mirror of
https://github.com/vladmandic/sdnext.git
synced 2026-01-27 15:02:48 +03:00
533c13a2d5
Fixes restoring parameters when your default hires sampler isn't "Same as primary". In those cases, only the main sampler would be updated because there's no value stored for the hires sampler. While technically you *could* assume that "Same as primary" is meant when other hires settings are loaded without a value for the sampler, it would be a lot more complicated to actually implement. Honestly, it's just better and probably more "expected behavior" to always include the information when using Hires.
206 lines
13 KiB
Python
206 lines
13 KiB
Python
import os
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from installer import git_commit
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from modules import shared, sd_samplers_common, sd_vae, generation_parameters_copypaste
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from modules.processing_class import StableDiffusionProcessing
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args = {} # maintain history
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infotext = '' # maintain history
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debug = shared.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None
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def get_last_args():
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return args, infotext
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def create_infotext(p: StableDiffusionProcessing, all_prompts=None, all_seeds=None, all_subseeds=None, comments=None, iteration=0, position_in_batch=0, index=None, all_negative_prompts=None, grid=None):
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global args, infotext # pylint: disable=global-statement
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if p is None:
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shared.log.warning('Processing info: no data')
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return ''
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if not hasattr(shared.sd_model, 'sd_checkpoint_info'):
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return ''
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if index is None:
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index = position_in_batch + iteration * p.batch_size
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if all_prompts is None:
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all_prompts = p.all_prompts or [p.prompt]
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if all_negative_prompts is None:
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all_negative_prompts = p.all_negative_prompts or [p.negative_prompt]
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if all_seeds is None:
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all_seeds = p.all_seeds or [p.seed]
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if all_subseeds is None:
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all_subseeds = p.all_subseeds or [p.subseed]
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while len(all_prompts) <= index:
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all_prompts.insert(0, p.prompt)
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while len(all_seeds) <= index:
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all_seeds.insert(0, int(p.seed))
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while len(all_subseeds) <= index:
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all_subseeds.insert(0, int(p.subseed))
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while len(all_negative_prompts) <= index:
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all_negative_prompts.insert(0, p.negative_prompt)
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comment = ', '.join(comments) if comments is not None and type(comments) is list else None
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ops = list(set(p.ops))
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args = {
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# basic
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"Steps": p.steps,
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"Size": f"{p.width}x{p.height}" if hasattr(p, 'width') and hasattr(p, 'height') else None,
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"Sampler": p.sampler_name if p.sampler_name != 'Default' else None,
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"Seed": all_seeds[index],
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"Seed resize from": None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}",
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"CFG scale": p.cfg_scale if p.cfg_scale > 1.0 else 1.0,
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"CFG rescale": p.diffusers_guidance_rescale if p.diffusers_guidance_rescale > 0 else None,
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"CFG end": p.cfg_end if p.cfg_end < 1.0 else None,
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"CFG true": p.pag_scale if p.pag_scale > 0 else None,
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"CFG adaptive": p.pag_adaptive if p.pag_adaptive != 0.5 else None,
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"Clip skip": p.clip_skip if p.clip_skip > 1 else None,
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"Batch": f'{p.n_iter}x{p.batch_size}' if p.n_iter > 1 or p.batch_size > 1 else None,
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"Refiner prompt": p.refiner_prompt if len(p.refiner_prompt) > 0 else None,
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"Refiner negative": p.refiner_negative if len(p.refiner_negative) > 0 else None,
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"Styles": "; ".join(p.styles) if p.styles is not None and len(p.styles) > 0 else None,
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"App": 'SD.Next',
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"Version": git_commit,
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"Parser": shared.opts.prompt_attention if shared.opts.prompt_attention != 'native' else None,
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"Comment": comment,
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"Pipeline": shared.sd_model.__class__.__name__,
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"TE": None if (shared.opts.sd_text_encoder is None or shared.opts.sd_text_encoder == 'Default') else shared.opts.sd_text_encoder,
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"UNet": None if (shared.opts.sd_unet is None or shared.opts.sd_unet == 'Default') else shared.opts.sd_unet,
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"Operations": '; '.join(ops).replace('"', '') if len(p.ops) > 0 else 'none',
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}
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if shared.opts.add_model_name_to_info:
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if getattr(shared.sd_model, 'sd_checkpoint_info', None) is not None:
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args["Model"] = shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')
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if shared.opts.add_model_hash_to_info:
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if getattr(p, 'sd_model_hash', None) is not None:
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args["Model hash"] = p.sd_model_hash
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elif getattr(shared.sd_model, 'sd_model_hash', None) is not None:
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args["Model hash"] = shared.sd_model.sd_model_hash
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if p.vae_type == 'Full':
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args["VAE"] = (None if not shared.opts.add_model_name_to_info or sd_vae.loaded_vae_file is None else os.path.splitext(os.path.basename(sd_vae.loaded_vae_file))[0])
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elif p.vae_type == 'Tiny':
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args["VAE"] = 'TAESD'
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elif p.vae_type == 'REPA-E':
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args["VAE"] = 'REPA-E'
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elif p.vae_type == 'Remote':
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args["VAE"] = 'Remote'
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if grid is None and (p.n_iter > 1 or p.batch_size > 1) and index >= 0:
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args['Index'] = f'{p.iteration + 1}x{index + 1}'
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if grid is not None:
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args['Grid'] = grid
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if 'txt2img' in p.ops:
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args["Variation seed"] = all_subseeds[index] if p.subseed_strength > 0 else None
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args["Variation strength"] = p.subseed_strength if p.subseed_strength > 0 else None
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if 'hires' in p.ops or 'upscale' in p.ops:
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is_resize = p.hr_resize_mode > 0 and (p.hr_upscaler != 'None' or p.hr_resize_mode == 5)
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is_fixed = p.hr_resize_x > 0 or p.hr_resize_y > 0
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args["Refine"] = p.enable_hr
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if is_resize:
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args["HiRes mode"] = p.hr_resize_mode
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args["HiRes context"] = p.hr_resize_context if p.hr_resize_mode == 5 else None
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args["Hires upscaler"] = p.hr_upscaler
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if is_fixed:
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args["Hires fixed"] = f"{p.hr_resize_x}x{p.hr_resize_y}"
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else:
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args["Hires scale"] = p.hr_scale
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args["Hires size"] = f"{p.hr_upscale_to_x}x{p.hr_upscale_to_y}"
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if p.hr_force or ('Latent' in p.hr_upscaler):
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args["Hires force"] = p.hr_force
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args["Hires steps"] = p.hr_second_pass_steps
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args["Hires strength"] = p.hr_denoising_strength
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args["Hires sampler"] = p.hr_sampler_name
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args["Hires CFG scale"] = p.image_cfg_scale
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if 'refine' in p.ops:
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args["Refine"] = p.enable_hr
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args["Refiner"] = None if (not shared.opts.add_model_name_to_info) or (not shared.sd_refiner) or (not shared.sd_refiner.sd_checkpoint_info.model_name) else shared.sd_refiner.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')
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args['Hires CFG scale'] = p.image_cfg_scale
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args['Refiner steps'] = p.refiner_steps
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args['Refiner start'] = p.refiner_start
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args["Hires steps"] = p.hr_second_pass_steps
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args["Hires sampler"] = p.hr_sampler_name
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if ('img2img' in p.ops or 'inpaint' in p.ops) and ('txt2img' not in p.ops and 'hires' not in p.ops): # real img2img/inpaint
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args["Init image size"] = f"{getattr(p, 'init_img_width', 0)}x{getattr(p, 'init_img_height', 0)}"
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args["Init image hash"] = getattr(p, 'init_img_hash', None)
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args['Image CFG scale'] = p.image_cfg_scale
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args["Mask weight"] = getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None
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args["Denoising strength"] = getattr(p, 'denoising_strength', None)
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if args["Size"] != args["Init image size"]:
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args['Resize scale'] = float(getattr(p, 'scale_by', None)) if getattr(p, 'scale_by', None) != 1 else None
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args['Resize mode'] = shared.resize_modes[p.resize_mode] if shared.resize_modes[p.resize_mode] != 'None' else None
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if args["Size"] is None:
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args["Size"] = args["Init image size"]
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if p.resize_mode_before != 0 and p.resize_name_before != 'None' and hasattr(p, 'init_images') and p.init_images is not None and len(p.init_images) > 0:
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args['Resize before'] = f"{p.width_before}x{p.height_before}"
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args['Resize mode before'] = p.resize_mode_before
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args['Resize name before'] = p.resize_name_before
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args['Resize scale before'] = float(p.scale_by_before) if p.scale_by_before != 1.0 else None
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if p.resize_mode_after != 0 and p.resize_name_after != 'None':
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args['Resize after'] = f"{p.width_after}x{p.height_after}"
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args['Resize mode after'] = p.resize_mode_after
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args['Resize name after'] = p.resize_name_after
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args['Resize scale after'] = float(p.scale_by_after) if p.scale_by_after != 1.0 else None
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if p.resize_name_mask != 'None' and p.scale_by_mask != 1.0:
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args['Resize mask'] = f"{p.width_mask}x{p.height_mask}"
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args['Resize mode mask'] = p.resize_mode_mask
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args['Resize name mask'] = p.resize_name_mask
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args['Resize scale mask'] = float(p.scale_by_mask)
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if 'detailer' in p.ops:
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args["Detailer"] = ', '.join(shared.opts.detailer_models) if len(shared.opts.detailer_args) == 0 else shared.opts.detailer_args
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args["Detailer steps"] = p.detailer_steps
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args["Detailer strength"] = p.detailer_strength
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args["Detailer resolution"] = p.detailer_resolution if p.detailer_resolution != 1024 else None
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args["Detailer prompt"] = p.detailer_prompt if len(p.detailer_prompt) > 0 else None
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args["Detailer negative"] = p.detailer_negative if len(p.detailer_negative) > 0 else None
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if 'color' in p.ops:
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args["Color correction"] = True
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if shared.opts.token_merging_method == 'ToMe': # tome/todo
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args['ToMe'] = shared.opts.tome_ratio if shared.opts.tome_ratio != 0 else None
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else:
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args['ToDo'] = shared.opts.todo_ratio if shared.opts.todo_ratio != 0 else None
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if hasattr(shared.sd_model, 'embedding_db') and len(shared.sd_model.embedding_db.embeddings_used) > 0: # register used embeddings
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args['Embeddings'] = ', '.join(shared.sd_model.embedding_db.embeddings_used)
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# samplers
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if getattr(p, 'sampler_name', None) is not None and p.sampler_name.lower() != 'default':
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args["Sampler eta delta"] = shared.opts.eta_noise_seed_delta if shared.opts.eta_noise_seed_delta != 0 and sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p) else None
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args["Sampler eta multiplier"] = p.initial_noise_multiplier if getattr(p, 'initial_noise_multiplier', 1.0) != 1.0 else None
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args['Sampler timesteps'] = shared.opts.schedulers_timesteps if shared.opts.schedulers_timesteps != shared.opts.data_labels.get('schedulers_timesteps').default else None
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args['Sampler spacing'] = shared.opts.schedulers_timestep_spacing if shared.opts.schedulers_timestep_spacing != shared.opts.data_labels.get('schedulers_timestep_spacing').default else None
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args['Sampler sigma'] = shared.opts.schedulers_sigma if shared.opts.schedulers_sigma != shared.opts.data_labels.get('schedulers_sigma').default else None
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args['Sampler order'] = shared.opts.schedulers_solver_order if shared.opts.schedulers_solver_order != shared.opts.data_labels.get('schedulers_solver_order').default else None
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args['Sampler type'] = shared.opts.schedulers_prediction_type if shared.opts.schedulers_prediction_type != shared.opts.data_labels.get('schedulers_prediction_type').default else None
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args['Sampler beta schedule'] = shared.opts.schedulers_beta_schedule if shared.opts.schedulers_beta_schedule != shared.opts.data_labels.get('schedulers_beta_schedule').default else None
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args['Sampler low order'] = shared.opts.schedulers_use_loworder if shared.opts.schedulers_use_loworder != shared.opts.data_labels.get('schedulers_use_loworder').default else None
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args['Sampler dynamic'] = shared.opts.schedulers_use_thresholding if shared.opts.schedulers_use_thresholding != shared.opts.data_labels.get('schedulers_use_thresholding').default else None
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args['Sampler rescale'] = shared.opts.schedulers_rescale_betas if shared.opts.schedulers_rescale_betas != shared.opts.data_labels.get('schedulers_rescale_betas').default else None
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args['Sampler beta start'] = shared.opts.schedulers_beta_start if shared.opts.schedulers_beta_start != shared.opts.data_labels.get('schedulers_beta_start').default else None
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args['Sampler beta end'] = shared.opts.schedulers_beta_end if shared.opts.schedulers_beta_end != shared.opts.data_labels.get('schedulers_beta_end').default else None
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args['Sampler range'] = shared.opts.schedulers_timesteps_range if shared.opts.schedulers_timesteps_range != shared.opts.data_labels.get('schedulers_timesteps_range').default else None
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args['Sampler shift'] = shared.opts.schedulers_shift if shared.opts.schedulers_shift != shared.opts.data_labels.get('schedulers_shift').default else None
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args['Sampler dynamic shift'] = shared.opts.schedulers_dynamic_shift if shared.opts.schedulers_dynamic_shift != shared.opts.data_labels.get('schedulers_dynamic_shift').default else None
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# model specific
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if shared.sd_model_type == 'h1':
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args['LLM'] = None if shared.opts.model_h1_llama_repo == 'Default' else shared.opts.model_h1_llama_repo
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args.update(p.extra_generation_params)
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for k, v in args.copy().items():
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if v is None:
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del args[k]
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if type(v) is float or type(v) is int:
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if v <= -1:
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del args[k]
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if isinstance(v, str):
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if len(v) == 0 or v == '0x0':
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del args[k]
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debug(f'Infotext: args={args}')
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params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in args.items()])
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if hasattr(p, 'original_prompt'):
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args['Original prompt'] = p.original_prompt
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if hasattr(p, 'original_negative'):
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args['Original negative'] = p.original_negative
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negative_prompt_text = f"\nNegative prompt: {all_negative_prompts[index] if all_negative_prompts[index] else ''}"
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infotext = f"{all_prompts[index]}{negative_prompt_text}\n{params_text}".strip()
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debug(f'Infotext: "{infotext}"')
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return infotext
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