_iter):
debug(f'Processing inner: iteration={n+1}/{p.n_iter}')
p.iteration = n
if shared.state.skipped:
shared.log.debug(f'Process skipped: {n}/{p.n_iter}')
shared.log.debug(f'Process skipped: {n+1}/{p.n_iter}')
shared.state.skipped = False
continue
if shared.state.interrupted:
shared.log.debug(f'Process interrupted: {n}/{p.n_iter}')
shared.log.debug(f'Process interrupted: {n+1}/{p.n_iter}')
break
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]
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 and isinstance(p.scripts, scripts.ScriptRunner):
p.scripts.before_process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
if len(p.prompts) == 0:
@@ -313,8 +314,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if p.scripts is not None and isinstance(p.scripts, scripts.ScriptRunner):
p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
if p.scripts is not None and isinstance(p.scripts, scripts.ScriptRunner):
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.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]
batch_params = scripts.PostprocessBatchListArgs(list(x_samples_ddim))
p.scripts.postprocess_batch_list(p, batch_params, batch_number=n)
x_samples_ddim = batch_params.images
@@ -326,6 +327,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
shared.sd_model.restore_pipeline()
for i, x_sample in enumerate(x_samples_ddim):
if hasattr(p, 'recursion'):
continue
debug(f'Processing result: index={i+1}/{len(x_samples_ddim)} iteration={n+1}/{p.n_iter}')
p.batch_index = i
if type(x_sample) == Image.Image:
image = x_sample
@@ -335,11 +339,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
image = Image.fromarray(x_sample)
if p.restore_faces:
if not p.do_not_save_samples and shared.opts.save_images_before_face_restoration:
orig = p.restore_faces
p.restore_faces = False
info = infotext(i)
p.restore_faces = orig
images.save_image(Image.fromarray(x_sample), path=p.outpath_samples, basename="", seed=p.seeds[i], prompt=p.prompts[i], extension=shared.opts.samples_format, info=info, p=p, suffix="-before-face-restore")
images.save_image(Image.fromarray(x_sample), path=p.outpath_samples, basename="", seed=p.seeds[i], prompt=p.prompts[i], extension=shared.opts.samples_format, info=infotext(i), p=p, suffix="-before-face-restore")
p.ops.append('face')
x_sample = face_restoration.restore_faces(x_sample, p)
image = Image.fromarray(x_sample)