mirror of
https://github.com/vladmandic/automatic
synced 2026-09-19 01:04:32 +02:00
bring metadata apply to speed
This commit is contained in:
Vladimir Mandic
+1
-1
@@ -4,7 +4,7 @@
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Examples:
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- sd15: train.py --type lora --tag girl --comments sdnext --input ~/generative/Input/mia --process original,interrogate,resize --name mia
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- sdxl: train.py --type lora --tag girl --comments sdnext --input ~/generative/Input/mia --process original,interrogate,resize --precision fp32 --optimizer Adafactor --sdxl --name miaxl
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- offline: train.py --type lora --tag girl --comments sdnext --input ~/generative/Input/mia --model /home/vlado/dev/sdnext/models/Stable-diffusion/sdxl/miaanimeSFWNSFWSDXL_v40.safetensors --dir /home/vlado/dev/sdnext/models/Lora/ --precision fp32 --optimizer Adafactor --sdxl --name miaxl
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- offline: train.py --type lora --tag girl --comments sdnext --input ~/generative/Input/mia --model /home/vlado/dev/sdnext/models/Stable-diffusion/sdxl/miaanimeSFWNSFWSDXL_v40.safetensors --dir /home/vlado/dev/sdnext/models/Lora/ --precision fp32 --optimizer Adafactor --sdxl --name miaxl
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"""
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# system imports
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Submodule extensions-builtin/sd-webui-agent-scheduler updated: 546e699747...b085fd31e6
+3
-3
@@ -576,9 +576,9 @@
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{"id":"","label":"GFPGAN","localized":"","hint":"Restore low quality faces using GFPGAN neural network"},
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{"id":"","label":"CodeFormer weight parameter","localized":"","hint":"0 = maximum effect; 1 = minimum effect"},
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{"id":"","label":"Move face restoration model from VRAM into RAM after processing","localized":"","hint":""},
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{"id":"","label":"Token merging ratio","localized":"","hint":"Enable redundant token merging via tomesd for speed and memory improvements, 0=disabled"},
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{"id":"","label":"Token merging ratio for img2img","localized":"","hint":"Enable redundant token merging for img2img via tomesd for speed and memory improvements, 0=disabled"},
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{"id":"","label":"Token merging ratio for hires pass","localized":"","hint":"Enable redundant token merging for hires pass via tomesd for speed and memory improvements, 0=disabled"},
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{"id":"","label":"Token merging ratio (txt2img)","localized":"","hint":"Enable redundant token merging via tomesd for speed and memory improvements, 0=disabled"},
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{"id":"","label":"Token merging ratio (img2img)","localized":"","hint":"Enable redundant token merging for img2img via tomesd for speed and memory improvements, 0=disabled"},
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{"id":"","label":"Token merging ratio (hires)","localized":"","hint":"Enable redundant token merging for hires pass via tomesd for speed and memory improvements, 0=disabled"},
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{"id":"","label":"Diffusers pipeline","localized":"","hint":"If autodetect does not detect model automatically, select model type before loading a model"},
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{"id":"","label":"Move base model to CPU when using refiner","localized":"","hint":""},
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{"id":"","label":"Move base model to CPU when using VAE","localized":"","hint":""},
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+9
-1
@@ -535,7 +535,7 @@ def install_packages():
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install('pi-heif', 'pi_heif', ignore=True)
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tensorflow_package = os.environ.get('TENSORFLOW_PACKAGE', 'tensorflow==2.13.0')
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install(tensorflow_package, 'tensorflow', ignore=True)
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install('nvidia-ml-py', 'pynvml', ignore=True)
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# install('nvidia-ml-py', 'pynvml', ignore=True)
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bitsandbytes_package = os.environ.get('BITSANDBYTES_PACKAGE', None)
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if bitsandbytes_package is not None:
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install(bitsandbytes_package, 'bitsandbytes', ignore=True)
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@@ -693,6 +693,14 @@ def install_submodules():
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def ensure_base_requirements():
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try:
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import setuptools # pylint: disable=unused-import
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except ImportError:
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install('setuptools', 'setuptools')
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try:
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import setuptools # pylint: disable=unused-import
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except ImportError:
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pass
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try:
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import rich # pylint: disable=unused-import
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except ImportError:
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@@ -101,7 +101,7 @@ svg.feather.feather-image, .feather .feather-image { display: none }
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#div.gradio-container { overflow-x: hidden; }
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#img2img_label_copy_to_img2img { font-weight: normal; }
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#txt2img_prompt, #txt2img_neg_prompt, #img2img_prompt, #img2img_neg_prompt { background-color: var(--background-color); box-shadow: 4px 4px 4px 0px #333333 !important; }
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#txt2img_prompt > label > textarea, #txt2img_neg_prompt > label > textarea, #img2img_prompt > label > textarea, #img2img_neg_prompt > label > textarea { font-size: 1.1rem; }
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#txt2img_prompt > label > textarea, #txt2img_neg_prompt > label > textarea, #img2img_prompt > label > textarea, #img2img_neg_prompt > label > textarea { font-size: 1.0em; line-height: 1.4em; }
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#img2img_settings { min-width: calc(2 * var(--left-column)); max-width: calc(2 * var(--left-column)); background-color: #111111; padding-top: 16px; }
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#interrogate, #deepbooru { margin: 0 0px 10px 0px; max-width: 80px; max-height: 80px; font-weight: normal; font-size: 0.95em; }
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#quicksettings .gr-button-tool { font-size: 1.6rem; box-shadow: none; margin-left: -20px; margin-top: -2px; height: 2.4em; }
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@@ -262,23 +262,45 @@ settings_map = {}
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infotext_to_setting_name_mapping = [
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('VAE', 'sd_vae'),
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('Conditional mask weight', 'inpainting_mask_weight'),
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('Model hash', 'sd_model_checkpoint'),
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('Backed', 'sd_backend'),
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('Model hash', 'sd_model_checkpoint'),
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('Refiner', 'sd_model_refiner'),
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('VAE', 'sd_vae'),
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('Parser', 'prompt_attention'),
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('ENSD', 'eta_noise_seed_delta'),
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('Noise multiplier', 'initial_noise_multiplier'),
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('Eta', 'scheduler_eta'),
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('Color correction', 'img2img_color_correction'),
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('LoRA method', 'diffusers_lora_loader'),
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('Discard penultimate sigma', 'discard_next_to_last_sigma'),
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('UniPC variant', 'uni_pc_variant'),
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# Samplers
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('Sampler Eta', 'scheduler_eta'),
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('Sampler ENSD', 'eta_noise_seed_delta'),
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('Sampler order', 'schedulers_solver_order'),
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# Samplers diffusers
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('Sampler beta schedule', 'schedulers_beta_schedule'),
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('Sampler beta start', 'schedulers_beta_start'),
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('Sampler beta end', 'schedulers_beta_end'),
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('Sampler DPM solver', 'schedulers_dpm_solver'),
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# Samplers original
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('Sampler brownian', 'schedulers_brownian_noise'),
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('Sampler discard', 'schedulers_discard_penultimate'),
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('Sampler dyn threshold', 'schedulers_use_thresholding'),
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('Sampler karras', 'schedulers_use_karras'),
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('Sampler low order', 'schedulers_use_loworder'),
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('Sampler quantization', 'enable_quantization'),
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('Sampler sigma', 'schedulers_sigma'),
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('Sampler sigma min', 's_min'),
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('Sampler sigma max', 's_max'),
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('Sampler sigma churn', 's_churn'),
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('Sampler sigma uncond', 's_min_uncond'),
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('Sampler sigma noise', 's_noise'),
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('Sampler sigma tmin', 's_tmin'),
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('Sampler ENSM', 'initial_noise_multiplier'), # img2img only
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('UniPC skip type', 'uni_pc_skip_type'),
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('UniPC order', 'schedulers_solver_order'),
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('UniPC lower order final', 'schedulers_use_loworder'),
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('UniPC variant', 'uni_pc_variant'),
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# Token Merging
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('Mask weight', 'inpainting_mask_weight'),
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('Token merging ratio', 'token_merging_ratio'),
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('Token merging ratio hr', 'token_merging_ratio_hr'),
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('ToMe', 'token_merging_ratio'),
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('ToMe hires', 'token_merging_ratio_hr'),
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('ToMe img2img', 'token_merging_ratio_img2img'),
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]
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@@ -348,12 +370,14 @@ def connect_paste(button, local_paste_fields, input_comp, override_settings_comp
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if v is None:
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continue
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if shared.opts.disable_weights_auto_swap:
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if setting_name == "sd_model_checkpoint" or setting_name == 'sd_model_refiner' or setting_name == 'sd_backend':
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if setting_name == "sd_model_checkpoint" or setting_name == 'sd_model_refiner' or setting_name == 'sd_backend' or setting_name == 'sd_vae':
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continue
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v = shared.opts.cast_value(setting_name, v)
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current_value = getattr(shared.opts, setting_name, None)
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if v == current_value:
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continue
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if type(current_value) == str and v == os.path.splitext(current_value)[0]:
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continue
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vals[param_name] = v
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vals_pairs = [f"{k}: {v}" for k, v in vals.items()]
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return gr.Dropdown.update(value=vals_pairs, choices=vals_pairs, visible=len(vals_pairs) > 0)
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+35
-8
@@ -483,11 +483,11 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts, all_seeds, all_su
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"Clip skip": p.clip_skip if p.clip_skip > 1 else None,
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"Prompt2": p.refiner_prompt if len(p.refiner_prompt) > 0 else None,
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"Negative2": p.refiner_negative if len(p.refiner_negative) > 0 else None,
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# other
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"ENSD": shared.opts.eta_noise_seed_delta if shared.opts.eta_noise_seed_delta != 0 and modules.sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p) 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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"Tiling": p.tiling if p.tiling else None,
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# sdnext
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"Backend": 'Diffusers' if shared.backend == shared.Backend.DIFFUSERS else 'Original',
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"App": 'SD.Next',
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"Version": git_commit,
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"Comment": comment,
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"Operations": '; '.join(ops).replace('"', '') if len(p.ops) > 0 else 'none',
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@@ -495,6 +495,8 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts, all_seeds, all_su
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if 'txt2img' in p.ops:
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pass
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if 'hires' in p.ops or 'upscale' in p.ops:
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args["Second pass"] = p.enable_hr
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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 upscaler"] = p.hr_upscaler
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args["Hires upscale"] = p.hr_scale
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@@ -505,6 +507,7 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts, all_seeds, all_su
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args["Image CFG scale"] = p.image_cfg_scale
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args["CFG rescale"] = p.diffusers_guidance_rescale if shared.backend == shared.Backend.DIFFUSERS else None
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if 'refine' in p.ops:
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args["Second pass"] = 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['Image CFG scale'] = p.image_cfg_scale
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args['Refiner steps'] = p.refiner_steps
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@@ -515,10 +518,9 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts, all_seeds, all_su
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if 'img2img' in p.ops or 'inpaint' in p.ops:
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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["Conditional 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["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['Resize mode'] = getattr(p, 'resize_mode', None)
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args["Mask blur"] = p.mask_blur if getattr(p, 'mask', None) is not None and getattr(p, 'mask_blur', 0) > 0 else None
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args["Noise multiplier"] = p.initial_noise_multiplier if getattr(p, 'initial_noise_multiplier', 1.0) != 1.0 else None
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args["Denoising strength"] = getattr(p, 'denoising_strength', None)
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if 'face' in p.ops:
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args["Face restoration"] = shared.opts.face_restoration_model
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@@ -528,11 +530,34 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts, all_seeds, all_su
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if hasattr(modules.sd_hijack.model_hijack, 'embedding_db') and len(modules.sd_hijack.model_hijack.embedding_db.embeddings_used) > 0: # this is for original hijaacked models only, diffusers are handled separately
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args["Embeddings"] = ', '.join(modules.sd_hijack.model_hijack.embedding_db.embeddings_used)
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# samplers
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args["Sampler ENSD"] = shared.opts.eta_noise_seed_delta if shared.opts.eta_noise_seed_delta != 0 and modules.sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p) else None
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args["Sampler ENSM"] = p.initial_noise_multiplier if getattr(p, 'initial_noise_multiplier', 1.0) != 1.0 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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if shared.backend == shared.Backend.DIFFUSERS:
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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 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 DPM solver'] = shared.opts.schedulers_dpm_solver if shared.opts.schedulers_dpm_solver != shared.opts.data_labels.get('schedulers_dpm_solver').default else None
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if shared.backend == shared.Backend.ORIGINAL:
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args['Sampler brownian'] = shared.opts.schedulers_brownian_noise if shared.opts.schedulers_brownian_noise != shared.opts.data_labels.get('schedulers_brownian_noise').default else None
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args['Sampler discard'] = shared.opts.schedulers_discard_penultimate if shared.opts.schedulers_discard_penultimate != shared.opts.data_labels.get('schedulers_discard_penultimate').default else None
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args['Sampler dyn threshold'] = 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 karras'] = shared.opts.schedulers_use_karras if shared.opts.schedulers_use_karras != shared.opts.data_labels.get('schedulers_use_karras').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 quantization'] = shared.opts.enable_quantization if shared.opts.enable_quantization != shared.opts.data_labels.get('enable_quantization').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 sigma min'] = shared.opts.s_min if shared.opts.s_min != shared.opts.data_labels.get('s_min').default else None
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args['Sampler sigma max'] = shared.opts.s_max if shared.opts.s_max != shared.opts.data_labels.get('s_max').default else None
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args['Sampler sigma churn'] = shared.opts.s_churn if shared.opts.s_churn != shared.opts.data_labels.get('s_churn').default else None
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args['Sampler sigma uncond'] = shared.opts.s_churn if shared.opts.s_churn != shared.opts.data_labels.get('s_churn').default else None
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args['Sampler sigma noise'] = shared.opts.s_noise if shared.opts.s_noise != shared.opts.data_labels.get('s_noise').default else None
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args['Sampler sigma tmin'] = shared.opts.s_tmin if shared.opts.s_tmin != shared.opts.data_labels.get('s_tmin').default else None
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# tome
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token_merging_ratio = p.get_token_merging_ratio()
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token_merging_ratio_hr = p.get_token_merging_ratio(for_hr=True) if p.enable_hr else None
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args['Token merging ratio'] = token_merging_ratio if token_merging_ratio != 0 else None
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args['Token merging ratio hr'] = token_merging_ratio_hr if token_merging_ratio_hr != 0 else None
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args['ToMe'] = token_merging_ratio if token_merging_ratio != 0 else None
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args['ToMe hires'] = token_merging_ratio_hr if token_merging_ratio_hr != 0 else None
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args.update(p.extra_generation_params)
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params_text = ", ".join([k if k == v else f'{k}: {modules.generation_parameters_copypaste.quote(v)}' for k, v in args.items() if v is not None])
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@@ -579,7 +604,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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stored_opts = {}
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for k, v in p.override_settings.copy().items():
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orig = shared.opts.data.get(k, None) or shared.opts.data_labels[k].default
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if orig == v or os.path.splitext(orig)[0] == v:
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if orig == v or (type(orig) == str and os.path.splitext(orig)[0] == v):
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p.override_settings.pop(k, None)
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for k in p.override_settings.keys():
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stored_opts[k] = shared.opts.data.get(k, None) or shared.opts.data_labels[k].default
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@@ -596,7 +621,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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if p.override_settings.get('sd_vae', None) is not None:
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if p.override_settings.get('sd_vae', None) == 'TAESD':
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p.full_quality = False
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# p.override_settings.pop('sd_vae', None)
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p.override_settings.pop('sd_vae', None)
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if p.override_settings.get('Hires upscaler', None) is not None:
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p.enable_hr = True
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if len(p.override_settings.keys()) > 0:
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shared.log.debug(f'Override: {p.override_settings}')
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for k, v in p.override_settings.items():
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@@ -197,12 +197,6 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
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return task_args
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def set_pipeline_args(model, prompts: list, negative_prompts: list, prompts_2: typing.Optional[list]=None, negative_prompts_2: typing.Optional[list]=None, desc:str='', **kwargs):
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# if hasattr(model, 'embedding_db'):
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# del model.embedding_db
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try:
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is_refiner = model.text_encoder.__class__.__name__ != 'CLIPTextModel'
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except Exception:
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is_refiner = False
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if hasattr(model, "set_progress_bar_config"):
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model.set_progress_bar_config(bar_format='Progress {rate_fmt}{postfix} {bar} {percentage:3.0f}% {n_fmt}/{total_fmt} {elapsed} {remaining} ' + '\x1b[38;5;71m' + desc, ncols=80, colour='#327fba')
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args = {}
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@@ -324,7 +318,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
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if sampler is None:
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sampler = sd_samplers.all_samplers_map.get("UniPC")
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sd_samplers.create_sampler(sampler.name, shared.sd_model) # TODO(Patrick): For wrapped pipelines this is currently a no-op
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# p.extra_generation_params['Sampler options'] = '' # TODO
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# p.extra_generation_params['Sampler options'] = '' # TODO sampler_options
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p.extra_generation_params['Pipeline'] = shared.sd_model.__class__.__name__
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@@ -375,7 +369,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
|
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)
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# p.steps = base_args['num_inference_steps']
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p.extra_generation_params['CFG rescale'] = p.diffusers_guidance_rescale
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p.extra_generation_params["Eta"] = shared.opts.scheduler_eta if shared.opts.scheduler_eta is not None and shared.opts.scheduler_eta > 0 and shared.opts.scheduler_eta < 1 else None
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p.extra_generation_params["Sampler Eta"] = shared.opts.scheduler_eta if shared.opts.scheduler_eta is not None and shared.opts.scheduler_eta > 0 and shared.opts.scheduler_eta < 1 else None
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try:
|
||||
output = shared.sd_model(**base_args) # pylint: disable=not-callable
|
||||
except AssertionError as e:
|
||||
|
||||
@@ -136,14 +136,14 @@ class VanillaStableDiffusionSampler:
|
||||
else:
|
||||
self.eta = 0.0
|
||||
if self.eta != 0.0:
|
||||
p.extra_generation_params["Eta DDIM"] = self.eta
|
||||
p.extra_generation_params["Sampler Eta"] = self.eta
|
||||
|
||||
if self.is_unipc:
|
||||
keys = [
|
||||
('Solver order', 'schedulers_solver_order'),
|
||||
('Sampler low order', 'schedulers_use_loworder'),
|
||||
('UniPC variant', 'uni_pc_variant'),
|
||||
('UniPC skip type', 'uni_pc_skip_type'),
|
||||
('UniPC order', 'schedulers_solver_order'),
|
||||
('UniPC lower order final', 'schedulers_use_loworder'),
|
||||
]
|
||||
|
||||
for name, key in keys:
|
||||
|
||||
@@ -294,7 +294,7 @@ class KDiffusionSampler:
|
||||
extra_params_kwargs[param_name] = getattr(p, param_name)
|
||||
if 'eta' in inspect.signature(self.func).parameters:
|
||||
if self.eta != 1.0:
|
||||
p.extra_generation_params["Eta"] = self.eta
|
||||
p.extra_generation_params["Sampler Eta"] = self.eta
|
||||
extra_params_kwargs['eta'] = self.eta
|
||||
|
||||
return extra_params_kwargs
|
||||
|
||||
+8
-11
@@ -407,9 +407,9 @@ options_templates.update(options_section(('optimizations', "Optimizations"), {
|
||||
"sub_quad_kv_chunk_size": OptionInfo(512, "cross-attention kv chunk size", gr.Slider, {"minimum": 0, "maximum": 8192, "step": 8}),
|
||||
"sub_quad_chunk_threshold": OptionInfo(80, "cross-attention chunking threshold", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1}),
|
||||
"token_merging_sep": OptionInfo("<h2>Token Merging</h2>", "", gr.HTML),
|
||||
"token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}),
|
||||
"token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}),
|
||||
"token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for hires pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}),
|
||||
"token_merging_ratio": OptionInfo(0.0, "Token merging ratio (txt2img)", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}),
|
||||
"token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio (img2img)", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}),
|
||||
"token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for (hires)", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}),
|
||||
"inference_mode": OptionInfo("no-grad", "Torch inference mode", gr.Radio, lambda: {"choices": ["no-grad", "inference-mode", "none"]}),
|
||||
"sd_vae_sliced_encode": OptionInfo(False, "VAE Slicing (original)"),
|
||||
}))
|
||||
@@ -564,9 +564,9 @@ options_templates.update(options_section(('ui', "User Interface"), {
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('live-preview', "Live Previews"), {
|
||||
"show_progressbar": OptionInfo(True, "Show progressbar"),
|
||||
"live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
|
||||
"show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
|
||||
"show_progressbar": OptionInfo(True, "Show progressbar", gr.Checkbox, {"visible": False}),
|
||||
"live_previews_enable": OptionInfo(True, "Show live previews of the created image", gr.Checkbox, {"visible": False}),
|
||||
"show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid", gr.Checkbox, {"visible": False}),
|
||||
"notification_audio_enable": OptionInfo(False, "Play a sound when images are finished generating"),
|
||||
"notification_audio_path": OptionInfo("html/notification.mp3","Path to notification sound", component_args=hide_dirs, folder=True),
|
||||
"show_progress_every_n_steps": OptionInfo(1, "Live preview display period", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
|
||||
@@ -587,18 +587,19 @@ options_templates.update(options_section(('sampler-params', "Sampler Settings"),
|
||||
"schedulers_brownian_noise": OptionInfo(True, "Use Brownian noise", gr.Checkbox, {"visible": False}),
|
||||
"schedulers_discard_penultimate": OptionInfo(True, "Discard penultimate sigma", gr.Checkbox, {"visible": False}),
|
||||
"schedulers_sigma": OptionInfo("default", "Sigma algorithm", gr.Radio, {"choices": ['default', 'karras', 'exponential', 'polyexponential'], "visible": False}),
|
||||
# managed from ui.py for backend diffusers
|
||||
"schedulers_use_karras": OptionInfo(True, "Use Karras sigmas", gr.Checkbox, {"visible": False}),
|
||||
"schedulers_use_thresholding": OptionInfo(False, "Use dynamic thresholding", gr.Checkbox, {"visible": False}),
|
||||
"schedulers_use_loworder": OptionInfo(True, "Use simplified solvers in final steps", gr.Checkbox, {"visible": False}),
|
||||
"schedulers_prediction_type": OptionInfo("default", "Override model prediction type", gr.Radio, lambda: {"choices": ['default', 'epsilon', 'sample', 'v-prediction'], "visible": False}),
|
||||
|
||||
# managed from ui.py for backend diffusers
|
||||
"schedulers_sep_diffusers": OptionInfo("<h2>Diffusers specific config</h2>", "", gr.HTML),
|
||||
"schedulers_dpm_solver": OptionInfo("sde-dpmsolver++", "DPM solver algorithm", gr.Radio, lambda: {"choices": ['dpmsolver', 'dpmsolver++', 'sde-dpmsolver', 'sde-dpmsolver++']}),
|
||||
"schedulers_beta_schedule": OptionInfo("default", "Override beta schedule", gr.Radio, lambda: {"choices": ['default', 'linear', 'scaled_linear', 'squaredcos_cap_v2']}),
|
||||
'schedulers_beta_start': OptionInfo(0, "Override beta start", gr.Number, {}),
|
||||
'schedulers_beta_end': OptionInfo(0, "Override beta end", gr.Number, {}),
|
||||
|
||||
# managed from ui.py for backend original k-diffusion
|
||||
"schedulers_sep_kdiffusers": OptionInfo("<h2>K-Diffusion specific config</h2>", "", gr.HTML),
|
||||
"always_batch_cond_uncond": OptionInfo(False, "Disable conditional batching enabled on low memory systems"),
|
||||
"enable_quantization": OptionInfo(True, "Enable quantization for sharper and cleaner results"),
|
||||
@@ -608,10 +609,6 @@ options_templates.update(options_section(('sampler-params', "Sampler Settings"),
|
||||
's_noise': OptionInfo(1.0, "Sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
's_min': OptionInfo(0.0, "Sigma min", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
's_max': OptionInfo(0.0, "Sigma max", gr.Slider, {"minimum": 0.0, "maximum": 100.0, "step": 1.0}),
|
||||
# 'discard_next_to_last_sigma': OptionInfo("default", "Discard penultimate sigma", gr.Radio, lambda: {"choices": ['default', 'always', 'never']}),
|
||||
# 'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
|
||||
# 'never_discard_next_to_last_sigma': OptionInfo(False, "Never discard next-to-last sigma"),
|
||||
|
||||
"schedulers_sep_compvis": OptionInfo("<h2>CompVis specific config</h2>", "", gr.HTML),
|
||||
'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
|
||||
'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}),
|
||||
|
||||
+14
-18
@@ -495,17 +495,11 @@ def create_ui(startup_timer = None):
|
||||
res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height], show_progress=False)
|
||||
batch_switch_btn.click(lambda w, h: (h, w), inputs=[batch_count, batch_size], outputs=[batch_count, batch_size], show_progress=False)
|
||||
txt_prompt_img.change(fn=modules.images.image_data, inputs=[txt_prompt_img], outputs=[txt2img_prompt, txt_prompt_img])
|
||||
"""
|
||||
show_sampler.change(gr_show, inputs=[show_sampler], outputs=[sampler_group], show_progress = False)
|
||||
show_batch.change(gr_show, inputs=[show_batch], outputs=[batch_group], show_progress = False)
|
||||
show_seed.change(gr_show, inputs=[show_seed], outputs=[seed_group], show_progress = False)
|
||||
show_advanced.change(gr_show, inputs=[show_advanced], outputs=[advanced_group], show_progress = False)
|
||||
show_second_pass.change(enable_hr_change, inputs=[show_second_pass, refiner_start], outputs=[second_pass_group, hr_refiner], show_progress = False)
|
||||
"""
|
||||
|
||||
txt2img_paste_fields = [
|
||||
(txt2img_prompt, "Prompt"),
|
||||
(txt2img_negative_prompt, "Negative prompt"),
|
||||
# (txt2img_prompt_styles, "Styles"),
|
||||
(steps, "Steps"),
|
||||
(seed, "Seed"),
|
||||
(sampler_index, "Sampler"),
|
||||
@@ -514,7 +508,6 @@ def create_ui(startup_timer = None):
|
||||
(height, "Size-2"),
|
||||
(subseed, "Variation seed"),
|
||||
(subseed_strength, "Variation strength"),
|
||||
(full_quality, "Full quality"),
|
||||
(clip_skip, "Clip skip"),
|
||||
(latent_index, "Latent sampler"),
|
||||
(latent_index, "Secondary sampler"),
|
||||
@@ -527,14 +520,15 @@ def create_ui(startup_timer = None):
|
||||
(batch_count, "Batch count"),
|
||||
(seed_resize_from_w, "Seed resize from-1"),
|
||||
(seed_resize_from_h, "Seed resize from-2"),
|
||||
(enable_hr, "Second pass"),
|
||||
(hr_force, "Hires force"),
|
||||
(hr_scale, "Hires upscale"),
|
||||
(hr_upscaler, "Hires upscaler"),
|
||||
(hr_second_pass_steps, "Hires steps"),
|
||||
(hr_second_pass_steps, "Hires steps"),
|
||||
(hr_resize_x, "Hires resize-1"),
|
||||
(hr_resize_y, "Hires resize-2"),
|
||||
(diffusers_guidance_rescale, "CFG rescale"),
|
||||
(image_cfg_scale, "Refiner CFG scale"),
|
||||
(image_cfg_scale, "Image CFG scale"),
|
||||
(refiner_steps, "Refiner steps"),
|
||||
(refiner_start, "Refiner start"),
|
||||
(tiling, "Tiling"),
|
||||
@@ -837,6 +831,7 @@ def create_ui(startup_timer = None):
|
||||
img2img_paste_fields = [
|
||||
(img2img_prompt, "Prompt"),
|
||||
(img2img_negative_prompt, "Negative prompt"),
|
||||
# (img2img_prompt_styles, "Styles"),
|
||||
(steps, "Steps"),
|
||||
(seed, "Seed"),
|
||||
(sampler_index, "Sampler"),
|
||||
@@ -850,28 +845,29 @@ def create_ui(startup_timer = None):
|
||||
(latent_index, "Latent sampler"),
|
||||
(latent_index, "Secondary sampler"),
|
||||
(denoising_strength, "Denoising strength"),
|
||||
(refiner_steps, "Refiner steps"),
|
||||
(refiner_start, "Refiner start"),
|
||||
(full_quality, "Full quality"),
|
||||
(restore_faces, "Face restoration"),
|
||||
(batch_size, "Batch size"),
|
||||
(batch_count, "Batch count"),
|
||||
(seed_resize_from_w, "Seed resize from-1"),
|
||||
(seed_resize_from_h, "Seed resize from-2"),
|
||||
(resize_mode, "Resize mode"),
|
||||
(image_cfg_scale, "Image CFG scale"),
|
||||
(diffusers_guidance_rescale, "CFG rescale"),
|
||||
(tiling, "Tiling"),
|
||||
(mask_blur, "Mask blur"),
|
||||
(scale_by, "UNKNOWN"), # TODO scale_by
|
||||
# from txt2img
|
||||
(hr_force, "Hires force"),
|
||||
(hr_scale, "Hires upscale"),
|
||||
(hr_upscaler, "Hires upscaler"),
|
||||
(hr_second_pass_steps, "Hires steps"),
|
||||
(hr_second_pass_steps, "Hires steps"),
|
||||
(hr_resize_x, "Hires resize-1"),
|
||||
(hr_resize_y, "Hires resize-2"),
|
||||
(diffusers_guidance_rescale, "CFG rescale"),
|
||||
(image_cfg_scale, "Image CFG scale"),
|
||||
(refiner_steps, "Refiner steps"),
|
||||
(refiner_start, "Refiner start"),
|
||||
(tiling, "Tiling"),
|
||||
(refiner_negative, "Negative2"),
|
||||
(refiner_prompt, "Prompt2"),
|
||||
(mask_blur, "Mask blur"),
|
||||
(refiner_negative, "Negative2"),
|
||||
*modules.scripts.scripts_img2img.infotext_fields
|
||||
]
|
||||
parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings)
|
||||
|
||||
+2
-2
@@ -221,8 +221,8 @@ axis_options = [
|
||||
AxisOption("Prompt order", str_permutations, apply_order, fmt=format_value_join_list),
|
||||
AxisOption("Upscaler", str, apply_upscaler, choices=lambda: [x.name for x in shared.sd_upscalers][1:]),
|
||||
AxisOption("Face restore", str, apply_face_restore, fmt=format_value),
|
||||
AxisOption("Token merging ratio high-res", float, apply_override('token_merging_ratio_hr')),
|
||||
AxisOption("Token merging ratio", float, apply_override('token_merging_ratio')),
|
||||
AxisOption("Token merging ratio (txt2img)", float, apply_override('token_merging_ratio')),
|
||||
AxisOption("Token merging ratio (hires)", float, apply_override('token_merging_ratio_hr')),
|
||||
AxisOptionImg2Img("Image mask weight", float, apply_field("inpainting_mask_weight")),
|
||||
AxisOption("Model dictionary", str, apply_dict, fmt=format_value, cost=1.0, choices=lambda: ['None'] + list(sd_models.checkpoints_list)),
|
||||
AxisOption("Sampler sigma min", float, apply_field("s_min")),
|
||||
|
||||
Reference in New Issue
Block a user