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refact

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This commit is contained in:
ljleb
2023-08-02 15:54:41 -04:00
parent 88cc6f63e1
commit 65aee8cf7b
2 changed files with 12 additions and 15 deletions
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modules/processing.py
+12 -14
View File
@@ -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]
modules/scripts.py
-1
View File
@@ -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
"""