diff --git "a/app.py" "b/app.py"
--- "a/app.py"
+++ "b/app.py"
@@ -1,1799 +1,1799 @@
-import spaces
-import os
-from stablepy import (
- Model_Diffusers,
- SCHEDULE_TYPE_OPTIONS,
- SCHEDULE_PREDICTION_TYPE_OPTIONS,
- check_scheduler_compatibility,
- TASK_AND_PREPROCESSORS,
- FACE_RESTORATION_MODELS,
- scheduler_names,
-)
-from constants import (
- DIRECTORY_UPSCALERS,
- TASK_STABLEPY,
- TASK_MODEL_LIST,
- UPSCALER_DICT_GUI,
- UPSCALER_KEYS,
- PROMPT_W_OPTIONS,
- WARNING_MSG_VAE,
- SDXL_TASK,
- MODEL_TYPE_TASK,
- POST_PROCESSING_SAMPLER,
- DIFFUSERS_CONTROLNET_MODEL,
- IP_MODELS,
- MODE_IP_OPTIONS,
-)
-from stablepy.diffusers_vanilla.style_prompt_config import STYLE_NAMES
-import torch
-import re
-import time
-from PIL import ImageFile
-from utils import (
- get_model_list,
- extract_parameters,
- get_model_type,
- extract_exif_data,
- create_mask_now,
- download_diffuser_repo,
- get_used_storage_gb,
- delete_model,
- progress_step_bar,
- html_template_message,
- escape_html,
-)
-from image_processor import preprocessor_tab
-from datetime import datetime
-import gradio as gr
-import logging
-import diffusers
-import warnings
-from stablepy import logger
-from diffusers import FluxPipeline
-# import urllib.parse
-import subprocess
-
-# 导入翻译模块
-import translate_ui
-from translate_ui import monkey_patch_gradio
-import translate_helper as th
-
-# 应用猴子补丁，使所有新创建的Gradio组件自动翻译
-monkey_patch_gradio()
-
-subprocess.run("rm -rf /data-nvme/zerogpu-offload/*", env={}, shell=True)
-
-ImageFile.LOAD_TRUNCATED_IMAGES = True
-torch.backends.cuda.matmul.allow_tf32 = True
-# os.environ["PYTORCH_NO_CUDA_MEMORY_CACHING"] = "1"
-print(os.getenv("SPACES_ZERO_GPU"))
-
-## BEGIN MOD
-from modutils import (list_uniq, download_private_repo, get_model_id_list, get_tupled_embed_list,
- get_lora_model_list, get_all_lora_tupled_list, update_loras, apply_lora_prompt, set_prompt_loras,
- get_my_lora, upload_file_lora, move_file_lora, search_civitai_lora, select_civitai_lora,
- update_civitai_selection, get_civitai_tag, CIVITAI_SORT, CIVITAI_PERIOD, CIVITAI_BASEMODEL,
- set_textual_inversion_prompt, get_model_pipeline, change_interface_mode, get_t2i_model_info, download_link_model,
- get_tupled_model_list, save_gallery_images, save_gallery_history, set_optimization, set_sampler_settings,
- set_quick_presets, process_style_prompt, optimization_list, save_images, download_things, valid_model_name,
- preset_styles, preset_quality, preset_sampler_setting, translate_to_en, EXAMPLES_GUI, RESOURCES)
-from env import (HF_TOKEN, CIVITAI_API_KEY, HF_LORA_ESSENTIAL_PRIVATE_REPO, HF_VAE_PRIVATE_REPO,
- HF_SDXL_EMBEDS_NEGATIVE_PRIVATE_REPO, HF_SDXL_EMBEDS_POSITIVE_PRIVATE_REPO,
- DIRECTORY_MODELS, DIRECTORY_LORAS, DIRECTORY_VAES, DIRECTORY_EMBEDS, DIRECTORY_EMBEDS_SDXL,
- DIRECTORY_EMBEDS_POSITIVE_SDXL, LOAD_DIFFUSERS_FORMAT_MODEL,
- DOWNLOAD_MODEL_LIST, DOWNLOAD_LORA_LIST, DOWNLOAD_VAE_LIST, DOWNLOAD_EMBEDS)
-
-download_private_repo(HF_LORA_ESSENTIAL_PRIVATE_REPO, DIRECTORY_LORAS, True)
-download_private_repo(HF_VAE_PRIVATE_REPO, DIRECTORY_VAES, False)
-## END MOD
-
-directories = [DIRECTORY_MODELS, DIRECTORY_LORAS, DIRECTORY_VAES, DIRECTORY_EMBEDS, DIRECTORY_UPSCALERS]
-for directory in directories:
- os.makedirs(directory, exist_ok=True)
-
-# - **Download Models**
-DOWNLOAD_MODEL = ", ".join(DOWNLOAD_MODEL_LIST)
-# - **Download VAEs**
-DOWNLOAD_VAE = ", ".join(DOWNLOAD_VAE_LIST)
-# - **Download LoRAs**
-DOWNLOAD_LORA = ", ".join(DOWNLOAD_LORA_LIST)
-
-# Download stuffs
-for url in [url.strip() for url in DOWNLOAD_MODEL.split(',')]:
- if not os.path.exists(f"./models/{url.split('/')[-1]}"):
- download_things(DIRECTORY_MODELS, url, HF_TOKEN, CIVITAI_API_KEY)
-for url in [url.strip() for url in DOWNLOAD_VAE.split(',')]:
- if not os.path.exists(f"./vaes/{url.split('/')[-1]}"):
- download_things(DIRECTORY_VAES, url, HF_TOKEN, CIVITAI_API_KEY)
-for url in [url.strip() for url in DOWNLOAD_LORA.split(',')]:
- if not os.path.exists(f"./loras/{url.split('/')[-1]}"):
- download_things(DIRECTORY_LORAS, url, HF_TOKEN, CIVITAI_API_KEY)
-
-# Download Embeddings
-for url_embed in DOWNLOAD_EMBEDS:
- if not os.path.exists(f"./embedings/{url_embed.split('/')[-1]}"):
- download_things(DIRECTORY_EMBEDS, url_embed, HF_TOKEN, CIVITAI_API_KEY)
-
-# Build list models
-embed_list = get_model_list(DIRECTORY_EMBEDS)
-lora_model_list = get_lora_model_list()
-vae_model_list = get_model_list(DIRECTORY_VAES)
-vae_model_list.insert(0, "BakedVAE")
-vae_model_list.insert(0, "None")
-
-## BEGIN MOD
-single_file_model_list = get_model_list(DIRECTORY_MODELS)
-model_list = list_uniq(get_model_id_list() + LOAD_DIFFUSERS_FORMAT_MODEL + single_file_model_list)
-download_private_repo(HF_SDXL_EMBEDS_NEGATIVE_PRIVATE_REPO, DIRECTORY_EMBEDS_SDXL, False)
-download_private_repo(HF_SDXL_EMBEDS_POSITIVE_PRIVATE_REPO, DIRECTORY_EMBEDS_POSITIVE_SDXL, False)
-embed_sdxl_list = get_model_list(DIRECTORY_EMBEDS_SDXL) + get_model_list(DIRECTORY_EMBEDS_POSITIVE_SDXL)
-
-def get_embed_list(pipeline_name):
- return get_tupled_embed_list(embed_sdxl_list if pipeline_name == "StableDiffusionXLPipeline" else embed_list)
-## END MOD
-
-print('\033[33m🏁 Download and listing of valid models completed.\033[0m')
-
-flux_repo = "camenduru/FLUX.1-dev-diffusers"
-flux_pipe = FluxPipeline.from_pretrained(
- flux_repo,
- transformer=None,
- torch_dtype=torch.bfloat16,
-)#.to("cuda")
-components = flux_pipe.components
-components.pop("transformer", None)
-components.pop("scheduler", None)
-delete_model(flux_repo)
-# components = None
-
-#######################
-# GUI
-#######################
-logging.getLogger("diffusers").setLevel(logging.ERROR)
-diffusers.utils.logging.set_verbosity(40)
-warnings.filterwarnings(action="ignore", category=FutureWarning, module="diffusers")
-warnings.filterwarnings(action="ignore", category=UserWarning, module="diffusers")
-warnings.filterwarnings(action="ignore", category=FutureWarning, module="transformers")
-## BEGIN MOD
-#logger.setLevel(logging.CRITICAL)
-logger.setLevel(logging.DEBUG)
-
-from tagger.v2 import V2_ALL_MODELS, v2_random_prompt, v2_upsampling_prompt
-from tagger.utils import (gradio_copy_text, COPY_ACTION_JS, gradio_copy_prompt,
- V2_ASPECT_RATIO_OPTIONS, V2_RATING_OPTIONS, V2_LENGTH_OPTIONS, V2_IDENTITY_OPTIONS)
-from tagger.tagger import (predict_tags_wd, convert_danbooru_to_e621_prompt,
- remove_specific_prompt, insert_recom_prompt, insert_model_recom_prompt,
- compose_prompt_to_copy, translate_prompt, select_random_character)
-def description_ui():
- gr.Markdown(
- th.t("""
-## Danbooru Tags Transformer V2 Demo with WD Tagger
-(Image =>) Prompt => Upsampled longer prompt
-- Mod of p1atdev's [Danbooru Tags Transformer V2 Demo](https://huggingface.co/spaces/p1atdev/danbooru-tags-transformer-v2) and [WD Tagger with 🤗 transformers](https://huggingface.co/spaces/p1atdev/wd-tagger-transformers).
-- Models: p1atdev's [wd-swinv2-tagger-v3-hf](https://huggingface.co/p1atdev/wd-swinv2-tagger-v3-hf), [dart-v2-moe-sft](https://huggingface.co/p1atdev/dart-v2-moe-sft)
-""")
- )
-## END MOD
-
-class GuiSD:
- def __init__(self, stream=True):
- self.model = None
- self.status_loading = False
- self.sleep_loading = 4
- self.last_load = datetime.now()
- self.inventory = []
-
- def update_storage_models(self, storage_floor_gb=24, required_inventory_for_purge=3):
- while get_used_storage_gb() > storage_floor_gb:
- if len(self.inventory) < required_inventory_for_purge:
- break
- removal_candidate = self.inventory.pop(0)
- delete_model(removal_candidate)
-
- def update_inventory(self, model_name):
- if model_name not in single_file_model_list:
- self.inventory = [
- m for m in self.inventory if m != model_name
- ] + [model_name]
- print(self.inventory)
-
- def load_new_model(self, model_name, vae_model, task, controlnet_model, progress=gr.Progress(track_tqdm=True)):
-
- # download link model > model_name
- if "http" in model_name: #
- model_name, model_type = download_link_model(model_name, DIRECTORY_MODELS) #
- is_link_model = True #
- else: is_link_model = False #
-
- self.update_storage_models()
-
- vae_model = vae_model if vae_model != "None" else None
- model_type = get_model_type(model_name) if not is_link_model else model_type #
- dtype_model = torch.bfloat16 if model_type == "FLUX" else torch.float16
-
- if not os.path.exists(model_name):
- _ = download_diffuser_repo(
- repo_name=model_name,
- model_type=model_type,
- revision="main",
- token=True,
- )
-
- self.update_inventory(model_name)
-
- for i in range(68):
- if not self.status_loading:
- self.status_loading = True
- if i > 0:
- time.sleep(self.sleep_loading)
- print("Previous model ops...")
- break
- time.sleep(0.5)
- print(f"Waiting queue {i}")
- yield "Waiting queue"
-
- self.status_loading = True
-
- yield f"Loading model: {model_name}"
-
- if vae_model == "BakedVAE":
- vae_model = model_name
- elif vae_model:
- vae_type = "SDXL" if "sdxl" in vae_model.lower() else "SD 1.5"
- if model_type != vae_type:
- gr.Warning(WARNING_MSG_VAE)
-
- print("Loading model...")
-
- try:
- start_time = time.time()
-
- if self.model is None:
- self.model = Model_Diffusers(
- base_model_id=model_name,
- task_name=TASK_STABLEPY[task],
- vae_model=vae_model,
- type_model_precision=dtype_model,
- retain_task_model_in_cache=False,
- controlnet_model=controlnet_model,
- device="cpu",
- env_components=components,
- )
- self.model.advanced_params(image_preprocessor_cuda_active=True)
- else:
- if self.model.base_model_id != model_name:
- load_now_time = datetime.now()
- elapsed_time = max((load_now_time - self.last_load).total_seconds(), 0)
-
- if elapsed_time <= 9:
- print("Waiting for the previous model's time ops...")
- time.sleep(9 - elapsed_time)
-
- self.model.device = torch.device("cpu")
- self.model.load_pipe(
- model_name,
- task_name=TASK_STABLEPY[task],
- vae_model=vae_model,
- type_model_precision=dtype_model,
- retain_task_model_in_cache=False,
- controlnet_model=controlnet_model,
- )
-
- end_time = time.time()
- self.sleep_loading = max(min(int(end_time - start_time), 10), 4)
- except Exception as e:
- self.last_load = datetime.now()
- self.status_loading = False
- self.sleep_loading = 4
- raise e
-
- self.last_load = datetime.now()
- self.status_loading = False
-
- yield f"Model loaded: {model_name}"
-
- #@spaces.GPU
- @torch.inference_mode()
- def generate_pipeline(
- self,
- prompt,
- neg_prompt,
- num_images,
- steps,
- cfg,
- clip_skip,
- seed,
- lora1,
- lora_scale1,
- lora2,
- lora_scale2,
- lora3,
- lora_scale3,
- lora4,
- lora_scale4,
- lora5,
- lora_scale5,
- lora6,
- lora_scale6,
- lora7,
- lora_scale7,
- sampler,
- schedule_type,
- schedule_prediction_type,
- img_height,
- img_width,
- model_name,
- vae_model,
- task,
- image_control,
- preprocessor_name,
- preprocess_resolution,
- image_resolution,
- style_prompt, # list []
- style_json_file,
- image_mask,
- strength,
- low_threshold,
- high_threshold,
- value_threshold,
- distance_threshold,
- recolor_gamma_correction,
- tile_blur_sigma,
- controlnet_output_scaling_in_unet,
- controlnet_start_threshold,
- controlnet_stop_threshold,
- textual_inversion,
- syntax_weights,
- upscaler_model_path,
- upscaler_increases_size,
- upscaler_tile_size,
- upscaler_tile_overlap,
- hires_steps,
- hires_denoising_strength,
- hires_sampler,
- hires_prompt,
- hires_negative_prompt,
- hires_before_adetailer,
- hires_after_adetailer,
- hires_schedule_type,
- hires_guidance_scale,
- controlnet_model,
- loop_generation,
- leave_progress_bar,
- disable_progress_bar,
- image_previews,
- display_images,
- save_generated_images,
- filename_pattern,
- image_storage_location,
- retain_compel_previous_load,
- retain_detailfix_model_previous_load,
- retain_hires_model_previous_load,
- t2i_adapter_preprocessor,
- t2i_adapter_conditioning_scale,
- t2i_adapter_conditioning_factor,
- xformers_memory_efficient_attention,
- freeu,
- generator_in_cpu,
- adetailer_inpaint_only,
- adetailer_verbose,
- adetailer_sampler,
- adetailer_active_a,
- prompt_ad_a,
- negative_prompt_ad_a,
- strength_ad_a,
- face_detector_ad_a,
- person_detector_ad_a,
- hand_detector_ad_a,
- mask_dilation_a,
- mask_blur_a,
- mask_padding_a,
- adetailer_active_b,
- prompt_ad_b,
- negative_prompt_ad_b,
- strength_ad_b,
- face_detector_ad_b,
- person_detector_ad_b,
- hand_detector_ad_b,
- mask_dilation_b,
- mask_blur_b,
- mask_padding_b,
- retain_task_cache_gui,
- guidance_rescale,
- image_ip1,
- mask_ip1,
- model_ip1,
- mode_ip1,
- scale_ip1,
- image_ip2,
- mask_ip2,
- model_ip2,
- mode_ip2,
- scale_ip2,
- pag_scale,
- face_restoration_model,
- face_restoration_visibility,
- face_restoration_weight,
- ):
- info_state = html_template_message("Navigating latent space...")
- yield info_state, gr.update(), gr.update()
-
- vae_model = vae_model if vae_model != "None" else None
- loras_list = [lora1, lora2, lora3, lora4, lora5, lora6, lora7]
- vae_msg = f"VAE: {vae_model}" if vae_model else ""
- msg_lora = ""
-
-## BEGIN MOD
- loras_list = [s if s else "None" for s in loras_list]
- global lora_model_list
- lora_model_list = get_lora_model_list()
- lora1, lora_scale1, lora2, lora_scale2, lora3, lora_scale3, lora4, lora_scale4, lora5, lora_scale5, lora6, lora_scale6, lora7, lora_scale7 = \
- set_prompt_loras(prompt, syntax_weights, model_name, lora1, lora_scale1, lora2, lora_scale2, lora3,
- lora_scale3, lora4, lora_scale4, lora5, lora_scale5, lora6, lora_scale6, lora7, lora_scale7)
-## END MOD
-
- print("Config model:", model_name, vae_model, loras_list)
-
- task = TASK_STABLEPY[task]
-
- params_ip_img = []
- params_ip_msk = []
- params_ip_model = []
- params_ip_mode = []
- params_ip_scale = []
-
- all_adapters = [
- (image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1),
- (image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2),
- ]
-
- if not hasattr(self.model.pipe, "transformer"):
- for imgip, mskip, modelip, modeip, scaleip in all_adapters:
- if imgip:
- params_ip_img.append(imgip)
- if mskip:
- params_ip_msk.append(mskip)
- params_ip_model.append(modelip)
- params_ip_mode.append(modeip)
- params_ip_scale.append(scaleip)
-
- concurrency = 5
- self.model.stream_config(concurrency=concurrency, latent_resize_by=1, vae_decoding=False)
-
- if task != "txt2img" and not image_control:
- raise ValueError("Reference image is required. Please upload one in 'Image ControlNet/Inpaint/Img2img'.")
-
- if task in ["inpaint", "repaint"] and not image_mask:
- raise ValueError("Mask image not found. Upload one in 'Image Mask' to proceed.")
-
- if "https://" not in str(UPSCALER_DICT_GUI[upscaler_model_path]):
- upscaler_model = upscaler_model_path
- else:
- url_upscaler = UPSCALER_DICT_GUI[upscaler_model_path]
-
- if not os.path.exists(f"./{DIRECTORY_UPSCALERS}/{url_upscaler.split('/')[-1]}"):
- download_things(DIRECTORY_UPSCALERS, url_upscaler, HF_TOKEN)
-
- upscaler_model = f"./{DIRECTORY_UPSCALERS}/{url_upscaler.split('/')[-1]}"
-
- logging.getLogger("ultralytics").setLevel(logging.INFO if adetailer_verbose else logging.ERROR)
-
- adetailer_params_A = {
- "face_detector_ad": face_detector_ad_a,
- "person_detector_ad": person_detector_ad_a,
- "hand_detector_ad": hand_detector_ad_a,
- "prompt": prompt_ad_a,
- "negative_prompt": negative_prompt_ad_a,
- "strength": strength_ad_a,
- # "image_list_task" : None,
- "mask_dilation": mask_dilation_a,
- "mask_blur": mask_blur_a,
- "mask_padding": mask_padding_a,
- "inpaint_only": adetailer_inpaint_only,
- "sampler": adetailer_sampler,
- }
-
- adetailer_params_B = {
- "face_detector_ad": face_detector_ad_b,
- "person_detector_ad": person_detector_ad_b,
- "hand_detector_ad": hand_detector_ad_b,
- "prompt": prompt_ad_b,
- "negative_prompt": negative_prompt_ad_b,
- "strength": strength_ad_b,
- # "image_list_task" : None,
- "mask_dilation": mask_dilation_b,
- "mask_blur": mask_blur_b,
- "mask_padding": mask_padding_b,
- }
- pipe_params = {
- "prompt": prompt,
- "negative_prompt": neg_prompt,
- "img_height": img_height,
- "img_width": img_width,
- "num_images": num_images,
- "num_steps": steps,
- "guidance_scale": cfg,
- "clip_skip": clip_skip,
- "pag_scale": float(pag_scale),
- "seed": seed,
- "image": image_control,
- "preprocessor_name": preprocessor_name,
- "preprocess_resolution": preprocess_resolution,
- "image_resolution": image_resolution,
- "style_prompt": style_prompt if style_prompt else "",
- "style_json_file": "",
- "image_mask": image_mask, # only for Inpaint
- "strength": strength, # only for Inpaint or ...
- "low_threshold": low_threshold,
- "high_threshold": high_threshold,
- "value_threshold": value_threshold,
- "distance_threshold": distance_threshold,
- "recolor_gamma_correction": float(recolor_gamma_correction),
- "tile_blur_sigma": int(tile_blur_sigma),
- "lora_A": lora1 if lora1 != "None" else None,
- "lora_scale_A": lora_scale1,
- "lora_B": lora2 if lora2 != "None" else None,
- "lora_scale_B": lora_scale2,
- "lora_C": lora3 if lora3 != "None" else None,
- "lora_scale_C": lora_scale3,
- "lora_D": lora4 if lora4 != "None" else None,
- "lora_scale_D": lora_scale4,
- "lora_E": lora5 if lora5 != "None" else None,
- "lora_scale_E": lora_scale5,
- "lora_F": lora6 if lora6 != "None" else None,
- "lora_scale_F": lora_scale6,
- "lora_G": lora7 if lora7 != "None" else None,
- "lora_scale_G": lora_scale7,
-## BEGIN MOD
- "textual_inversion": get_embed_list(self.model.class_name) if textual_inversion else [],
-## END MOD
- "syntax_weights": syntax_weights, # "Classic"
- "sampler": sampler,
- "schedule_type": schedule_type,
- "schedule_prediction_type": schedule_prediction_type,
- "xformers_memory_efficient_attention": xformers_memory_efficient_attention,
- "gui_active": True,
- "loop_generation": loop_generation,
- "controlnet_conditioning_scale": float(controlnet_output_scaling_in_unet),
- "control_guidance_start": float(controlnet_start_threshold),
- "control_guidance_end": float(controlnet_stop_threshold),
- "generator_in_cpu": generator_in_cpu,
- "FreeU": freeu,
- "adetailer_A": adetailer_active_a,
- "adetailer_A_params": adetailer_params_A,
- "adetailer_B": adetailer_active_b,
- "adetailer_B_params": adetailer_params_B,
- "leave_progress_bar": leave_progress_bar,
- "disable_progress_bar": disable_progress_bar,
- "image_previews": image_previews,
- "display_images": display_images,
- "save_generated_images": save_generated_images,
- "filename_pattern": filename_pattern,
- "image_storage_location": image_storage_location,
- "retain_compel_previous_load": retain_compel_previous_load,
- "retain_detailfix_model_previous_load": retain_detailfix_model_previous_load,
- "retain_hires_model_previous_load": retain_hires_model_previous_load,
- "t2i_adapter_preprocessor": t2i_adapter_preprocessor,
- "t2i_adapter_conditioning_scale": float(t2i_adapter_conditioning_scale),
- "t2i_adapter_conditioning_factor": float(t2i_adapter_conditioning_factor),
- "upscaler_model_path": upscaler_model,
- "upscaler_increases_size": upscaler_increases_size,
- "upscaler_tile_size": upscaler_tile_size,
- "upscaler_tile_overlap": upscaler_tile_overlap,
- "hires_steps": hires_steps,
- "hires_denoising_strength": hires_denoising_strength,
- "hires_prompt": hires_prompt,
- "hires_negative_prompt": hires_negative_prompt,
- "hires_sampler": hires_sampler,
- "hires_before_adetailer": hires_before_adetailer,
- "hires_after_adetailer": hires_after_adetailer,
- "hires_schedule_type": hires_schedule_type,
- "hires_guidance_scale": hires_guidance_scale,
- "ip_adapter_image": params_ip_img,
- "ip_adapter_mask": params_ip_msk,
- "ip_adapter_model": params_ip_model,
- "ip_adapter_mode": params_ip_mode,
- "ip_adapter_scale": params_ip_scale,
- "face_restoration_model": face_restoration_model,
- "face_restoration_visibility": face_restoration_visibility,
- "face_restoration_weight": face_restoration_weight,
- }
-
- # kwargs for diffusers pipeline
- if guidance_rescale:
- pipe_params["guidance_rescale"] = guidance_rescale
-
- self.model.device = torch.device("cuda:0")
- if hasattr(self.model.pipe, "transformer") and loras_list != ["None"] * self.model.num_loras:
- self.model.pipe.transformer.to(self.model.device)
- print("transformer to cuda")
-
- actual_progress = 0
- info_images = gr.update()
- for img, [seed, image_path, metadata] in self.model(**pipe_params):
- info_state = progress_step_bar(actual_progress, steps)
- actual_progress += concurrency
- if image_path:
- info_images = f"Seeds: {str(seed)}"
- if vae_msg:
- info_images = info_images + "<br>" + vae_msg
-
- if "Cannot copy out of meta tensor; no data!" in self.model.last_lora_error:
- msg_ram = "Unable to process the LoRAs due to high RAM usage; please try again later."
- print(msg_ram)
- msg_lora += f"<br>{msg_ram}"
-
- for status, lora in zip(self.model.lora_status, self.model.lora_memory):
- if status:
- msg_lora += f"<br>Loaded: {lora}"
- elif status is not None:
- msg_lora += f"<br>Error with: {lora}"
-
- if msg_lora:
- info_images += msg_lora
-
- info_images = info_images + "<br>" + "GENERATION DATA:<br>" + escape_html(metadata[-1]) + "<br>-------<br>"
-
- download_links = "<br>".join(
- [
- f'<a href="{path.replace("/images/", "/file=/home/user/app/images/")}" download="{os.path.basename(path)}">Download Image {i + 1}</a>'
- for i, path in enumerate(image_path)
- ]
- )
- if save_generated_images:
- info_images += f"<br>{download_links}"
-
-## BEGIN MOD
- img = save_images(img, metadata)
-## END MOD
-
- info_state = "COMPLETE"
-
- yield info_state, img, info_images
-
-
-def dynamic_gpu_duration(func, duration, *args):
-
- @spaces.GPU(duration=duration)
- def wrapped_func():
- yield from func(*args)
-
- return wrapped_func()
-
-
-@spaces.GPU
-def dummy_gpu():
- return None
-
-
-def sd_gen_generate_pipeline(*args):
- gpu_duration_arg = int(args[-1]) if args[-1] else 59
- verbose_arg = int(args[-2])
- load_lora_cpu = args[-3]
- generation_args = args[:-3]
- lora_list = [
- None if item == "None" or item == "" else item # MOD
- for item in [args[7], args[9], args[11], args[13], args[15], args[17], args[19]]
- ]
- lora_status = [None] * sd_gen.model.num_loras
-
- msg_load_lora = "Updating LoRAs in GPU..."
- if load_lora_cpu:
- msg_load_lora = "Updating LoRAs in CPU..."
-
- if lora_list != sd_gen.model.lora_memory and lora_list != [None] * sd_gen.model.num_loras:
- yield msg_load_lora, gr.update(), gr.update()
-
- # Load lora in CPU
- if load_lora_cpu:
- lora_status = sd_gen.model.load_lora_on_the_fly(
- lora_A=lora_list[0], lora_scale_A=args[8],
- lora_B=lora_list[1], lora_scale_B=args[10],
- lora_C=lora_list[2], lora_scale_C=args[12],
- lora_D=lora_list[3], lora_scale_D=args[14],
- lora_E=lora_list[4], lora_scale_E=args[16],
- lora_F=lora_list[5], lora_scale_F=args[18],
- lora_G=lora_list[6], lora_scale_G=args[20],
- )
- print(lora_status)
-
- sampler_name = args[21]
- schedule_type_name = args[22]
- _, _, msg_sampler = check_scheduler_compatibility(
- sd_gen.model.class_name, sampler_name, schedule_type_name
- )
- if msg_sampler:
- gr.Warning(msg_sampler)
-
- if verbose_arg:
- for status, lora in zip(lora_status, lora_list):
- if status:
- gr.Info(f"LoRA loaded in CPU: {lora}")
- elif status is not None:
- gr.Warning(f"Failed to load LoRA: {lora}")
-
- if lora_status == [None] * sd_gen.model.num_loras and sd_gen.model.lora_memory != [None] * sd_gen.model.num_loras and load_lora_cpu:
- lora_cache_msg = ", ".join(
- str(x) for x in sd_gen.model.lora_memory if x is not None
- )
- gr.Info(f"LoRAs in cache: {lora_cache_msg}")
-
- msg_request = f"Requesting {gpu_duration_arg}s. of GPU time.\nModel: {sd_gen.model.base_model_id}"
- if verbose_arg:
- gr.Info(msg_request)
- print(msg_request)
- yield msg_request.replace("\n", "<br>"), gr.update(), gr.update()
-
- start_time = time.time()
-
- # yield from sd_gen.generate_pipeline(*generation_args)
- yield from dynamic_gpu_duration(
- sd_gen.generate_pipeline,
- gpu_duration_arg,
- *generation_args,
- )
-
- end_time = time.time()
- execution_time = end_time - start_time
- msg_task_complete = (
- f"GPU task complete in: {int(round(execution_time, 0) + 1)} seconds"
- )
-
- if verbose_arg:
- gr.Info(msg_task_complete)
- print(msg_task_complete)
-
- yield msg_task_complete, gr.update(), gr.update()
-
-
-@spaces.GPU(duration=15)
-def process_upscale(image, upscaler_name, upscaler_size):
- if image is None: return None
-
- from stablepy.diffusers_vanilla.utils import save_pil_image_with_metadata
- from stablepy import load_upscaler_model
-
- image = image.convert("RGB")
- exif_image = extract_exif_data(image)
-
- name_upscaler = UPSCALER_DICT_GUI[upscaler_name]
-
- if "https://" in str(name_upscaler):
-
- if not os.path.exists(f"./{DIRECTORY_UPSCALERS}/{name_upscaler.split('/')[-1]}"):
- download_things(DIRECTORY_UPSCALERS, name_upscaler, HF_TOKEN)
-
- name_upscaler = f"./{DIRECTORY_UPSCALERS}/{name_upscaler.split('/')[-1]}"
-
- scaler_beta = load_upscaler_model(model=name_upscaler, tile=0, tile_overlap=8, device="cuda", half=True)
- image_up = scaler_beta.upscale(image, upscaler_size, True)
-
- image_path = save_pil_image_with_metadata(image_up, f'{os.getcwd()}/up_images', exif_image)
-
- return image_path
-
-
-# https://huggingface.co/spaces/BestWishYsh/ConsisID-preview-Space/discussions/1#674969a022b99c122af5d407
-dynamic_gpu_duration.zerogpu = True
-sd_gen_generate_pipeline.zerogpu = True
-sd_gen = GuiSD()
-
-
-## BEGIN MOD
-CSS ="""
-.gradio-container, #main { width:100%; height:100%; max-width:100%; padding-left:0; padding-right:0; margin-left:0; margin-right:0; }
-.contain { display:flex; flex-direction:column; }
-#component-0 { width:100%; height:100%; }
-#gallery { flex-grow:1; }
-#load_model { height: 50px; }
-.lora { min-width:480px; }
-#model-info { text-align:center; }
-.title { font-size: 3em; align-items: center; text-align: center; }
-.info { align-items: center; text-align: center; }
-.desc [src$='#float'] { float: right; margin: 20px; }
-"""
-
-with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, css=CSS, delete_cache=(60, 3600)) as app:
- title, description = th.translate_app_title()
- gr.Markdown(title, elem_classes="title")
- gr.Markdown(description, elem_classes="info")
- with gr.Column():
- with gr.Tab(th.t("Generation")):
- with gr.Row():
- with gr.Column(scale=1):
-
- def update_task_options(model_name, task_name):
- new_choices = MODEL_TYPE_TASK[get_model_type(valid_model_name(model_name))]
-
- if task_name not in new_choices:
- task_name = "txt2img"
-
- return gr.update(value=task_name, choices=new_choices)
-
- interface_mode_gui = gr.Radio(label="Quick settings", choices=["Simple", "Standard", "Fast", "LoRA"], value="Standard")
- with gr.Accordion("Model and Task", open=False) as menu_model:
- task_gui = gr.Dropdown(label="Task", choices=SDXL_TASK, value=TASK_MODEL_LIST[0])
- with gr.Group():
- model_name_gui = gr.Dropdown(label="Model", info="You can enter a huggingface model repo_id to want to use.", choices=get_tupled_model_list(model_list), value="votepurchase/animagine-xl-3.1", allow_custom_value=True)
- model_info_gui = gr.Markdown(elem_classes="info")
- with gr.Row():
- quick_model_type_gui = gr.Radio(label="Model Type", choices=["None", "Auto", "Animagine", "Pony"], value="Auto", interactive=True)
- quick_genre_gui = gr.Radio(label="Genre", choices=["Anime", "Photo"], value="Anime", interactive=True)
- quick_speed_gui = gr.Radio(label="Speed", choices=["Fast", "Standard", "Heavy"], value="Standard", interactive=True)
- quick_aspect_gui = gr.Radio(label="Aspect Ratio", choices=["1:1", "3:4"], value="1:1", interactive=True)
- with gr.Row():
- quality_selector_gui = gr.Dropdown(label="Quality Tags Presets", interactive=True, choices=list(preset_quality.keys()), value="None")
- style_selector_gui = gr.Dropdown(label="Style Preset", interactive=True, choices=list(preset_styles.keys()), value="None")
- sampler_selector_gui = gr.Dropdown(label="Sampler Quick Settings", interactive=True, choices=list(preset_sampler_setting.keys()), value="None")
- optimization_gui = gr.Dropdown(label="Optimization for SDXL", choices=list(optimization_list.keys()), value="None", interactive=True)
- with gr.Group():
- with gr.Accordion("Prompt from Image", open=False) as menu_from_image:
- input_image_gui = gr.Image(label="Input image", type="pil", sources=["upload", "clipboard"], height=256)
- with gr.Accordion(label="Advanced options", open=False):
- with gr.Row():
- general_threshold_gui = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.01, interactive=True)
- character_threshold_gui = gr.Slider(label="Character threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.01, interactive=True)
- with gr.Row():
- tag_type_gui = gr.Radio(label="Convert tags to", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="danbooru")
- recom_prompt_gui = gr.Radio(label="Insert reccomended prompt", choices=["None", "Animagine", "Pony"], value="None", interactive=True)
- keep_tags_gui = gr.Radio(label="Remove tags leaving only the following", choices=["body", "dress", "all"], value="all")
- image_algorithms = gr.CheckboxGroup(["Use WD Tagger"], label="Algorithms", value=["Use WD Tagger"], visible=False)
- generate_from_image_btn_gui = gr.Button(value="GENERATE TAGS FROM IMAGE")
- prompt_gui = gr.Textbox(lines=6, placeholder="1girl, solo, ...", label="Prompt", show_copy_button=True)
- with gr.Accordion("Negative prompt, etc.", open=False) as menu_negative:
- neg_prompt_gui = gr.Textbox(lines=3, placeholder="Enter Neg prompt", label="Negative prompt", value="lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, worst quality, low quality, very displeasing, (bad)", show_copy_button=True)
- translate_prompt_button = gr.Button(value="Translate prompt to English", size="sm", variant="secondary")
- with gr.Row():
- insert_prompt_gui = gr.Radio(label="Insert reccomended positive / negative prompt", choices=["None", "Auto", "Animagine", "Pony"], value="Auto", interactive=True)
- prompt_type_gui = gr.Radio(label="Convert tags to", choices=["danbooru", "e621"], value="e621", visible=False)
- prompt_type_button = gr.Button(value="Convert prompt to Pony e621 style", size="sm", variant="secondary")
- with gr.Row():
- character_dbt = gr.Textbox(lines=1, placeholder="kafuu chino, ...", label="Character names")
- series_dbt = gr.Textbox(lines=1, placeholder="Is the order a rabbit?, ...", label="Series names")
- random_character_gui = gr.Button(value="Random character 🎲", size="sm", variant="secondary")
- model_name_dbt = gr.Dropdown(label="Model", choices=list(V2_ALL_MODELS.keys()), value=list(V2_ALL_MODELS.keys())[0], visible=False)
- aspect_ratio_dbt = gr.Radio(label="Aspect ratio", choices=list(V2_ASPECT_RATIO_OPTIONS), value="square", visible=False)
- length_dbt = gr.Radio(label="Length", choices=list(V2_LENGTH_OPTIONS), value="very_long", visible=False)
- identity_dbt = gr.Radio(label="Keep identity", choices=list(V2_IDENTITY_OPTIONS), value="lax", visible=False)
- ban_tags_dbt = gr.Textbox(label="Ban tags", placeholder="alternate costumen, ...", value="futanari, censored, furry, furrification", visible=False)
- copy_button_dbt = gr.Button(value="Copy to clipboard", visible=False)
- rating_dbt = gr.Radio(label="Rating", choices=list(V2_RATING_OPTIONS), value="sfw")
- generate_db_random_button = gr.Button(value="EXTEND PROMPT 🎲")
- with gr.Row():
- translate_prompt_gui = gr.Button(value="Translate Prompt 📝", variant="secondary", size="sm")
- set_random_seed = gr.Button(value="Seed 🎲", variant="secondary", size="sm")
- set_params_gui = gr.Button(value="Params ↙️", variant="secondary", size="sm")
- clear_prompt_gui = gr.Button(value="Clear 🗑️", variant="secondary", size="sm")
-
- generate_button = gr.Button(value="GENERATE IMAGE", size="lg", variant="primary")
-
- model_name_gui.change(
- update_task_options,
- [model_name_gui, task_gui],
- [task_gui],
- )
-
- load_model_gui = gr.HTML(elem_id="load_model", elem_classes="contain")
-
- result_images = gr.Gallery(
- label="Generated images",
- show_label=False,
- elem_id="gallery",
- #columns=[2],
- columns=[1],
- #rows=[2],
- rows=[1],
- object_fit="contain",
- # height="auto",
- interactive=False,
- preview=False,
- show_share_button=False,
- show_download_button=True,
- selected_index=50,
- format="png",
- )
-
- result_images_files = gr.Files(interactive=False, visible=False)
-
- actual_task_info = gr.HTML()
-
- with gr.Accordion("History", open=False):
- history_files = gr.Files(interactive=False, visible=False)
- history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", format="png", interactive=False, show_share_button=False, show_download_button=True)
- history_clear_button = gr.Button(value="Clear History", variant="secondary")
- history_clear_button.click(lambda: ([], []), None, [history_gallery, history_files], queue=False, show_api=False)
-
- with gr.Row(equal_height=False, variant="default"):
- gpu_duration_gui = gr.Number(minimum=5, maximum=240, value=59, show_label=False, container=False, info="GPU time duration (seconds)")
- with gr.Column():
- verbose_info_gui = gr.Checkbox(value=False, container=False, label="Status info")
- load_lora_cpu_gui = gr.Checkbox(value=False, container=False, label="Load LoRAs on CPU")
-
- with gr.Column(scale=1):
- with gr.Accordion("Generation settings", open=False, visible=True) as menu_gen:
- with gr.Row():
- img_width_gui = gr.Slider(minimum=64, maximum=4096, step=8, value=1024, label="Img Width")
- img_height_gui = gr.Slider(minimum=64, maximum=4096, step=8, value=1024, label="Img Height")
- steps_gui = gr.Slider(minimum=1, maximum=100, step=1, value=28, label="Steps")
- cfg_gui = gr.Slider(minimum=0, maximum=30, step=0.5, value=7.0, label="CFG")
- guidance_rescale_gui = gr.Slider(label="CFG rescale:", value=0., step=0.01, minimum=0., maximum=1.5)
- with gr.Row():
- seed_gui = gr.Number(minimum=-1, maximum=2**32-1, value=-1, label="Seed")
- pag_scale_gui = gr.Slider(minimum=0.0, maximum=10.0, step=0.1, value=0.0, label="PAG Scale")
- num_images_gui = gr.Slider(minimum=1, maximum=5, step=1, value=1, label="Images")
- clip_skip_gui = gr.Checkbox(value=False, label="Layer 2 Clip Skip")
- free_u_gui = gr.Checkbox(value=False, label="FreeU")
- with gr.Row():
- sampler_gui = gr.Dropdown(label="Sampler", choices=scheduler_names, value="Euler")
- schedule_type_gui = gr.Dropdown(label="Schedule type", choices=SCHEDULE_TYPE_OPTIONS, value=SCHEDULE_TYPE_OPTIONS[0])
- schedule_prediction_type_gui = gr.Dropdown(label="Discrete Sampling Type", choices=SCHEDULE_PREDICTION_TYPE_OPTIONS, value=SCHEDULE_PREDICTION_TYPE_OPTIONS[0])
- vae_model_gui = gr.Dropdown(label="VAE Model", choices=vae_model_list, value=vae_model_list[0])
- prompt_syntax_gui = gr.Dropdown(label="Prompt Syntax", choices=PROMPT_W_OPTIONS, value=PROMPT_W_OPTIONS[1][1])
-
- with gr.Row(equal_height=False):
-
- def run_set_params_gui(base_prompt, name_model):
- valid_receptors = { # default values
- "prompt": gr.update(value=base_prompt),
- "neg_prompt": gr.update(value=""),
- "Steps": gr.update(value=30),
- "width": gr.update(value=1024),
- "height": gr.update(value=1024),
- "Seed": gr.update(value=-1),
- "Sampler": gr.update(value="Euler"),
- "CFG scale": gr.update(value=7.), # cfg
- "Clip skip": gr.update(value=True),
- "Model": gr.update(value=name_model),
- "Schedule type": gr.update(value="Automatic"),
- "PAG": gr.update(value=.0),
- "FreeU": gr.update(value=False),
- }
- valid_keys = list(valid_receptors.keys())
-
- parameters = extract_parameters(base_prompt)
- # print(parameters)
-
- if "Sampler" in parameters:
- value_sampler = parameters["Sampler"]
- for s_type in SCHEDULE_TYPE_OPTIONS:
- if s_type in value_sampler:
- value_sampler = value_sampler.replace(s_type, "").strip()
- parameters["Sampler"] = value_sampler
- parameters["Schedule type"] = s_type
-
- for key, val in parameters.items():
- # print(val)
- if key in valid_keys:
- try:
- if key == "Sampler":
- if val not in scheduler_names:
- continue
- if key == "Schedule type":
- if val not in SCHEDULE_TYPE_OPTIONS:
- val = "Automatic"
- elif key == "Clip skip":
- if "," in str(val):
- val = val.replace(",", "")
- if int(val) >= 2:
- val = True
- if key == "prompt":
- if ">" in val and "<" in val:
- val = re.sub(r'<[^>]+>', '', val)
- print("Removed LoRA written in the prompt")
- if key in ["prompt", "neg_prompt"]:
- val = re.sub(r'\s+', ' ', re.sub(r',+', ',', val)).strip()
- if key in ["Steps", "width", "height", "Seed"]:
- val = int(val)
- if key == "FreeU":
- val = True
- if key in ["CFG scale", "PAG"]:
- val = float(val)
- if key == "Model":
- filtered_models = [m for m in model_list if val in m]
- if filtered_models:
- val = filtered_models[0]
- else:
- val = name_model
- if key == "Seed":
- continue
- valid_receptors[key] = gr.update(value=val)
- # print(val, type(val))
- # print(valid_receptors)
- except Exception as e:
- print(str(e))
- return [value for value in valid_receptors.values()]
-
- set_params_gui.click(
- run_set_params_gui, [prompt_gui, model_name_gui], [
- prompt_gui,
- neg_prompt_gui,
- steps_gui,
- img_width_gui,
- img_height_gui,
- seed_gui,
- sampler_gui,
- cfg_gui,
- clip_skip_gui,
- model_name_gui,
- schedule_type_gui,
- pag_scale_gui,
- free_u_gui,
- ],
- )
-
- def run_clear_prompt_gui():
- return gr.update(value=""), gr.update(value="")
- clear_prompt_gui.click(
- run_clear_prompt_gui, [], [prompt_gui, neg_prompt_gui]
- )
-
- def run_set_random_seed():
- return -1
- set_random_seed.click(
- run_set_random_seed, [], seed_gui
- )
-
- with gr.Accordion("LoRA", open=False, visible=True) as menu_lora:
- def lora_dropdown(label, visible=True):
- return gr.Dropdown(label=label, choices=get_all_lora_tupled_list(), value="", allow_custom_value=True, elem_classes="lora", min_width=320, visible=visible)
-
- def lora_scale_slider(label, visible=True):
- return gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label=label, visible=visible)
-
- def lora_textbox(label):
- return gr.Textbox(label=label, info="Example of prompt:", value="None", show_copy_button=True, interactive=False, visible=False)
-
- with gr.Row():
- with gr.Column():
- lora1_gui = lora_dropdown("LoRA1")
- lora_scale_1_gui = lora_scale_slider("LoRA Scale 1")
- with gr.Row():
- with gr.Group():
- lora1_info_gui = lora_textbox("LoRA1 prompts")
- lora1_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
- lora1_desc_gui = gr.Markdown(value="", visible=False)
- with gr.Column():
- lora2_gui = lora_dropdown("LoRA2")
- lora_scale_2_gui = lora_scale_slider("LoRA Scale 2")
- with gr.Row():
- with gr.Group():
- lora2_info_gui = lora_textbox("LoRA2 prompts")
- lora2_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
- lora2_desc_gui = gr.Markdown(value="", visible=False)
- with gr.Column():
- lora3_gui = lora_dropdown("LoRA3")
- lora_scale_3_gui = lora_scale_slider("LoRA Scale 3")
- with gr.Row():
- with gr.Group():
- lora3_info_gui = lora_textbox("LoRA3 prompts")
- lora3_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
- lora3_desc_gui = gr.Markdown(value="", visible=False)
- with gr.Column():
- lora4_gui = lora_dropdown("LoRA4")
- lora_scale_4_gui = lora_scale_slider("LoRA Scale 4")
- with gr.Row():
- with gr.Group():
- lora4_info_gui = lora_textbox("LoRA4 prompts")
- lora4_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
- lora4_desc_gui = gr.Markdown(value="", visible=False)
- with gr.Column():
- lora5_gui = lora_dropdown("LoRA5")
- lora_scale_5_gui = lora_scale_slider("LoRA Scale 5")
- with gr.Row():
- with gr.Group():
- lora5_info_gui = lora_textbox("LoRA5 prompts")
- lora5_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
- lora5_desc_gui = gr.Markdown(value="", visible=False)
- with gr.Column():
- lora6_gui = lora_dropdown("LoRA6", visible=False)
- lora_scale_6_gui = lora_scale_slider("LoRA Scale 6", visible=False)
- with gr.Row():
- with gr.Group():
- lora6_info_gui = lora_textbox("LoRA6 prompts")
- lora6_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
- lora6_desc_gui = gr.Markdown(value="", visible=False)
- with gr.Column():
- lora7_gui = lora_dropdown("LoRA7", visible=False)
- lora_scale_7_gui = lora_scale_slider("LoRA Scale 7", visible=False)
- with gr.Row():
- with gr.Group():
- lora7_info_gui = lora_textbox("LoRA7 prompts")
- lora7_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
- lora7_desc_gui = gr.Markdown(value="", visible=False)
- with gr.Accordion("From URL", open=True, visible=True):
- with gr.Row():
- search_civitai_basemodel_lora = gr.CheckboxGroup(label="Search LoRA for", choices=CIVITAI_BASEMODEL, value=["Pony", "Illustrious", "SDXL 1.0"])
- search_civitai_sort_lora = gr.Radio(label="Sort", choices=CIVITAI_SORT, value="Highest Rated")
- search_civitai_period_lora = gr.Radio(label="Period", choices=CIVITAI_PERIOD, value="AllTime")
- with gr.Row():
- search_civitai_query_lora = gr.Textbox(label="Query", placeholder="oomuro sakurako...", lines=1)
- search_civitai_tag_lora = gr.Dropdown(label="Tag", choices=get_civitai_tag(), value=get_civitai_tag()[0], allow_custom_value=True)
- search_civitai_user_lora = gr.Textbox(label="Username", lines=1)
- search_civitai_button_lora = gr.Button("Search on Civitai")
- search_civitai_desc_lora = gr.Markdown(value="", visible=False, elem_classes="desc")
- with gr.Accordion("Select from Gallery", open=False):
- search_civitai_gallery_lora = gr.Gallery([], label="Results", allow_preview=False, columns=5, show_share_button=False, interactive=False)
- search_civitai_result_lora = gr.Dropdown(label="Search Results", choices=[("", "")], value="", allow_custom_value=True, visible=False)
- with gr.Row():
- text_lora = gr.Textbox(label="LoRA's download URL", placeholder="https://civitai.com/api/download/models/28907", info="It has to be .safetensors files, and you can also download them from Hugging Face.", lines=1, scale=4)
- romanize_text = gr.Checkbox(value=False, label="Transliterate name", scale=1, visible=False)
- button_lora = gr.Button("Get and Refresh the LoRA Lists")
- new_lora_status = gr.HTML()
- with gr.Accordion("From Local", open=True, visible=True):
- file_output_lora = gr.File(label="Uploaded LoRA", file_types=['.ckpt', '.pt', '.pth', '.safetensors', '.bin'], file_count="multiple", interactive=False, visible=False)
- upload_button_lora = gr.UploadButton(label="Upload LoRA from your disk (very slow)", file_types=['.ckpt', '.pt', '.pth', '.safetensors', '.bin'], file_count="multiple")
-
- with gr.Column() as menu_advanced:
- with gr.Accordion("Hires fix", open=False, visible=True) as menu_hires:
- upscaler_model_path_gui = gr.Dropdown(label="Upscaler", choices=UPSCALER_KEYS, value=UPSCALER_KEYS[0])
- with gr.Row():
- upscaler_increases_size_gui = gr.Slider(minimum=1.1, maximum=6., step=0.1, value=1.0, label="Upscale by")
- upscaler_tile_size_gui = gr.Slider(minimum=0, maximum=512, step=16, value=0, label="Upscaler Tile Size", info="0 = no tiling")
- upscaler_tile_overlap_gui = gr.Slider(minimum=0, maximum=48, step=1, value=8, label="Upscaler Tile Overlap")
- with gr.Row():
- hires_steps_gui = gr.Slider(minimum=0, value=30, maximum=100, step=1, label="Hires Steps")
- hires_denoising_strength_gui = gr.Slider(minimum=0.1, maximum=1.0, step=0.01, value=0.55, label="Hires Denoising Strength")
- hires_sampler_gui = gr.Dropdown(label="Hires Sampler", choices=POST_PROCESSING_SAMPLER, value=POST_PROCESSING_SAMPLER[0])
- hires_schedule_list = ["Use same schedule type"] + SCHEDULE_TYPE_OPTIONS
- hires_schedule_type_gui = gr.Dropdown(label="Hires Schedule type", choices=hires_schedule_list, value=hires_schedule_list[0])
- hires_guidance_scale_gui = gr.Slider(minimum=-1., maximum=30., step=0.5, value=-1., label="Hires CFG", info="If the value is -1, the main CFG will be used")
- hires_prompt_gui = gr.Textbox(label="Hires Prompt", placeholder="Main prompt will be use", lines=3)
- hires_negative_prompt_gui = gr.Textbox(label="Hires Negative Prompt", placeholder="Main negative prompt will be use", lines=3)
-
- with gr.Accordion("Detailfix", open=False, visible=True) as menu_detail:
- with gr.Row():
-
- # Adetailer Inpaint Only
- adetailer_inpaint_only_gui = gr.Checkbox(label="Inpaint only", value=True)
-
- # Adetailer Verbose
- adetailer_verbose_gui = gr.Checkbox(label="Verbose", value=False)
-
- # Adetailer Sampler
- adetailer_sampler_gui = gr.Dropdown(label="Adetailer sampler:", choices=POST_PROCESSING_SAMPLER, value=POST_PROCESSING_SAMPLER[0])
-
- with gr.Accordion("Detailfix A", open=True, visible=True):
- # Adetailer A
- adetailer_active_a_gui = gr.Checkbox(label="Enable Adetailer A", value=False)
- prompt_ad_a_gui = gr.Textbox(label="Main prompt", placeholder="Main prompt will be use", lines=3)
- negative_prompt_ad_a_gui = gr.Textbox(label="Negative prompt", placeholder="Main negative prompt will be use", lines=3)
- with gr.Row():
- strength_ad_a_gui = gr.Number(label="Strength:", value=0.35, step=0.01, minimum=0.01, maximum=1.0)
- face_detector_ad_a_gui = gr.Checkbox(label="Face detector", value=False)
- person_detector_ad_a_gui = gr.Checkbox(label="Person detector", value=True)
- hand_detector_ad_a_gui = gr.Checkbox(label="Hand detector", value=False)
- with gr.Row():
- mask_dilation_a_gui = gr.Number(label="Mask dilation:", value=4, minimum=1)
- mask_blur_a_gui = gr.Number(label="Mask blur:", value=4, minimum=1)
- mask_padding_a_gui = gr.Number(label="Mask padding:", value=32, minimum=1)
-
- with gr.Accordion("Detailfix B", open=True, visible=True):
- # Adetailer B
- adetailer_active_b_gui = gr.Checkbox(label="Enable Adetailer B", value=False)
- prompt_ad_b_gui = gr.Textbox(label="Main prompt", placeholder="Main prompt will be use", lines=3)
- negative_prompt_ad_b_gui = gr.Textbox(label="Negative prompt", placeholder="Main negative prompt will be use", lines=3)
- with gr.Row():
- strength_ad_b_gui = gr.Number(label="Strength:", value=0.35, step=0.01, minimum=0.01, maximum=1.0)
- face_detector_ad_b_gui = gr.Checkbox(label="Face detector", value=False)
- person_detector_ad_b_gui = gr.Checkbox(label="Person detector", value=True)
- hand_detector_ad_b_gui = gr.Checkbox(label="Hand detector", value=False)
- with gr.Row():
- mask_dilation_b_gui = gr.Number(label="Mask dilation:", value=4, minimum=1)
- mask_blur_b_gui = gr.Number(label="Mask blur:", value=4, minimum=1)
- mask_padding_b_gui = gr.Number(label="Mask padding:", value=32, minimum=1)
-
- with gr.Accordion("Face restoration", open=False, visible=True):
-
- face_rest_options = [None] + FACE_RESTORATION_MODELS
-
- face_restoration_model_gui = gr.Dropdown(label="Face restoration model", choices=face_rest_options, value=face_rest_options[0])
- with gr.Row():
- face_restoration_visibility_gui = gr.Slider(minimum=0., maximum=1., step=0.001, value=1., label="Visibility")
- face_restoration_weight_gui = gr.Slider(minimum=0., maximum=1., step=0.001, value=.5, label="Weight", info="(0 = maximum effect, 1 = minimum effect)")
-
- with gr.Accordion("Textual inversion", open=False, visible=True) as menu_ti:
- active_textual_inversion_gui = gr.Checkbox(value=False, label="Active Textual Inversion in prompt")
- use_textual_inversion_gui = gr.CheckboxGroup(choices=get_embed_list(get_model_pipeline(model_name_gui.value)) if active_textual_inversion_gui.value else [], value=None, label="Use Textual Invertion in prompt")
- def update_textual_inversion_gui(active_textual_inversion_gui, model_name_gui):
- return gr.update(choices=get_embed_list(get_model_pipeline(model_name_gui)) if active_textual_inversion_gui else [])
- active_textual_inversion_gui.change(update_textual_inversion_gui, [active_textual_inversion_gui, model_name_gui], [use_textual_inversion_gui])
- model_name_gui.change(update_textual_inversion_gui, [active_textual_inversion_gui, model_name_gui], [use_textual_inversion_gui])
-
- with gr.Accordion("ControlNet / Img2img / Inpaint", open=False, visible=True) as menu_i2i:
- with gr.Row():
- image_control = gr.Image(label="Image ControlNet/Inpaint/Img2img", type="filepath")
- image_mask_gui = gr.Image(label="Image Mask", type="filepath")
- with gr.Row():
- strength_gui = gr.Slider(
- minimum=0.01, maximum=1.0, step=0.01, value=0.55, label="Strength",
- info="This option adjusts the level of changes for img2img, repaint and inpaint."
- )
- image_resolution_gui = gr.Slider(
- minimum=64, maximum=2048, step=64, value=1024, label="Image Resolution",
- info="The maximum proportional size of the generated image based on the uploaded image."
- )
- with gr.Row():
- controlnet_model_gui = gr.Dropdown(label="ControlNet model", choices=DIFFUSERS_CONTROLNET_MODEL, value=DIFFUSERS_CONTROLNET_MODEL[0], allow_custom_value=True)
- control_net_output_scaling_gui = gr.Slider(minimum=0, maximum=5.0, step=0.1, value=1, label="ControlNet Output Scaling in UNet")
- control_net_start_threshold_gui = gr.Slider(minimum=0, maximum=1, step=0.01, value=0, label="ControlNet Start Threshold (%)")
- control_net_stop_threshold_gui = gr.Slider(minimum=0, maximum=1, step=0.01, value=1, label="ControlNet Stop Threshold (%)")
- with gr.Row():
- preprocessor_name_gui = gr.Dropdown(label="Preprocessor Name", choices=TASK_AND_PREPROCESSORS["canny"])
- preprocess_resolution_gui = gr.Slider(minimum=64, maximum=2048, step=64, value=512, label="Preprocessor Resolution")
- low_threshold_gui = gr.Slider(minimum=1, maximum=255, step=1, value=100, label="'CANNY' low threshold")
- high_threshold_gui = gr.Slider(minimum=1, maximum=255, step=1, value=200, label="'CANNY' high threshold")
-
- def change_preprocessor_choices(task):
- task = TASK_STABLEPY[task]
- if task in TASK_AND_PREPROCESSORS.keys():
- choices_task = TASK_AND_PREPROCESSORS[task]
- else:
- choices_task = TASK_AND_PREPROCESSORS["canny"]
- return gr.update(choices=choices_task, value=choices_task[0])
-
- task_gui.change(
- change_preprocessor_choices,
- [task_gui],
- [preprocessor_name_gui],
- )
-
- with gr.Row():
- value_threshold_gui = gr.Slider(minimum=1, maximum=2.0, step=0.01, value=0.1, label="'MLSD' Hough value threshold")
- distance_threshold_gui = gr.Slider(minimum=1, maximum=20.0, step=0.01, value=0.1, label="'MLSD' Hough distance threshold")
- recolor_gamma_correction_gui = gr.Number(minimum=0., maximum=25., value=1., step=0.001, label="'RECOLOR' gamma correction")
- tile_blur_sigma_gui = gr.Number(minimum=0, maximum=100, value=9, step=1, label="'TILE' blur sigma")
-
- with gr.Accordion("IP-Adapter", open=False, visible=True) as menu_ipa:
-
- with gr.Accordion("IP-Adapter 1", open=True, visible=True):
- with gr.Row():
- image_ip1 = gr.Image(label="IP Image", type="filepath")
- mask_ip1 = gr.Image(label="IP Mask", type="filepath")
- with gr.Row():
- model_ip1 = gr.Dropdown(value="plus_face", label="Model", choices=IP_MODELS)
- mode_ip1 = gr.Dropdown(value="original", label="Mode", choices=MODE_IP_OPTIONS)
- scale_ip1 = gr.Slider(minimum=0., maximum=2., step=0.01, value=0.7, label="Scale")
- with gr.Accordion("IP-Adapter 2", open=True, visible=True):
- with gr.Row():
- image_ip2 = gr.Image(label="IP Image", type="filepath")
- mask_ip2 = gr.Image(label="IP Mask (optional)", type="filepath")
- with gr.Row():
- model_ip2 = gr.Dropdown(value="base", label="Model", choices=IP_MODELS)
- mode_ip2 = gr.Dropdown(value="style", label="Mode", choices=MODE_IP_OPTIONS)
- scale_ip2 = gr.Slider(minimum=0., maximum=2., step=0.01, value=0.7, label="Scale")
-
- with gr.Accordion("T2I adapter", open=False, visible=False) as menu_t2i:
- t2i_adapter_preprocessor_gui = gr.Checkbox(value=True, label="T2i Adapter Preprocessor")
- with gr.Row():
- adapter_conditioning_scale_gui = gr.Slider(minimum=0, maximum=5., step=0.1, value=1, label="Adapter Conditioning Scale")
- adapter_conditioning_factor_gui = gr.Slider(minimum=0, maximum=1., step=0.01, value=0.55, label="Adapter Conditioning Factor (%)")
-
- with gr.Accordion("Styles", open=False, visible=True) as menu_styles:
-
- try:
- style_names_found = sd_gen.model.STYLE_NAMES
- except Exception:
- style_names_found = STYLE_NAMES
-
- style_prompt_gui = gr.Dropdown(
- style_names_found,
- multiselect=True,
- value=None,
- label="Style Prompt",
- interactive=True,
- )
- style_json_gui = gr.File(label="Style JSON File")
- style_button = gr.Button("Load styles")
-
- def load_json_style_file(json):
- if not sd_gen.model:
- gr.Info("First load the model")
- return gr.update(value=None, choices=STYLE_NAMES)
-
- sd_gen.model.load_style_file(json)
- gr.Info(f"{len(sd_gen.model.STYLE_NAMES)} styles loaded")
- return gr.update(value=None, choices=sd_gen.model.STYLE_NAMES)
-
- style_button.click(load_json_style_file, [style_json_gui], [style_prompt_gui])
-
- with gr.Accordion("Other settings", open=False, visible=True) as menu_other:
- with gr.Row():
- save_generated_images_gui = gr.Checkbox(value=False, label="Save Generated Images")
- filename_pattern_gui = gr.Textbox(label="Filename pattern", value="model,seed", placeholder="model,seed,sampler,schedule_type,img_width,img_height,guidance_scale,num_steps,vae,prompt_section,neg_prompt_section", lines=1)
- with gr.Row():
- hires_before_adetailer_gui = gr.Checkbox(value=False, label="Hires Before Adetailer")
- hires_after_adetailer_gui = gr.Checkbox(value=True, label="Hires After Adetailer")
- generator_in_cpu_gui = gr.Checkbox(value=False, label="Generator in CPU")
-
- with gr.Accordion("More settings", open=False, visible=False):
- loop_generation_gui = gr.Slider(minimum=1, value=1, label="Loop Generation")
- retain_task_cache_gui = gr.Checkbox(value=True, label="Retain task model in cache")
- leave_progress_bar_gui = gr.Checkbox(value=True, label="Leave Progress Bar")
- disable_progress_bar_gui = gr.Checkbox(value=False, label="Disable Progress Bar")
- display_images_gui = gr.Checkbox(value=False, label="Display Images")
- image_previews_gui = gr.Checkbox(value=True, label="Image Previews")
- image_storage_location_gui = gr.Textbox(value="./images", label="Image Storage Location")
- retain_compel_previous_load_gui = gr.Checkbox(value=False, label="Retain Compel Previous Load")
- retain_detailfix_model_previous_load_gui = gr.Checkbox(value=False, label="Retain Detailfix Model Previous Load")
- retain_hires_model_previous_load_gui = gr.Checkbox(value=False, label="Retain Hires Model Previous Load")
- xformers_memory_efficient_attention_gui = gr.Checkbox(value=False, label="Xformers Memory Efficient Attention")
-
- with gr.Accordion("Examples and help", open=True, visible=True) as menu_example:
- gr.Examples(
- examples=EXAMPLES_GUI,
- fn=sd_gen.generate_pipeline,
- inputs=[
- prompt_gui,
- neg_prompt_gui,
- num_images_gui,
- steps_gui,
- cfg_gui,
- clip_skip_gui,
- seed_gui,
- sampler_gui,
- img_height_gui,
- img_width_gui,
- model_name_gui,
- ],
- outputs=[load_model_gui, result_images, actual_task_info],
- cache_examples=False,
- #elem_id="examples",
- )
-
- gr.Markdown(RESOURCES)
-## END MOD
-
- with gr.Tab(th.t("Inpaint mask maker"), render=True):
-
- def create_mask_now(img, invert):
- import numpy as np
- import time
-
- time.sleep(0.5)
-
- transparent_image = img["layers"][0]
-
- # Extract the alpha channel
- alpha_channel = np.array(transparent_image)[:, :, 3]
-
- # Create a binary mask by thresholding the alpha channel
- binary_mask = alpha_channel > 1
-
- if invert:
- print("Invert")
- # Invert the binary mask so that the drawn shape is white and the rest is black
- binary_mask = np.invert(binary_mask)
-
- # Convert the binary mask to a 3-channel RGB mask
- rgb_mask = np.stack((binary_mask,) * 3, axis=-1)
-
- # Convert the mask to uint8
- rgb_mask = rgb_mask.astype(np.uint8) * 255
-
- return img["background"], rgb_mask
-
- with gr.Row():
- with gr.Column(scale=2):
- # image_base = gr.ImageEditor(label="Base image", show_label=True, brush=gr.Brush(colors=["#000000"]))
- image_base = gr.ImageEditor(
- sources=["upload", "clipboard"],
- # crop_size="1:1",
- # enable crop (or disable it)
- # transforms=["crop"],
- brush=gr.Brush(
- default_size="16", # or leave it as 'auto'
- color_mode="fixed", # 'fixed' hides the user swatches and colorpicker, 'defaults' shows it
- # default_color="black", # html names are supported
- colors=[
- "rgba(0, 0, 0, 1)", # rgb(a)
- "rgba(0, 0, 0, 0.1)",
- "rgba(255, 255, 255, 0.1)",
- # "hsl(360, 120, 120)" # in fact any valid colorstring
- ]
- ),
- eraser=gr.Eraser(default_size="16")
- )
- invert_mask = gr.Checkbox(value=False, label="Invert mask")
- btn = gr.Button("Create mask")
- with gr.Column(scale=1):
- img_source = gr.Image(interactive=False)
- img_result = gr.Image(label="Mask image", show_label=True, interactive=False)
- btn_send = gr.Button("Send to the first tab")
-
- btn.click(create_mask_now, [image_base, invert_mask], [img_source, img_result])
-
- def send_img(img_source, img_result):
- return img_source, img_result
- btn_send.click(send_img, [img_source, img_result], [image_control, image_mask_gui])
-
- with gr.Tab(th.t("PNG Info")):
- with gr.Row():
- with gr.Column():
- image_metadata = gr.Image(label="Image with metadata", type="pil", sources=["upload"])
-
- with gr.Column():
- result_metadata = gr.Textbox(label="Metadata", show_label=True, show_copy_button=True, interactive=False, container=True, max_lines=99)
-
- image_metadata.change(
- fn=extract_exif_data,
- inputs=[image_metadata],
- outputs=[result_metadata],
- )
-
- with gr.Tab(th.t("Upscaler")):
- with gr.Row():
- with gr.Column():
-
- USCALER_TAB_KEYS = [name for name in UPSCALER_KEYS[9:]]
-
- image_up_tab = gr.Image(label="Image", type="pil", sources=["upload"])
- upscaler_tab = gr.Dropdown(label="Upscaler", choices=USCALER_TAB_KEYS, value=USCALER_TAB_KEYS[5])
- upscaler_size_tab = gr.Slider(minimum=1., maximum=4., step=0.1, value=1.1, label="Upscale by")
- generate_button_up_tab = gr.Button(value="START UPSCALE", variant="primary")
-
- with gr.Column():
- result_up_tab = gr.Image(label="Result", type="pil", interactive=False, format="png")
-
- generate_button_up_tab.click(
- fn=process_upscale,
- inputs=[image_up_tab, upscaler_tab, upscaler_size_tab],
- outputs=[result_up_tab],
- )
-
- with gr.Tab(th.t("Preprocessor"), render=True):
- preprocessor_tab()
-
-## BEGIN MOD
- interface_mode_gui.change(
- change_interface_mode,
- [interface_mode_gui],
- [menu_model, menu_from_image, menu_negative, menu_gen, menu_hires, menu_lora, menu_advanced,
- menu_example, task_gui, quick_speed_gui],
- queue=False,
- )
- model_name_gui.change(get_t2i_model_info, [model_name_gui], [model_info_gui], queue=False)
- translate_prompt_gui.click(translate_to_en, [prompt_gui], [prompt_gui], queue=False)\
- .then(translate_to_en, [neg_prompt_gui], [neg_prompt_gui], queue=False)
-
- gr.on(
- triggers=[quick_model_type_gui.change, quick_genre_gui.change, quick_speed_gui.change, quick_aspect_gui.change],
- fn=set_quick_presets,
- inputs=[quick_genre_gui, quick_model_type_gui, quick_speed_gui, quick_aspect_gui],
- outputs=[quality_selector_gui, style_selector_gui, sampler_selector_gui, optimization_gui, insert_prompt_gui],
- queue=False,
- trigger_mode="once",
- )
- gr.on(
- triggers=[quality_selector_gui.change, style_selector_gui.change, insert_prompt_gui.change],
- fn=process_style_prompt,
- inputs=[prompt_gui, neg_prompt_gui, style_selector_gui, quality_selector_gui, insert_prompt_gui],
- outputs=[prompt_gui, neg_prompt_gui, quick_model_type_gui],
- queue=False,
- trigger_mode="once",
- )
- sampler_selector_gui.change(set_sampler_settings, [sampler_selector_gui], [sampler_gui, steps_gui, cfg_gui, clip_skip_gui, img_width_gui, img_height_gui, optimization_gui], queue=False)
- optimization_gui.change(set_optimization, [optimization_gui, steps_gui, cfg_gui, sampler_gui, clip_skip_gui, lora5_gui, lora_scale_5_gui], [steps_gui, cfg_gui, sampler_gui, clip_skip_gui, lora5_gui, lora_scale_5_gui], queue=False)
-
- gr.on(
- triggers=[lora1_gui.change, lora_scale_1_gui.change, lora2_gui.change, lora_scale_2_gui.change,
- lora3_gui.change, lora_scale_3_gui.change, lora4_gui.change, lora_scale_4_gui.change,
- lora5_gui.change, lora_scale_5_gui.change, lora6_gui.change, lora_scale_6_gui.change,
- lora7_gui.change, lora_scale_7_gui.change, prompt_syntax_gui.change],
- fn=update_loras,
- inputs=[prompt_gui, prompt_syntax_gui, lora1_gui, lora_scale_1_gui, lora2_gui, lora_scale_2_gui,
- lora3_gui, lora_scale_3_gui, lora4_gui, lora_scale_4_gui, lora5_gui, lora_scale_5_gui,
- lora6_gui, lora_scale_6_gui, lora7_gui, lora_scale_7_gui],
- outputs=[prompt_gui, lora1_gui, lora_scale_1_gui, lora1_info_gui, lora1_copy_gui, lora1_desc_gui,
- lora2_gui, lora_scale_2_gui, lora2_info_gui, lora2_copy_gui, lora2_desc_gui,
- lora3_gui, lora_scale_3_gui, lora3_info_gui, lora3_copy_gui, lora3_desc_gui,
- lora4_gui, lora_scale_4_gui, lora4_info_gui, lora4_copy_gui, lora4_desc_gui,
- lora5_gui, lora_scale_5_gui, lora5_info_gui, lora5_copy_gui, lora5_desc_gui,
- lora6_gui, lora_scale_6_gui, lora6_info_gui, lora6_copy_gui, lora6_desc_gui,
- lora7_gui, lora_scale_7_gui, lora7_info_gui, lora7_copy_gui, lora7_desc_gui],
- queue=False,
- trigger_mode="once",
- )
- lora1_copy_gui.click(apply_lora_prompt, [prompt_gui, lora1_info_gui], [prompt_gui], queue=False)
- lora2_copy_gui.click(apply_lora_prompt, [prompt_gui, lora2_info_gui], [prompt_gui], queue=False)
- lora3_copy_gui.click(apply_lora_prompt, [prompt_gui, lora3_info_gui], [prompt_gui], queue=False)
- lora4_copy_gui.click(apply_lora_prompt, [prompt_gui, lora4_info_gui], [prompt_gui], queue=False)
- lora5_copy_gui.click(apply_lora_prompt, [prompt_gui, lora5_info_gui], [prompt_gui], queue=False)
- lora6_copy_gui.click(apply_lora_prompt, [prompt_gui, lora6_info_gui], [prompt_gui], queue=False)
- lora7_copy_gui.click(apply_lora_prompt, [prompt_gui, lora7_info_gui], [prompt_gui], queue=False)
- gr.on(
- triggers=[search_civitai_button_lora.click, search_civitai_query_lora.submit],
- fn=search_civitai_lora,
- inputs=[search_civitai_query_lora, search_civitai_basemodel_lora, search_civitai_sort_lora, search_civitai_period_lora,
- search_civitai_tag_lora, search_civitai_user_lora, search_civitai_gallery_lora],
- outputs=[search_civitai_result_lora, search_civitai_desc_lora, search_civitai_button_lora, search_civitai_query_lora, search_civitai_gallery_lora],
- queue=True,
- scroll_to_output=True,
- )
- search_civitai_result_lora.change(select_civitai_lora, [search_civitai_result_lora], [text_lora, search_civitai_desc_lora], queue=False, scroll_to_output=True)
- search_civitai_gallery_lora.select(update_civitai_selection, None, [search_civitai_result_lora], queue=False, show_api=False)
- button_lora.click(get_my_lora, [text_lora, romanize_text], [lora1_gui, lora2_gui, lora3_gui, lora4_gui, lora5_gui, lora6_gui, lora7_gui, new_lora_status], scroll_to_output=True)
- upload_button_lora.upload(upload_file_lora, [upload_button_lora], [file_output_lora, upload_button_lora]).success(
- move_file_lora, [file_output_lora], [lora1_gui, lora2_gui, lora3_gui, lora4_gui, lora5_gui, lora6_gui, lora7_gui], scroll_to_output=True)
-
- use_textual_inversion_gui.change(set_textual_inversion_prompt, [use_textual_inversion_gui, prompt_gui, neg_prompt_gui, prompt_syntax_gui], [prompt_gui, neg_prompt_gui])
-
- generate_from_image_btn_gui.click(
- lambda: ("", "", ""), None, [series_dbt, character_dbt, prompt_gui], queue=False,
- ).success(
- predict_tags_wd,
- [input_image_gui, prompt_gui, image_algorithms, general_threshold_gui, character_threshold_gui],
- [series_dbt, character_dbt, prompt_gui, copy_button_dbt],
- ).success(
- compose_prompt_to_copy, [character_dbt, series_dbt, prompt_gui], [prompt_gui], queue=False,
- ).success(
- remove_specific_prompt, [prompt_gui, keep_tags_gui], [prompt_gui], queue=False,
- ).success(
- convert_danbooru_to_e621_prompt, [prompt_gui, tag_type_gui], [prompt_gui], queue=False,
- ).success(
- insert_recom_prompt, [prompt_gui, neg_prompt_gui, recom_prompt_gui], [prompt_gui, neg_prompt_gui], queue=False,
- )
-
- prompt_type_button.click(convert_danbooru_to_e621_prompt, [prompt_gui, prompt_type_gui], [prompt_gui], queue=False)
- random_character_gui.click(select_random_character, [series_dbt, character_dbt], [series_dbt, character_dbt], queue=False)
- generate_db_random_button.click(
- v2_random_prompt,
- [prompt_gui, series_dbt, character_dbt,
- rating_dbt, aspect_ratio_dbt, length_dbt, identity_dbt, ban_tags_dbt, model_name_dbt],
- [prompt_gui, series_dbt, character_dbt],
- ).success(
- convert_danbooru_to_e621_prompt, [prompt_gui, tag_type_gui], [prompt_gui], queue=False,
- )
-
- translate_prompt_button.click(translate_prompt, [prompt_gui], [prompt_gui], queue=False)
- translate_prompt_button.click(translate_prompt, [character_dbt], [character_dbt], queue=False)
- translate_prompt_button.click(translate_prompt, [series_dbt], [series_dbt], queue=False)
-
- generate_button.click(
- fn=insert_model_recom_prompt,
- inputs=[prompt_gui, neg_prompt_gui, model_name_gui, recom_prompt_gui],
- outputs=[prompt_gui, neg_prompt_gui],
- queue=False,
- ).success(
- fn=sd_gen.load_new_model,
- inputs=[
- model_name_gui,
- vae_model_gui,
- task_gui,
- controlnet_model_gui,
- ],
- outputs=[load_model_gui],
- queue=True,
- show_progress="minimal",
- ).success(
- fn=sd_gen_generate_pipeline,
- inputs=[
- prompt_gui,
- neg_prompt_gui,
- num_images_gui,
- steps_gui,
- cfg_gui,
- clip_skip_gui,
- seed_gui,
- lora1_gui,
- lora_scale_1_gui,
- lora2_gui,
- lora_scale_2_gui,
- lora3_gui,
- lora_scale_3_gui,
- lora4_gui,
- lora_scale_4_gui,
- lora5_gui,
- lora_scale_5_gui,
- lora6_gui,
- lora_scale_6_gui,
- lora7_gui,
- lora_scale_7_gui,
- sampler_gui,
- schedule_type_gui,
- schedule_prediction_type_gui,
- img_height_gui,
- img_width_gui,
- model_name_gui,
- vae_model_gui,
- task_gui,
- image_control,
- preprocessor_name_gui,
- preprocess_resolution_gui,
- image_resolution_gui,
- style_prompt_gui,
- style_json_gui,
- image_mask_gui,
- strength_gui,
- low_threshold_gui,
- high_threshold_gui,
- value_threshold_gui,
- distance_threshold_gui,
- recolor_gamma_correction_gui,
- tile_blur_sigma_gui,
- control_net_output_scaling_gui,
- control_net_start_threshold_gui,
- control_net_stop_threshold_gui,
- active_textual_inversion_gui,
- prompt_syntax_gui,
- upscaler_model_path_gui,
- upscaler_increases_size_gui,
- upscaler_tile_size_gui,
- upscaler_tile_overlap_gui,
- hires_steps_gui,
- hires_denoising_strength_gui,
- hires_sampler_gui,
- hires_prompt_gui,
- hires_negative_prompt_gui,
- hires_before_adetailer_gui,
- hires_after_adetailer_gui,
- hires_schedule_type_gui,
- hires_guidance_scale_gui,
- controlnet_model_gui,
- loop_generation_gui,
- leave_progress_bar_gui,
- disable_progress_bar_gui,
- image_previews_gui,
- display_images_gui,
- save_generated_images_gui,
- filename_pattern_gui,
- image_storage_location_gui,
- retain_compel_previous_load_gui,
- retain_detailfix_model_previous_load_gui,
- retain_hires_model_previous_load_gui,
- t2i_adapter_preprocessor_gui,
- adapter_conditioning_scale_gui,
- adapter_conditioning_factor_gui,
- xformers_memory_efficient_attention_gui,
- free_u_gui,
- generator_in_cpu_gui,
- adetailer_inpaint_only_gui,
- adetailer_verbose_gui,
- adetailer_sampler_gui,
- adetailer_active_a_gui,
- prompt_ad_a_gui,
- negative_prompt_ad_a_gui,
- strength_ad_a_gui,
- face_detector_ad_a_gui,
- person_detector_ad_a_gui,
- hand_detector_ad_a_gui,
- mask_dilation_a_gui,
- mask_blur_a_gui,
- mask_padding_a_gui,
- adetailer_active_b_gui,
- prompt_ad_b_gui,
- negative_prompt_ad_b_gui,
- strength_ad_b_gui,
- face_detector_ad_b_gui,
- person_detector_ad_b_gui,
- hand_detector_ad_b_gui,
- mask_dilation_b_gui,
- mask_blur_b_gui,
- mask_padding_b_gui,
- retain_task_cache_gui,
- guidance_rescale_gui,
- image_ip1,
- mask_ip1,
- model_ip1,
- mode_ip1,
- scale_ip1,
- image_ip2,
- mask_ip2,
- model_ip2,
- mode_ip2,
- scale_ip2,
- pag_scale_gui,
- face_restoration_model_gui,
- face_restoration_visibility_gui,
- face_restoration_weight_gui,
- load_lora_cpu_gui,
- verbose_info_gui,
- gpu_duration_gui,
- ],
- outputs=[load_model_gui, result_images, actual_task_info],
- queue=True,
- show_progress="full",
- ).success(save_gallery_images, [result_images, model_name_gui], [result_images, result_images_files], queue=False, show_api=False)\
- .success(save_gallery_history, [result_images, result_images_files, history_gallery, history_files], [history_gallery, history_files], queue=False, show_api=False)
-
- with gr.Tab(th.t("Danbooru Tags Transformer with WD Tagger"), render=True):
- with gr.Column(scale=2):
- with gr.Group():
- input_image = gr.Image(label="Input image", type="pil", sources=["upload", "clipboard"], height=256)
- with gr.Accordion(label="Advanced options", open=False):
- general_threshold = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.01, interactive=True)
- character_threshold = gr.Slider(label="Character threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.01, interactive=True)
- input_tag_type = gr.Radio(label="Convert tags to", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="danbooru")
- recom_prompt = gr.Radio(label="Insert reccomended prompt", choices=["None", "Animagine", "Pony"], value="None", interactive=True)
- image_algorithms = gr.CheckboxGroup(["Use WD Tagger"], label="Algorithms", value=["Use WD Tagger"], visible=False)
- keep_tags = gr.Radio(label="Remove tags leaving only the following", choices=["body", "dress", "all"], value="all")
- generate_from_image_btn = gr.Button(value="GENERATE TAGS FROM IMAGE", size="lg", variant="primary")
- with gr.Group():
- with gr.Row():
- input_character = gr.Textbox(label="Character tags", placeholder="hatsune miku")
- input_copyright = gr.Textbox(label="Copyright tags", placeholder="vocaloid")
- pick_random_character = gr.Button(value="Random character 🎲", size="sm")
- input_general = gr.TextArea(label="General tags", lines=4, placeholder="1girl, ...", value="")
- input_tags_to_copy = gr.Textbox(value="", visible=False)
- with gr.Row():
- copy_input_btn = gr.Button(value="Copy to clipboard", size="sm", interactive=False)
- copy_prompt_btn_input = gr.Button(value="Copy to primary prompt", size="sm", interactive=False)
- translate_input_prompt_button = gr.Button(value="Translate prompt to English", size="sm", variant="secondary")
- tag_type = gr.Radio(label="Output tag conversion", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="e621", visible=False)
- input_rating = gr.Radio(label="Rating", choices=list(V2_RATING_OPTIONS), value="explicit")
- with gr.Accordion(label="Advanced options", open=False):
- input_aspect_ratio = gr.Radio(label="Aspect ratio", info="The aspect ratio of the image.", choices=list(V2_ASPECT_RATIO_OPTIONS), value="square")
- input_length = gr.Radio(label="Length", info="The total length of the tags.", choices=list(V2_LENGTH_OPTIONS), value="very_long")
- input_identity = gr.Radio(label="Keep identity", info="How strictly to keep the identity of the character or subject. If you specify the detail of subject in the prompt, you should choose `strict`. Otherwise, choose `none` or `lax`. `none` is very creative but sometimes ignores the input prompt.", choices=list(V2_IDENTITY_OPTIONS), value="lax")
- input_ban_tags = gr.Textbox(label="Ban tags", info="Tags to ban from the output.", placeholder="alternate costumen, ...", value="censored")
- model_name = gr.Dropdown(label="Model", choices=list(V2_ALL_MODELS.keys()), value=list(V2_ALL_MODELS.keys())[0])
- dummy_np = gr.Textbox(label="Negative prompt", value="", visible=False)
- recom_animagine = gr.Textbox(label="Animagine reccomended prompt", value="Animagine", visible=False)
- recom_pony = gr.Textbox(label="Pony reccomended prompt", value="Pony", visible=False)
- generate_btn = gr.Button(value="GENERATE TAGS", size="lg", variant="primary")
- with gr.Row():
- with gr.Group():
- output_text = gr.TextArea(label="Output tags", interactive=False, show_copy_button=True)
- with gr.Row():
- copy_btn = gr.Button(value="Copy to clipboard", size="sm", interactive=False)
- copy_prompt_btn = gr.Button(value="Copy to primary prompt", size="sm", interactive=False)
- with gr.Group():
- output_text_pony = gr.TextArea(label="Output tags (Pony e621 style)", interactive=False, show_copy_button=True)
- with gr.Row():
- copy_btn_pony = gr.Button(value="Copy to clipboard", size="sm", interactive=False)
- copy_prompt_btn_pony = gr.Button(value="Copy to primary prompt", size="sm", interactive=False)
- description_ui()
-
- translate_input_prompt_button.click(translate_prompt, inputs=[input_general], outputs=[input_general], queue=False)
- translate_input_prompt_button.click(translate_prompt, inputs=[input_character], outputs=[input_character], queue=False)
- translate_input_prompt_button.click(translate_prompt, inputs=[input_copyright], outputs=[input_copyright], queue=False)
-
- generate_from_image_btn.click(
- lambda: ("", "", ""), None, [input_copyright, input_character, input_general], queue=False,
- ).success(
- predict_tags_wd,
- [input_image, input_general, image_algorithms, general_threshold, character_threshold],
- [input_copyright, input_character, input_general, copy_input_btn],
- ).success(
- remove_specific_prompt, inputs=[input_general, keep_tags], outputs=[input_general], queue=False,
- ).success(
- convert_danbooru_to_e621_prompt, inputs=[input_general, input_tag_type], outputs=[input_general], queue=False,
- ).success(
- insert_recom_prompt, inputs=[input_general, dummy_np, recom_prompt], outputs=[input_general, dummy_np], queue=False,
- ).success(lambda: gr.update(interactive=True), None, [copy_prompt_btn_input], queue=False)
- copy_input_btn.click(compose_prompt_to_copy, inputs=[input_character, input_copyright, input_general], outputs=[input_tags_to_copy])\
- .success(gradio_copy_text, inputs=[input_tags_to_copy], js=COPY_ACTION_JS)
- copy_prompt_btn_input.click(compose_prompt_to_copy, inputs=[input_character, input_copyright, input_general], outputs=[input_tags_to_copy])\
- .success(gradio_copy_prompt, inputs=[input_tags_to_copy], outputs=[prompt_gui])
-
- pick_random_character.click(select_random_character, [input_copyright, input_character], [input_copyright, input_character])
-
- generate_btn.click(
- v2_upsampling_prompt,
- [model_name, input_copyright, input_character, input_general,
- input_rating, input_aspect_ratio, input_length, input_identity, input_ban_tags],
- [output_text],
- ).success(
- convert_danbooru_to_e621_prompt, inputs=[output_text, tag_type], outputs=[output_text_pony], queue=False,
- ).success(
- insert_recom_prompt, inputs=[output_text, dummy_np, recom_animagine], outputs=[output_text, dummy_np], queue=False,
- ).success(
- insert_recom_prompt, inputs=[output_text_pony, dummy_np, recom_pony], outputs=[output_text_pony, dummy_np], queue=False,
- ).success(lambda: (gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True)),
- None, [copy_btn, copy_btn_pony, copy_prompt_btn, copy_prompt_btn_pony], queue=False)
- copy_btn.click(gradio_copy_text, inputs=[output_text], js=COPY_ACTION_JS)
- copy_btn_pony.click(gradio_copy_text, inputs=[output_text_pony], js=COPY_ACTION_JS)
- copy_prompt_btn.click(gradio_copy_prompt, inputs=[output_text], outputs=[prompt_gui])
- copy_prompt_btn_pony.click(gradio_copy_prompt, inputs=[output_text_pony], outputs=[prompt_gui])
-
- gr.LoginButton()
- gr.DuplicateButton(value="Duplicate Space for private use (This demo does not work on CPU. Requires GPU Space)")
-
-app.queue()
-th.translate_civitai_constants(app)
-app.launch(show_error=True, debug=True) # allowed_paths=["./images/"], show_error=True, debug=True
-## END MOD
-
-# 自定义一些额外的翻译
-def custom_translate(text):
- """自定义翻译函数，以处理特殊情况"""
- return translate_ui.translate_text(text)
+import spaces
+import os
+from stablepy import (
+ Model_Diffusers,
+ SCHEDULE_TYPE_OPTIONS,
+ SCHEDULE_PREDICTION_TYPE_OPTIONS,
+ check_scheduler_compatibility,
+ TASK_AND_PREPROCESSORS,
+ FACE_RESTORATION_MODELS,
+ scheduler_names,
+)
+from constants import (
+ DIRECTORY_UPSCALERS,
+ TASK_STABLEPY,
+ TASK_MODEL_LIST,
+ UPSCALER_DICT_GUI,
+ UPSCALER_KEYS,
+ PROMPT_W_OPTIONS,
+ WARNING_MSG_VAE,
+ SDXL_TASK,
+ MODEL_TYPE_TASK,
+ POST_PROCESSING_SAMPLER,
+ DIFFUSERS_CONTROLNET_MODEL,
+ IP_MODELS,
+ MODE_IP_OPTIONS,
+)
+from stablepy.diffusers_vanilla.style_prompt_config import STYLE_NAMES
+import torch
+import re
+import time
+from PIL import ImageFile
+from utils import (
+ get_model_list,
+ extract_parameters,
+ get_model_type,
+ extract_exif_data,
+ create_mask_now,
+ download_diffuser_repo,
+ get_used_storage_gb,
+ delete_model,
+ progress_step_bar,
+ html_template_message,
+ escape_html,
+)
+from image_processor import preprocessor_tab
+from datetime import datetime
+import gradio as gr
+import logging
+import diffusers
+import warnings
+from stablepy import logger
+from diffusers import FluxPipeline
+# import urllib.parse
+import subprocess
+
+# 导入翻译模块
+import translate_ui
+from translate_ui import monkey_patch_gradio
+import translate_helper as th
+
+# 应用猴子补丁，使所有新创建的Gradio组件自动翻译
+monkey_patch_gradio()
+
+subprocess.run("rm -rf /data-nvme/zerogpu-offload/*", env={}, shell=True)
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+torch.backends.cuda.matmul.allow_tf32 = True
+# os.environ["PYTORCH_NO_CUDA_MEMORY_CACHING"] = "1"
+print(os.getenv("SPACES_ZERO_GPU"))
+
+## BEGIN MOD
+from modutils import (list_uniq, download_private_repo, get_model_id_list, get_tupled_embed_list,
+ get_lora_model_list, get_all_lora_tupled_list, update_loras, apply_lora_prompt, set_prompt_loras,
+ get_my_lora, upload_file_lora, move_file_lora, search_civitai_lora, select_civitai_lora,
+ update_civitai_selection, get_civitai_tag, CIVITAI_SORT, CIVITAI_PERIOD, CIVITAI_BASEMODEL,
+ set_textual_inversion_prompt, get_model_pipeline, change_interface_mode, get_t2i_model_info, download_link_model,
+ get_tupled_model_list, save_gallery_images, save_gallery_history, set_optimization, set_sampler_settings,
+ set_quick_presets, process_style_prompt, optimization_list, save_images, download_things, valid_model_name,
+ preset_styles, preset_quality, preset_sampler_setting, translate_to_en, EXAMPLES_GUI, RESOURCES)
+from env import (HF_TOKEN, CIVITAI_API_KEY, HF_LORA_ESSENTIAL_PRIVATE_REPO, HF_VAE_PRIVATE_REPO,
+ HF_SDXL_EMBEDS_NEGATIVE_PRIVATE_REPO, HF_SDXL_EMBEDS_POSITIVE_PRIVATE_REPO,
+ DIRECTORY_MODELS, DIRECTORY_LORAS, DIRECTORY_VAES, DIRECTORY_EMBEDS, DIRECTORY_EMBEDS_SDXL,
+ DIRECTORY_EMBEDS_POSITIVE_SDXL, LOAD_DIFFUSERS_FORMAT_MODEL,
+ DOWNLOAD_MODEL_LIST, DOWNLOAD_LORA_LIST, DOWNLOAD_VAE_LIST, DOWNLOAD_EMBEDS)
+
+download_private_repo(HF_LORA_ESSENTIAL_PRIVATE_REPO, DIRECTORY_LORAS, True)
+download_private_repo(HF_VAE_PRIVATE_REPO, DIRECTORY_VAES, False)
+## END MOD
+
+directories = [DIRECTORY_MODELS, DIRECTORY_LORAS, DIRECTORY_VAES, DIRECTORY_EMBEDS, DIRECTORY_UPSCALERS]
+for directory in directories:
+ os.makedirs(directory, exist_ok=True)
+
+# - **Download Models**
+DOWNLOAD_MODEL = ", ".join(DOWNLOAD_MODEL_LIST)
+# - **Download VAEs**
+DOWNLOAD_VAE = ", ".join(DOWNLOAD_VAE_LIST)
+# - **Download LoRAs**
+DOWNLOAD_LORA = ", ".join(DOWNLOAD_LORA_LIST)
+
+# Download stuffs
+for url in [url.strip() for url in DOWNLOAD_MODEL.split(',')]:
+ if not os.path.exists(f"./models/{url.split('/')[-1]}"):
+ download_things(DIRECTORY_MODELS, url, HF_TOKEN, CIVITAI_API_KEY)
+for url in [url.strip() for url in DOWNLOAD_VAE.split(',')]:
+ if not os.path.exists(f"./vaes/{url.split('/')[-1]}"):
+ download_things(DIRECTORY_VAES, url, HF_TOKEN, CIVITAI_API_KEY)
+for url in [url.strip() for url in DOWNLOAD_LORA.split(',')]:
+ if not os.path.exists(f"./loras/{url.split('/')[-1]}"):
+ download_things(DIRECTORY_LORAS, url, HF_TOKEN, CIVITAI_API_KEY)
+
+# Download Embeddings
+for url_embed in DOWNLOAD_EMBEDS:
+ if not os.path.exists(f"./embedings/{url_embed.split('/')[-1]}"):
+ download_things(DIRECTORY_EMBEDS, url_embed, HF_TOKEN, CIVITAI_API_KEY)
+
+# Build list models
+embed_list = get_model_list(DIRECTORY_EMBEDS)
+lora_model_list = get_lora_model_list()
+vae_model_list = get_model_list(DIRECTORY_VAES)
+vae_model_list.insert(0, "BakedVAE")
+vae_model_list.insert(0, "None")
+
+## BEGIN MOD
+single_file_model_list = get_model_list(DIRECTORY_MODELS)
+model_list = list_uniq(get_model_id_list() + LOAD_DIFFUSERS_FORMAT_MODEL + single_file_model_list)
+download_private_repo(HF_SDXL_EMBEDS_NEGATIVE_PRIVATE_REPO, DIRECTORY_EMBEDS_SDXL, False)
+download_private_repo(HF_SDXL_EMBEDS_POSITIVE_PRIVATE_REPO, DIRECTORY_EMBEDS_POSITIVE_SDXL, False)
+embed_sdxl_list = get_model_list(DIRECTORY_EMBEDS_SDXL) + get_model_list(DIRECTORY_EMBEDS_POSITIVE_SDXL)
+
+def get_embed_list(pipeline_name):
+ return get_tupled_embed_list(embed_sdxl_list if pipeline_name == "StableDiffusionXLPipeline" else embed_list)
+## END MOD
+
+print('\033[33m🏁 Download and listing of valid models completed.\033[0m')
+
+flux_repo = "camenduru/FLUX.1-dev-diffusers"
+flux_pipe = FluxPipeline.from_pretrained(
+ flux_repo,
+ transformer=None,
+ torch_dtype=torch.bfloat16,
+)#.to("cuda")
+components = flux_pipe.components
+components.pop("transformer", None)
+components.pop("scheduler", None)
+delete_model(flux_repo)
+# components = None
+
+#######################
+# GUI
+#######################
+logging.getLogger("diffusers").setLevel(logging.ERROR)
+diffusers.utils.logging.set_verbosity(40)
+warnings.filterwarnings(action="ignore", category=FutureWarning, module="diffusers")
+warnings.filterwarnings(action="ignore", category=UserWarning, module="diffusers")
+warnings.filterwarnings(action="ignore", category=FutureWarning, module="transformers")
+## BEGIN MOD
+#logger.setLevel(logging.CRITICAL)
+logger.setLevel(logging.DEBUG)
+
+from tagger.v2 import V2_ALL_MODELS, v2_random_prompt, v2_upsampling_prompt
+from tagger.utils import (gradio_copy_text, COPY_ACTION_JS, gradio_copy_prompt,
+ V2_ASPECT_RATIO_OPTIONS, V2_RATING_OPTIONS, V2_LENGTH_OPTIONS, V2_IDENTITY_OPTIONS)
+from tagger.tagger import (predict_tags_wd, convert_danbooru_to_e621_prompt,
+ remove_specific_prompt, insert_recom_prompt, insert_model_recom_prompt,
+ compose_prompt_to_copy, translate_prompt, select_random_character)
+def description_ui():
+ gr.Markdown(
+ th.t("""
+## Danbooru Tags Transformer V2 Demo with WD Tagger
+(Image =>) Prompt => Upsampled longer prompt
+- Mod of p1atdev's [Danbooru Tags Transformer V2 Demo](https://huggingface.co/spaces/p1atdev/danbooru-tags-transformer-v2) and [WD Tagger with 🤗 transformers](https://huggingface.co/spaces/p1atdev/wd-tagger-transformers).
+- Models: p1atdev's [wd-swinv2-tagger-v3-hf](https://huggingface.co/p1atdev/wd-swinv2-tagger-v3-hf), [dart-v2-moe-sft](https://huggingface.co/p1atdev/dart-v2-moe-sft)
+""")
+ )
+## END MOD
+
+class GuiSD:
+ def __init__(self, stream=True):
+ self.model = None
+ self.status_loading = False
+ self.sleep_loading = 4
+ self.last_load = datetime.now()
+ self.inventory = []
+
+ def update_storage_models(self, storage_floor_gb=24, required_inventory_for_purge=3):
+ while get_used_storage_gb() > storage_floor_gb:
+ if len(self.inventory) < required_inventory_for_purge:
+ break
+ removal_candidate = self.inventory.pop(0)
+ delete_model(removal_candidate)
+
+ def update_inventory(self, model_name):
+ if model_name not in single_file_model_list:
+ self.inventory = [
+ m for m in self.inventory if m != model_name
+ ] + [model_name]
+ print(self.inventory)
+
+ def load_new_model(self, model_name, vae_model, task, controlnet_model, progress=gr.Progress(track_tqdm=True)):
+
+ # download link model > model_name
+ if "http" in model_name: #
+ model_name, model_type = download_link_model(model_name, DIRECTORY_MODELS) #
+ is_link_model = True #
+ else: is_link_model = False #
+
+ self.update_storage_models()
+
+ vae_model = vae_model if vae_model != "None" else None
+ model_type = get_model_type(model_name) if not is_link_model else model_type #
+ dtype_model = torch.bfloat16 if model_type == "FLUX" else torch.float16
+
+ if not os.path.exists(model_name):
+ _ = download_diffuser_repo(
+ repo_name=model_name,
+ model_type=model_type,
+ revision="main",
+ token=True,
+ )
+
+ self.update_inventory(model_name)
+
+ for i in range(68):
+ if not self.status_loading:
+ self.status_loading = True
+ if i > 0:
+ time.sleep(self.sleep_loading)
+ print("Previous model ops...")
+ break
+ time.sleep(0.5)
+ print(f"Waiting queue {i}")
+ yield "Waiting queue"
+
+ self.status_loading = True
+
+ yield f"Loading model: {model_name}"
+
+ if vae_model == "BakedVAE":
+ vae_model = model_name
+ elif vae_model:
+ vae_type = "SDXL" if "sdxl" in vae_model.lower() else "SD 1.5"
+ if model_type != vae_type:
+ gr.Warning(WARNING_MSG_VAE)
+
+ print("Loading model...")
+
+ try:
+ start_time = time.time()
+
+ if self.model is None:
+ self.model = Model_Diffusers(
+ base_model_id=model_name,
+ task_name=TASK_STABLEPY[task],
+ vae_model=vae_model,
+ type_model_precision=dtype_model,
+ retain_task_model_in_cache=False,
+ controlnet_model=controlnet_model,
+ device="cpu",
+ env_components=components,
+ )
+ self.model.advanced_params(image_preprocessor_cuda_active=True)
+ else:
+ if self.model.base_model_id != model_name:
+ load_now_time = datetime.now()
+ elapsed_time = max((load_now_time - self.last_load).total_seconds(), 0)
+
+ if elapsed_time <= 9:
+ print("Waiting for the previous model's time ops...")
+ time.sleep(9 - elapsed_time)
+
+ self.model.device = torch.device("cpu")
+ self.model.load_pipe(
+ model_name,
+ task_name=TASK_STABLEPY[task],
+ vae_model=vae_model,
+ type_model_precision=dtype_model,
+ retain_task_model_in_cache=False,
+ controlnet_model=controlnet_model,
+ )
+
+ end_time = time.time()
+ self.sleep_loading = max(min(int(end_time - start_time), 10), 4)
+ except Exception as e:
+ self.last_load = datetime.now()
+ self.status_loading = False
+ self.sleep_loading = 4
+ raise e
+
+ self.last_load = datetime.now()
+ self.status_loading = False
+
+ yield f"Model loaded: {model_name}"
+
+ #@spaces.GPU
+ @torch.inference_mode()
+ def generate_pipeline(
+ self,
+ prompt,
+ neg_prompt,
+ num_images,
+ steps,
+ cfg,
+ clip_skip,
+ seed,
+ lora1,
+ lora_scale1,
+ lora2,
+ lora_scale2,
+ lora3,
+ lora_scale3,
+ lora4,
+ lora_scale4,
+ lora5,
+ lora_scale5,
+ lora6,
+ lora_scale6,
+ lora7,
+ lora_scale7,
+ sampler,
+ schedule_type,
+ schedule_prediction_type,
+ img_height,
+ img_width,
+ model_name,
+ vae_model,
+ task,
+ image_control,
+ preprocessor_name,
+ preprocess_resolution,
+ image_resolution,
+ style_prompt, # list []
+ style_json_file,
+ image_mask,
+ strength,
+ low_threshold,
+ high_threshold,
+ value_threshold,
+ distance_threshold,
+ recolor_gamma_correction,
+ tile_blur_sigma,
+ controlnet_output_scaling_in_unet,
+ controlnet_start_threshold,
+ controlnet_stop_threshold,
+ textual_inversion,
+ syntax_weights,
+ upscaler_model_path,
+ upscaler_increases_size,
+ upscaler_tile_size,
+ upscaler_tile_overlap,
+ hires_steps,
+ hires_denoising_strength,
+ hires_sampler,
+ hires_prompt,
+ hires_negative_prompt,
+ hires_before_adetailer,
+ hires_after_adetailer,
+ hires_schedule_type,
+ hires_guidance_scale,
+ controlnet_model,
+ loop_generation,
+ leave_progress_bar,
+ disable_progress_bar,
+ image_previews,
+ display_images,
+ save_generated_images,
+ filename_pattern,
+ image_storage_location,
+ retain_compel_previous_load,
+ retain_detailfix_model_previous_load,
+ retain_hires_model_previous_load,
+ t2i_adapter_preprocessor,
+ t2i_adapter_conditioning_scale,
+ t2i_adapter_conditioning_factor,
+ xformers_memory_efficient_attention,
+ freeu,
+ generator_in_cpu,
+ adetailer_inpaint_only,
+ adetailer_verbose,
+ adetailer_sampler,
+ adetailer_active_a,
+ prompt_ad_a,
+ negative_prompt_ad_a,
+ strength_ad_a,
+ face_detector_ad_a,
+ person_detector_ad_a,
+ hand_detector_ad_a,
+ mask_dilation_a,
+ mask_blur_a,
+ mask_padding_a,
+ adetailer_active_b,
+ prompt_ad_b,
+ negative_prompt_ad_b,
+ strength_ad_b,
+ face_detector_ad_b,
+ person_detector_ad_b,
+ hand_detector_ad_b,
+ mask_dilation_b,
+ mask_blur_b,
+ mask_padding_b,
+ retain_task_cache_gui,
+ guidance_rescale,
+ image_ip1,
+ mask_ip1,
+ model_ip1,
+ mode_ip1,
+ scale_ip1,
+ image_ip2,
+ mask_ip2,
+ model_ip2,
+ mode_ip2,
+ scale_ip2,
+ pag_scale,
+ face_restoration_model,
+ face_restoration_visibility,
+ face_restoration_weight,
+ ):
+ info_state = html_template_message("Navigating latent space...")
+ yield info_state, gr.update(), gr.update()
+
+ vae_model = vae_model if vae_model != "None" else None
+ loras_list = [lora1, lora2, lora3, lora4, lora5, lora6, lora7]
+ vae_msg = f"VAE: {vae_model}" if vae_model else ""
+ msg_lora = ""
+
+## BEGIN MOD
+ loras_list = [s if s else "None" for s in loras_list]
+ global lora_model_list
+ lora_model_list = get_lora_model_list()
+ lora1, lora_scale1, lora2, lora_scale2, lora3, lora_scale3, lora4, lora_scale4, lora5, lora_scale5, lora6, lora_scale6, lora7, lora_scale7 = \
+ set_prompt_loras(prompt, syntax_weights, model_name, lora1, lora_scale1, lora2, lora_scale2, lora3,
+ lora_scale3, lora4, lora_scale4, lora5, lora_scale5, lora6, lora_scale6, lora7, lora_scale7)
+## END MOD
+
+ print("Config model:", model_name, vae_model, loras_list)
+
+ task = TASK_STABLEPY[task]
+
+ params_ip_img = []
+ params_ip_msk = []
+ params_ip_model = []
+ params_ip_mode = []
+ params_ip_scale = []
+
+ all_adapters = [
+ (image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1),
+ (image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2),
+ ]
+
+ if not hasattr(self.model.pipe, "transformer"):
+ for imgip, mskip, modelip, modeip, scaleip in all_adapters:
+ if imgip:
+ params_ip_img.append(imgip)
+ if mskip:
+ params_ip_msk.append(mskip)
+ params_ip_model.append(modelip)
+ params_ip_mode.append(modeip)
+ params_ip_scale.append(scaleip)
+
+ concurrency = 5
+ self.model.stream_config(concurrency=concurrency, latent_resize_by=1, vae_decoding=False)
+
+ if task != "txt2img" and not image_control:
+ raise ValueError("Reference image is required. Please upload one in 'Image ControlNet/Inpaint/Img2img'.")
+
+ if task in ["inpaint", "repaint"] and not image_mask:
+ raise ValueError("Mask image not found. Upload one in 'Image Mask' to proceed.")
+
+ if "https://" not in str(UPSCALER_DICT_GUI[upscaler_model_path]):
+ upscaler_model = upscaler_model_path
+ else:
+ url_upscaler = UPSCALER_DICT_GUI[upscaler_model_path]
+
+ if not os.path.exists(f"./{DIRECTORY_UPSCALERS}/{url_upscaler.split('/')[-1]}"):
+ download_things(DIRECTORY_UPSCALERS, url_upscaler, HF_TOKEN)
+
+ upscaler_model = f"./{DIRECTORY_UPSCALERS}/{url_upscaler.split('/')[-1]}"
+
+ logging.getLogger("ultralytics").setLevel(logging.INFO if adetailer_verbose else logging.ERROR)
+
+ adetailer_params_A = {
+ "face_detector_ad": face_detector_ad_a,
+ "person_detector_ad": person_detector_ad_a,
+ "hand_detector_ad": hand_detector_ad_a,
+ "prompt": prompt_ad_a,
+ "negative_prompt": negative_prompt_ad_a,
+ "strength": strength_ad_a,
+ # "image_list_task" : None,
+ "mask_dilation": mask_dilation_a,
+ "mask_blur": mask_blur_a,
+ "mask_padding": mask_padding_a,
+ "inpaint_only": adetailer_inpaint_only,
+ "sampler": adetailer_sampler,
+ }
+
+ adetailer_params_B = {
+ "face_detector_ad": face_detector_ad_b,
+ "person_detector_ad": person_detector_ad_b,
+ "hand_detector_ad": hand_detector_ad_b,
+ "prompt": prompt_ad_b,
+ "negative_prompt": negative_prompt_ad_b,
+ "strength": strength_ad_b,
+ # "image_list_task" : None,
+ "mask_dilation": mask_dilation_b,
+ "mask_blur": mask_blur_b,
+ "mask_padding": mask_padding_b,
+ }
+ pipe_params = {
+ "prompt": prompt,
+ "negative_prompt": neg_prompt,
+ "img_height": img_height,
+ "img_width": img_width,
+ "num_images": num_images,
+ "num_steps": steps,
+ "guidance_scale": cfg,
+ "clip_skip": clip_skip,
+ "pag_scale": float(pag_scale),
+ "seed": seed,
+ "image": image_control,
+ "preprocessor_name": preprocessor_name,
+ "preprocess_resolution": preprocess_resolution,
+ "image_resolution": image_resolution,
+ "style_prompt": style_prompt if style_prompt else "",
+ "style_json_file": "",
+ "image_mask": image_mask, # only for Inpaint
+ "strength": strength, # only for Inpaint or ...
+ "low_threshold": low_threshold,
+ "high_threshold": high_threshold,
+ "value_threshold": value_threshold,
+ "distance_threshold": distance_threshold,
+ "recolor_gamma_correction": float(recolor_gamma_correction),
+ "tile_blur_sigma": int(tile_blur_sigma),
+ "lora_A": lora1 if lora1 != "None" else None,
+ "lora_scale_A": lora_scale1,
+ "lora_B": lora2 if lora2 != "None" else None,
+ "lora_scale_B": lora_scale2,
+ "lora_C": lora3 if lora3 != "None" else None,
+ "lora_scale_C": lora_scale3,
+ "lora_D": lora4 if lora4 != "None" else None,
+ "lora_scale_D": lora_scale4,
+ "lora_E": lora5 if lora5 != "None" else None,
+ "lora_scale_E": lora_scale5,
+ "lora_F": lora6 if lora6 != "None" else None,
+ "lora_scale_F": lora_scale6,
+ "lora_G": lora7 if lora7 != "None" else None,
+ "lora_scale_G": lora_scale7,
+## BEGIN MOD
+ "textual_inversion": get_embed_list(self.model.class_name) if textual_inversion else [],
+## END MOD
+ "syntax_weights": syntax_weights, # "Classic"
+ "sampler": sampler,
+ "schedule_type": schedule_type,
+ "schedule_prediction_type": schedule_prediction_type,
+ "xformers_memory_efficient_attention": xformers_memory_efficient_attention,
+ "gui_active": True,
+ "loop_generation": loop_generation,
+ "controlnet_conditioning_scale": float(controlnet_output_scaling_in_unet),
+ "control_guidance_start": float(controlnet_start_threshold),
+ "control_guidance_end": float(controlnet_stop_threshold),
+ "generator_in_cpu": generator_in_cpu,
+ "FreeU": freeu,
+ "adetailer_A": adetailer_active_a,
+ "adetailer_A_params": adetailer_params_A,
+ "adetailer_B": adetailer_active_b,
+ "adetailer_B_params": adetailer_params_B,
+ "leave_progress_bar": leave_progress_bar,
+ "disable_progress_bar": disable_progress_bar,
+ "image_previews": image_previews,
+ "display_images": display_images,
+ "save_generated_images": save_generated_images,
+ "filename_pattern": filename_pattern,
+ "image_storage_location": image_storage_location,
+ "retain_compel_previous_load": retain_compel_previous_load,
+ "retain_detailfix_model_previous_load": retain_detailfix_model_previous_load,
+ "retain_hires_model_previous_load": retain_hires_model_previous_load,
+ "t2i_adapter_preprocessor": t2i_adapter_preprocessor,
+ "t2i_adapter_conditioning_scale": float(t2i_adapter_conditioning_scale),
+ "t2i_adapter_conditioning_factor": float(t2i_adapter_conditioning_factor),
+ "upscaler_model_path": upscaler_model,
+ "upscaler_increases_size": upscaler_increases_size,
+ "upscaler_tile_size": upscaler_tile_size,
+ "upscaler_tile_overlap": upscaler_tile_overlap,
+ "hires_steps": hires_steps,
+ "hires_denoising_strength": hires_denoising_strength,
+ "hires_prompt": hires_prompt,
+ "hires_negative_prompt": hires_negative_prompt,
+ "hires_sampler": hires_sampler,
+ "hires_before_adetailer": hires_before_adetailer,
+ "hires_after_adetailer": hires_after_adetailer,
+ "hires_schedule_type": hires_schedule_type,
+ "hires_guidance_scale": hires_guidance_scale,
+ "ip_adapter_image": params_ip_img,
+ "ip_adapter_mask": params_ip_msk,
+ "ip_adapter_model": params_ip_model,
+ "ip_adapter_mode": params_ip_mode,
+ "ip_adapter_scale": params_ip_scale,
+ "face_restoration_model": face_restoration_model,
+ "face_restoration_visibility": face_restoration_visibility,
+ "face_restoration_weight": face_restoration_weight,
+ }
+
+ # kwargs for diffusers pipeline
+ if guidance_rescale:
+ pipe_params["guidance_rescale"] = guidance_rescale
+
+ self.model.device = torch.device("cuda:0")
+ if hasattr(self.model.pipe, "transformer") and loras_list != ["None"] * self.model.num_loras:
+ self.model.pipe.transformer.to(self.model.device)
+ print("transformer to cuda")
+
+ actual_progress = 0
+ info_images = gr.update()
+ for img, [seed, image_path, metadata] in self.model(**pipe_params):
+ info_state = progress_step_bar(actual_progress, steps)
+ actual_progress += concurrency
+ if image_path:
+ info_images = f"Seeds: {str(seed)}"
+ if vae_msg:
+ info_images = info_images + "<br>" + vae_msg
+
+ if "Cannot copy out of meta tensor; no data!" in self.model.last_lora_error:
+ msg_ram = "Unable to process the LoRAs due to high RAM usage; please try again later."
+ print(msg_ram)
+ msg_lora += f"<br>{msg_ram}"
+
+ for status, lora in zip(self.model.lora_status, self.model.lora_memory):
+ if status:
+ msg_lora += f"<br>Loaded: {lora}"
+ elif status is not None:
+ msg_lora += f"<br>Error with: {lora}"
+
+ if msg_lora:
+ info_images += msg_lora
+
+ info_images = info_images + "<br>" + "GENERATION DATA:<br>" + escape_html(metadata[-1]) + "<br>-------<br>"
+
+ download_links = "<br>".join(
+ [
+ f'<a href="{path.replace("/images/", "/file=/home/user/app/images/")}" download="{os.path.basename(path)}">Download Image {i + 1}</a>'
+ for i, path in enumerate(image_path)
+ ]
+ )
+ if save_generated_images:
+ info_images += f"<br>{download_links}"
+
+## BEGIN MOD
+ img = save_images(img, metadata)
+## END MOD
+
+ info_state = "COMPLETE"
+
+ yield info_state, img, info_images
+
+
+def dynamic_gpu_duration(func, duration, *args):
+
+ @spaces.GPU(duration=duration)
+ def wrapped_func():
+ yield from func(*args)
+
+ return wrapped_func()
+
+
+@spaces.GPU
+def dummy_gpu():
+ return None
+
+
+def sd_gen_generate_pipeline(*args):
+ gpu_duration_arg = int(args[-1]) if args[-1] else 59
+ verbose_arg = int(args[-2])
+ load_lora_cpu = args[-3]
+ generation_args = args[:-3]
+ lora_list = [
+ None if item == "None" or item == "" else item # MOD
+ for item in [args[7], args[9], args[11], args[13], args[15], args[17], args[19]]
+ ]
+ lora_status = [None] * sd_gen.model.num_loras
+
+ msg_load_lora = "Updating LoRAs in GPU..."
+ if load_lora_cpu:
+ msg_load_lora = "Updating LoRAs in CPU..."
+
+ if lora_list != sd_gen.model.lora_memory and lora_list != [None] * sd_gen.model.num_loras:
+ yield msg_load_lora, gr.update(), gr.update()
+
+ # Load lora in CPU
+ if load_lora_cpu:
+ lora_status = sd_gen.model.load_lora_on_the_fly(
+ lora_A=lora_list[0], lora_scale_A=args[8],
+ lora_B=lora_list[1], lora_scale_B=args[10],
+ lora_C=lora_list[2], lora_scale_C=args[12],
+ lora_D=lora_list[3], lora_scale_D=args[14],
+ lora_E=lora_list[4], lora_scale_E=args[16],
+ lora_F=lora_list[5], lora_scale_F=args[18],
+ lora_G=lora_list[6], lora_scale_G=args[20],
+ )
+ print(lora_status)
+
+ sampler_name = args[21]
+ schedule_type_name = args[22]
+ _, _, msg_sampler = check_scheduler_compatibility(
+ sd_gen.model.class_name, sampler_name, schedule_type_name
+ )
+ if msg_sampler:
+ gr.Warning(msg_sampler)
+
+ if verbose_arg:
+ for status, lora in zip(lora_status, lora_list):
+ if status:
+ gr.Info(f"LoRA loaded in CPU: {lora}")
+ elif status is not None:
+ gr.Warning(f"Failed to load LoRA: {lora}")
+
+ if lora_status == [None] * sd_gen.model.num_loras and sd_gen.model.lora_memory != [None] * sd_gen.model.num_loras and load_lora_cpu:
+ lora_cache_msg = ", ".join(
+ str(x) for x in sd_gen.model.lora_memory if x is not None
+ )
+ gr.Info(f"LoRAs in cache: {lora_cache_msg}")
+
+ msg_request = f"Requesting {gpu_duration_arg}s. of GPU time.\nModel: {sd_gen.model.base_model_id}"
+ if verbose_arg:
+ gr.Info(msg_request)
+ print(msg_request)
+ yield msg_request.replace("\n", "<br>"), gr.update(), gr.update()
+
+ start_time = time.time()
+
+ # yield from sd_gen.generate_pipeline(*generation_args)
+ yield from dynamic_gpu_duration(
+ sd_gen.generate_pipeline,
+ gpu_duration_arg,
+ *generation_args,
+ )
+
+ end_time = time.time()
+ execution_time = end_time - start_time
+ msg_task_complete = (
+ f"GPU task complete in: {int(round(execution_time, 0) + 1)} seconds"
+ )
+
+ if verbose_arg:
+ gr.Info(msg_task_complete)
+ print(msg_task_complete)
+
+ yield msg_task_complete, gr.update(), gr.update()
+
+
+@spaces.GPU(duration=15)
+def process_upscale(image, upscaler_name, upscaler_size):
+ if image is None: return None
+
+ from stablepy.diffusers_vanilla.utils import save_pil_image_with_metadata
+ from stablepy import load_upscaler_model
+
+ image = image.convert("RGB")
+ exif_image = extract_exif_data(image)
+
+ name_upscaler = UPSCALER_DICT_GUI[upscaler_name]
+
+ if "https://" in str(name_upscaler):
+
+ if not os.path.exists(f"./{DIRECTORY_UPSCALERS}/{name_upscaler.split('/')[-1]}"):
+ download_things(DIRECTORY_UPSCALERS, name_upscaler, HF_TOKEN)
+
+ name_upscaler = f"./{DIRECTORY_UPSCALERS}/{name_upscaler.split('/')[-1]}"
+
+ scaler_beta = load_upscaler_model(model=name_upscaler, tile=0, tile_overlap=8, device="cuda", half=True)
+ image_up = scaler_beta.upscale(image, upscaler_size, True)
+
+ image_path = save_pil_image_with_metadata(image_up, f'{os.getcwd()}/up_images', exif_image)
+
+ return image_path
+
+
+# https://huggingface.co/spaces/BestWishYsh/ConsisID-preview-Space/discussions/1#674969a022b99c122af5d407
+dynamic_gpu_duration.zerogpu = True
+sd_gen_generate_pipeline.zerogpu = True
+sd_gen = GuiSD()
+
+
+## BEGIN MOD
+CSS ="""
+.gradio-container, #main { width:100%; height:100%; max-width:100%; padding-left:0; padding-right:0; margin-left:0; margin-right:0; }
+.contain { display:flex; flex-direction:column; }
+#component-0 { width:100%; height:100%; }
+#gallery { flex-grow:1; }
+#load_model { height: 50px; }
+.lora { min-width:480px; }
+#model-info { text-align:center; }
+.title { font-size: 3em; align-items: center; text-align: center; }
+.info { align-items: center; text-align: center; }
+.desc [src$='#float'] { float: right; margin: 20px; }
+"""
+
+with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, css=CSS, delete_cache=(60, 3600)) as app:
+ title, description = th.translate_app_title()
+ gr.Markdown(title, elem_classes="title")
+ gr.Markdown(description, elem_classes="info")
+ with gr.Column():
+ with gr.Tab(th.t("Generation")):
+ with gr.Row():
+ with gr.Column(scale=1):
+
+ def update_task_options(model_name, task_name):
+ new_choices = MODEL_TYPE_TASK[get_model_type(valid_model_name(model_name))]
+
+ if task_name not in new_choices:
+ task_name = "txt2img"
+
+ return gr.update(value=task_name, choices=new_choices)
+
+ interface_mode_gui = gr.Radio(label="Quick settings", choices=["Simple", "Standard", "Fast", "LoRA"], value="Standard")
+ with gr.Accordion("Model and Task", open=False) as menu_model:
+ task_gui = gr.Dropdown(label="Task", choices=SDXL_TASK, value=TASK_MODEL_LIST[0])
+ with gr.Group():
+ model_name_gui = gr.Dropdown(label="Model", info="You can enter a huggingface model repo_id to want to use.", choices=get_tupled_model_list(model_list), value="votepurchase/animagine-xl-3.1", allow_custom_value=True)
+ model_info_gui = gr.Markdown(elem_classes="info")
+ with gr.Row():
+ quick_model_type_gui = gr.Radio(label="Model Type", choices=["None", "Auto", "Animagine", "Pony"], value="Auto", interactive=True)
+ quick_genre_gui = gr.Radio(label="Genre", choices=["Anime", "Photo"], value="Anime", interactive=True)
+ quick_speed_gui = gr.Radio(label="Speed", choices=["Fast", "Standard", "Heavy"], value="标准", interactive=True)
+ quick_aspect_gui = gr.Radio(label="Aspect Ratio", choices=["1:1", "3:4"], value="1:1", interactive=True)
+ with gr.Row():
+ quality_selector_gui = gr.Dropdown(label="Quality Tags Presets", interactive=True, choices=list(preset_quality.keys()), value="None")
+ style_selector_gui = gr.Dropdown(label="Style Preset", interactive=True, choices=list(preset_styles.keys()), value="None")
+ sampler_selector_gui = gr.Dropdown(label="Sampler Quick Settings", interactive=True, choices=list(preset_sampler_setting.keys()), value="None")
+ optimization_gui = gr.Dropdown(label="Optimization for SDXL", choices=list(optimization_list.keys()), value="None", interactive=True)
+ with gr.Group():
+ with gr.Accordion("Prompt from Image", open=False) as menu_from_image:
+ input_image_gui = gr.Image(label="Input image", type="pil", sources=["upload", "clipboard"], height=256)
+ with gr.Accordion(label="Advanced options", open=False):
+ with gr.Row():
+ general_threshold_gui = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.01, interactive=True)
+ character_threshold_gui = gr.Slider(label="Character threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.01, interactive=True)
+ with gr.Row():
+ tag_type_gui = gr.Radio(label="Convert tags to", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="danbooru")
+ recom_prompt_gui = gr.Radio(label="Insert reccomended prompt", choices=["None", "Animagine", "Pony"], value="None", interactive=True)
+ keep_tags_gui = gr.Radio(label="Remove tags leaving only the following", choices=["body", "dress", "all"], value="all")
+ image_algorithms = gr.CheckboxGroup(["Use WD Tagger"], label="Algorithms", value=["Use WD Tagger"], visible=False)
+ generate_from_image_btn_gui = gr.Button(value="GENERATE TAGS FROM IMAGE")
+ prompt_gui = gr.Textbox(lines=6, placeholder="1girl, solo, ...", label="Prompt", show_copy_button=True)
+ with gr.Accordion("Negative prompt, etc.", open=False) as menu_negative:
+ neg_prompt_gui = gr.Textbox(lines=3, placeholder="Enter Neg prompt", label="Negative prompt", value="lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, worst quality, low quality, very displeasing, (bad)", show_copy_button=True)
+ translate_prompt_button = gr.Button(value="Translate prompt to English", size="sm", variant="secondary")
+ with gr.Row():
+ insert_prompt_gui = gr.Radio(label="Insert reccomended positive / negative prompt", choices=["None", "Auto", "Animagine", "Pony"], value="Auto", interactive=True)
+ prompt_type_gui = gr.Radio(label="Convert tags to", choices=["danbooru", "e621"], value="e621", visible=False)
+ prompt_type_button = gr.Button(value="Convert prompt to Pony e621 style", size="sm", variant="secondary")
+ with gr.Row():
+ character_dbt = gr.Textbox(lines=1, placeholder="kafuu chino, ...", label="Character names")
+ series_dbt = gr.Textbox(lines=1, placeholder="Is the order a rabbit?, ...", label="Series names")
+ random_character_gui = gr.Button(value="Random character 🎲", size="sm", variant="secondary")
+ model_name_dbt = gr.Dropdown(label="Model", choices=list(V2_ALL_MODELS.keys()), value=list(V2_ALL_MODELS.keys())[0], visible=False)
+ aspect_ratio_dbt = gr.Radio(label="Aspect ratio", choices=list(V2_ASPECT_RATIO_OPTIONS), value="square", visible=False)
+ length_dbt = gr.Radio(label="Length", choices=list(V2_LENGTH_OPTIONS), value="very_long", visible=False)
+ identity_dbt = gr.Radio(label="Keep identity", choices=list(V2_IDENTITY_OPTIONS), value="lax", visible=False)
+ ban_tags_dbt = gr.Textbox(label="Ban tags", placeholder="alternate costumen, ...", value="futanari, censored, furry, furrification", visible=False)
+ copy_button_dbt = gr.Button(value="Copy to clipboard", visible=False)
+ rating_dbt = gr.Radio(label="Rating", choices=list(V2_RATING_OPTIONS), value="sfw")
+ generate_db_random_button = gr.Button(value="EXTEND PROMPT 🎲")
+ with gr.Row():
+ translate_prompt_gui = gr.Button(value="Translate Prompt 📝", variant="secondary", size="sm")
+ set_random_seed = gr.Button(value="Seed 🎲", variant="secondary", size="sm")
+ set_params_gui = gr.Button(value="Params ↙️", variant="secondary", size="sm")
+ clear_prompt_gui = gr.Button(value="Clear 🗑️", variant="secondary", size="sm")
+
+ generate_button = gr.Button(value="GENERATE IMAGE", size="lg", variant="primary")
+
+ model_name_gui.change(
+ update_task_options,
+ [model_name_gui, task_gui],
+ [task_gui],
+ )
+
+ load_model_gui = gr.HTML(elem_id="load_model", elem_classes="contain")
+
+ result_images = gr.Gallery(
+ label="Generated images",
+ show_label=False,
+ elem_id="gallery",
+ #columns=[2],
+ columns=[1],
+ #rows=[2],
+ rows=[1],
+ object_fit="contain",
+ # height="auto",
+ interactive=False,
+ preview=False,
+ show_share_button=False,
+ show_download_button=True,
+ selected_index=50,
+ format="png",
+ )
+
+ result_images_files = gr.Files(interactive=False, visible=False)
+
+ actual_task_info = gr.HTML()
+
+ with gr.Accordion("History", open=False):
+ history_files = gr.Files(interactive=False, visible=False)
+ history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", format="png", interactive=False, show_share_button=False, show_download_button=True)
+ history_clear_button = gr.Button(value="Clear History", variant="secondary")
+ history_clear_button.click(lambda: ([], []), None, [history_gallery, history_files], queue=False, show_api=False)
+
+ with gr.Row(equal_height=False, variant="default"):
+ gpu_duration_gui = gr.Number(minimum=5, maximum=240, value=59, show_label=False, container=False, info="GPU time duration (seconds)")
+ with gr.Column():
+ verbose_info_gui = gr.Checkbox(value=False, container=False, label="Status info")
+ load_lora_cpu_gui = gr.Checkbox(value=False, container=False, label="Load LoRAs on CPU")
+
+ with gr.Column(scale=1):
+ with gr.Accordion("Generation settings", open=False, visible=True) as menu_gen:
+ with gr.Row():
+ img_width_gui = gr.Slider(minimum=64, maximum=4096, step=8, value=1024, label="Img Width")
+ img_height_gui = gr.Slider(minimum=64, maximum=4096, step=8, value=1024, label="Img Height")
+ steps_gui = gr.Slider(minimum=1, maximum=100, step=1, value=28, label="Steps")
+ cfg_gui = gr.Slider(minimum=0, maximum=30, step=0.5, value=7.0, label="CFG")
+ guidance_rescale_gui = gr.Slider(label="CFG rescale:", value=0., step=0.01, minimum=0., maximum=1.5)
+ with gr.Row():
+ seed_gui = gr.Number(minimum=-1, maximum=2**32-1, value=-1, label="Seed")
+ pag_scale_gui = gr.Slider(minimum=0.0, maximum=10.0, step=0.1, value=0.0, label="PAG Scale")
+ num_images_gui = gr.Slider(minimum=1, maximum=5, step=1, value=1, label="Images")
+ clip_skip_gui = gr.Checkbox(value=False, label="Layer 2 Clip Skip")
+ free_u_gui = gr.Checkbox(value=False, label="FreeU")
+ with gr.Row():
+ sampler_gui = gr.Dropdown(label="Sampler", choices=scheduler_names, value="Euler")
+ schedule_type_gui = gr.Dropdown(label="Schedule type", choices=SCHEDULE_TYPE_OPTIONS, value=SCHEDULE_TYPE_OPTIONS[0])
+ schedule_prediction_type_gui = gr.Dropdown(label="Discrete Sampling Type", choices=SCHEDULE_PREDICTION_TYPE_OPTIONS, value=SCHEDULE_PREDICTION_TYPE_OPTIONS[0])
+ vae_model_gui = gr.Dropdown(label="VAE Model", choices=vae_model_list, value=vae_model_list[0])
+ prompt_syntax_gui = gr.Dropdown(label="Prompt Syntax", choices=PROMPT_W_OPTIONS, value=PROMPT_W_OPTIONS[1][1])
+
+ with gr.Row(equal_height=False):
+
+ def run_set_params_gui(base_prompt, name_model):
+ valid_receptors = { # default values
+ "prompt": gr.update(value=base_prompt),
+ "neg_prompt": gr.update(value=""),
+ "Steps": gr.update(value=30),
+ "width": gr.update(value=1024),
+ "height": gr.update(value=1024),
+ "Seed": gr.update(value=-1),
+ "Sampler": gr.update(value="Euler"),
+ "CFG scale": gr.update(value=7.), # cfg
+ "Clip skip": gr.update(value=True),
+ "Model": gr.update(value=name_model),
+ "Schedule type": gr.update(value="Automatic"),
+ "PAG": gr.update(value=.0),
+ "FreeU": gr.update(value=False),
+ }
+ valid_keys = list(valid_receptors.keys())
+
+ parameters = extract_parameters(base_prompt)
+ # print(parameters)
+
+ if "Sampler" in parameters:
+ value_sampler = parameters["Sampler"]
+ for s_type in SCHEDULE_TYPE_OPTIONS:
+ if s_type in value_sampler:
+ value_sampler = value_sampler.replace(s_type, "").strip()
+ parameters["Sampler"] = value_sampler
+ parameters["Schedule type"] = s_type
+
+ for key, val in parameters.items():
+ # print(val)
+ if key in valid_keys:
+ try:
+ if key == "Sampler":
+ if val not in scheduler_names:
+ continue
+ if key == "Schedule type":
+ if val not in SCHEDULE_TYPE_OPTIONS:
+ val = "Automatic"
+ elif key == "Clip skip":
+ if "," in str(val):
+ val = val.replace(",", "")
+ if int(val) >= 2:
+ val = True
+ if key == "prompt":
+ if ">" in val and "<" in val:
+ val = re.sub(r'<[^>]+>', '', val)
+ print("Removed LoRA written in the prompt")
+ if key in ["prompt", "neg_prompt"]:
+ val = re.sub(r'\s+', ' ', re.sub(r',+', ',', val)).strip()
+ if key in ["Steps", "width", "height", "Seed"]:
+ val = int(val)
+ if key == "FreeU":
+ val = True
+ if key in ["CFG scale", "PAG"]:
+ val = float(val)
+ if key == "Model":
+ filtered_models = [m for m in model_list if val in m]
+ if filtered_models:
+ val = filtered_models[0]
+ else:
+ val = name_model
+ if key == "Seed":
+ continue
+ valid_receptors[key] = gr.update(value=val)
+ # print(val, type(val))
+ # print(valid_receptors)
+ except Exception as e:
+ print(str(e))
+ return [value for value in valid_receptors.values()]
+
+ set_params_gui.click(
+ run_set_params_gui, [prompt_gui, model_name_gui], [
+ prompt_gui,
+ neg_prompt_gui,
+ steps_gui,
+ img_width_gui,
+ img_height_gui,
+ seed_gui,
+ sampler_gui,
+ cfg_gui,
+ clip_skip_gui,
+ model_name_gui,
+ schedule_type_gui,
+ pag_scale_gui,
+ free_u_gui,
+ ],
+ )
+
+ def run_clear_prompt_gui():
+ return gr.update(value=""), gr.update(value="")
+ clear_prompt_gui.click(
+ run_clear_prompt_gui, [], [prompt_gui, neg_prompt_gui]
+ )
+
+ def run_set_random_seed():
+ return -1
+ set_random_seed.click(
+ run_set_random_seed, [], seed_gui
+ )
+
+ with gr.Accordion("LoRA", open=False, visible=True) as menu_lora:
+ def lora_dropdown(label, visible=True):
+ return gr.Dropdown(label=label, choices=get_all_lora_tupled_list(), value="", allow_custom_value=True, elem_classes="lora", min_width=320, visible=visible)
+
+ def lora_scale_slider(label, visible=True):
+ return gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label=label, visible=visible)
+
+ def lora_textbox(label):
+ return gr.Textbox(label=label, info="Example of prompt:", value="None", show_copy_button=True, interactive=False, visible=False)
+
+ with gr.Row():
+ with gr.Column():
+ lora1_gui = lora_dropdown("LoRA1")
+ lora_scale_1_gui = lora_scale_slider("LoRA Scale 1")
+ with gr.Row():
+ with gr.Group():
+ lora1_info_gui = lora_textbox("LoRA1 prompts")
+ lora1_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
+ lora1_desc_gui = gr.Markdown(value="", visible=False)
+ with gr.Column():
+ lora2_gui = lora_dropdown("LoRA2")
+ lora_scale_2_gui = lora_scale_slider("LoRA Scale 2")
+ with gr.Row():
+ with gr.Group():
+ lora2_info_gui = lora_textbox("LoRA2 prompts")
+ lora2_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
+ lora2_desc_gui = gr.Markdown(value="", visible=False)
+ with gr.Column():
+ lora3_gui = lora_dropdown("LoRA3")
+ lora_scale_3_gui = lora_scale_slider("LoRA Scale 3")
+ with gr.Row():
+ with gr.Group():
+ lora3_info_gui = lora_textbox("LoRA3 prompts")
+ lora3_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
+ lora3_desc_gui = gr.Markdown(value="", visible=False)
+ with gr.Column():
+ lora4_gui = lora_dropdown("LoRA4")
+ lora_scale_4_gui = lora_scale_slider("LoRA Scale 4")
+ with gr.Row():
+ with gr.Group():
+ lora4_info_gui = lora_textbox("LoRA4 prompts")
+ lora4_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
+ lora4_desc_gui = gr.Markdown(value="", visible=False)
+ with gr.Column():
+ lora5_gui = lora_dropdown("LoRA5")
+ lora_scale_5_gui = lora_scale_slider("LoRA Scale 5")
+ with gr.Row():
+ with gr.Group():
+ lora5_info_gui = lora_textbox("LoRA5 prompts")
+ lora5_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
+ lora5_desc_gui = gr.Markdown(value="", visible=False)
+ with gr.Column():
+ lora6_gui = lora_dropdown("LoRA6", visible=False)
+ lora_scale_6_gui = lora_scale_slider("LoRA Scale 6", visible=False)
+ with gr.Row():
+ with gr.Group():
+ lora6_info_gui = lora_textbox("LoRA6 prompts")
+ lora6_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
+ lora6_desc_gui = gr.Markdown(value="", visible=False)
+ with gr.Column():
+ lora7_gui = lora_dropdown("LoRA7", visible=False)
+ lora_scale_7_gui = lora_scale_slider("LoRA Scale 7", visible=False)
+ with gr.Row():
+ with gr.Group():
+ lora7_info_gui = lora_textbox("LoRA7 prompts")
+ lora7_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
+ lora7_desc_gui = gr.Markdown(value="", visible=False)
+ with gr.Accordion("From URL", open=True, visible=True):
+ with gr.Row():
+ search_civitai_basemodel_lora = gr.CheckboxGroup(label="Search LoRA for", choices=CIVITAI_BASEMODEL, value=["Pony", "Illustrious", "SDXL 1.0"])
+ search_civitai_sort_lora = gr.Radio(label="Sort", choices=CIVITAI_SORT, value="Highest Rated")
+ search_civitai_period_lora = gr.Radio(label="Period", choices=CIVITAI_PERIOD, value="AllTime")
+ with gr.Row():
+ search_civitai_query_lora = gr.Textbox(label="Query", placeholder="oomuro sakurako...", lines=1)
+ search_civitai_tag_lora = gr.Dropdown(label="Tag", choices=get_civitai_tag(), value=get_civitai_tag()[0], allow_custom_value=True)
+ search_civitai_user_lora = gr.Textbox(label="Username", lines=1)
+ search_civitai_button_lora = gr.Button("Search on Civitai")
+ search_civitai_desc_lora = gr.Markdown(value="", visible=False, elem_classes="desc")
+ with gr.Accordion("Select from Gallery", open=False):
+ search_civitai_gallery_lora = gr.Gallery([], label="Results", allow_preview=False, columns=5, show_share_button=False, interactive=False)
+ search_civitai_result_lora = gr.Dropdown(label="Search Results", choices=[("", "")], value="", allow_custom_value=True, visible=False)
+ with gr.Row():
+ text_lora = gr.Textbox(label="LoRA's download URL", placeholder="https://civitai.com/api/download/models/28907", info="It has to be .safetensors files, and you can also download them from Hugging Face.", lines=1, scale=4)
+ romanize_text = gr.Checkbox(value=False, label="Transliterate name", scale=1, visible=False)
+ button_lora = gr.Button("Get and Refresh the LoRA Lists")
+ new_lora_status = gr.HTML()
+ with gr.Accordion("From Local", open=True, visible=True):
+ file_output_lora = gr.File(label="Uploaded LoRA", file_types=['.ckpt', '.pt', '.pth', '.safetensors', '.bin'], file_count="multiple", interactive=False, visible=False)
+ upload_button_lora = gr.UploadButton(label="Upload LoRA from your disk (very slow)", file_types=['.ckpt', '.pt', '.pth', '.safetensors', '.bin'], file_count="multiple")
+
+ with gr.Column() as menu_advanced:
+ with gr.Accordion("Hires fix", open=False, visible=True) as menu_hires:
+ upscaler_model_path_gui = gr.Dropdown(label="Upscaler", choices=UPSCALER_KEYS, value=UPSCALER_KEYS[0])
+ with gr.Row():
+ upscaler_increases_size_gui = gr.Slider(minimum=1.1, maximum=6., step=0.1, value=1.0, label="Upscale by")
+ upscaler_tile_size_gui = gr.Slider(minimum=0, maximum=512, step=16, value=0, label="Upscaler Tile Size", info="0 = no tiling")
+ upscaler_tile_overlap_gui = gr.Slider(minimum=0, maximum=48, step=1, value=8, label="Upscaler Tile Overlap")
+ with gr.Row():
+ hires_steps_gui = gr.Slider(minimum=0, value=30, maximum=100, step=1, label="Hires Steps")
+ hires_denoising_strength_gui = gr.Slider(minimum=0.1, maximum=1.0, step=0.01, value=0.55, label="Hires Denoising Strength")
+ hires_sampler_gui = gr.Dropdown(label="Hires Sampler", choices=POST_PROCESSING_SAMPLER, value=POST_PROCESSING_SAMPLER[0])
+ hires_schedule_list = ["Use same schedule type"] + SCHEDULE_TYPE_OPTIONS
+ hires_schedule_type_gui = gr.Dropdown(label="Hires Schedule type", choices=hires_schedule_list, value=hires_schedule_list[0])
+ hires_guidance_scale_gui = gr.Slider(minimum=-1., maximum=30., step=0.5, value=-1., label="Hires CFG", info="If the value is -1, the main CFG will be used")
+ hires_prompt_gui = gr.Textbox(label="Hires Prompt", placeholder="Main prompt will be use", lines=3)
+ hires_negative_prompt_gui = gr.Textbox(label="Hires Negative Prompt", placeholder="Main negative prompt will be use", lines=3)
+
+ with gr.Accordion("Detailfix", open=False, visible=True) as menu_detail:
+ with gr.Row():
+
+ # Adetailer Inpaint Only
+ adetailer_inpaint_only_gui = gr.Checkbox(label="Inpaint only", value=True)
+
+ # Adetailer Verbose
+ adetailer_verbose_gui = gr.Checkbox(label="Verbose", value=False)
+
+ # Adetailer Sampler
+ adetailer_sampler_gui = gr.Dropdown(label="Adetailer sampler:", choices=POST_PROCESSING_SAMPLER, value=POST_PROCESSING_SAMPLER[0])
+
+ with gr.Accordion("Detailfix A", open=True, visible=True):
+ # Adetailer A
+ adetailer_active_a_gui = gr.Checkbox(label="Enable Adetailer A", value=False)
+ prompt_ad_a_gui = gr.Textbox(label="Main prompt", placeholder="Main prompt will be use", lines=3)
+ negative_prompt_ad_a_gui = gr.Textbox(label="Negative prompt", placeholder="Main negative prompt will be use", lines=3)
+ with gr.Row():
+ strength_ad_a_gui = gr.Number(label="Strength:", value=0.35, step=0.01, minimum=0.01, maximum=1.0)
+ face_detector_ad_a_gui = gr.Checkbox(label="Face detector", value=False)
+ person_detector_ad_a_gui = gr.Checkbox(label="Person detector", value=True)
+ hand_detector_ad_a_gui = gr.Checkbox(label="Hand detector", value=False)
+ with gr.Row():
+ mask_dilation_a_gui = gr.Number(label="Mask dilation:", value=4, minimum=1)
+ mask_blur_a_gui = gr.Number(label="Mask blur:", value=4, minimum=1)
+ mask_padding_a_gui = gr.Number(label="Mask padding:", value=32, minimum=1)
+
+ with gr.Accordion("Detailfix B", open=True, visible=True):
+ # Adetailer B
+ adetailer_active_b_gui = gr.Checkbox(label="Enable Adetailer B", value=False)
+ prompt_ad_b_gui = gr.Textbox(label="Main prompt", placeholder="Main prompt will be use", lines=3)
+ negative_prompt_ad_b_gui = gr.Textbox(label="Negative prompt", placeholder="Main negative prompt will be use", lines=3)
+ with gr.Row():
+ strength_ad_b_gui = gr.Number(label="Strength:", value=0.35, step=0.01, minimum=0.01, maximum=1.0)
+ face_detector_ad_b_gui = gr.Checkbox(label="Face detector", value=False)
+ person_detector_ad_b_gui = gr.Checkbox(label="Person detector", value=True)
+ hand_detector_ad_b_gui = gr.Checkbox(label="Hand detector", value=False)
+ with gr.Row():
+ mask_dilation_b_gui = gr.Number(label="Mask dilation:", value=4, minimum=1)
+ mask_blur_b_gui = gr.Number(label="Mask blur:", value=4, minimum=1)
+ mask_padding_b_gui = gr.Number(label="Mask padding:", value=32, minimum=1)
+
+ with gr.Accordion("Face restoration", open=False, visible=True):
+
+ face_rest_options = [None] + FACE_RESTORATION_MODELS
+
+ face_restoration_model_gui = gr.Dropdown(label="Face restoration model", choices=face_rest_options, value=face_rest_options[0])
+ with gr.Row():
+ face_restoration_visibility_gui = gr.Slider(minimum=0., maximum=1., step=0.001, value=1., label="Visibility")
+ face_restoration_weight_gui = gr.Slider(minimum=0., maximum=1., step=0.001, value=.5, label="Weight", info="(0 = maximum effect, 1 = minimum effect)")
+
+ with gr.Accordion("Textual inversion", open=False, visible=True) as menu_ti:
+ active_textual_inversion_gui = gr.Checkbox(value=False, label="Active Textual Inversion in prompt")
+ use_textual_inversion_gui = gr.CheckboxGroup(choices=get_embed_list(get_model_pipeline(model_name_gui.value)) if active_textual_inversion_gui.value else [], value=None, label="Use Textual Invertion in prompt")
+ def update_textual_inversion_gui(active_textual_inversion_gui, model_name_gui):
+ return gr.update(choices=get_embed_list(get_model_pipeline(model_name_gui)) if active_textual_inversion_gui else [])
+ active_textual_inversion_gui.change(update_textual_inversion_gui, [active_textual_inversion_gui, model_name_gui], [use_textual_inversion_gui])
+ model_name_gui.change(update_textual_inversion_gui, [active_textual_inversion_gui, model_name_gui], [use_textual_inversion_gui])
+
+ with gr.Accordion("ControlNet / Img2img / Inpaint", open=False, visible=True) as menu_i2i:
+ with gr.Row():
+ image_control = gr.Image(label="Image ControlNet/Inpaint/Img2img", type="filepath")
+ image_mask_gui = gr.Image(label="Image Mask", type="filepath")
+ with gr.Row():
+ strength_gui = gr.Slider(
+ minimum=0.01, maximum=1.0, step=0.01, value=0.55, label="Strength",
+ info="This option adjusts the level of changes for img2img, repaint and inpaint."
+ )
+ image_resolution_gui = gr.Slider(
+ minimum=64, maximum=2048, step=64, value=1024, label="Image Resolution",
+ info="The maximum proportional size of the generated image based on the uploaded image."
+ )
+ with gr.Row():
+ controlnet_model_gui = gr.Dropdown(label="ControlNet model", choices=DIFFUSERS_CONTROLNET_MODEL, value=DIFFUSERS_CONTROLNET_MODEL[0], allow_custom_value=True)
+ control_net_output_scaling_gui = gr.Slider(minimum=0, maximum=5.0, step=0.1, value=1, label="ControlNet Output Scaling in UNet")
+ control_net_start_threshold_gui = gr.Slider(minimum=0, maximum=1, step=0.01, value=0, label="ControlNet Start Threshold (%)")
+ control_net_stop_threshold_gui = gr.Slider(minimum=0, maximum=1, step=0.01, value=1, label="ControlNet Stop Threshold (%)")
+ with gr.Row():
+ preprocessor_name_gui = gr.Dropdown(label="Preprocessor Name", choices=TASK_AND_PREPROCESSORS["canny"])
+ preprocess_resolution_gui = gr.Slider(minimum=64, maximum=2048, step=64, value=512, label="Preprocessor Resolution")
+ low_threshold_gui = gr.Slider(minimum=1, maximum=255, step=1, value=100, label="'CANNY' low threshold")
+ high_threshold_gui = gr.Slider(minimum=1, maximum=255, step=1, value=200, label="'CANNY' high threshold")
+
+ def change_preprocessor_choices(task):
+ task = TASK_STABLEPY[task]
+ if task in TASK_AND_PREPROCESSORS.keys():
+ choices_task = TASK_AND_PREPROCESSORS[task]
+ else:
+ choices_task = TASK_AND_PREPROCESSORS["canny"]
+ return gr.update(choices=choices_task, value=choices_task[0])
+
+ task_gui.change(
+ change_preprocessor_choices,
+ [task_gui],
+ [preprocessor_name_gui],
+ )
+
+ with gr.Row():
+ value_threshold_gui = gr.Slider(minimum=1, maximum=2.0, step=0.01, value=0.1, label="'MLSD' Hough value threshold")
+ distance_threshold_gui = gr.Slider(minimum=1, maximum=20.0, step=0.01, value=0.1, label="'MLSD' Hough distance threshold")
+ recolor_gamma_correction_gui = gr.Number(minimum=0., maximum=25., value=1., step=0.001, label="'RECOLOR' gamma correction")
+ tile_blur_sigma_gui = gr.Number(minimum=0, maximum=100, value=9, step=1, label="'TILE' blur sigma")
+
+ with gr.Accordion("IP-Adapter", open=False, visible=True) as menu_ipa:
+
+ with gr.Accordion("IP-Adapter 1", open=True, visible=True):
+ with gr.Row():
+ image_ip1 = gr.Image(label="IP Image", type="filepath")
+ mask_ip1 = gr.Image(label="IP Mask", type="filepath")
+ with gr.Row():
+ model_ip1 = gr.Dropdown(value="plus_face", label="Model", choices=IP_MODELS)
+ mode_ip1 = gr.Dropdown(value="original", label="Mode", choices=MODE_IP_OPTIONS)
+ scale_ip1 = gr.Slider(minimum=0., maximum=2., step=0.01, value=0.7, label="Scale")
+ with gr.Accordion("IP-Adapter 2", open=True, visible=True):
+ with gr.Row():
+ image_ip2 = gr.Image(label="IP Image", type="filepath")
+ mask_ip2 = gr.Image(label="IP Mask (optional)", type="filepath")
+ with gr.Row():
+ model_ip2 = gr.Dropdown(value="base", label="Model", choices=IP_MODELS)
+ mode_ip2 = gr.Dropdown(value="style", label="Mode", choices=MODE_IP_OPTIONS)
+ scale_ip2 = gr.Slider(minimum=0., maximum=2., step=0.01, value=0.7, label="Scale")
+
+ with gr.Accordion("T2I adapter", open=False, visible=False) as menu_t2i:
+ t2i_adapter_preprocessor_gui = gr.Checkbox(value=True, label="T2i Adapter Preprocessor")
+ with gr.Row():
+ adapter_conditioning_scale_gui = gr.Slider(minimum=0, maximum=5., step=0.1, value=1, label="Adapter Conditioning Scale")
+ adapter_conditioning_factor_gui = gr.Slider(minimum=0, maximum=1., step=0.01, value=0.55, label="Adapter Conditioning Factor (%)")
+
+ with gr.Accordion("Styles", open=False, visible=True) as menu_styles:
+
+ try:
+ style_names_found = sd_gen.model.STYLE_NAMES
+ except Exception:
+ style_names_found = STYLE_NAMES
+
+ style_prompt_gui = gr.Dropdown(
+ style_names_found,
+ multiselect=True,
+ value=None,
+ label="Style Prompt",
+ interactive=True,
+ )
+ style_json_gui = gr.File(label="Style JSON File")
+ style_button = gr.Button("Load styles")
+
+ def load_json_style_file(json):
+ if not sd_gen.model:
+ gr.Info("First load the model")
+ return gr.update(value=None, choices=STYLE_NAMES)
+
+ sd_gen.model.load_style_file(json)
+ gr.Info(f"{len(sd_gen.model.STYLE_NAMES)} styles loaded")
+ return gr.update(value=None, choices=sd_gen.model.STYLE_NAMES)
+
+ style_button.click(load_json_style_file, [style_json_gui], [style_prompt_gui])
+
+ with gr.Accordion("Other settings", open=False, visible=True) as menu_other:
+ with gr.Row():
+ save_generated_images_gui = gr.Checkbox(value=False, label="Save Generated Images")
+ filename_pattern_gui = gr.Textbox(label="Filename pattern", value="model,seed", placeholder="model,seed,sampler,schedule_type,img_width,img_height,guidance_scale,num_steps,vae,prompt_section,neg_prompt_section", lines=1)
+ with gr.Row():
+ hires_before_adetailer_gui = gr.Checkbox(value=False, label="Hires Before Adetailer")
+ hires_after_adetailer_gui = gr.Checkbox(value=True, label="Hires After Adetailer")
+ generator_in_cpu_gui = gr.Checkbox(value=False, label="Generator in CPU")
+
+ with gr.Accordion("More settings", open=False, visible=False):
+ loop_generation_gui = gr.Slider(minimum=1, value=1, label="Loop Generation")
+ retain_task_cache_gui = gr.Checkbox(value=True, label="Retain task model in cache")
+ leave_progress_bar_gui = gr.Checkbox(value=True, label="Leave Progress Bar")
+ disable_progress_bar_gui = gr.Checkbox(value=False, label="Disable Progress Bar")
+ display_images_gui = gr.Checkbox(value=False, label="Display Images")
+ image_previews_gui = gr.Checkbox(value=True, label="Image Previews")
+ image_storage_location_gui = gr.Textbox(value="./images", label="Image Storage Location")
+ retain_compel_previous_load_gui = gr.Checkbox(value=False, label="Retain Compel Previous Load")
+ retain_detailfix_model_previous_load_gui = gr.Checkbox(value=False, label="Retain Detailfix Model Previous Load")
+ retain_hires_model_previous_load_gui = gr.Checkbox(value=False, label="Retain Hires Model Previous Load")
+ xformers_memory_efficient_attention_gui = gr.Checkbox(value=False, label="Xformers Memory Efficient Attention")
+
+ with gr.Accordion("Examples and help", open=True, visible=True) as menu_example:
+ gr.Examples(
+ examples=EXAMPLES_GUI,
+ fn=sd_gen.generate_pipeline,
+ inputs=[
+ prompt_gui,
+ neg_prompt_gui,
+ num_images_gui,
+ steps_gui,
+ cfg_gui,
+ clip_skip_gui,
+ seed_gui,
+ sampler_gui,
+ img_height_gui,
+ img_width_gui,
+ model_name_gui,
+ ],
+ outputs=[load_model_gui, result_images, actual_task_info],
+ cache_examples=False,
+ #elem_id="examples",
+ )
+
+ gr.Markdown(RESOURCES)
+## END MOD
+
+ with gr.Tab(th.t("Inpaint mask maker"), render=True):
+
+ def create_mask_now(img, invert):
+ import numpy as np
+ import time
+
+ time.sleep(0.5)
+
+ transparent_image = img["layers"][0]
+
+ # Extract the alpha channel
+ alpha_channel = np.array(transparent_image)[:, :, 3]
+
+ # Create a binary mask by thresholding the alpha channel
+ binary_mask = alpha_channel > 1
+
+ if invert:
+ print("Invert")
+ # Invert the binary mask so that the drawn shape is white and the rest is black
+ binary_mask = np.invert(binary_mask)
+
+ # Convert the binary mask to a 3-channel RGB mask
+ rgb_mask = np.stack((binary_mask,) * 3, axis=-1)
+
+ # Convert the mask to uint8
+ rgb_mask = rgb_mask.astype(np.uint8) * 255
+
+ return img["background"], rgb_mask
+
+ with gr.Row():
+ with gr.Column(scale=2):
+ # image_base = gr.ImageEditor(label="Base image", show_label=True, brush=gr.Brush(colors=["#000000"]))
+ image_base = gr.ImageEditor(
+ sources=["upload", "clipboard"],
+ # crop_size="1:1",
+ # enable crop (or disable it)
+ # transforms=["crop"],
+ brush=gr.Brush(
+ default_size="16", # or leave it as 'auto'
+ color_mode="fixed", # 'fixed' hides the user swatches and colorpicker, 'defaults' shows it
+ # default_color="black", # html names are supported
+ colors=[
+ "rgba(0, 0, 0, 1)", # rgb(a)
+ "rgba(0, 0, 0, 0.1)",
+ "rgba(255, 255, 255, 0.1)",
+ # "hsl(360, 120, 120)" # in fact any valid colorstring
+ ]
+ ),
+ eraser=gr.Eraser(default_size="16")
+ )
+ invert_mask = gr.Checkbox(value=False, label="Invert mask")
+ btn = gr.Button("Create mask")
+ with gr.Column(scale=1):
+ img_source = gr.Image(interactive=False)
+ img_result = gr.Image(label="Mask image", show_label=True, interactive=False)
+ btn_send = gr.Button("Send to the first tab")
+
+ btn.click(create_mask_now, [image_base, invert_mask], [img_source, img_result])
+
+ def send_img(img_source, img_result):
+ return img_source, img_result
+ btn_send.click(send_img, [img_source, img_result], [image_control, image_mask_gui])
+
+ with gr.Tab(th.t("PNG Info")):
+ with gr.Row():
+ with gr.Column():
+ image_metadata = gr.Image(label="Image with metadata", type="pil", sources=["upload"])
+
+ with gr.Column():
+ result_metadata = gr.Textbox(label="Metadata", show_label=True, show_copy_button=True, interactive=False, container=True, max_lines=99)
+
+ image_metadata.change(
+ fn=extract_exif_data,
+ inputs=[image_metadata],
+ outputs=[result_metadata],
+ )
+
+ with gr.Tab(th.t("Upscaler")):
+ with gr.Row():
+ with gr.Column():
+
+ USCALER_TAB_KEYS = [name for name in UPSCALER_KEYS[9:]]
+
+ image_up_tab = gr.Image(label="Image", type="pil", sources=["upload"])
+ upscaler_tab = gr.Dropdown(label="Upscaler", choices=USCALER_TAB_KEYS, value=USCALER_TAB_KEYS[5])
+ upscaler_size_tab = gr.Slider(minimum=1., maximum=4., step=0.1, value=1.1, label="Upscale by")
+ generate_button_up_tab = gr.Button(value="START UPSCALE", variant="primary")
+
+ with gr.Column():
+ result_up_tab = gr.Image(label="Result", type="pil", interactive=False, format="png")
+
+ generate_button_up_tab.click(
+ fn=process_upscale,
+ inputs=[image_up_tab, upscaler_tab, upscaler_size_tab],
+ outputs=[result_up_tab],
+ )
+
+ with gr.Tab(th.t("Preprocessor"), render=True):
+ preprocessor_tab()
+
+## BEGIN MOD
+ interface_mode_gui.change(
+ change_interface_mode,
+ [interface_mode_gui],
+ [menu_model, menu_from_image, menu_negative, menu_gen, menu_hires, menu_lora, menu_advanced,
+ menu_example, task_gui, quick_speed_gui],
+ queue=False,
+ )
+ model_name_gui.change(get_t2i_model_info, [model_name_gui], [model_info_gui], queue=False)
+ translate_prompt_gui.click(translate_to_en, [prompt_gui], [prompt_gui], queue=False)\
+ .then(translate_to_en, [neg_prompt_gui], [neg_prompt_gui], queue=False)
+
+ gr.on(
+ triggers=[quick_model_type_gui.change, quick_genre_gui.change, quick_speed_gui.change, quick_aspect_gui.change],
+ fn=set_quick_presets,
+ inputs=[quick_genre_gui, quick_model_type_gui, quick_speed_gui, quick_aspect_gui],
+ outputs=[quality_selector_gui, style_selector_gui, sampler_selector_gui, optimization_gui, insert_prompt_gui],
+ queue=False,
+ trigger_mode="once",
+ )
+ gr.on(
+ triggers=[quality_selector_gui.change, style_selector_gui.change, insert_prompt_gui.change],
+ fn=process_style_prompt,
+ inputs=[prompt_gui, neg_prompt_gui, style_selector_gui, quality_selector_gui, insert_prompt_gui],
+ outputs=[prompt_gui, neg_prompt_gui, quick_model_type_gui],
+ queue=False,
+ trigger_mode="once",
+ )
+ sampler_selector_gui.change(set_sampler_settings, [sampler_selector_gui], [sampler_gui, steps_gui, cfg_gui, clip_skip_gui, img_width_gui, img_height_gui, optimization_gui], queue=False)
+ optimization_gui.change(set_optimization, [optimization_gui, steps_gui, cfg_gui, sampler_gui, clip_skip_gui, lora5_gui, lora_scale_5_gui], [steps_gui, cfg_gui, sampler_gui, clip_skip_gui, lora5_gui, lora_scale_5_gui], queue=False)
+
+ gr.on(
+ triggers=[lora1_gui.change, lora_scale_1_gui.change, lora2_gui.change, lora_scale_2_gui.change,
+ lora3_gui.change, lora_scale_3_gui.change, lora4_gui.change, lora_scale_4_gui.change,
+ lora5_gui.change, lora_scale_5_gui.change, lora6_gui.change, lora_scale_6_gui.change,
+ lora7_gui.change, lora_scale_7_gui.change, prompt_syntax_gui.change],
+ fn=update_loras,
+ inputs=[prompt_gui, prompt_syntax_gui, lora1_gui, lora_scale_1_gui, lora2_gui, lora_scale_2_gui,
+ lora3_gui, lora_scale_3_gui, lora4_gui, lora_scale_4_gui, lora5_gui, lora_scale_5_gui,
+ lora6_gui, lora_scale_6_gui, lora7_gui, lora_scale_7_gui],
+ outputs=[prompt_gui, lora1_gui, lora_scale_1_gui, lora1_info_gui, lora1_copy_gui, lora1_desc_gui,
+ lora2_gui, lora_scale_2_gui, lora2_info_gui, lora2_copy_gui, lora2_desc_gui,
+ lora3_gui, lora_scale_3_gui, lora3_info_gui, lora3_copy_gui, lora3_desc_gui,
+ lora4_gui, lora_scale_4_gui, lora4_info_gui, lora4_copy_gui, lora4_desc_gui,
+ lora5_gui, lora_scale_5_gui, lora5_info_gui, lora5_copy_gui, lora5_desc_gui,
+ lora6_gui, lora_scale_6_gui, lora6_info_gui, lora6_copy_gui, lora6_desc_gui,
+ lora7_gui, lora_scale_7_gui, lora7_info_gui, lora7_copy_gui, lora7_desc_gui],
+ queue=False,
+ trigger_mode="once",
+ )
+ lora1_copy_gui.click(apply_lora_prompt, [prompt_gui, lora1_info_gui], [prompt_gui], queue=False)
+ lora2_copy_gui.click(apply_lora_prompt, [prompt_gui, lora2_info_gui], [prompt_gui], queue=False)
+ lora3_copy_gui.click(apply_lora_prompt, [prompt_gui, lora3_info_gui], [prompt_gui], queue=False)
+ lora4_copy_gui.click(apply_lora_prompt, [prompt_gui, lora4_info_gui], [prompt_gui], queue=False)
+ lora5_copy_gui.click(apply_lora_prompt, [prompt_gui, lora5_info_gui], [prompt_gui], queue=False)
+ lora6_copy_gui.click(apply_lora_prompt, [prompt_gui, lora6_info_gui], [prompt_gui], queue=False)
+ lora7_copy_gui.click(apply_lora_prompt, [prompt_gui, lora7_info_gui], [prompt_gui], queue=False)
+ gr.on(
+ triggers=[search_civitai_button_lora.click, search_civitai_query_lora.submit],
+ fn=search_civitai_lora,
+ inputs=[search_civitai_query_lora, search_civitai_basemodel_lora, search_civitai_sort_lora, search_civitai_period_lora,
+ search_civitai_tag_lora, search_civitai_user_lora, search_civitai_gallery_lora],
+ outputs=[search_civitai_result_lora, search_civitai_desc_lora, search_civitai_button_lora, search_civitai_query_lora, search_civitai_gallery_lora],
+ queue=True,
+ scroll_to_output=True,
+ )
+ search_civitai_result_lora.change(select_civitai_lora, [search_civitai_result_lora], [text_lora, search_civitai_desc_lora], queue=False, scroll_to_output=True)
+ search_civitai_gallery_lora.select(update_civitai_selection, None, [search_civitai_result_lora], queue=False, show_api=False)
+ button_lora.click(get_my_lora, [text_lora, romanize_text], [lora1_gui, lora2_gui, lora3_gui, lora4_gui, lora5_gui, lora6_gui, lora7_gui, new_lora_status], scroll_to_output=True)
+ upload_button_lora.upload(upload_file_lora, [upload_button_lora], [file_output_lora, upload_button_lora]).success(
+ move_file_lora, [file_output_lora], [lora1_gui, lora2_gui, lora3_gui, lora4_gui, lora5_gui, lora6_gui, lora7_gui], scroll_to_output=True)
+
+ use_textual_inversion_gui.change(set_textual_inversion_prompt, [use_textual_inversion_gui, prompt_gui, neg_prompt_gui, prompt_syntax_gui], [prompt_gui, neg_prompt_gui])
+
+ generate_from_image_btn_gui.click(
+ lambda: ("", "", ""), None, [series_dbt, character_dbt, prompt_gui], queue=False,
+ ).success(
+ predict_tags_wd,
+ [input_image_gui, prompt_gui, image_algorithms, general_threshold_gui, character_threshold_gui],
+ [series_dbt, character_dbt, prompt_gui, copy_button_dbt],
+ ).success(
+ compose_prompt_to_copy, [character_dbt, series_dbt, prompt_gui], [prompt_gui], queue=False,
+ ).success(
+ remove_specific_prompt, [prompt_gui, keep_tags_gui], [prompt_gui], queue=False,
+ ).success(
+ convert_danbooru_to_e621_prompt, [prompt_gui, tag_type_gui], [prompt_gui], queue=False,
+ ).success(
+ insert_recom_prompt, [prompt_gui, neg_prompt_gui, recom_prompt_gui], [prompt_gui, neg_prompt_gui], queue=False,
+ )
+
+ prompt_type_button.click(convert_danbooru_to_e621_prompt, [prompt_gui, prompt_type_gui], [prompt_gui], queue=False)
+ random_character_gui.click(select_random_character, [series_dbt, character_dbt], [series_dbt, character_dbt], queue=False)
+ generate_db_random_button.click(
+ v2_random_prompt,
+ [prompt_gui, series_dbt, character_dbt,
+ rating_dbt, aspect_ratio_dbt, length_dbt, identity_dbt, ban_tags_dbt, model_name_dbt],
+ [prompt_gui, series_dbt, character_dbt],
+ ).success(
+ convert_danbooru_to_e621_prompt, [prompt_gui, tag_type_gui], [prompt_gui], queue=False,
+ )
+
+ translate_prompt_button.click(translate_prompt, [prompt_gui], [prompt_gui], queue=False)
+ translate_prompt_button.click(translate_prompt, [character_dbt], [character_dbt], queue=False)
+ translate_prompt_button.click(translate_prompt, [series_dbt], [series_dbt], queue=False)
+
+ generate_button.click(
+ fn=insert_model_recom_prompt,
+ inputs=[prompt_gui, neg_prompt_gui, model_name_gui, recom_prompt_gui],
+ outputs=[prompt_gui, neg_prompt_gui],
+ queue=False,
+ ).success(
+ fn=sd_gen.load_new_model,
+ inputs=[
+ model_name_gui,
+ vae_model_gui,
+ task_gui,
+ controlnet_model_gui,
+ ],
+ outputs=[load_model_gui],
+ queue=True,
+ show_progress="minimal",
+ ).success(
+ fn=sd_gen_generate_pipeline,
+ inputs=[
+ prompt_gui,
+ neg_prompt_gui,
+ num_images_gui,
+ steps_gui,
+ cfg_gui,
+ clip_skip_gui,
+ seed_gui,
+ lora1_gui,
+ lora_scale_1_gui,
+ lora2_gui,
+ lora_scale_2_gui,
+ lora3_gui,
+ lora_scale_3_gui,
+ lora4_gui,
+ lora_scale_4_gui,
+ lora5_gui,
+ lora_scale_5_gui,
+ lora6_gui,
+ lora_scale_6_gui,
+ lora7_gui,
+ lora_scale_7_gui,
+ sampler_gui,
+ schedule_type_gui,
+ schedule_prediction_type_gui,
+ img_height_gui,
+ img_width_gui,
+ model_name_gui,
+ vae_model_gui,
+ task_gui,
+ image_control,
+ preprocessor_name_gui,
+ preprocess_resolution_gui,
+ image_resolution_gui,
+ style_prompt_gui,
+ style_json_gui,
+ image_mask_gui,
+ strength_gui,
+ low_threshold_gui,
+ high_threshold_gui,
+ value_threshold_gui,
+ distance_threshold_gui,
+ recolor_gamma_correction_gui,
+ tile_blur_sigma_gui,
+ control_net_output_scaling_gui,
+ control_net_start_threshold_gui,
+ control_net_stop_threshold_gui,
+ active_textual_inversion_gui,
+ prompt_syntax_gui,
+ upscaler_model_path_gui,
+ upscaler_increases_size_gui,
+ upscaler_tile_size_gui,
+ upscaler_tile_overlap_gui,
+ hires_steps_gui,
+ hires_denoising_strength_gui,
+ hires_sampler_gui,
+ hires_prompt_gui,
+ hires_negative_prompt_gui,
+ hires_before_adetailer_gui,
+ hires_after_adetailer_gui,
+ hires_schedule_type_gui,
+ hires_guidance_scale_gui,
+ controlnet_model_gui,
+ loop_generation_gui,
+ leave_progress_bar_gui,
+ disable_progress_bar_gui,
+ image_previews_gui,
+ display_images_gui,
+ save_generated_images_gui,
+ filename_pattern_gui,
+ image_storage_location_gui,
+ retain_compel_previous_load_gui,
+ retain_detailfix_model_previous_load_gui,
+ retain_hires_model_previous_load_gui,
+ t2i_adapter_preprocessor_gui,
+ adapter_conditioning_scale_gui,
+ adapter_conditioning_factor_gui,
+ xformers_memory_efficient_attention_gui,
+ free_u_gui,
+ generator_in_cpu_gui,
+ adetailer_inpaint_only_gui,
+ adetailer_verbose_gui,
+ adetailer_sampler_gui,
+ adetailer_active_a_gui,
+ prompt_ad_a_gui,
+ negative_prompt_ad_a_gui,
+ strength_ad_a_gui,
+ face_detector_ad_a_gui,
+ person_detector_ad_a_gui,
+ hand_detector_ad_a_gui,
+ mask_dilation_a_gui,
+ mask_blur_a_gui,
+ mask_padding_a_gui,
+ adetailer_active_b_gui,
+ prompt_ad_b_gui,
+ negative_prompt_ad_b_gui,
+ strength_ad_b_gui,
+ face_detector_ad_b_gui,
+ person_detector_ad_b_gui,
+ hand_detector_ad_b_gui,
+ mask_dilation_b_gui,
+ mask_blur_b_gui,
+ mask_padding_b_gui,
+ retain_task_cache_gui,
+ guidance_rescale_gui,
+ image_ip1,
+ mask_ip1,
+ model_ip1,
+ mode_ip1,
+ scale_ip1,
+ image_ip2,
+ mask_ip2,
+ model_ip2,
+ mode_ip2,
+ scale_ip2,
+ pag_scale_gui,
+ face_restoration_model_gui,
+ face_restoration_visibility_gui,
+ face_restoration_weight_gui,
+ load_lora_cpu_gui,
+ verbose_info_gui,
+ gpu_duration_gui,
+ ],
+ outputs=[load_model_gui, result_images, actual_task_info],
+ queue=True,
+ show_progress="full",
+ ).success(save_gallery_images, [result_images, model_name_gui], [result_images, result_images_files], queue=False, show_api=False)\
+ .success(save_gallery_history, [result_images, result_images_files, history_gallery, history_files], [history_gallery, history_files], queue=False, show_api=False)
+
+ with gr.Tab(th.t("Danbooru Tags Transformer with WD Tagger"), render=True):
+ with gr.Column(scale=2):
+ with gr.Group():
+ input_image = gr.Image(label="Input image", type="pil", sources=["upload", "clipboard"], height=256)
+ with gr.Accordion(label="Advanced options", open=False):
+ general_threshold = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.01, interactive=True)
+ character_threshold = gr.Slider(label="Character threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.01, interactive=True)
+ input_tag_type = gr.Radio(label="Convert tags to", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="danbooru")
+ recom_prompt = gr.Radio(label="Insert reccomended prompt", choices=["None", "Animagine", "Pony"], value="None", interactive=True)
+ image_algorithms = gr.CheckboxGroup(["Use WD Tagger"], label="Algorithms", value=["Use WD Tagger"], visible=False)
+ keep_tags = gr.Radio(label="Remove tags leaving only the following", choices=["body", "dress", "all"], value="all")
+ generate_from_image_btn = gr.Button(value="GENERATE TAGS FROM IMAGE", size="lg", variant="primary")
+ with gr.Group():
+ with gr.Row():
+ input_character = gr.Textbox(label="Character tags", placeholder="hatsune miku")
+ input_copyright = gr.Textbox(label="Copyright tags", placeholder="vocaloid")
+ pick_random_character = gr.Button(value="Random character 🎲", size="sm")
+ input_general = gr.TextArea(label="General tags", lines=4, placeholder="1girl, ...", value="")
+ input_tags_to_copy = gr.Textbox(value="", visible=False)
+ with gr.Row():
+ copy_input_btn = gr.Button(value="Copy to clipboard", size="sm", interactive=False)
+ copy_prompt_btn_input = gr.Button(value="Copy to primary prompt", size="sm", interactive=False)
+ translate_input_prompt_button = gr.Button(value="Translate prompt to English", size="sm", variant="secondary")
+ tag_type = gr.Radio(label="Output tag conversion", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="e621", visible=False)
+ input_rating = gr.Radio(label="Rating", choices=list(V2_RATING_OPTIONS), value="explicit")
+ with gr.Accordion(label="Advanced options", open=False):
+ input_aspect_ratio = gr.Radio(label="Aspect ratio", info="The aspect ratio of the image.", choices=list(V2_ASPECT_RATIO_OPTIONS), value="square")
+ input_length = gr.Radio(label="Length", info="The total length of the tags.", choices=list(V2_LENGTH_OPTIONS), value="very_long")
+ input_identity = gr.Radio(label="Keep identity", info="How strictly to keep the identity of the character or subject. If you specify the detail of subject in the prompt, you should choose `strict`. Otherwise, choose `none` or `lax`. `none` is very creative but sometimes ignores the input prompt.", choices=list(V2_IDENTITY_OPTIONS), value="lax")
+ input_ban_tags = gr.Textbox(label="Ban tags", info="Tags to ban from the output.", placeholder="alternate costumen, ...", value="censored")
+ model_name = gr.Dropdown(label="Model", choices=list(V2_ALL_MODELS.keys()), value=list(V2_ALL_MODELS.keys())[0])
+ dummy_np = gr.Textbox(label="Negative prompt", value="", visible=False)
+ recom_animagine = gr.Textbox(label="Animagine reccomended prompt", value="Animagine", visible=False)
+ recom_pony = gr.Textbox(label="Pony reccomended prompt", value="Pony", visible=False)
+ generate_btn = gr.Button(value="GENERATE TAGS", size="lg", variant="primary")
+ with gr.Row():
+ with gr.Group():
+ output_text = gr.TextArea(label="Output tags", interactive=False, show_copy_button=True)
+ with gr.Row():
+ copy_btn = gr.Button(value="Copy to clipboard", size="sm", interactive=False)
+ copy_prompt_btn = gr.Button(value="Copy to primary prompt", size="sm", interactive=False)
+ with gr.Group():
+ output_text_pony = gr.TextArea(label="Output tags (Pony e621 style)", interactive=False, show_copy_button=True)
+ with gr.Row():
+ copy_btn_pony = gr.Button(value="Copy to clipboard", size="sm", interactive=False)
+ copy_prompt_btn_pony = gr.Button(value="Copy to primary prompt", size="sm", interactive=False)
+ description_ui()
+
+ translate_input_prompt_button.click(translate_prompt, inputs=[input_general], outputs=[input_general], queue=False)
+ translate_input_prompt_button.click(translate_prompt, inputs=[input_character], outputs=[input_character], queue=False)
+ translate_input_prompt_button.click(translate_prompt, inputs=[input_copyright], outputs=[input_copyright], queue=False)
+
+ generate_from_image_btn.click(
+ lambda: ("", "", ""), None, [input_copyright, input_character, input_general], queue=False,
+ ).success(
+ predict_tags_wd,
+ [input_image, input_general, image_algorithms, general_threshold, character_threshold],
+ [input_copyright, input_character, input_general, copy_input_btn],
+ ).success(
+ remove_specific_prompt, inputs=[input_general, keep_tags], outputs=[input_general], queue=False,
+ ).success(
+ convert_danbooru_to_e621_prompt, inputs=[input_general, input_tag_type], outputs=[input_general], queue=False,
+ ).success(
+ insert_recom_prompt, inputs=[input_general, dummy_np, recom_prompt], outputs=[input_general, dummy_np], queue=False,
+ ).success(lambda: gr.update(interactive=True), None, [copy_prompt_btn_input], queue=False)
+ copy_input_btn.click(compose_prompt_to_copy, inputs=[input_character, input_copyright, input_general], outputs=[input_tags_to_copy])\
+ .success(gradio_copy_text, inputs=[input_tags_to_copy], js=COPY_ACTION_JS)
+ copy_prompt_btn_input.click(compose_prompt_to_copy, inputs=[input_character, input_copyright, input_general], outputs=[input_tags_to_copy])\
+ .success(gradio_copy_prompt, inputs=[input_tags_to_copy], outputs=[prompt_gui])
+
+ pick_random_character.click(select_random_character, [input_copyright, input_character], [input_copyright, input_character])
+
+ generate_btn.click(
+ v2_upsampling_prompt,
+ [model_name, input_copyright, input_character, input_general,
+ input_rating, input_aspect_ratio, input_length, input_identity, input_ban_tags],
+ [output_text],
+ ).success(
+ convert_danbooru_to_e621_prompt, inputs=[output_text, tag_type], outputs=[output_text_pony], queue=False,
+ ).success(
+ insert_recom_prompt, inputs=[output_text, dummy_np, recom_animagine], outputs=[output_text, dummy_np], queue=False,
+ ).success(
+ insert_recom_prompt, inputs=[output_text_pony, dummy_np, recom_pony], outputs=[output_text_pony, dummy_np], queue=False,
+ ).success(lambda: (gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True)),
+ None, [copy_btn, copy_btn_pony, copy_prompt_btn, copy_prompt_btn_pony], queue=False)
+ copy_btn.click(gradio_copy_text, inputs=[output_text], js=COPY_ACTION_JS)
+ copy_btn_pony.click(gradio_copy_text, inputs=[output_text_pony], js=COPY_ACTION_JS)
+ copy_prompt_btn.click(gradio_copy_prompt, inputs=[output_text], outputs=[prompt_gui])
+ copy_prompt_btn_pony.click(gradio_copy_prompt, inputs=[output_text_pony], outputs=[prompt_gui])
+
+ gr.LoginButton()
+ gr.DuplicateButton(value="Duplicate Space for private use (This demo does not work on CPU. Requires GPU Space)")
+
+app.queue()
+th.translate_civitai_constants(app)
+app.launch(show_error=True, debug=True) # allowed_paths=["./images/"], show_error=True, debug=True
+## END MOD
+
+# 自定义一些额外的翻译
+def custom_translate(text):
+ """自定义翻译函数，以处理特殊情况"""
+ return translate_ui.translate_text(text)