import os import torch import gradio as gr from gradio.context import Context from modules import shared_items, shared, ui_common, sd_models, processing, infotext_utils, paths, ui_loadsave from backend import memory_management, stream from backend.args import dynamic_args from modules.shared import cmd_opts total_vram = int(memory_management.total_vram) ui_forge_preset: gr.Radio = None ui_checkpoint: gr.Dropdown = None ui_vae: gr.Dropdown = None ui_clip_skip: gr.Slider = None ui_forge_unet_storage_dtype_options: gr.Radio = None ui_forge_async_loading: gr.Radio = None ui_forge_pin_shared_memory: gr.Radio = None ui_forge_inference_memory: gr.Slider = None forge_unet_storage_dtype_options = { 'Automatic': (None, False), 'Automatic (fp16 LoRA)': (None, True), 'bnb-nf4': ('nf4', False), 'bnb-nf4 (fp16 LoRA)': ('nf4', True), 'float8-e4m3fn': (torch.float8_e4m3fn, False), 'float8-e4m3fn (fp16 LoRA)': (torch.float8_e4m3fn, True), 'bnb-fp4': ('fp4', False), 'bnb-fp4 (fp16 LoRA)': ('fp4', True), 'float8-e5m2': (torch.float8_e5m2, False), 'float8-e5m2 (fp16 LoRA)': (torch.float8_e5m2, True), } module_list = {} def bind_to_opts(comp, k, save=False, callback=None): def on_change(v): shared.opts.set(k, v) if save: shared.opts.save(shared.config_filename) if callback is not None: callback() return comp.change(on_change, inputs=[comp], queue=False, show_progress=False) return def make_checkpoint_manager_ui(): global ui_checkpoint, ui_vae, ui_clip_skip, ui_forge_unet_storage_dtype_options, ui_forge_async_loading, ui_forge_pin_shared_memory, ui_forge_inference_memory, ui_forge_preset if shared.opts.sd_model_checkpoint in [None, 'None', 'none', '']: if len(sd_models.checkpoints_list) == 0: sd_models.list_models() if len(sd_models.checkpoints_list) > 0: shared.opts.set('sd_model_checkpoint', next(iter(sd_models.checkpoints_list.values())).name) ui_forge_preset = gr.Radio(label="UI", value=lambda: shared.opts.forge_preset, choices=['sd', 'xl', 'flux', 'all'], elem_id="forge_ui_preset") ckpt_list, vae_list = refresh_models() ui_checkpoint = gr.Dropdown( value=lambda: shared.opts.sd_model_checkpoint, label="Checkpoint", elem_classes=['model_selection'], choices=ckpt_list ) ui_vae = gr.Dropdown( value=lambda: [os.path.basename(x) for x in shared.opts.forge_additional_modules], multiselect=True, label="VAE / Text Encoder", render=False, choices=vae_list ) def gr_refresh_models(): a, b = refresh_models() return gr.update(choices=a), gr.update(choices=b) refresh_button = ui_common.ToolButton(value=ui_common.refresh_symbol, elem_id=f"forge_refresh_checkpoint", tooltip="Refresh") refresh_button.click( fn=gr_refresh_models, inputs=[], outputs=[ui_checkpoint, ui_vae], show_progress=False, queue=False ) Context.root_block.load( fn=gr_refresh_models, inputs=[], outputs=[ui_checkpoint, ui_vae], show_progress=False, queue=False ) ui_vae.render() ui_forge_unet_storage_dtype_options = gr.Dropdown(label="Diffusion in Low Bits", value=lambda: shared.opts.forge_unet_storage_dtype, choices=list(forge_unet_storage_dtype_options.keys())) bind_to_opts(ui_forge_unet_storage_dtype_options, 'forge_unet_storage_dtype', save=True, callback=refresh_model_loading_parameters) ui_forge_async_loading = gr.Radio(label="Swap Method", value=lambda: shared.opts.forge_async_loading, choices=['Queue', 'Async']) ui_forge_pin_shared_memory = gr.Radio(label="Swap Location", value=lambda: shared.opts.forge_pin_shared_memory, choices=['CPU', 'Shared']) ui_forge_inference_memory = gr.Slider(label="GPU Weights (MB)", value=lambda: total_vram - shared.opts.forge_inference_memory, minimum=0, maximum=int(memory_management.total_vram), step=1) mem_comps = [ui_forge_inference_memory, ui_forge_async_loading, ui_forge_pin_shared_memory] ui_forge_inference_memory.change(ui_refresh_memory_management_settings, inputs=mem_comps, queue=False, show_progress=False) ui_forge_async_loading.change(ui_refresh_memory_management_settings, inputs=mem_comps, queue=False, show_progress=False) ui_forge_pin_shared_memory.change(ui_refresh_memory_management_settings, inputs=mem_comps, queue=False, show_progress=False) Context.root_block.load(ui_refresh_memory_management_settings, inputs=mem_comps, queue=False, show_progress=False) ui_clip_skip = gr.Slider(label="Clip skip", value=lambda: shared.opts.CLIP_stop_at_last_layers, **{"minimum": 1, "maximum": 12, "step": 1}) bind_to_opts(ui_clip_skip, 'CLIP_stop_at_last_layers', save=True) ui_checkpoint.change(checkpoint_change, inputs=[ui_checkpoint], show_progress=False) ui_vae.change(modules_change, inputs=[ui_vae], queue=False, show_progress=False) return def find_files_with_extensions(base_path, extensions): found_files = {} for root, _, files in os.walk(base_path): for file in files: if any(file.endswith(ext) for ext in extensions): full_path = os.path.join(root, file) found_files[file] = full_path return found_files def refresh_models(): global module_list shared_items.refresh_checkpoints() ckpt_list = shared_items.list_checkpoint_tiles(shared.opts.sd_checkpoint_dropdown_use_short) file_extensions = ['ckpt', 'pt', 'bin', 'safetensors', 'gguf'] module_list.clear() module_paths = [ os.path.abspath(os.path.join(paths.models_path, "VAE")), os.path.abspath(os.path.join(paths.models_path, "text_encoder")), ] if isinstance(shared.cmd_opts.vae_dir, str): module_paths.append(os.path.abspath(shared.cmd_opts.vae_dir)) if isinstance(shared.cmd_opts.text_encoder_dir, str): module_paths.append(os.path.abspath(shared.cmd_opts.text_encoder_dir)) for vae_path in module_paths: vae_files = find_files_with_extensions(vae_path, file_extensions) module_list.update(vae_files) return ckpt_list, module_list.keys() def ui_refresh_memory_management_settings(model_memory, async_loading, pin_shared_memory): """ Passes precalculated 'model_memory' from "GPU Weights" UI slider (skip redundant calculation) """ refresh_memory_management_settings( async_loading=async_loading, pin_shared_memory=pin_shared_memory, model_memory=model_memory # Use model_memory directly from UI slider value ) def refresh_memory_management_settings(async_loading=None, inference_memory=None, pin_shared_memory=None, model_memory=None): # Fallback to defaults if values are not passed async_loading = async_loading if async_loading is not None else shared.opts.forge_async_loading inference_memory = inference_memory if inference_memory is not None else shared.opts.forge_inference_memory pin_shared_memory = pin_shared_memory if pin_shared_memory is not None else shared.opts.forge_pin_shared_memory # If model_memory is provided, calculate inference memory accordingly, otherwise use inference_memory directly if model_memory is None: model_memory = total_vram - inference_memory else: inference_memory = total_vram - model_memory shared.opts.set('forge_async_loading', async_loading) shared.opts.set('forge_inference_memory', inference_memory) shared.opts.set('forge_pin_shared_memory', pin_shared_memory) stream.stream_activated = async_loading == 'Async' memory_management.current_inference_memory = inference_memory * 1024 * 1024 # Convert MB to bytes memory_management.PIN_SHARED_MEMORY = pin_shared_memory == 'Shared' log_dict = dict( stream=stream.should_use_stream(), inference_memory=memory_management.minimum_inference_memory() / (1024 * 1024), pin_shared_memory=memory_management.PIN_SHARED_MEMORY ) print(f'Environment vars changed: {log_dict}') if inference_memory < min(512, total_vram * 0.05): print('------------------') print(f'[Low VRAM Warning] You just set Forge to use 100% GPU memory ({model_memory:.2f} MB) to load model weights.') print('[Low VRAM Warning] This means you will have 0% GPU memory (0.00 MB) to do matrix computation. Computations may fallback to CPU or go Out of Memory.') print('[Low VRAM Warning] In many cases, image generation will be 10x slower.') print("[Low VRAM Warning] To solve the problem, you can set the 'GPU Weights' (on the top of page) to a lower value.") print("[Low VRAM Warning] If you cannot find 'GPU Weights', you can click the 'all' option in the 'UI' area on the left-top corner of the webpage.") print('[Low VRAM Warning] Make sure that you know what you are testing.') print('------------------') else: compute_percentage = (inference_memory / total_vram) * 100.0 print(f'[GPU Setting] You will use {(100 - compute_percentage):.2f}% GPU memory ({model_memory:.2f} MB) to load weights, and use {compute_percentage:.2f}% GPU memory ({inference_memory:.2f} MB) to do matrix computation.') processing.need_global_unload = True return def refresh_model_loading_parameters(): from modules.sd_models import select_checkpoint, model_data checkpoint_info = select_checkpoint() unet_storage_dtype, lora_fp16 = forge_unet_storage_dtype_options.get(shared.opts.forge_unet_storage_dtype, (None, False)) dynamic_args['online_lora'] = lora_fp16 model_data.forge_loading_parameters = dict( checkpoint_info=checkpoint_info, additional_modules=shared.opts.forge_additional_modules, unet_storage_dtype=unet_storage_dtype ) print(f'Model selected: {model_data.forge_loading_parameters}') print(f'Using online LoRAs in FP16: {lora_fp16}') processing.need_global_unload = True return def checkpoint_change(ckpt_name:str, save=True, refresh=True): """ checkpoint name can be a number of valid aliases. Returns True if checkpoint changed. """ new_ckpt_info = sd_models.get_closet_checkpoint_match(ckpt_name) current_ckpt_info = sd_models.get_closet_checkpoint_match(shared.opts.data.get('sd_model_checkpoint', '')) if new_ckpt_info == current_ckpt_info: return False shared.opts.set('sd_model_checkpoint', ckpt_name) if save: shared.opts.save(shared.config_filename) if refresh: refresh_model_loading_parameters() return True def modules_change(module_values:list, save=True, refresh=True) -> bool: """ module values may be provided as file paths, or just the module names. Returns True if modules changed. """ modules = [] for v in module_values: module_name = os.path.basename(v) # If the input is a filepath, extract the file name if module_name in module_list: modules.append(module_list[module_name]) # skip further processing if value unchanged if sorted(modules) == sorted(shared.opts.data.get('forge_additional_modules', [])): return False shared.opts.set('forge_additional_modules', modules) if save: shared.opts.save(shared.config_filename) if refresh: refresh_model_loading_parameters() return True def get_a1111_ui_component(tab, label): fields = infotext_utils.paste_fields[tab]['fields'] for f in fields: if f.label == label or f.api == label: return f.component def forge_main_entry(): ui_txt2img_width = get_a1111_ui_component('txt2img', 'Size-1') ui_txt2img_height = get_a1111_ui_component('txt2img', 'Size-2') ui_txt2img_cfg = get_a1111_ui_component('txt2img', 'CFG scale') ui_txt2img_distilled_cfg = get_a1111_ui_component('txt2img', 'Distilled CFG Scale') ui_txt2img_sampler = get_a1111_ui_component('txt2img', 'sampler_name') ui_txt2img_scheduler = get_a1111_ui_component('txt2img', 'scheduler') ui_img2img_width = get_a1111_ui_component('img2img', 'Size-1') ui_img2img_height = get_a1111_ui_component('img2img', 'Size-2') ui_img2img_cfg = get_a1111_ui_component('img2img', 'CFG scale') ui_img2img_distilled_cfg = get_a1111_ui_component('img2img', 'Distilled CFG Scale') ui_img2img_sampler = get_a1111_ui_component('img2img', 'sampler_name') ui_img2img_scheduler = get_a1111_ui_component('img2img', 'scheduler') ui_txt2img_hr_cfg = get_a1111_ui_component('txt2img', 'Hires CFG Scale') ui_txt2img_hr_distilled_cfg = get_a1111_ui_component('txt2img', 'Hires Distilled CFG Scale') output_targets = [ ui_vae, ui_clip_skip, ui_forge_unet_storage_dtype_options, ui_forge_async_loading, ui_forge_pin_shared_memory, ui_forge_inference_memory, ui_txt2img_width, ui_img2img_width, ui_txt2img_height, ui_img2img_height, ui_txt2img_cfg, ui_img2img_cfg, ui_txt2img_distilled_cfg, ui_img2img_distilled_cfg, ui_txt2img_sampler, ui_img2img_sampler, ui_txt2img_scheduler, ui_img2img_scheduler, ui_txt2img_hr_cfg, ui_txt2img_hr_distilled_cfg, ] ui_forge_preset.change(on_preset_change, inputs=[ui_forge_preset], outputs=output_targets, queue=False, show_progress=False) ui_forge_preset.change(js="clickLoraRefresh", fn=None, queue=False, show_progress=False) Context.root_block.load(on_preset_change, inputs=None, outputs=output_targets, queue=False, show_progress=False) refresh_model_loading_parameters() return def on_preset_change(preset=None): if preset is not None: shared.opts.set('forge_preset', preset) shared.opts.save(shared.config_filename) if shared.opts.forge_preset == 'sd': return [ gr.update(visible=True), # ui_vae gr.update(visible=True, value=1), # ui_clip_skip gr.update(visible=False, value='Automatic'), # ui_forge_unet_storage_dtype_options gr.update(visible=False, value='Queue'), # ui_forge_async_loading gr.update(visible=False, value='CPU'), # ui_forge_pin_shared_memory gr.update(visible=False, value=total_vram - 1024), # ui_forge_inference_memory gr.update(value=getattr(shared.opts, "sd_t2i_width", 512)), # ui_txt2img_width gr.update(value=getattr(shared.opts, "sd_i2i_width", 512)), # ui_img2img_width gr.update(value=getattr(shared.opts, "sd_t2i_height", 640)), # ui_txt2img_height gr.update(value=getattr(shared.opts, "sd_i2i_height", 512)), # ui_img2img_height gr.update(value=getattr(shared.opts, "sd_t2i_cfg", 7)), # ui_txt2img_cfg gr.update(value=getattr(shared.opts, "sd_i2i_cfg", 7)), # ui_img2img_cfg gr.update(visible=False, value=3.5), # ui_txt2img_distilled_cfg gr.update(visible=False, value=3.5), # ui_img2img_distilled_cfg gr.update(value=getattr(shared.opts, "sd_t2i_sampler", 'Euler a')), # ui_txt2img_sampler gr.update(value=getattr(shared.opts, "sd_i2i_sampler", 'Euler a')), # ui_img2img_sampler gr.update(value=getattr(shared.opts, "sd_t2i_scheduler", 'Automatic')), # ui_txt2img_scheduler gr.update(value=getattr(shared.opts, "sd_i2i_scheduler", 'Automatic')), # ui_img2img_scheduler gr.update(visible=True, value=getattr(shared.opts, "sd_t2i_hr_cfg", 7.0)), # ui_txt2img_hr_cfg gr.update(visible=False, value=3.5), # ui_txt2img_hr_distilled_cfg ] if shared.opts.forge_preset == 'xl': model_mem = getattr(shared.opts, "xl_GPU_MB", total_vram - 1024) if model_mem < 0 or model_mem > total_vram: model_mem = total_vram - 1024 return [ gr.update(visible=True), # ui_vae gr.update(visible=False, value=1), # ui_clip_skip gr.update(visible=True, value='Automatic'), # ui_forge_unet_storage_dtype_options gr.update(visible=False, value='Queue'), # ui_forge_async_loading gr.update(visible=False, value='CPU'), # ui_forge_pin_shared_memory gr.update(visible=True, value=model_mem), # ui_forge_inference_memory gr.update(value=getattr(shared.opts, "xl_t2i_width", 896)), # ui_txt2img_width gr.update(value=getattr(shared.opts, "xl_i2i_width", 1024)), # ui_img2img_width gr.update(value=getattr(shared.opts, "xl_t2i_height", 1152)), # ui_txt2img_height gr.update(value=getattr(shared.opts, "xl_i2i_height", 1024)), # ui_img2img_height gr.update(value=getattr(shared.opts, "xl_t2i_cfg", 5)), # ui_txt2img_cfg gr.update(value=getattr(shared.opts, "xl_i2i_cfg", 5)), # ui_img2img_cfg gr.update(visible=False, value=3.5), # ui_txt2img_distilled_cfg gr.update(visible=False, value=3.5), # ui_img2img_distilled_cfg gr.update(value=getattr(shared.opts, "xl_t2i_sampler", 'Euler a')), # ui_txt2img_sampler gr.update(value=getattr(shared.opts, "xl_i2i_sampler", 'Euler a')), # ui_img2img_sampler gr.update(value=getattr(shared.opts, "xl_t2i_scheduler", 'Automatic')), # ui_txt2img_scheduler gr.update(value=getattr(shared.opts, "xl_i2i_scheduler", 'Automatic')), # ui_img2img_scheduler gr.update(visible=True, value=getattr(shared.opts, "xl_t2i_hr_cfg", 5.0)), # ui_txt2img_hr_cfg gr.update(visible=False, value=3.5), # ui_txt2img_hr_distilled_cfg ] if shared.opts.forge_preset == 'flux': model_mem = getattr(shared.opts, "flux_GPU_MB", total_vram - 1024) if model_mem < 0 or model_mem > total_vram: model_mem = total_vram - 1024 return [ gr.update(visible=True), # ui_vae gr.update(visible=False, value=1), # ui_clip_skip gr.update(visible=True, value='Automatic'), # ui_forge_unet_storage_dtype_options gr.update(visible=True, value='Queue'), # ui_forge_async_loading gr.update(visible=True, value='CPU'), # ui_forge_pin_shared_memory gr.update(visible=True, value=model_mem), # ui_forge_inference_memory gr.update(value=getattr(shared.opts, "flux_t2i_width", 896)), # ui_txt2img_width gr.update(value=getattr(shared.opts, "flux_i2i_width", 1024)), # ui_img2img_width gr.update(value=getattr(shared.opts, "flux_t2i_height", 1152)), # ui_txt2img_height gr.update(value=getattr(shared.opts, "flux_i2i_height", 1024)), # ui_img2img_height gr.update(value=getattr(shared.opts, "flux_t2i_cfg", 1)), # ui_txt2img_cfg gr.update(value=getattr(shared.opts, "flux_i2i_cfg", 1)), # ui_img2img_cfg gr.update(visible=True, value=getattr(shared.opts, "flux_t2i_d_cfg", 3.5)), # ui_txt2img_distilled_cfg gr.update(visible=True, value=getattr(shared.opts, "flux_i2i_d_cfg", 3.5)), # ui_img2img_distilled_cfg gr.update(value=getattr(shared.opts, "flux_t2i_sampler", 'Euler')), # ui_txt2img_sampler gr.update(value=getattr(shared.opts, "flux_i2i_sampler", 'Euler')), # ui_img2img_sampler gr.update(value=getattr(shared.opts, "flux_t2i_scheduler", 'Simple')), # ui_txt2img_scheduler gr.update(value=getattr(shared.opts, "flux_i2i_scheduler", 'Simple')), # ui_img2img_scheduler gr.update(visible=True, value=getattr(shared.opts, "flux_t2i_hr_cfg", 1.0)), # ui_txt2img_hr_cfg gr.update(visible=True, value=getattr(shared.opts, "flux_t2i_hr_d_cfg", 3.5)), # ui_txt2img_hr_distilled_cfg ] loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file) ui_settings_from_file = loadsave.ui_settings.copy() return [ gr.update(visible=True), # ui_vae gr.update(visible=True, value=1), # ui_clip_skip gr.update(visible=True, value='Automatic'), # ui_forge_unet_storage_dtype_options gr.update(visible=True, value='Queue'), # ui_forge_async_loading gr.update(visible=True, value='CPU'), # ui_forge_pin_shared_memory gr.update(visible=True, value=total_vram - 1024), # ui_forge_inference_memory gr.update(value=ui_settings_from_file['txt2img/Width/value']), # ui_txt2img_width gr.update(value=ui_settings_from_file['img2img/Width/value']), # ui_img2img_width gr.update(value=ui_settings_from_file['txt2img/Height/value']), # ui_txt2img_height gr.update(value=ui_settings_from_file['img2img/Height/value']), # ui_img2img_height gr.update(value=ui_settings_from_file['txt2img/CFG Scale/value']), # ui_txt2img_cfg gr.update(value=ui_settings_from_file['img2img/CFG Scale/value']), # ui_img2img_cfg gr.update(visible=True, value=ui_settings_from_file['txt2img/Distilled CFG Scale/value']), # ui_txt2img_distilled_cfg gr.update(visible=True, value=ui_settings_from_file['img2img/Distilled CFG Scale/value']), # ui_img2img_distilled_cfg gr.update(value=ui_settings_from_file['customscript/sampler.py/txt2img/Sampling method/value']), # ui_txt2img_sampler gr.update(value=ui_settings_from_file['customscript/sampler.py/img2img/Sampling method/value']), # ui_img2img_sampler gr.update(value=ui_settings_from_file['customscript/sampler.py/txt2img/Schedule type/value']), # ui_txt2img_scheduler gr.update(value=ui_settings_from_file['customscript/sampler.py/img2img/Schedule type/value']), # ui_img2img_scheduler gr.update(visible=True, value=ui_settings_from_file['txt2img/Hires CFG Scale/value']), # ui_txt2img_hr_cfg gr.update(visible=True, value=ui_settings_from_file['txt2img/Hires Distilled CFG Scale/value']), # ui_txt2img_hr_distilled_cfg ] shared.options_templates.update(shared.options_section(('ui_sd', "UI defaults 'sd'", "ui"), { "sd_t2i_width": shared.OptionInfo(512, "txt2img width", gr.Slider, {"minimum": 64, "maximum": 2048, "step": 8}), "sd_t2i_height": shared.OptionInfo(640, "txt2img height", gr.Slider, {"minimum": 64, "maximum": 2048, "step": 8}), "sd_t2i_cfg": shared.OptionInfo(7, "txt2img CFG", gr.Slider, {"minimum": 1, "maximum": 30, "step": 0.1}), "sd_t2i_hr_cfg": shared.OptionInfo(7, "txt2img HiRes CFG", gr.Slider, {"minimum": 1, "maximum": 30, "step": 0.1}), "sd_i2i_width": shared.OptionInfo(512, "img2img width", gr.Slider, {"minimum": 64, "maximum": 2048, "step": 8}), "sd_i2i_height": shared.OptionInfo(512, "img2img height", gr.Slider, {"minimum": 64, "maximum": 2048, "step": 8}), "sd_i2i_cfg": shared.OptionInfo(7, "img2img CFG", gr.Slider, {"minimum": 1, "maximum": 30, "step": 0.1}), })) shared.options_templates.update(shared.options_section(('ui_xl', "UI defaults 'xl'", "ui"), { "xl_t2i_width": shared.OptionInfo(896, "txt2img width", gr.Slider, {"minimum": 64, "maximum": 2048, "step": 8}), "xl_t2i_height": shared.OptionInfo(1152, "txt2img height", gr.Slider, {"minimum": 64, "maximum": 2048, "step": 8}), "xl_t2i_cfg": shared.OptionInfo(5, "txt2img CFG", gr.Slider, {"minimum": 1, "maximum": 30, "step": 0.1}), "xl_t2i_hr_cfg": shared.OptionInfo(5, "txt2img HiRes CFG", gr.Slider, {"minimum": 1, "maximum": 30, "step": 0.1}), "xl_i2i_width": shared.OptionInfo(1024, "img2img width", gr.Slider, {"minimum": 64, "maximum": 2048, "step": 8}), "xl_i2i_height": shared.OptionInfo(1024, "img2img height", gr.Slider, {"minimum": 64, "maximum": 2048, "step": 8}), "xl_i2i_cfg": shared.OptionInfo(5, "img2img CFG", gr.Slider, {"minimum": 1, "maximum": 30, "step": 0.1}), "xl_GPU_MB": shared.OptionInfo(total_vram - 1024, "GPU Weights (MB)", gr.Slider, {"minimum": 0, "maximum": total_vram, "step": 1}), })) shared.options_templates.update(shared.options_section(('ui_flux', "UI defaults 'flux'", "ui"), { "flux_t2i_width": shared.OptionInfo(896, "txt2img width", gr.Slider, {"minimum": 64, "maximum": 2048, "step": 8}), "flux_t2i_height": shared.OptionInfo(1152, "txt2img height", gr.Slider, {"minimum": 64, "maximum": 2048, "step": 8}), "flux_t2i_cfg": shared.OptionInfo(1, "txt2img CFG", gr.Slider, {"minimum": 1, "maximum": 30, "step": 0.1}), "flux_t2i_hr_cfg": shared.OptionInfo(1, "txt2img HiRes CFG", gr.Slider, {"minimum": 1, "maximum": 30, "step": 0.1}), "flux_t2i_d_cfg": shared.OptionInfo(3.5, "txt2img Distilled CFG", gr.Slider, {"minimum": 0, "maximum": 30, "step": 0.1}), "flux_t2i_hr_d_cfg": shared.OptionInfo(3.5, "txt2img Distilled HiRes CFG", gr.Slider, {"minimum": 0, "maximum": 30, "step": 0.1}), "flux_i2i_width": shared.OptionInfo(1024, "img2img width", gr.Slider, {"minimum": 64, "maximum": 2048, "step": 8}), "flux_i2i_height": shared.OptionInfo(1024, "img2img height", gr.Slider, {"minimum": 64, "maximum": 2048, "step": 8}), "flux_i2i_cfg": shared.OptionInfo(1, "img2img CFG", gr.Slider, {"minimum": 1, "maximum": 30, "step": 0.1}), "flux_i2i_d_cfg": shared.OptionInfo(3.5, "img2img Distilled CFG", gr.Slider, {"minimum": 0, "maximum": 30, "step": 0.1}), "flux_GPU_MB": shared.OptionInfo(total_vram - 1024, "GPU Weights (MB)",gr.Slider, {"minimum": 0, "maximum": total_vram, "step": 1}), }))