mirror of
https://github.com/vladmandic/automatic
synced 2026-08-25 22:20:46 +02:00
Vladimir Mandic
91 lines
4.4 KiB
Python
91 lines
4.4 KiB
Python
import numpy as np
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from PIL import Image
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from modules import scripts_postprocessing, shared
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from modules.logger import log
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class ScriptPostprocessingDetailer(scripts_postprocessing.ScriptPostprocessing):
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name = "Detailer"
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order = 15000
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def ui(self):
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# The detailer accordion (built by yolo.ui) now contains the Sampler sub-accordion too, so for 'extras'
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# it returns a 7th element: a dict of the sampler-block controls. Spread it into the control map; their
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# values are stamped onto the synthetic p in process()/make_processing(), applying to this pass only.
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enabled, prompt, negative, steps, strength, resolution, classes, sampler_block = shared.detailer.ui('extras')
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return {
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"enabled": enabled,
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"prompt": prompt,
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"negative": negative,
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"steps": steps,
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"strength": strength,
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"resolution": resolution,
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"classes": classes,
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**sampler_block,
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}
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def process(self, pp: scripts_postprocessing.PostprocessedImage, # pylint: disable=arguments-differ
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enabled=False, prompt='', negative='', steps=10, strength=0.3, resolution=1024, classes='',
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sampler='Default', prediction='default', shift=3.0, cfg_scale=6.0, options=None, seed=-1):
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if not enabled:
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return pp
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if not shared.sd_loaded:
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log.warning('Detailer postprocess: SD model not loaded')
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pp.info["Detailer"] = "skipped (SD model not loaded)"
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return pp
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if shared.sd_model is None or not hasattr(shared.sd_model, 'sd_checkpoint_info'):
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log.warning('Detailer postprocess: no base model selected')
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pp.info["Detailer"] = "skipped (no base model selected)"
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return pp
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# The sampler block is stamped onto the synthetic p. The schedulers_* values become per-job overrides in
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# processing_helpers (they beat the global opts for this pass only); a named sampler is required for them
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# to take effect, 'Default' keeps the model scheduler. cfg_scale and hr_sampler_name apply directly.
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options = options or []
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overrides = {
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'hr_sampler_name': sampler,
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'schedulers_prediction_type': prediction,
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'schedulers_shift': shift,
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'cfg_scale': cfg_scale,
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'schedulers_use_loworder': 'low order' in options,
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'schedulers_use_thresholding': 'thresholding' in options,
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'schedulers_dynamic_shift': 'dynamic' in options,
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'schedulers_rescale_betas': 'rescale' in options,
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}
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log.info(f'Detailer postprocess: strength={strength} steps={steps} resolution={resolution} sampler={sampler} cfg={cfg_scale}')
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p = shared.detailer.make_processing(pp.image,
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prompt=prompt,
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negative=negative,
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steps=steps,
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strength=strength,
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resolution=resolution,
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classes=classes,
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seed=int(seed) if seed is not None else -1,
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overrides=overrides,
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)
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try:
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result = shared.detailer.restore(np.array(pp.image), p)
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except Exception as e:
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log.error(f'Detailer postprocess: {e}')
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return pp
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# restore() returns list[ndarray] (detailed image at [0], annotated debug at [1] when enabled)
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# on success, or a single ndarray on early-return paths. The postprocessing pipeline is one
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# image per input, so the annotated debug image is dropped here; use /sdapi/v1/detail for it.
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if isinstance(result, list) and len(result) > 0:
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pp.image = Image.fromarray(result[0])
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elif isinstance(result, np.ndarray):
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pp.image = Image.fromarray(result)
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pp.info["Detailer"] = "Enabled"
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pp.info["Detailer strength"] = strength
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pp.info["Detailer steps"] = steps
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pp.info["Detailer resolution"] = resolution
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pp.info["Detailer sampler"] = sampler
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if prompt:
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pp.info["Detailer prompt"] = prompt
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if negative:
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pp.info["Detailer negative"] = negative
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return pp
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