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correct metadata before/after

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This commit is contained in:
Vladimir Mandic
2023-05-09 09:09:06 -04:00
parent 373223a3d3
commit 1288aec459
6 changed files with 23 additions and 60 deletions
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modules/processing.py
+16 -51
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@@ -449,7 +449,6 @@ def fix_seed(p):
def create_infotext(p: StableDiffusionProcessing, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0): # pylint: disable=unused-argument
index = position_in_batch + iteration * p.batch_size
generation_params = {
"Steps": p.steps,
"Sampler": p.sampler_name,
@@ -479,11 +478,8 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts, all_seeds, all_su
"Token merging stride y": None if opts.token_merging_stride_y == 2 else opts.token_merging_stride_y
}
generation_params.update(p.extra_generation_params)
generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[index] if p.all_negative_prompts[index] else ""
return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
@@ -542,17 +538,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
assert len(p.prompt) > 0
else:
assert p.prompt is not None
devices.torch_gc()
seed = get_fixed_seed(p.seed)
subseed = get_fixed_seed(p.subseed)
modules.sd_hijack.model_hijack.apply_circular(p.tiling)
modules.sd_hijack.model_hijack.clear_comments()
comments = {}
if type(p.prompt) == list:
p.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.prompt]
else:
@@ -562,12 +553,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
p.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.negative_prompt]
else:
p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]
if type(seed) == list:
p.all_seeds = seed
else:
p.all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(p.all_prompts))]
if type(subseed) == list:
p.all_subseeds = subseed
else:
@@ -578,13 +567,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if os.path.exists(opts.embeddings_dir) and not p.do_not_reload_embeddings:
model_hijack.embedding_db.load_textual_inversion_embeddings()
if p.scripts is not None:
p.scripts.process(p)
infotexts = []
output_images = []
cached_uc = [None, None]
cached_c = [None, None]
@@ -598,13 +584,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
have been used before. The second element is where the previously
computed result is stored.
"""
if cache[0] is not None and (required_prompts, steps) == cache[0]:
return cache[1]
with devices.autocast():
cache[1] = function(shared.sd_model, required_prompts, steps)
cache[0] = (required_prompts, steps)
return cache[1]
@@ -613,49 +596,33 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
sd_vae_approx.model()
if state.job_count == -1:
state.job_count = p.n_iter
extra_network_data = None
for n in range(p.n_iter):
p.iteration = n
if state.skipped:
state.skipped = False
if state.interrupted:
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]
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:
break
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))
step_multiplier = 1
if not shared.opts.dont_fix_second_order_samplers_schedule:
try:
@@ -664,17 +631,13 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
pass
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)
if len(model_hijack.comments) > 0:
for comment in model_hijack.comments:
comments[comment] = 1
if p.n_iter > 1:
shared.state.job = f"Batch {n+1} out of {p.n_iter}"
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)
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:
@@ -690,45 +653,41 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
devices.test_for_nans(x, "vae")
else:
raise e
x_samples_ddim = torch.stack(x_samples_ddim).float()
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
del samples_ddim
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
lowvram.send_everything_to_cpu()
devices.torch_gc()
if p.scripts is not None:
p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
for i, x_sample in enumerate(x_samples_ddim):
p.batch_index = i
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8)
if p.restore_faces:
if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
orig = p.restore_faces
p.restore_faces = False
info=infotext(n, i)
p.restore_faces = orig
images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=info, p=p, suffix="-before-face-restoration")
devices.torch_gc()
x_sample = modules.face_restoration.restore_faces(x_sample)
devices.torch_gc()
image = Image.fromarray(x_sample)
if p.scripts is not None:
pp = scripts.PostprocessImageArgs(image)
p.scripts.postprocess_image(p, pp)
image = pp.image
if p.color_corrections is not None and i < len(p.color_corrections):
if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
orig = p.color_corrections
p.color_corrections = None
info=infotext(n, i)
p.color_corrections = orig
image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
images.save_image(image_without_cc, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
images.save_image(image_without_cc, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=info, p=p, suffix="-before-color-correction")
image = apply_color_correction(p.color_corrections[i], image)
image = apply_overlay(image, p.paste_to, i, p.overlay_images)
if opts.samples_save and not p.do_not_save_samples:
@@ -878,7 +837,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
return
if not isinstance(image, Image.Image):
image = sd_samplers.sample_to_image(image, index, approximation=0)
orig1 = self.extra_generation_params
orig2 = self.restore_faces
self.extra_generation_params = {}
self.restore_faces = False
info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index)
self.extra_generation_params = orig1
self.restore_faces = orig2
images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, suffix="-before-highres-fix")
if latent_scale_mode is not None: