import os from installer import git_commit from modules import shared, sd_samplers_common, sd_vae, generation_parameters_copypaste from modules.processing_class import StableDiffusionProcessing debug = shared.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None if not shared.native: from modules import sd_hijack else: sd_hijack = None 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): if p is None: shared.log.warning('Processing info: no data') return '' if not hasattr(shared.sd_model, 'sd_checkpoint_info'): return '' if index is None: index = position_in_batch + iteration * p.batch_size if all_prompts is None: all_prompts = p.all_prompts or [p.prompt] if all_negative_prompts is None: all_negative_prompts = p.all_negative_prompts or [p.negative_prompt] if all_seeds is None: all_seeds = p.all_seeds or [p.seed] if all_subseeds is None: all_subseeds = p.all_subseeds or [p.subseed] while len(all_prompts) <= index: all_prompts.append(all_prompts[-1]) while len(all_seeds) <= index: all_seeds.append(all_seeds[-1]) while len(all_subseeds) <= index: all_subseeds.append(all_subseeds[-1]) while len(all_negative_prompts) <= index: all_negative_prompts.append(all_negative_prompts[-1]) comment = ', '.join(comments) if comments is not None and type(comments) is list else None ops = list(set(p.ops)) ops.reverse() args = { # basic "Size": f"{p.width}x{p.height}" if hasattr(p, 'width') and hasattr(p, 'height') else None, "Sampler": p.sampler_name if p.sampler_name != 'Default' else None, "Steps": p.steps, "Seed": all_seeds[index], "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}", "CFG scale": p.cfg_scale if p.cfg_scale > 1.0 else None, "CFG end": p.cfg_end if p.cfg_end < 1.0 else None, "Clip skip": p.clip_skip if p.clip_skip > 1 else None, "Batch": f'{p.n_iter}x{p.batch_size}' if p.n_iter > 1 or p.batch_size > 1 else None, "Model": None if (not shared.opts.add_model_name_to_info) or (not shared.sd_model.sd_checkpoint_info.model_name) else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', ''), "Model hash": getattr(p, 'sd_model_hash', None if (not shared.opts.add_model_hash_to_info) or (not shared.sd_model.sd_model_hash) else shared.sd_model.sd_model_hash), "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]) if p.full_quality else 'TAESD', "Prompt2": p.refiner_prompt if len(p.refiner_prompt) > 0 else None, "Negative2": p.refiner_negative if len(p.refiner_negative) > 0 else None, "Styles": "; ".join(p.styles) if p.styles is not None and len(p.styles) > 0 else None, # sdnext "App": 'SD.Next', "Version": git_commit, "Backend": 'Diffusers' if shared.native else 'Original', "Pipeline": 'LDM', "Parser": shared.opts.prompt_attention.split()[0], "Comment": comment, "Operations": '; '.join(ops).replace('"', '') if len(p.ops) > 0 else 'none', } if shared.opts.add_model_name_to_info and getattr(shared.sd_model, 'sd_checkpoint_info', None) is not None: args["Model"] = shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '') if shared.opts.add_model_hash_to_info and getattr(shared.sd_model, 'sd_model_hash', None) is not None: args["Model hash"] = shared.sd_model.sd_model_hash # native if grid is None and (p.n_iter > 1 or p.batch_size > 1) and index >= 0: args['Index'] = f'{p.iteration + 1}x{index + 1}' if grid is not None: args['Grid'] = grid if shared.native: args['Pipeline'] = shared.sd_model.__class__.__name__ args['TE'] = None if (not shared.opts.add_model_name_to_info or shared.opts.sd_text_encoder is None or shared.opts.sd_text_encoder == 'None') else shared.opts.sd_text_encoder args['UNet'] = None if (not shared.opts.add_model_name_to_info or shared.opts.sd_unet is None or shared.opts.sd_unet == 'None') else shared.opts.sd_unet if 'txt2img' in p.ops: args["Variation seed"] = all_subseeds[index] if p.subseed_strength > 0 else None args["Variation strength"] = p.subseed_strength if p.subseed_strength > 0 else None if 'hires' in p.ops or 'upscale' in p.ops: is_resize = p.hr_resize_mode > 0 and (p.hr_upscaler != 'None' or p.hr_resize_mode == 5) args["Refine"] = p.enable_hr args["Hires force"] = p.hr_force args["Hires steps"] = p.hr_second_pass_steps args["HiRes resize mode"] = p.hr_resize_mode if is_resize else None args["HiRes resize context"] = p.hr_resize_context if p.hr_resize_mode == 5 else None args["Hires upscaler"] = p.hr_upscaler if is_resize else None args["Hires scale"] = p.hr_scale if is_resize else None args["Hires resize"] = f"{p.hr_resize_x}x{p.hr_resize_y}" if is_resize else None args["Hires size"] = f"{p.hr_upscale_to_x}x{p.hr_upscale_to_y}" if is_resize else None args["Denoising strength"] = p.denoising_strength args["Hires sampler"] = p.hr_sampler_name args["Image CFG scale"] = p.image_cfg_scale args["CFG rescale"] = p.diffusers_guidance_rescale if 'refine' in p.ops: args["Refine"] = p.enable_hr 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(':', '') args['Image CFG scale'] = p.image_cfg_scale args['Refiner steps'] = p.refiner_steps args['Refiner start'] = p.refiner_start args["Hires steps"] = p.hr_second_pass_steps args["Hires sampler"] = p.hr_sampler_name args["CFG rescale"] = p.diffusers_guidance_rescale if 'img2img' in p.ops or 'inpaint' in p.ops: args["Init image size"] = f"{getattr(p, 'init_img_width', 0)}x{getattr(p, 'init_img_height', 0)}" args["Init image hash"] = getattr(p, 'init_img_hash', None) args['Resize scale'] = getattr(p, 'scale_by', None) args["Mask weight"] = getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None args["Denoising strength"] = getattr(p, 'denoising_strength', None) if args["Size"] is None: args["Size"] = args["Init image size"] # lookup by index if getattr(p, 'resize_mode', None) is not None: args['Resize mode'] = shared.resize_modes[p.resize_mode] if shared.resize_modes[p.resize_mode] != 'None' else None if hasattr(p, 'width_before') and hasattr(p, 'height_before'): args['Size'] = f"{p.width_before}x{p.height_before}" # override size if getattr(p, 'resize_mode_before', None) is not None: args['Size before'] = f"{p.width_before}x{p.height_before}" args['Size mode before'] = p.resize_mode_before args['Size scale before'] = p.scale_by_before if p.scale_by_before != 1.0 else None args['Size name before'] = p.resize_name_before if getattr(p, 'resize_mode_after', None) is not None: args['Size after'] = f"{p.width_after}x{p.height_after}" if hasattr(p, 'width_after') and hasattr(p, 'height_after') else None args['Size mode after'] = p.resize_mode_after args['Size scale after'] = p.scale_by_after if p.scale_by_after != 1.0 else None args['Size name after'] = p.resize_name_after if getattr(p, 'resize_mode_mask', None) is not None: args['Size mask'] = f"{p.width_mask}x{p.height_mask}" if hasattr(p, 'width_mask') and hasattr(p, 'height_mask') else None args['Size mode mask'] = p.resize_mode_mask args['Size scale mask'] = p.scale_by_mask args['Size name mask'] = p.resize_name_mask if 'detailer' in p.ops: args["Detailer"] = ', '.join(shared.opts.detailer_models) if 'color' in p.ops: args["Color correction"] = True # embeddings if sd_hijack is not None and hasattr(sd_hijack.model_hijack, 'embedding_db') and len(sd_hijack.model_hijack.embedding_db.embeddings_used) > 0: # this is for original hijaacked models only, diffusers are handled separately args["Embeddings"] = ', '.join(sd_hijack.model_hijack.embedding_db.embeddings_used) # samplers if getattr(p, 'sampler_name', None) is not None: 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 args["Sampler eta multiplier"] = p.initial_noise_multiplier if getattr(p, 'initial_noise_multiplier', 1.0) != 1.0 else None args['Sampler timesteps'] = shared.opts.schedulers_timesteps if shared.opts.schedulers_timesteps != shared.opts.data_labels.get('schedulers_timesteps').default else None 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 args['Sampler sigma'] = shared.opts.schedulers_sigma if shared.opts.schedulers_sigma != shared.opts.data_labels.get('schedulers_sigma').default else None 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 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 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 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 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 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 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 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 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 args['Sampler shift'] = shared.opts.schedulers_shift if shared.opts.schedulers_shift != shared.opts.data_labels.get('schedulers_shift').default else None 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 # tome/todo if shared.opts.token_merging_method == 'ToMe': args['ToMe'] = shared.opts.tome_ratio if shared.opts.tome_ratio != 0 else None else: args['ToDo'] = shared.opts.todo_ratio if shared.opts.todo_ratio != 0 else None args.update(p.extra_generation_params) for k, v in args.copy().items(): if v is None: del args[k] if isinstance(v, str): if len(v) == 0 or v == '0x0': del args[k] debug(f'Infotext: args={args}') params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in args.items()]) negative_prompt_text = f"\nNegative prompt: {all_negative_prompts[index]}" if all_negative_prompts[index] else "" infotext = f"{all_prompts[index]}{negative_prompt_text}\n{params_text}".strip() debug(f'Infotext: "{infotext}"') return infotext