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cleanup

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Signed-off-by: vladmandic <mandic00@live.com>
This commit is contained in:
vladmandic
2026-03-13 14:44:58 +01:00
parent 35803746df
commit e0faa149dd
11 changed files with 69 additions and 73 deletions
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modules/processing_diffusers.py
+10 -7
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@@ -143,6 +143,7 @@ def process_base(p: processing.StableDiffusionProcessing):
update_sampler(p, shared.sd_model)
timer.process.record('prepare')
process_pre(p)
sched_eta = p.scheduler_eta if p.scheduler_eta is not None else shared.opts.scheduler_eta
desc = 'Base'
if 'detailer' in p.ops:
desc = 'Detail'
@@ -154,7 +155,7 @@ def process_base(p: processing.StableDiffusionProcessing):
prompts_2=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else p.prompts,
negative_prompts_2=[p.refiner_negative] if len(p.refiner_negative) > 0 else p.negative_prompts,
num_inference_steps=calculate_base_steps(p, use_refiner_start=use_refiner_start, use_denoise_start=use_denoise_start),
eta=shared.opts.scheduler_eta,
eta=sched_eta,
guidance_scale=p.cfg_scale,
guidance_rescale=p.diffusers_guidance_rescale,
true_cfg_scale=p.pag_scale,
@@ -163,12 +164,13 @@ def process_base(p: processing.StableDiffusionProcessing):
num_frames=getattr(p, 'frames', 1),
output_type=output_type,
clip_skip=p.clip_skip,
prompt_attention=getattr(p, 'prompt_attention', None),
desc=desc,
)
base_steps = base_args.get('prior_num_inference_steps', None) or p.steps or base_args.get('num_inference_steps', None)
shared.state.update(get_job_name(p, shared.sd_model), base_steps, 1)
if shared.opts.scheduler_eta is not None and shared.opts.scheduler_eta > 0 and shared.opts.scheduler_eta < 1:
p.extra_generation_params["Sampler Eta"] = shared.opts.scheduler_eta
if sched_eta is not None and sched_eta > 0 and sched_eta < 1:
p.extra_generation_params["Sampler Eta"] = sched_eta
output = None
if debug:
modelstats.analyze()
@@ -304,6 +306,7 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
prompts = p.prompts
reset_prompts = False
sched_eta = p.scheduler_eta if p.scheduler_eta is not None else shared.opts.scheduler_eta
if len(p.refiner_prompt) > 0:
prompts = len(output.images)* [p.refiner_prompt]
prompts, p.network_data = extra_networks.parse_prompts(prompts)
@@ -319,13 +322,14 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
prompts_2=len(output.images) * [p.refiner_prompt] if len(p.refiner_prompt) > 0 else p.prompts,
negative_prompts_2=len(output.images) * [p.refiner_negative] if len(p.refiner_negative) > 0 else p.negative_prompts,
num_inference_steps=calculate_hires_steps(p),
eta=shared.opts.scheduler_eta,
eta=sched_eta,
guidance_scale=p.image_cfg_scale if p.image_cfg_scale is not None else p.cfg_scale,
guidance_rescale=p.diffusers_guidance_rescale,
output_type=output_type,
clip_skip=p.clip_skip,
image=output.images,
strength=strength,
prompt_attention=getattr(p, 'prompt_attention', None),
desc='Hires',
)
@@ -397,15 +401,14 @@ def process_refine(p: processing.StableDiffusionProcessing, output):
p.extra_generation_params['Noise level'] = noise_level
refiner_output_type = 'np'
update_sampler(p, shared.sd_refiner, second_pass=True)
shared.opts.prompt_attention = 'fixed'
sched_eta = p.scheduler_eta if p.scheduler_eta is not None else shared.opts.scheduler_eta
refiner_args = set_pipeline_args(
p=p,
model=shared.sd_refiner,
prompts=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else p.prompts[i],
negative_prompts=[p.refiner_negative] if len(p.refiner_negative) > 0 else p.negative_prompts[i],
num_inference_steps=calculate_refiner_steps(p),
eta=shared.opts.scheduler_eta,
# strength=p.denoising_strength,
eta=sched_eta,
noise_level=noise_level, # StableDiffusionUpscalePipeline only
guidance_scale=p.image_cfg_scale if p.image_cfg_scale is not None else p.cfg_scale,
guidance_rescale=p.diffusers_guidance_rescale,