Spaces:
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Browse files
README.md
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---
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title:
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emoji:
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colorFrom: red
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colorTo: pink
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sdk: gradio
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app_file: app.py
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pinned: true
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license: mit
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short_description: Stunning images using stable diffusion.
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: 😈️ Ivan's DiffuseCraft
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emoji: 😈️
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colorFrom: red
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colorTo: pink
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sdk: gradio
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app_file: app.py
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pinned: true
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license: mit
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short_description: (ivan) Stunning images using stable diffusion.
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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@@ -25,55 +25,55 @@ from stablepy import (
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import urllib.parse
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preprocessor_controlnet = {
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}
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task_stablepy = {
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@@ -106,7 +106,7 @@ task_model_list = list(task_stablepy.keys())
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def download_things(directory, url, hf_token="", civitai_api_key=""):
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url = url.strip()
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if "drive.google.com" in url:
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original_dir = os.getcwd()
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os.chdir(directory)
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url = url.replace("/blob/", "/resolve/")
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user_header = f'"Authorization: Bearer {hf_token}"'
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if hf_token:
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os.system(
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else:
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os.system
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elif "civitai.com" in url:
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if "?" in url:
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url = url.split("?")[0]
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if civitai_api_key:
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url = url + f"?token={civitai_api_key}"
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os.system(
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else:
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print("\033[91mYou need an API key to download Civitai models.\033[0m")
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else:
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def get_model_list(directory_path):
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model_list = []
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valid_extensions = {'.ckpt'
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for filename in os.listdir(directory_path):
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if os.path.splitext(filename)[1] in valid_extensions:
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'https://huggingface.co/datasets/Nerfgun3/bad_prompt/blob/main/bad_prompt_version2.pt',
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'https://huggingface.co/embed/negative/resolve/main/EasyNegativeV2.safetensors',
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'https://huggingface.co/embed/negative/resolve/main/bad-hands-5.pt',
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-
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for url_embed in download_embeds:
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if not os.path.exists(f"./embedings/{url_embed.split('/')[-1]}"):
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@@ -243,13 +246,14 @@ lora_model_list.insert(0, "None")
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vae_model_list = get_model_list(directory_vaes)
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vae_model_list.insert(0, "None")
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def get_my_lora(link_url):
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for url in [url.strip() for url in link_url.split(',')]:
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if not os.path.exists(f"./loras/{url.split('/')[-1]}"):
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download_things(directory_loras, url, hf_token, CIVITAI_API_KEY)
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new_lora_model_list = get_model_list(directory_loras)
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new_lora_model_list.insert(0, "None")
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return gr.update(
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choices=new_lora_model_list
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), gr.update(
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choices=new_lora_model_list
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),
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print('\033[33m🏁 Download and listing of valid models completed.\033[0m')
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upscaler_dict_gui = {
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None
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"Lanczos"
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"Nearest"
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"RealESRGAN_x4plus"
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"RealESRNet_x4plus"
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"RealESRGAN_x4plus_anime_6B": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
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"RealESRGAN_x2plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
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"realesr-animevideov3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
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"realesr-general-x4v3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
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"realesr-general-wdn-x4v3"
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"4x-UltraSharp"
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"4x_foolhardy_Remacri"
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"Remacri4xExtraSmoother"
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"AnimeSharp4x"
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"lollypop"
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"RealisticRescaler4x"
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"NickelbackFS4x"
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}
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import time, json
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from IPython.utils import capture
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import logging
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logging.getLogger("diffusers").setLevel(logging.ERROR)
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import diffusers
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diffusers.utils.logging.set_verbosity(40)
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import warnings
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warnings.filterwarnings(action="ignore", category=FutureWarning, module="diffusers")
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warnings.filterwarnings(action="ignore", category=UserWarning, module="diffusers")
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warnings.filterwarnings(action="ignore", category=FutureWarning, module="transformers")
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from stablepy import logger
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logger.setLevel(logging.DEBUG)
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def info_html(json_data, title, subtitle):
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return f"""
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<div style='padding: 0; border-radius: 10px;'>
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</div>
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"""
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class GuiSD:
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def __init__(self, stream=True):
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self.model = None
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print("Loading model...")
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self.model = Model_Diffusers(
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base_model_id="cagliostrolab/animagine-xl-3.1",
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def load_new_model(self, model_name, vae_model, task, progress=gr.Progress(track_tqdm=True)):
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yield f"Loading model: {model_name}"
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vae_model = vae_model if vae_model != "None" else None
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if model_name in model_list:
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if incompatible_vae:
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vae_model = None
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self.model.load_pipe(
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model_name,
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task_name=task_stablepy[task],
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retain_task_model_in_cache=False,
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)
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yield f"Model loaded: {model_name}"
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@spaces.GPU
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def generate_pipeline(
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):
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vae_model = vae_model if vae_model != "None" else None
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loras_list = [lora1, lora2, lora3, lora4, lora5]
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vae_msg = f"VAE: {vae_model}" if vae_model else ""
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msg_lora = []
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-
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if model_name in model_list:
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model_is_xl = "xl" in model_name.lower()
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sdxl_in_vae = vae_model and "sdxl" in vae_model.lower()
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if incompatible_vae:
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msg_inc_vae = (
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f"The selected VAE is for a {
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f" are using a {
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"will be used."
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)
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gr.Info(msg_inc_vae)
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print(la)
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lora_type = ("animetarot" in la.lower() or "Hyper-SD15-8steps".lower() in la.lower())
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if (model_is_xl and lora_type) or (not model_is_xl and not lora_type):
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msg_inc_lora = f"The LoRA {la} is for {
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gr.Info(msg_inc_lora)
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msg_lora.append(msg_inc_lora)
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)
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if task != "txt2img" and not image_control:
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raise ValueError(
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if task == "inpaint" and not image_mask:
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raise ValueError("No mask image found: Specify one in 'Image Mask'")
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print("No Textual inversion for SDXL")
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adetailer_params_A = {
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"face_detector_ad"
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"person_detector_ad"
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"hand_detector_ad"
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"prompt": prompt_ad_a,
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"negative_prompt"
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"strength"
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# "image_list_task" : None,
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"mask_dilation"
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"mask_blur"
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"mask_padding"
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"inpaint_only"
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"sampler"
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}
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adetailer_params_B = {
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"face_detector_ad"
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"person_detector_ad"
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"hand_detector_ad"
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"prompt": prompt_ad_b,
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"negative_prompt"
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"strength"
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# "image_list_task" : None,
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"mask_dilation"
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"mask_blur"
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"mask_padding"
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}
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pipe_params = {
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"prompt": prompt,
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random_number = random.randint(1, 100)
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if random_number < 25 and num_images < 3:
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if not upscaler_model and steps < 45 and task in ["txt2img",
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-
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pipe_params["num_images"] = num_images
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gr.Info("Num images x 2 🎉")
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@@ -731,13 +744,15 @@ class GuiSD:
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sd_gen = GuiSD()
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CSS ="""
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.contain { display: flex; flex-direction: column; }
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#component-0 { height: 100%; }
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#gallery { flex-grow: 1; }
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"""
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sdxl_task = [k for k, v in task_stablepy.items() if v in SDXL_TASKS
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sd_task = [k for k, v in task_stablepy.items() if v in SD15_TASKS
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def update_task_options(model_name, task_name):
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if model_name in model_list:
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if "xl" in model_name.lower():
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@@ -754,7 +769,7 @@ def update_task_options(model_name, task_name):
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with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
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gr.Markdown("# 🧩 DiffuseCraft")
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gr.Markdown(
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f"""
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### This demo uses [diffusers](https://github.com/huggingface/diffusers) to perform different tasks in image generation.
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with gr.Column(scale=2):
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task_gui = gr.Dropdown(label="Task", choices=sdxl_task, value=task_model_list[0])
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model_name_gui = gr.Dropdown(label="Model", choices=model_list, value=model_list[0],
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prompt_gui = gr.Textbox(lines=5, placeholder="Enter prompt", label="Prompt")
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neg_prompt_gui = gr.Textbox(lines=3, placeholder="Enter Neg prompt", label="Negative prompt")
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with gr.Row(equal_height=False):
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clear_prompt_gui = gr.Button(value="🗑️")
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set_random_seed = gr.Button(value="🎲")
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generate_button = gr.Button(value="GENERATE", variant="primary")
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-
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model_name_gui.change(
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update_task_options,
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[model_name_gui, task_gui],
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)
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load_model_gui = gr.HTML()
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-
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result_images = gr.Gallery(
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label="Generated images",
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show_label=False,
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)
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actual_task_info = gr.HTML()
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-
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with gr.Column(scale=1):
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steps_gui = gr.Slider(minimum=1, maximum=100, step=1, value=30, label="Steps")
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cfg_gui = gr.Slider(minimum=0, maximum=30, step=0.5, value=7.5, label="CFG")
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free_u_gui = gr.Checkbox(value=True, label="FreeU")
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with gr.Row(equal_height=False):
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-
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-
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def run_set_params_gui(base_prompt):
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valid_receptors = { # default values
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"height": gr.update(value=1024),
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"Seed": gr.update(value=-1),
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"Sampler": gr.update(value="Euler a"),
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"scale": gr.update(value=7.5),
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"skip": gr.update(value=True),
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}
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valid_keys = list(valid_receptors.keys())
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# print(val)
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if key in valid_keys:
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if key == "Sampler":
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elif key == "skip":
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if key == "prompt":
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if key in ["prompt", "neg_prompt"]:
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val = val.strip()
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if key in ["Steps", "width", "height", "Seed"]:
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|
854 |
# print(valid_receptors)
|
855 |
return [value for value in valid_receptors.values()]
|
856 |
|
|
|
857 |
set_params_gui.click(
|
858 |
-
run_set_params_gui, [prompt_gui],[
|
859 |
prompt_gui,
|
860 |
neg_prompt_gui,
|
861 |
steps_gui,
|
@@ -867,16 +882,21 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
867 |
clip_skip_gui,
|
868 |
],
|
869 |
)
|
870 |
-
|
871 |
-
|
872 |
def run_clear_prompt_gui():
|
873 |
return gr.update(value=""), gr.update(value="")
|
|
|
|
|
874 |
clear_prompt_gui.click(
|
875 |
run_clear_prompt_gui, [], [prompt_gui, neg_prompt_gui]
|
876 |
)
|
877 |
|
|
|
878 |
def run_set_random_seed():
|
879 |
return -1
|
|
|
|
|
880 |
set_random_seed.click(
|
881 |
run_set_random_seed, [], seed_gui
|
882 |
)
|
@@ -890,22 +910,30 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
890 |
("Classic-ignore", "Classic-ignore"),
|
891 |
("None", "None"),
|
892 |
]
|
893 |
-
prompt_syntax_gui = gr.Dropdown(label="Prompt Syntax", choices=prompt_s_options,
|
|
|
894 |
vae_model_gui = gr.Dropdown(label="VAE Model", choices=vae_model_list)
|
895 |
|
896 |
with gr.Accordion("Hires fix", open=False, visible=True):
|
897 |
|
898 |
upscaler_keys = list(upscaler_dict_gui.keys())
|
899 |
|
900 |
-
upscaler_model_path_gui = gr.Dropdown(label="Upscaler", choices=upscaler_keys,
|
901 |
-
|
|
|
|
|
902 |
esrgan_tile_gui = gr.Slider(minimum=0, value=100, maximum=500, step=1, label="ESRGAN Tile")
|
903 |
-
esrgan_tile_overlap_gui = gr.Slider(minimum=1, maximum=200, step=1, value=10,
|
|
|
904 |
hires_steps_gui = gr.Slider(minimum=0, value=30, maximum=100, step=1, label="Hires Steps")
|
905 |
-
hires_denoising_strength_gui = gr.Slider(minimum=0.1, maximum=1.0, step=0.01, value=0.55,
|
906 |
-
|
|
|
|
|
|
|
907 |
hires_prompt_gui = gr.Textbox(label="Hires Prompt", placeholder="Main prompt will be use", lines=3)
|
908 |
-
hires_negative_prompt_gui = gr.Textbox(label="Hires Negative Prompt",
|
|
|
909 |
|
910 |
with gr.Accordion("LoRA", open=False, visible=True):
|
911 |
lora1_gui = gr.Dropdown(label="Lora1", choices=lora_model_list)
|
@@ -928,7 +956,7 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
928 |
[lora1_gui, lora2_gui, lora3_gui, lora4_gui, lora5_gui]
|
929 |
)
|
930 |
|
931 |
-
with gr.Accordion("IP-Adapter", open=False, visible=True)
|
932 |
|
933 |
IP_MODELS = sorted(list(set(IP_ADAPTERS_SD + IP_ADAPTERS_SDXL)))
|
934 |
MODE_IP_OPTIONS = ["original", "style", "layout", "style+layout"]
|
@@ -953,8 +981,11 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
953 |
minimum=0.01, maximum=1.0, step=0.01, value=0.55, label="Strength",
|
954 |
info="This option adjusts the level of changes for img2img and inpainting."
|
955 |
)
|
956 |
-
image_resolution_gui = gr.Slider(minimum=64, maximum=2048, step=64, value=1024,
|
957 |
-
|
|
|
|
|
|
|
958 |
|
959 |
def change_preprocessor_choices(task):
|
960 |
task = task_stablepy[task]
|
@@ -964,24 +995,35 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
964 |
choices_task = preprocessor_controlnet["canny"]
|
965 |
return gr.update(choices=choices_task, value=choices_task[0])
|
966 |
|
|
|
967 |
task_gui.change(
|
968 |
change_preprocessor_choices,
|
969 |
[task_gui],
|
970 |
[preprocessor_name_gui],
|
971 |
)
|
972 |
-
preprocess_resolution_gui = gr.Slider(minimum=64, maximum=2048, step=64, value=512,
|
973 |
-
|
974 |
-
|
975 |
-
|
976 |
-
|
977 |
-
|
978 |
-
|
979 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
980 |
|
981 |
with gr.Accordion("T2I adapter", open=False, visible=True):
|
982 |
t2i_adapter_preprocessor_gui = gr.Checkbox(value=True, label="T2i Adapter Preprocessor")
|
983 |
-
adapter_conditioning_scale_gui = gr.Slider(minimum=0, maximum=5., step=0.1, value=1,
|
984 |
-
|
|
|
|
|
985 |
|
986 |
with gr.Accordion("Styles", open=False, visible=True):
|
987 |
|
@@ -1000,6 +1042,7 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
1000 |
style_json_gui = gr.File(label="Style JSON File")
|
1001 |
style_button = gr.Button("Load styles")
|
1002 |
|
|
|
1003 |
def load_json_style_file(json):
|
1004 |
if not sd_gen.model:
|
1005 |
gr.Info("First load the model")
|
@@ -1009,7 +1052,8 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
1009 |
gr.Info(f"{len(sd_gen.model.STYLE_NAMES)} styles loaded")
|
1010 |
return gr.update(value=None, choices=sd_gen.model.STYLE_NAMES)
|
1011 |
|
1012 |
-
|
|
|
1013 |
|
1014 |
with gr.Accordion("Textual inversion", open=False, visible=False):
|
1015 |
active_textual_inversion_gui = gr.Checkbox(value=False, label="Active Textual Inversion in prompt")
|
@@ -1024,14 +1068,18 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
1024 |
|
1025 |
# Adetailer Sampler
|
1026 |
adetailer_sampler_options = ["Use same sampler"] + scheduler_names[:-1]
|
1027 |
-
adetailer_sampler_gui = gr.Dropdown(label="Adetailer sampler:", choices=adetailer_sampler_options,
|
|
|
1028 |
|
1029 |
with gr.Accordion("Detailfix A", open=False, visible=True):
|
1030 |
# Adetailer A
|
1031 |
adetailer_active_a_gui = gr.Checkbox(label="Enable Adetailer A", value=False)
|
1032 |
-
prompt_ad_a_gui = gr.Textbox(label="Main prompt", placeholder="Main prompt will be use",
|
1033 |
-
|
1034 |
-
|
|
|
|
|
|
|
1035 |
face_detector_ad_a_gui = gr.Checkbox(label="Face detector", value=True)
|
1036 |
person_detector_ad_a_gui = gr.Checkbox(label="Person detector", value=True)
|
1037 |
hand_detector_ad_a_gui = gr.Checkbox(label="Hand detector", value=False)
|
@@ -1042,9 +1090,12 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
1042 |
with gr.Accordion("Detailfix B", open=False, visible=True):
|
1043 |
# Adetailer B
|
1044 |
adetailer_active_b_gui = gr.Checkbox(label="Enable Adetailer B", value=False)
|
1045 |
-
prompt_ad_b_gui = gr.Textbox(label="Main prompt", placeholder="Main prompt will be use",
|
1046 |
-
|
1047 |
-
|
|
|
|
|
|
|
1048 |
face_detector_ad_b_gui = gr.Checkbox(label="Face detector", value=True)
|
1049 |
person_detector_ad_b_gui = gr.Checkbox(label="Person detector", value=True)
|
1050 |
hand_detector_ad_b_gui = gr.Checkbox(label="Hand detector", value=False)
|
@@ -1067,9 +1118,12 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
1067 |
save_generated_images_gui = gr.Checkbox(value=False, label="Save Generated Images")
|
1068 |
image_storage_location_gui = gr.Textbox(value="./images", label="Image Storage Location")
|
1069 |
retain_compel_previous_load_gui = gr.Checkbox(value=False, label="Retain Compel Previous Load")
|
1070 |
-
retain_detailfix_model_previous_load_gui = gr.Checkbox(value=False,
|
1071 |
-
|
1072 |
-
|
|
|
|
|
|
|
1073 |
|
1074 |
with gr.Accordion("Examples and help", open=False, visible=True):
|
1075 |
gr.Markdown(
|
@@ -1106,7 +1160,7 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
1106 |
None,
|
1107 |
1.0,
|
1108 |
None,
|
1109 |
-
1.0,
|
1110 |
None,
|
1111 |
1.0,
|
1112 |
None,
|
@@ -1115,24 +1169,24 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
1115 |
1152,
|
1116 |
896,
|
1117 |
"cagliostrolab/animagine-xl-3.1",
|
1118 |
-
None,
|
1119 |
"txt2img",
|
1120 |
-
None,
|
1121 |
-
"Canny",
|
1122 |
-
512,
|
1123 |
-
1024,
|
1124 |
-
None,
|
1125 |
-
None,
|
1126 |
-
None,
|
1127 |
-
0.35,
|
1128 |
-
100,
|
1129 |
-
200,
|
1130 |
-
0.1,
|
1131 |
-
0.1,
|
1132 |
-
1.0,
|
1133 |
-
0.,
|
1134 |
-
1.,
|
1135 |
-
False,
|
1136 |
"Classic",
|
1137 |
"Nearest",
|
1138 |
],
|
@@ -1149,7 +1203,7 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
1149 |
None,
|
1150 |
1.0,
|
1151 |
None,
|
1152 |
-
1.0,
|
1153 |
None,
|
1154 |
1.0,
|
1155 |
None,
|
@@ -1158,24 +1212,24 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
1158 |
1024,
|
1159 |
1024,
|
1160 |
"kitty7779/ponyDiffusionV6XL",
|
1161 |
-
None,
|
1162 |
"txt2img",
|
1163 |
-
None,
|
1164 |
-
"Canny",
|
1165 |
-
512,
|
1166 |
-
1024,
|
1167 |
-
None,
|
1168 |
-
None,
|
1169 |
-
None,
|
1170 |
-
0.35,
|
1171 |
-
100,
|
1172 |
-
200,
|
1173 |
-
0.1,
|
1174 |
-
0.1,
|
1175 |
-
1.0,
|
1176 |
-
0.,
|
1177 |
-
1.,
|
1178 |
-
False,
|
1179 |
"Classic",
|
1180 |
"Nearest",
|
1181 |
],
|
@@ -1192,7 +1246,7 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
1192 |
None,
|
1193 |
1.0,
|
1194 |
None,
|
1195 |
-
1.0,
|
1196 |
None,
|
1197 |
1.0,
|
1198 |
None,
|
@@ -1201,24 +1255,24 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
1201 |
1024,
|
1202 |
1024,
|
1203 |
"misri/epicrealismXL_v7FinalDestination",
|
1204 |
-
None,
|
1205 |
"canny ControlNet",
|
1206 |
-
"image.webp",
|
1207 |
-
"Canny",
|
1208 |
-
1024,
|
1209 |
-
1024,
|
1210 |
-
None,
|
1211 |
-
None,
|
1212 |
-
None,
|
1213 |
-
0.35,
|
1214 |
-
100,
|
1215 |
-
200,
|
1216 |
-
0.1,
|
1217 |
-
0.1,
|
1218 |
-
1.0,
|
1219 |
-
0.,
|
1220 |
-
1.,
|
1221 |
-
False,
|
1222 |
"Classic",
|
1223 |
None,
|
1224 |
],
|
@@ -1235,7 +1289,7 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
1235 |
None,
|
1236 |
1.0,
|
1237 |
None,
|
1238 |
-
1.0,
|
1239 |
None,
|
1240 |
1.0,
|
1241 |
None,
|
@@ -1244,24 +1298,24 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
1244 |
1024,
|
1245 |
1024,
|
1246 |
"misri/juggernautXL_juggernautX",
|
1247 |
-
None,
|
1248 |
"optical pattern ControlNet",
|
1249 |
-
"spiral_no_transparent.png",
|
1250 |
-
"Canny",
|
1251 |
-
512,
|
1252 |
-
1024,
|
1253 |
-
None,
|
1254 |
-
None,
|
1255 |
-
None,
|
1256 |
-
0.35,
|
1257 |
-
100,
|
1258 |
-
200,
|
1259 |
-
0.1,
|
1260 |
-
0.1,
|
1261 |
-
1.0,
|
1262 |
-
0.05,
|
1263 |
-
0.75,
|
1264 |
-
False,
|
1265 |
"Classic",
|
1266 |
None,
|
1267 |
],
|
@@ -1278,7 +1332,7 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
1278 |
None,
|
1279 |
1.0,
|
1280 |
None,
|
1281 |
-
1.0,
|
1282 |
None,
|
1283 |
1.0,
|
1284 |
None,
|
@@ -1287,24 +1341,24 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
1287 |
1024,
|
1288 |
1024,
|
1289 |
"cagliostrolab/animagine-xl-3.1",
|
1290 |
-
None,
|
1291 |
"lineart ControlNet",
|
1292 |
-
"color_image.png",
|
1293 |
-
"Lineart",
|
1294 |
-
512,
|
1295 |
-
896,
|
1296 |
-
None,
|
1297 |
-
None,
|
1298 |
-
None,
|
1299 |
-
0.35,
|
1300 |
-
100,
|
1301 |
-
200,
|
1302 |
-
0.1,
|
1303 |
-
0.1,
|
1304 |
-
1.0,
|
1305 |
-
0.,
|
1306 |
-
1.,
|
1307 |
-
False,
|
1308 |
"Compel",
|
1309 |
None,
|
1310 |
],
|
@@ -1321,7 +1375,7 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
1321 |
None,
|
1322 |
1.0,
|
1323 |
None,
|
1324 |
-
1.0,
|
1325 |
None,
|
1326 |
1.0,
|
1327 |
None,
|
@@ -1330,24 +1384,24 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
1330 |
512,
|
1331 |
512,
|
1332 |
"digiplay/majicMIX_realistic_v7",
|
1333 |
-
None,
|
1334 |
"openpose ControlNet",
|
1335 |
-
"image.webp",
|
1336 |
-
"Canny",
|
1337 |
-
512,
|
1338 |
-
1024,
|
1339 |
-
None,
|
1340 |
-
None,
|
1341 |
-
None,
|
1342 |
-
0.35,
|
1343 |
-
100,
|
1344 |
-
200,
|
1345 |
-
0.1,
|
1346 |
-
0.1,
|
1347 |
-
1.0,
|
1348 |
-
0.,
|
1349 |
-
0.9,
|
1350 |
-
False,
|
1351 |
"Compel",
|
1352 |
"Nearest",
|
1353 |
],
|
@@ -1402,7 +1456,7 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
1402 |
|
1403 |
with gr.Tab("Inpaint mask maker", render=True):
|
1404 |
|
1405 |
-
def create_mask_now(img, invert):
|
1406 |
import numpy as np
|
1407 |
import time
|
1408 |
|
@@ -1429,6 +1483,7 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
1429 |
|
1430 |
return img["background"], rgb_mask
|
1431 |
|
|
|
1432 |
with gr.Row():
|
1433 |
with gr.Column(scale=2):
|
1434 |
# image_base = gr.ImageEditor(label="Base image", show_label=True, brush=gr.Brush(colors=["#000000"]))
|
@@ -1438,15 +1493,15 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
1438 |
# enable crop (or disable it)
|
1439 |
# transforms=["crop"],
|
1440 |
brush=gr.Brush(
|
1441 |
-
|
1442 |
-
|
1443 |
-
|
1444 |
-
|
1445 |
-
|
1446 |
-
|
1447 |
-
|
1448 |
-
|
1449 |
-
|
1450 |
),
|
1451 |
eraser=gr.Eraser(default_size="16")
|
1452 |
)
|
@@ -1459,10 +1514,13 @@ with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
|
1459 |
|
1460 |
btn.click(create_mask_now, [image_base, invert_mask], [img_source, img_result])
|
1461 |
|
|
|
1462 |
def send_img(img_source, img_result):
|
1463 |
return img_source, img_result
|
|
|
|
|
1464 |
btn_send.click(send_img, [img_source, img_result], [image_control, image_mask_gui])
|
1465 |
-
|
1466 |
generate_button.click(
|
1467 |
fn=sd_gen.load_new_model,
|
1468 |
inputs=[
|
|
|
25 |
import urllib.parse
|
26 |
|
27 |
preprocessor_controlnet = {
|
28 |
+
"openpose": [
|
29 |
+
"Openpose",
|
30 |
+
"None",
|
31 |
+
],
|
32 |
+
"scribble": [
|
33 |
+
"HED",
|
34 |
+
"Pidinet",
|
35 |
+
"None",
|
36 |
+
],
|
37 |
+
"softedge": [
|
38 |
+
"Pidinet",
|
39 |
+
"HED",
|
40 |
+
"HED safe",
|
41 |
+
"Pidinet safe",
|
42 |
+
"None",
|
43 |
+
],
|
44 |
+
"segmentation": [
|
45 |
+
"UPerNet",
|
46 |
+
"None",
|
47 |
+
],
|
48 |
+
"depth": [
|
49 |
+
"DPT",
|
50 |
+
"Midas",
|
51 |
+
"None",
|
52 |
+
],
|
53 |
+
"normalbae": [
|
54 |
+
"NormalBae",
|
55 |
+
"None",
|
56 |
+
],
|
57 |
+
"lineart": [
|
58 |
+
"Lineart",
|
59 |
+
"Lineart coarse",
|
60 |
+
"Lineart (anime)",
|
61 |
+
"None",
|
62 |
+
"None (anime)",
|
63 |
+
],
|
64 |
+
"shuffle": [
|
65 |
+
"ContentShuffle",
|
66 |
+
"None",
|
67 |
+
],
|
68 |
+
"canny": [
|
69 |
+
"Canny"
|
70 |
+
],
|
71 |
+
"mlsd": [
|
72 |
+
"MLSD"
|
73 |
+
],
|
74 |
+
"ip2p": [
|
75 |
+
"ip2p"
|
76 |
+
]
|
77 |
}
|
78 |
|
79 |
task_stablepy = {
|
|
|
106 |
|
107 |
def download_things(directory, url, hf_token="", civitai_api_key=""):
|
108 |
url = url.strip()
|
109 |
+
|
110 |
if "drive.google.com" in url:
|
111 |
original_dir = os.getcwd()
|
112 |
os.chdir(directory)
|
|
|
119 |
url = url.replace("/blob/", "/resolve/")
|
120 |
user_header = f'"Authorization: Bearer {hf_token}"'
|
121 |
if hf_token:
|
122 |
+
os.system(
|
123 |
+
f"aria2c --console-log-level=error --summary-interval=10 --header={user_header} -c -x 16 -k 1M -s 16 {url} -d {directory} -o {url.split('/')[-1]}")
|
124 |
else:
|
125 |
+
os.system(
|
126 |
+
f"aria2c --optimize-concurrent-downloads --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 {url} -d {directory} -o {url.split('/')[-1]}")
|
127 |
elif "civitai.com" in url:
|
128 |
if "?" in url:
|
129 |
url = url.split("?")[0]
|
130 |
if civitai_api_key:
|
131 |
url = url + f"?token={civitai_api_key}"
|
132 |
+
os.system(
|
133 |
+
f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
|
134 |
else:
|
135 |
print("\033[91mYou need an API key to download Civitai models.\033[0m")
|
136 |
else:
|
|
|
139 |
|
140 |
def get_model_list(directory_path):
|
141 |
model_list = []
|
142 |
+
valid_extensions = {'.ckpt', '.pt', '.pth', '.safetensors', '.bin'}
|
143 |
|
144 |
for filename in os.listdir(directory_path):
|
145 |
if os.path.splitext(filename)[1] in valid_extensions:
|
|
|
231 |
'https://huggingface.co/datasets/Nerfgun3/bad_prompt/blob/main/bad_prompt_version2.pt',
|
232 |
'https://huggingface.co/embed/negative/resolve/main/EasyNegativeV2.safetensors',
|
233 |
'https://huggingface.co/embed/negative/resolve/main/bad-hands-5.pt',
|
234 |
+
]
|
235 |
|
236 |
for url_embed in download_embeds:
|
237 |
if not os.path.exists(f"./embedings/{url_embed.split('/')[-1]}"):
|
|
|
246 |
vae_model_list = get_model_list(directory_vaes)
|
247 |
vae_model_list.insert(0, "None")
|
248 |
|
249 |
+
|
250 |
def get_my_lora(link_url):
|
251 |
for url in [url.strip() for url in link_url.split(',')]:
|
252 |
if not os.path.exists(f"./loras/{url.split('/')[-1]}"):
|
253 |
download_things(directory_loras, url, hf_token, CIVITAI_API_KEY)
|
254 |
new_lora_model_list = get_model_list(directory_loras)
|
255 |
new_lora_model_list.insert(0, "None")
|
256 |
+
|
257 |
return gr.update(
|
258 |
choices=new_lora_model_list
|
259 |
), gr.update(
|
|
|
266 |
choices=new_lora_model_list
|
267 |
),
|
268 |
|
269 |
+
|
270 |
print('\033[33m🏁 Download and listing of valid models completed.\033[0m')
|
271 |
|
272 |
upscaler_dict_gui = {
|
273 |
+
None: None,
|
274 |
+
"Lanczos": "Lanczos",
|
275 |
+
"Nearest": "Nearest",
|
276 |
+
"RealESRGAN_x4plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
|
277 |
+
"RealESRNet_x4plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth",
|
278 |
"RealESRGAN_x4plus_anime_6B": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
|
279 |
"RealESRGAN_x2plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
|
280 |
"realesr-animevideov3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
|
281 |
"realesr-general-x4v3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
|
282 |
+
"realesr-general-wdn-x4v3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
|
283 |
+
"4x-UltraSharp": "https://huggingface.co/Shandypur/ESRGAN-4x-UltraSharp/resolve/main/4x-UltraSharp.pth",
|
284 |
+
"4x_foolhardy_Remacri": "https://huggingface.co/FacehugmanIII/4x_foolhardy_Remacri/resolve/main/4x_foolhardy_Remacri.pth",
|
285 |
+
"Remacri4xExtraSmoother": "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/Remacri%204x%20ExtraSmoother.pth",
|
286 |
+
"AnimeSharp4x": "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/AnimeSharp%204x.pth",
|
287 |
+
"lollypop": "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/lollypop.pth",
|
288 |
+
"RealisticRescaler4x": "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/RealisticRescaler%204x.pth",
|
289 |
+
"NickelbackFS4x": "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/NickelbackFS%204x.pth"
|
290 |
}
|
291 |
|
292 |
|
|
|
340 |
import time, json
|
341 |
from IPython.utils import capture
|
342 |
import logging
|
343 |
+
|
344 |
logging.getLogger("diffusers").setLevel(logging.ERROR)
|
345 |
import diffusers
|
346 |
+
|
347 |
diffusers.utils.logging.set_verbosity(40)
|
348 |
import warnings
|
349 |
+
|
350 |
warnings.filterwarnings(action="ignore", category=FutureWarning, module="diffusers")
|
351 |
warnings.filterwarnings(action="ignore", category=UserWarning, module="diffusers")
|
352 |
warnings.filterwarnings(action="ignore", category=FutureWarning, module="transformers")
|
353 |
from stablepy import logger
|
354 |
+
|
355 |
logger.setLevel(logging.DEBUG)
|
356 |
|
357 |
+
|
358 |
def info_html(json_data, title, subtitle):
|
359 |
return f"""
|
360 |
<div style='padding: 0; border-radius: 10px;'>
|
|
|
366 |
</div>
|
367 |
"""
|
368 |
|
369 |
+
|
370 |
class GuiSD:
|
371 |
def __init__(self, stream=True):
|
372 |
self.model = None
|
373 |
+
|
374 |
print("Loading model...")
|
375 |
self.model = Model_Diffusers(
|
376 |
base_model_id="cagliostrolab/animagine-xl-3.1",
|
|
|
383 |
def load_new_model(self, model_name, vae_model, task, progress=gr.Progress(track_tqdm=True)):
|
384 |
|
385 |
yield f"Loading model: {model_name}"
|
386 |
+
|
387 |
vae_model = vae_model if vae_model != "None" else None
|
388 |
|
389 |
if model_name in model_list:
|
|
|
395 |
if incompatible_vae:
|
396 |
vae_model = None
|
397 |
|
|
|
398 |
self.model.load_pipe(
|
399 |
model_name,
|
400 |
task_name=task_stablepy[task],
|
|
|
403 |
retain_task_model_in_cache=False,
|
404 |
)
|
405 |
yield f"Model loaded: {model_name}"
|
406 |
+
|
407 |
@spaces.GPU
|
408 |
def generate_pipeline(
|
409 |
+
self,
|
410 |
+
prompt,
|
411 |
+
neg_prompt,
|
412 |
+
num_images,
|
413 |
+
steps,
|
414 |
+
cfg,
|
415 |
+
clip_skip,
|
416 |
+
seed,
|
417 |
+
lora1,
|
418 |
+
lora_scale1,
|
419 |
+
lora2,
|
420 |
+
lora_scale2,
|
421 |
+
lora3,
|
422 |
+
lora_scale3,
|
423 |
+
lora4,
|
424 |
+
lora_scale4,
|
425 |
+
lora5,
|
426 |
+
lora_scale5,
|
427 |
+
sampler,
|
428 |
+
img_height,
|
429 |
+
img_width,
|
430 |
+
model_name,
|
431 |
+
vae_model,
|
432 |
+
task,
|
433 |
+
image_control,
|
434 |
+
preprocessor_name,
|
435 |
+
preprocess_resolution,
|
436 |
+
image_resolution,
|
437 |
+
style_prompt, # list []
|
438 |
+
style_json_file,
|
439 |
+
image_mask,
|
440 |
+
strength,
|
441 |
+
low_threshold,
|
442 |
+
high_threshold,
|
443 |
+
value_threshold,
|
444 |
+
distance_threshold,
|
445 |
+
controlnet_output_scaling_in_unet,
|
446 |
+
controlnet_start_threshold,
|
447 |
+
controlnet_stop_threshold,
|
448 |
+
textual_inversion,
|
449 |
+
syntax_weights,
|
450 |
+
upscaler_model_path,
|
451 |
+
upscaler_increases_size,
|
452 |
+
esrgan_tile,
|
453 |
+
esrgan_tile_overlap,
|
454 |
+
hires_steps,
|
455 |
+
hires_denoising_strength,
|
456 |
+
hires_sampler,
|
457 |
+
hires_prompt,
|
458 |
+
hires_negative_prompt,
|
459 |
+
hires_before_adetailer,
|
460 |
+
hires_after_adetailer,
|
461 |
+
loop_generation,
|
462 |
+
leave_progress_bar,
|
463 |
+
disable_progress_bar,
|
464 |
+
image_previews,
|
465 |
+
display_images,
|
466 |
+
save_generated_images,
|
467 |
+
image_storage_location,
|
468 |
+
retain_compel_previous_load,
|
469 |
+
retain_detailfix_model_previous_load,
|
470 |
+
retain_hires_model_previous_load,
|
471 |
+
t2i_adapter_preprocessor,
|
472 |
+
t2i_adapter_conditioning_scale,
|
473 |
+
t2i_adapter_conditioning_factor,
|
474 |
+
xformers_memory_efficient_attention,
|
475 |
+
freeu,
|
476 |
+
generator_in_cpu,
|
477 |
+
adetailer_inpaint_only,
|
478 |
+
adetailer_verbose,
|
479 |
+
adetailer_sampler,
|
480 |
+
adetailer_active_a,
|
481 |
+
prompt_ad_a,
|
482 |
+
negative_prompt_ad_a,
|
483 |
+
strength_ad_a,
|
484 |
+
face_detector_ad_a,
|
485 |
+
person_detector_ad_a,
|
486 |
+
hand_detector_ad_a,
|
487 |
+
mask_dilation_a,
|
488 |
+
mask_blur_a,
|
489 |
+
mask_padding_a,
|
490 |
+
adetailer_active_b,
|
491 |
+
prompt_ad_b,
|
492 |
+
negative_prompt_ad_b,
|
493 |
+
strength_ad_b,
|
494 |
+
face_detector_ad_b,
|
495 |
+
person_detector_ad_b,
|
496 |
+
hand_detector_ad_b,
|
497 |
+
mask_dilation_b,
|
498 |
+
mask_blur_b,
|
499 |
+
mask_padding_b,
|
500 |
+
retain_task_cache_gui,
|
501 |
+
image_ip1,
|
502 |
+
mask_ip1,
|
503 |
+
model_ip1,
|
504 |
+
mode_ip1,
|
505 |
+
scale_ip1,
|
506 |
+
image_ip2,
|
507 |
+
mask_ip2,
|
508 |
+
model_ip2,
|
509 |
+
mode_ip2,
|
510 |
+
scale_ip2,
|
511 |
):
|
512 |
+
|
513 |
vae_model = vae_model if vae_model != "None" else None
|
514 |
loras_list = [lora1, lora2, lora3, lora4, lora5]
|
515 |
vae_msg = f"VAE: {vae_model}" if vae_model else ""
|
516 |
msg_lora = []
|
517 |
|
|
|
518 |
if model_name in model_list:
|
519 |
model_is_xl = "xl" in model_name.lower()
|
520 |
sdxl_in_vae = vae_model and "sdxl" in vae_model.lower()
|
|
|
523 |
|
524 |
if incompatible_vae:
|
525 |
msg_inc_vae = (
|
526 |
+
f"The selected VAE is for a {'SD 1.5' if model_is_xl else 'SDXL'} model, but you"
|
527 |
+
f" are using a {model_type} model. The default VAE "
|
528 |
"will be used."
|
529 |
)
|
530 |
gr.Info(msg_inc_vae)
|
|
|
536 |
print(la)
|
537 |
lora_type = ("animetarot" in la.lower() or "Hyper-SD15-8steps".lower() in la.lower())
|
538 |
if (model_is_xl and lora_type) or (not model_is_xl and not lora_type):
|
539 |
+
msg_inc_lora = f"The LoRA {la} is for {'SD 1.5' if model_is_xl else 'SDXL'}, but you are using {model_type}."
|
540 |
gr.Info(msg_inc_lora)
|
541 |
msg_lora.append(msg_inc_lora)
|
542 |
|
|
|
577 |
)
|
578 |
|
579 |
if task != "txt2img" and not image_control:
|
580 |
+
raise ValueError(
|
581 |
+
"No control image found: To use this function, "
|
582 |
+
"you have to upload an image in 'Image ControlNet/Inpaint/Img2img'"
|
583 |
+
)
|
584 |
|
585 |
if task == "inpaint" and not image_mask:
|
586 |
raise ValueError("No mask image found: Specify one in 'Image Mask'")
|
|
|
614 |
print("No Textual inversion for SDXL")
|
615 |
|
616 |
adetailer_params_A = {
|
617 |
+
"face_detector_ad": face_detector_ad_a,
|
618 |
+
"person_detector_ad": person_detector_ad_a,
|
619 |
+
"hand_detector_ad": hand_detector_ad_a,
|
620 |
"prompt": prompt_ad_a,
|
621 |
+
"negative_prompt": negative_prompt_ad_a,
|
622 |
+
"strength": strength_ad_a,
|
623 |
# "image_list_task" : None,
|
624 |
+
"mask_dilation": mask_dilation_a,
|
625 |
+
"mask_blur": mask_blur_a,
|
626 |
+
"mask_padding": mask_padding_a,
|
627 |
+
"inpaint_only": adetailer_inpaint_only,
|
628 |
+
"sampler": adetailer_sampler,
|
629 |
}
|
630 |
|
631 |
adetailer_params_B = {
|
632 |
+
"face_detector_ad": face_detector_ad_b,
|
633 |
+
"person_detector_ad": person_detector_ad_b,
|
634 |
+
"hand_detector_ad": hand_detector_ad_b,
|
635 |
"prompt": prompt_ad_b,
|
636 |
+
"negative_prompt": negative_prompt_ad_b,
|
637 |
+
"strength": strength_ad_b,
|
638 |
# "image_list_task" : None,
|
639 |
+
"mask_dilation": mask_dilation_b,
|
640 |
+
"mask_blur": mask_blur_b,
|
641 |
+
"mask_padding": mask_padding_b,
|
642 |
}
|
643 |
pipe_params = {
|
644 |
"prompt": prompt,
|
|
|
721 |
|
722 |
random_number = random.randint(1, 100)
|
723 |
if random_number < 25 and num_images < 3:
|
724 |
+
if not upscaler_model and steps < 45 and task in ["txt2img",
|
725 |
+
"img2img"] and not adetailer_active_a and not adetailer_active_b:
|
726 |
+
num_images *= 2
|
727 |
pipe_params["num_images"] = num_images
|
728 |
gr.Info("Num images x 2 🎉")
|
729 |
|
|
|
744 |
|
745 |
sd_gen = GuiSD()
|
746 |
|
747 |
+
CSS = """
|
748 |
.contain { display: flex; flex-direction: column; }
|
749 |
#component-0 { height: 100%; }
|
750 |
#gallery { flex-grow: 1; }
|
751 |
"""
|
752 |
+
sdxl_task = [k for k, v in task_stablepy.items() if v in SDXL_TASKS]
|
753 |
+
sd_task = [k for k, v in task_stablepy.items() if v in SD15_TASKS]
|
754 |
+
|
755 |
+
|
756 |
def update_task_options(model_name, task_name):
|
757 |
if model_name in model_list:
|
758 |
if "xl" in model_name.lower():
|
|
|
769 |
|
770 |
|
771 |
with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
|
772 |
+
gr.Markdown("# 🧩 (Ivan) DiffuseCraft")
|
773 |
gr.Markdown(
|
774 |
f"""
|
775 |
### This demo uses [diffusers](https://github.com/huggingface/diffusers) to perform different tasks in image generation.
|
|
|
781 |
with gr.Column(scale=2):
|
782 |
|
783 |
task_gui = gr.Dropdown(label="Task", choices=sdxl_task, value=task_model_list[0])
|
784 |
+
model_name_gui = gr.Dropdown(label="Model", choices=model_list, value=model_list[0],
|
785 |
+
allow_custom_value=True)
|
786 |
prompt_gui = gr.Textbox(lines=5, placeholder="Enter prompt", label="Prompt")
|
787 |
neg_prompt_gui = gr.Textbox(lines=3, placeholder="Enter Neg prompt", label="Negative prompt")
|
788 |
with gr.Row(equal_height=False):
|
|
|
790 |
clear_prompt_gui = gr.Button(value="🗑️")
|
791 |
set_random_seed = gr.Button(value="🎲")
|
792 |
generate_button = gr.Button(value="GENERATE", variant="primary")
|
793 |
+
|
794 |
model_name_gui.change(
|
795 |
update_task_options,
|
796 |
[model_name_gui, task_gui],
|
|
|
798 |
)
|
799 |
|
800 |
load_model_gui = gr.HTML()
|
801 |
+
|
802 |
result_images = gr.Gallery(
|
803 |
label="Generated images",
|
804 |
show_label=False,
|
|
|
813 |
)
|
814 |
|
815 |
actual_task_info = gr.HTML()
|
816 |
+
|
817 |
with gr.Column(scale=1):
|
818 |
steps_gui = gr.Slider(minimum=1, maximum=100, step=1, value=30, label="Steps")
|
819 |
cfg_gui = gr.Slider(minimum=0, maximum=30, step=0.5, value=7.5, label="CFG")
|
|
|
826 |
free_u_gui = gr.Checkbox(value=True, label="FreeU")
|
827 |
|
828 |
with gr.Row(equal_height=False):
|
|
|
|
|
829 |
|
830 |
def run_set_params_gui(base_prompt):
|
831 |
valid_receptors = { # default values
|
|
|
836 |
"height": gr.update(value=1024),
|
837 |
"Seed": gr.update(value=-1),
|
838 |
"Sampler": gr.update(value="Euler a"),
|
839 |
+
"scale": gr.update(value=7.5), # cfg
|
840 |
"skip": gr.update(value=True),
|
841 |
}
|
842 |
valid_keys = list(valid_receptors.keys())
|
|
|
846 |
# print(val)
|
847 |
if key in valid_keys:
|
848 |
if key == "Sampler":
|
849 |
+
if val not in scheduler_names:
|
850 |
+
continue
|
851 |
elif key == "skip":
|
852 |
+
if int(val) >= 2:
|
853 |
+
val = True
|
854 |
if key == "prompt":
|
855 |
+
if ">" in val and "<" in val:
|
856 |
+
val = re.sub(r'<[^>]+>', '', val)
|
857 |
+
print("Removed LoRA written in the prompt")
|
858 |
if key in ["prompt", "neg_prompt"]:
|
859 |
val = val.strip()
|
860 |
if key in ["Steps", "width", "height", "Seed"]:
|
|
|
868 |
# print(valid_receptors)
|
869 |
return [value for value in valid_receptors.values()]
|
870 |
|
871 |
+
|
872 |
set_params_gui.click(
|
873 |
+
run_set_params_gui, [prompt_gui], [
|
874 |
prompt_gui,
|
875 |
neg_prompt_gui,
|
876 |
steps_gui,
|
|
|
882 |
clip_skip_gui,
|
883 |
],
|
884 |
)
|
885 |
+
|
886 |
+
|
887 |
def run_clear_prompt_gui():
|
888 |
return gr.update(value=""), gr.update(value="")
|
889 |
+
|
890 |
+
|
891 |
clear_prompt_gui.click(
|
892 |
run_clear_prompt_gui, [], [prompt_gui, neg_prompt_gui]
|
893 |
)
|
894 |
|
895 |
+
|
896 |
def run_set_random_seed():
|
897 |
return -1
|
898 |
+
|
899 |
+
|
900 |
set_random_seed.click(
|
901 |
run_set_random_seed, [], seed_gui
|
902 |
)
|
|
|
910 |
("Classic-ignore", "Classic-ignore"),
|
911 |
("None", "None"),
|
912 |
]
|
913 |
+
prompt_syntax_gui = gr.Dropdown(label="Prompt Syntax", choices=prompt_s_options,
|
914 |
+
value=prompt_s_options[0][1])
|
915 |
vae_model_gui = gr.Dropdown(label="VAE Model", choices=vae_model_list)
|
916 |
|
917 |
with gr.Accordion("Hires fix", open=False, visible=True):
|
918 |
|
919 |
upscaler_keys = list(upscaler_dict_gui.keys())
|
920 |
|
921 |
+
upscaler_model_path_gui = gr.Dropdown(label="Upscaler", choices=upscaler_keys,
|
922 |
+
value=upscaler_keys[0])
|
923 |
+
upscaler_increases_size_gui = gr.Slider(minimum=1.1, maximum=6., step=0.1, value=1.4,
|
924 |
+
label="Upscale by")
|
925 |
esrgan_tile_gui = gr.Slider(minimum=0, value=100, maximum=500, step=1, label="ESRGAN Tile")
|
926 |
+
esrgan_tile_overlap_gui = gr.Slider(minimum=1, maximum=200, step=1, value=10,
|
927 |
+
label="ESRGAN Tile Overlap")
|
928 |
hires_steps_gui = gr.Slider(minimum=0, value=30, maximum=100, step=1, label="Hires Steps")
|
929 |
+
hires_denoising_strength_gui = gr.Slider(minimum=0.1, maximum=1.0, step=0.01, value=0.55,
|
930 |
+
label="Hires Denoising Strength")
|
931 |
+
hires_sampler_gui = gr.Dropdown(label="Hires Sampler",
|
932 |
+
choices=["Use same sampler"] + scheduler_names[:-1],
|
933 |
+
value="Use same sampler")
|
934 |
hires_prompt_gui = gr.Textbox(label="Hires Prompt", placeholder="Main prompt will be use", lines=3)
|
935 |
+
hires_negative_prompt_gui = gr.Textbox(label="Hires Negative Prompt",
|
936 |
+
placeholder="Main negative prompt will be use", lines=3)
|
937 |
|
938 |
with gr.Accordion("LoRA", open=False, visible=True):
|
939 |
lora1_gui = gr.Dropdown(label="Lora1", choices=lora_model_list)
|
|
|
956 |
[lora1_gui, lora2_gui, lora3_gui, lora4_gui, lora5_gui]
|
957 |
)
|
958 |
|
959 |
+
with gr.Accordion("IP-Adapter", open=False, visible=True): ##############
|
960 |
|
961 |
IP_MODELS = sorted(list(set(IP_ADAPTERS_SD + IP_ADAPTERS_SDXL)))
|
962 |
MODE_IP_OPTIONS = ["original", "style", "layout", "style+layout"]
|
|
|
981 |
minimum=0.01, maximum=1.0, step=0.01, value=0.55, label="Strength",
|
982 |
info="This option adjusts the level of changes for img2img and inpainting."
|
983 |
)
|
984 |
+
image_resolution_gui = gr.Slider(minimum=64, maximum=2048, step=64, value=1024,
|
985 |
+
label="Image Resolution")
|
986 |
+
preprocessor_name_gui = gr.Dropdown(label="Preprocessor Name",
|
987 |
+
choices=preprocessor_controlnet["canny"])
|
988 |
+
|
989 |
|
990 |
def change_preprocessor_choices(task):
|
991 |
task = task_stablepy[task]
|
|
|
995 |
choices_task = preprocessor_controlnet["canny"]
|
996 |
return gr.update(choices=choices_task, value=choices_task[0])
|
997 |
|
998 |
+
|
999 |
task_gui.change(
|
1000 |
change_preprocessor_choices,
|
1001 |
[task_gui],
|
1002 |
[preprocessor_name_gui],
|
1003 |
)
|
1004 |
+
preprocess_resolution_gui = gr.Slider(minimum=64, maximum=2048, step=64, value=512,
|
1005 |
+
label="Preprocess Resolution")
|
1006 |
+
low_threshold_gui = gr.Slider(minimum=1, maximum=255, step=1, value=100,
|
1007 |
+
label="Canny low threshold")
|
1008 |
+
high_threshold_gui = gr.Slider(minimum=1, maximum=255, step=1, value=200,
|
1009 |
+
label="Canny high threshold")
|
1010 |
+
value_threshold_gui = gr.Slider(minimum=1, maximum=2.0, step=0.01, value=0.1,
|
1011 |
+
label="Hough value threshold (MLSD)")
|
1012 |
+
distance_threshold_gui = gr.Slider(minimum=1, maximum=20.0, step=0.01, value=0.1,
|
1013 |
+
label="Hough distance threshold (MLSD)")
|
1014 |
+
control_net_output_scaling_gui = gr.Slider(minimum=0, maximum=5.0, step=0.1, value=1,
|
1015 |
+
label="ControlNet Output Scaling in UNet")
|
1016 |
+
control_net_start_threshold_gui = gr.Slider(minimum=0, maximum=1, step=0.01, value=0,
|
1017 |
+
label="ControlNet Start Threshold (%)")
|
1018 |
+
control_net_stop_threshold_gui = gr.Slider(minimum=0, maximum=1, step=0.01, value=1,
|
1019 |
+
label="ControlNet Stop Threshold (%)")
|
1020 |
|
1021 |
with gr.Accordion("T2I adapter", open=False, visible=True):
|
1022 |
t2i_adapter_preprocessor_gui = gr.Checkbox(value=True, label="T2i Adapter Preprocessor")
|
1023 |
+
adapter_conditioning_scale_gui = gr.Slider(minimum=0, maximum=5., step=0.1, value=1,
|
1024 |
+
label="Adapter Conditioning Scale")
|
1025 |
+
adapter_conditioning_factor_gui = gr.Slider(minimum=0, maximum=1., step=0.01, value=0.55,
|
1026 |
+
label="Adapter Conditioning Factor (%)")
|
1027 |
|
1028 |
with gr.Accordion("Styles", open=False, visible=True):
|
1029 |
|
|
|
1042 |
style_json_gui = gr.File(label="Style JSON File")
|
1043 |
style_button = gr.Button("Load styles")
|
1044 |
|
1045 |
+
|
1046 |
def load_json_style_file(json):
|
1047 |
if not sd_gen.model:
|
1048 |
gr.Info("First load the model")
|
|
|
1052 |
gr.Info(f"{len(sd_gen.model.STYLE_NAMES)} styles loaded")
|
1053 |
return gr.update(value=None, choices=sd_gen.model.STYLE_NAMES)
|
1054 |
|
1055 |
+
|
1056 |
+
style_button.click(load_json_style_file, [style_json_gui], [style_prompt_gui])
|
1057 |
|
1058 |
with gr.Accordion("Textual inversion", open=False, visible=False):
|
1059 |
active_textual_inversion_gui = gr.Checkbox(value=False, label="Active Textual Inversion in prompt")
|
|
|
1068 |
|
1069 |
# Adetailer Sampler
|
1070 |
adetailer_sampler_options = ["Use same sampler"] + scheduler_names[:-1]
|
1071 |
+
adetailer_sampler_gui = gr.Dropdown(label="Adetailer sampler:", choices=adetailer_sampler_options,
|
1072 |
+
value="Use same sampler")
|
1073 |
|
1074 |
with gr.Accordion("Detailfix A", open=False, visible=True):
|
1075 |
# Adetailer A
|
1076 |
adetailer_active_a_gui = gr.Checkbox(label="Enable Adetailer A", value=False)
|
1077 |
+
prompt_ad_a_gui = gr.Textbox(label="Main prompt", placeholder="Main prompt will be use",
|
1078 |
+
lines=3)
|
1079 |
+
negative_prompt_ad_a_gui = gr.Textbox(label="Negative prompt",
|
1080 |
+
placeholder="Main negative prompt will be use", lines=3)
|
1081 |
+
strength_ad_a_gui = gr.Number(label="Strength:", value=0.35, step=0.01, minimum=0.01,
|
1082 |
+
maximum=1.0)
|
1083 |
face_detector_ad_a_gui = gr.Checkbox(label="Face detector", value=True)
|
1084 |
person_detector_ad_a_gui = gr.Checkbox(label="Person detector", value=True)
|
1085 |
hand_detector_ad_a_gui = gr.Checkbox(label="Hand detector", value=False)
|
|
|
1090 |
with gr.Accordion("Detailfix B", open=False, visible=True):
|
1091 |
# Adetailer B
|
1092 |
adetailer_active_b_gui = gr.Checkbox(label="Enable Adetailer B", value=False)
|
1093 |
+
prompt_ad_b_gui = gr.Textbox(label="Main prompt", placeholder="Main prompt will be use",
|
1094 |
+
lines=3)
|
1095 |
+
negative_prompt_ad_b_gui = gr.Textbox(label="Negative prompt",
|
1096 |
+
placeholder="Main negative prompt will be use", lines=3)
|
1097 |
+
strength_ad_b_gui = gr.Number(label="Strength:", value=0.35, step=0.01, minimum=0.01,
|
1098 |
+
maximum=1.0)
|
1099 |
face_detector_ad_b_gui = gr.Checkbox(label="Face detector", value=True)
|
1100 |
person_detector_ad_b_gui = gr.Checkbox(label="Person detector", value=True)
|
1101 |
hand_detector_ad_b_gui = gr.Checkbox(label="Hand detector", value=False)
|
|
|
1118 |
save_generated_images_gui = gr.Checkbox(value=False, label="Save Generated Images")
|
1119 |
image_storage_location_gui = gr.Textbox(value="./images", label="Image Storage Location")
|
1120 |
retain_compel_previous_load_gui = gr.Checkbox(value=False, label="Retain Compel Previous Load")
|
1121 |
+
retain_detailfix_model_previous_load_gui = gr.Checkbox(value=False,
|
1122 |
+
label="Retain Detailfix Model Previous Load")
|
1123 |
+
retain_hires_model_previous_load_gui = gr.Checkbox(value=False,
|
1124 |
+
label="Retain Hires Model Previous Load")
|
1125 |
+
xformers_memory_efficient_attention_gui = gr.Checkbox(value=False,
|
1126 |
+
label="Xformers Memory Efficient Attention")
|
1127 |
|
1128 |
with gr.Accordion("Examples and help", open=False, visible=True):
|
1129 |
gr.Markdown(
|
|
|
1160 |
None,
|
1161 |
1.0,
|
1162 |
None,
|
1163 |
+
1.0,
|
1164 |
None,
|
1165 |
1.0,
|
1166 |
None,
|
|
|
1169 |
1152,
|
1170 |
896,
|
1171 |
"cagliostrolab/animagine-xl-3.1",
|
1172 |
+
None, # vae
|
1173 |
"txt2img",
|
1174 |
+
None, # img conttol
|
1175 |
+
"Canny", # preprocessor
|
1176 |
+
512, # preproc resolution
|
1177 |
+
1024, # img resolution
|
1178 |
+
None, # Style prompt
|
1179 |
+
None, # Style json
|
1180 |
+
None, # img Mask
|
1181 |
+
0.35, # strength
|
1182 |
+
100, # low th canny
|
1183 |
+
200, # high th canny
|
1184 |
+
0.1, # value mstd
|
1185 |
+
0.1, # distance mstd
|
1186 |
+
1.0, # cn scale
|
1187 |
+
0., # cn start
|
1188 |
+
1., # cn end
|
1189 |
+
False, # ti
|
1190 |
"Classic",
|
1191 |
"Nearest",
|
1192 |
],
|
|
|
1203 |
None,
|
1204 |
1.0,
|
1205 |
None,
|
1206 |
+
1.0,
|
1207 |
None,
|
1208 |
1.0,
|
1209 |
None,
|
|
|
1212 |
1024,
|
1213 |
1024,
|
1214 |
"kitty7779/ponyDiffusionV6XL",
|
1215 |
+
None, # vae
|
1216 |
"txt2img",
|
1217 |
+
None, # img conttol
|
1218 |
+
"Canny", # preprocessor
|
1219 |
+
512, # preproc resolution
|
1220 |
+
1024, # img resolution
|
1221 |
+
None, # Style prompt
|
1222 |
+
None, # Style json
|
1223 |
+
None, # img Mask
|
1224 |
+
0.35, # strength
|
1225 |
+
100, # low th canny
|
1226 |
+
200, # high th canny
|
1227 |
+
0.1, # value mstd
|
1228 |
+
0.1, # distance mstd
|
1229 |
+
1.0, # cn scale
|
1230 |
+
0., # cn start
|
1231 |
+
1., # cn end
|
1232 |
+
False, # ti
|
1233 |
"Classic",
|
1234 |
"Nearest",
|
1235 |
],
|
|
|
1246 |
None,
|
1247 |
1.0,
|
1248 |
None,
|
1249 |
+
1.0,
|
1250 |
None,
|
1251 |
1.0,
|
1252 |
None,
|
|
|
1255 |
1024,
|
1256 |
1024,
|
1257 |
"misri/epicrealismXL_v7FinalDestination",
|
1258 |
+
None, # vae
|
1259 |
"canny ControlNet",
|
1260 |
+
"image.webp", # img conttol
|
1261 |
+
"Canny", # preprocessor
|
1262 |
+
1024, # preproc resolution
|
1263 |
+
1024, # img resolution
|
1264 |
+
None, # Style prompt
|
1265 |
+
None, # Style json
|
1266 |
+
None, # img Mask
|
1267 |
+
0.35, # strength
|
1268 |
+
100, # low th canny
|
1269 |
+
200, # high th canny
|
1270 |
+
0.1, # value mstd
|
1271 |
+
0.1, # distance mstd
|
1272 |
+
1.0, # cn scale
|
1273 |
+
0., # cn start
|
1274 |
+
1., # cn end
|
1275 |
+
False, # ti
|
1276 |
"Classic",
|
1277 |
None,
|
1278 |
],
|
|
|
1289 |
None,
|
1290 |
1.0,
|
1291 |
None,
|
1292 |
+
1.0,
|
1293 |
None,
|
1294 |
1.0,
|
1295 |
None,
|
|
|
1298 |
1024,
|
1299 |
1024,
|
1300 |
"misri/juggernautXL_juggernautX",
|
1301 |
+
None, # vae
|
1302 |
"optical pattern ControlNet",
|
1303 |
+
"spiral_no_transparent.png", # img conttol
|
1304 |
+
"Canny", # preprocessor
|
1305 |
+
512, # preproc resolution
|
1306 |
+
1024, # img resolution
|
1307 |
+
None, # Style prompt
|
1308 |
+
None, # Style json
|
1309 |
+
None, # img Mask
|
1310 |
+
0.35, # strength
|
1311 |
+
100, # low th canny
|
1312 |
+
200, # high th canny
|
1313 |
+
0.1, # value mstd
|
1314 |
+
0.1, # distance mstd
|
1315 |
+
1.0, # cn scale
|
1316 |
+
0.05, # cn start
|
1317 |
+
0.75, # cn end
|
1318 |
+
False, # ti
|
1319 |
"Classic",
|
1320 |
None,
|
1321 |
],
|
|
|
1332 |
None,
|
1333 |
1.0,
|
1334 |
None,
|
1335 |
+
1.0,
|
1336 |
None,
|
1337 |
1.0,
|
1338 |
None,
|
|
|
1341 |
1024,
|
1342 |
1024,
|
1343 |
"cagliostrolab/animagine-xl-3.1",
|
1344 |
+
None, # vae
|
1345 |
"lineart ControlNet",
|
1346 |
+
"color_image.png", # img conttol
|
1347 |
+
"Lineart", # preprocessor
|
1348 |
+
512, # preproc resolution
|
1349 |
+
896, # img resolution
|
1350 |
+
None, # Style prompt
|
1351 |
+
None, # Style json
|
1352 |
+
None, # img Mask
|
1353 |
+
0.35, # strength
|
1354 |
+
100, # low th canny
|
1355 |
+
200, # high th canny
|
1356 |
+
0.1, # value mstd
|
1357 |
+
0.1, # distance mstd
|
1358 |
+
1.0, # cn scale
|
1359 |
+
0., # cn start
|
1360 |
+
1., # cn end
|
1361 |
+
False, # ti
|
1362 |
"Compel",
|
1363 |
None,
|
1364 |
],
|
|
|
1375 |
None,
|
1376 |
1.0,
|
1377 |
None,
|
1378 |
+
1.0,
|
1379 |
None,
|
1380 |
1.0,
|
1381 |
None,
|
|
|
1384 |
512,
|
1385 |
512,
|
1386 |
"digiplay/majicMIX_realistic_v7",
|
1387 |
+
None, # vae
|
1388 |
"openpose ControlNet",
|
1389 |
+
"image.webp", # img conttol
|
1390 |
+
"Canny", # preprocessor
|
1391 |
+
512, # preproc resolution
|
1392 |
+
1024, # img resolution
|
1393 |
+
None, # Style prompt
|
1394 |
+
None, # Style json
|
1395 |
+
None, # img Mask
|
1396 |
+
0.35, # strength
|
1397 |
+
100, # low th canny
|
1398 |
+
200, # high th canny
|
1399 |
+
0.1, # value mstd
|
1400 |
+
0.1, # distance mstd
|
1401 |
+
1.0, # cn scale
|
1402 |
+
0., # cn start
|
1403 |
+
0.9, # cn end
|
1404 |
+
False, # ti
|
1405 |
"Compel",
|
1406 |
"Nearest",
|
1407 |
],
|
|
|
1456 |
|
1457 |
with gr.Tab("Inpaint mask maker", render=True):
|
1458 |
|
1459 |
+
def create_mask_now(img, invert):
|
1460 |
import numpy as np
|
1461 |
import time
|
1462 |
|
|
|
1483 |
|
1484 |
return img["background"], rgb_mask
|
1485 |
|
1486 |
+
|
1487 |
with gr.Row():
|
1488 |
with gr.Column(scale=2):
|
1489 |
# image_base = gr.ImageEditor(label="Base image", show_label=True, brush=gr.Brush(colors=["#000000"]))
|
|
|
1493 |
# enable crop (or disable it)
|
1494 |
# transforms=["crop"],
|
1495 |
brush=gr.Brush(
|
1496 |
+
default_size="16", # or leave it as 'auto'
|
1497 |
+
color_mode="fixed", # 'fixed' hides the user swatches and colorpicker, 'defaults' shows it
|
1498 |
+
# default_color="black", # html names are supported
|
1499 |
+
colors=[
|
1500 |
+
"rgba(0, 0, 0, 1)", # rgb(a)
|
1501 |
+
"rgba(0, 0, 0, 0.1)",
|
1502 |
+
"rgba(255, 255, 255, 0.1)",
|
1503 |
+
# "hsl(360, 120, 120)" # in fact any valid colorstring
|
1504 |
+
]
|
1505 |
),
|
1506 |
eraser=gr.Eraser(default_size="16")
|
1507 |
)
|
|
|
1514 |
|
1515 |
btn.click(create_mask_now, [image_base, invert_mask], [img_source, img_result])
|
1516 |
|
1517 |
+
|
1518 |
def send_img(img_source, img_result):
|
1519 |
return img_source, img_result
|
1520 |
+
|
1521 |
+
|
1522 |
btn_send.click(send_img, [img_source, img_result], [image_control, image_mask_gui])
|
1523 |
+
|
1524 |
generate_button.click(
|
1525 |
fn=sd_gen.load_new_model,
|
1526 |
inputs=[
|