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votepurchase-multiple-model

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John6666 commited on Oct 27

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9f748cc

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1 Parent(s): d04e577

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Files changed (8) hide show

app.py +16 -8
constants.py +453 -0
dc.py +278 -334
env.py +15 -18
llmdolphin.py +218 -196
modutils.py +263 -50
requirements.txt +2 -2
utils.py +421 -0

app.py CHANGED Viewed

@@ -3,11 +3,11 @@ import gradio as gr
 import numpy as np
 # DiffuseCraft
-from dc import (infer, _infer, pass_result, get_diffusers_model_list, get_samplers,
     get_vaes, enable_model_recom_prompt, enable_diffusers_model_detail, extract_exif_data, esrgan_upscale, UPSCALER_KEYS,
     preset_quality, preset_styles, process_style_prompt, get_all_lora_tupled_list, update_loras, apply_lora_prompt,
-    download_my_lora, search_civitai_lora, update_civitai_selection, select_civitai_lora, search_civitai_lora_json)
-from modutils import get_t2i_model_info, get_civitai_tag, CIVITAI_SORT, CIVITAI_PERIOD, CIVITAI_BASEMODEL
 # Translator
 from llmdolphin import (dolphin_respond_auto, dolphin_parse_simple,
     get_llm_formats, get_dolphin_model_format, get_dolphin_models,
@@ -57,9 +57,15 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
                 run_translate_button = gr.Button("Run with LLM Enhance", variant="secondary", scale=3)
                 auto_trans = gr.Checkbox(label="Auto translate to English", value=False, scale=2)
-            result = gr.Image(label="Result", elem_id="result", format="png", show_label=False, interactive=False,
-                              show_download_button=True, show_share_button=False, container=True)
             with gr.Accordion("Advanced Settings", open=False):
                 with gr.Row():
                     negative_prompt = gr.Text(label="Negative prompt", lines=1, max_lines=6, placeholder="Enter a negative prompt",
@@ -215,7 +221,7 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
     ).success(
         fn=dolphin_respond_auto,
         inputs=[prompt, chatbot],
-        outputs=[chatbot],
         queue=True,
         show_progress="full",
         show_api=False,
@@ -238,6 +244,8 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
     ).success(lambda: None, None, chatbot, queue=False, show_api=False)\
     .success(pass_result, [result], [result], queue=False, show_api=False) # dummy fn for api
     gr.on(
         triggers=[lora1.change, lora1_wt.change, lora2.change, lora2_wt.change, lora3.change, lora3_wt.change,
                    lora4.change, lora4_wt.change, lora5.change, lora5_wt.change],
@@ -425,4 +433,4 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
     gr.DuplicateButton(value="Duplicate Space for private use (This demo does not work on CPU. Requires GPU Space)")
 demo.queue()
-demo.launch()

 import numpy as np
 # DiffuseCraft
+from dc import (infer, _infer, pass_result, get_diffusers_model_list, get_samplers, save_image_history,
     get_vaes, enable_model_recom_prompt, enable_diffusers_model_detail, extract_exif_data, esrgan_upscale, UPSCALER_KEYS,
     preset_quality, preset_styles, process_style_prompt, get_all_lora_tupled_list, update_loras, apply_lora_prompt,
+    download_my_lora, search_civitai_lora, update_civitai_selection, select_civitai_lora, search_civitai_lora_json,
+    get_t2i_model_info, get_civitai_tag, CIVITAI_SORT, CIVITAI_PERIOD, CIVITAI_BASEMODEL)
 # Translator
 from llmdolphin import (dolphin_respond_auto, dolphin_parse_simple,
     get_llm_formats, get_dolphin_model_format, get_dolphin_models,
                 run_translate_button = gr.Button("Run with LLM Enhance", variant="secondary", scale=3)
                 auto_trans = gr.Checkbox(label="Auto translate to English", value=False, scale=2)
+            result = gr.Image(label="Result", elem_id="result", format="png", type="filepath", show_label=False, interactive=False,
+                            show_download_button=True, show_share_button=False, container=True)
+            with gr.Accordion("History", open=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_files = gr.Files(interactive=False, visible=False)
+                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.Accordion("Advanced Settings", open=False):
                 with gr.Row():
                     negative_prompt = gr.Text(label="Negative prompt", lines=1, max_lines=6, placeholder="Enter a negative prompt",
     ).success(
         fn=dolphin_respond_auto,
         inputs=[prompt, chatbot],
+        outputs=[chatbot, result, prompt],
         queue=True,
         show_progress="full",
         show_api=False,
     ).success(lambda: None, None, chatbot, queue=False, show_api=False)\
     .success(pass_result, [result], [result], queue=False, show_api=False) # dummy fn for api
+    result.change(save_image_history, [result, history_gallery, history_files, model_name], [history_gallery, history_files], queue=False, show_api=False)
     gr.on(
         triggers=[lora1.change, lora1_wt.change, lora2.change, lora2_wt.change, lora3.change, lora3_wt.change,
                    lora4.change, lora4_wt.change, lora5.change, lora5_wt.change],
     gr.DuplicateButton(value="Duplicate Space for private use (This demo does not work on CPU. Requires GPU Space)")
 demo.queue()
+demo.launch(show_error=True, debug=True)

constants.py ADDED Viewed

	@@ -0,0 +1,453 @@

+import os
+from stablepy.diffusers_vanilla.constants import FLUX_CN_UNION_MODES
+from stablepy import (
+    scheduler_names,
+    SD15_TASKS,
+    SDXL_TASKS,
+)
+# - **Download Models**
+DOWNLOAD_MODEL = "https://civitai.com/api/download/models/574369, https://huggingface.co/TechnoByte/MilkyWonderland/resolve/main/milkyWonderland_v40.safetensors"
+# - **Download VAEs**
+DOWNLOAD_VAE = "https://huggingface.co/nubby/blessed-sdxl-vae-fp16-fix/resolve/main/sdxl_vae-fp16fix-c-1.1-b-0.5.safetensors?download=true, https://huggingface.co/nubby/blessed-sdxl-vae-fp16-fix/resolve/main/sdxl_vae-fp16fix-blessed.safetensors?download=true, https://huggingface.co/digiplay/VAE/resolve/main/vividReal_v20.safetensors?download=true, https://huggingface.co/fp16-guy/anything_kl-f8-anime2_vae-ft-mse-840000-ema-pruned_blessed_clearvae_fp16_cleaned/resolve/main/vae-ft-mse-840000-ema-pruned_fp16.safetensors?download=true"
+# - **Download LoRAs**
+DOWNLOAD_LORA = "https://huggingface.co/Leopain/color/resolve/main/Coloring_book_-_LineArt.safetensors, https://civitai.com/api/download/models/135867, https://huggingface.co/Linaqruf/anime-detailer-xl-lora/resolve/main/anime-detailer-xl.safetensors?download=true, https://huggingface.co/Linaqruf/style-enhancer-xl-lora/resolve/main/style-enhancer-xl.safetensors?download=true, https://huggingface.co/ByteDance/Hyper-SD/resolve/main/Hyper-SD15-8steps-CFG-lora.safetensors?download=true, https://huggingface.co/ByteDance/Hyper-SD/resolve/main/Hyper-SDXL-8steps-CFG-lora.safetensors?download=true"
+LOAD_DIFFUSERS_FORMAT_MODEL = [
+    'stabilityai/stable-diffusion-xl-base-1.0',
+    'black-forest-labs/FLUX.1-dev',
+    'John6666/blue-pencil-flux1-v021-fp8-flux',
+    'John6666/wai-ani-flux-v10forfp8-fp8-flux',
+    'John6666/xe-anime-flux-v04-fp8-flux',
+    'John6666/lyh-anime-flux-v2a1-fp8-flux',
+    'John6666/carnival-unchained-v10-fp8-flux',
+    'cagliostrolab/animagine-xl-3.1',
+    'John6666/epicrealism-xl-v8kiss-sdxl',
+    'misri/epicrealismXL_v7FinalDestination',
+    'misri/juggernautXL_juggernautX',
+    'misri/zavychromaxl_v80',
+    'SG161222/RealVisXL_V4.0',
+    'SG161222/RealVisXL_V5.0',
+    'misri/newrealityxlAllInOne_Newreality40',
+    'eienmojiki/Anything-XL',
+    'eienmojiki/Starry-XL-v5.2',
+    'gsdf/CounterfeitXL',
+    'KBlueLeaf/Kohaku-XL-Zeta',
+    'John6666/silvermoon-mix-01xl-v11-sdxl',
+    'WhiteAiZ/autismmixSDXL_autismmixConfetti_diffusers',
+    'kitty7779/ponyDiffusionV6XL',
+    'GraydientPlatformAPI/aniverse-pony',
+    'John6666/ras-real-anime-screencap-v1-sdxl',
+    'John6666/duchaiten-pony-xl-no-score-v60-sdxl',
+    'John6666/mistoon-anime-ponyalpha-sdxl',
+    'John6666/3x3x3mixxl-v2-sdxl',
+    'John6666/3x3x3mixxl-3dv01-sdxl',
+    'John6666/ebara-mfcg-pony-mix-v12-sdxl',
+    'John6666/t-ponynai3-v51-sdxl',
+    'John6666/t-ponynai3-v65-sdxl',
+    'John6666/prefect-pony-xl-v3-sdxl',
+    'John6666/mala-anime-mix-nsfw-pony-xl-v5-sdxl',
+    'John6666/wai-real-mix-v11-sdxl',
+    'John6666/wai-c-v6-sdxl',
+    'John6666/iniverse-mix-xl-sfwnsfw-pony-guofeng-v43-sdxl',
+    'John6666/photo-realistic-pony-v5-sdxl',
+    'John6666/pony-realism-v21main-sdxl',
+    'John6666/pony-realism-v22main-sdxl',
+    'John6666/cyberrealistic-pony-v63-sdxl',
+    'John6666/cyberrealistic-pony-v64-sdxl',
+    'John6666/cyberrealistic-pony-v65-sdxl',
+    'GraydientPlatformAPI/realcartoon-pony-diffusion',
+    'John6666/nova-anime-xl-pony-v5-sdxl',
+    'John6666/autismmix-sdxl-autismmix-pony-sdxl',
+    'John6666/aimz-dream-real-pony-mix-v3-sdxl',
+    'John6666/duchaiten-pony-real-v11fix-sdxl',
+    'John6666/duchaiten-pony-real-v20-sdxl',
+    'yodayo-ai/kivotos-xl-2.0',
+    'yodayo-ai/holodayo-xl-2.1',
+    'yodayo-ai/clandestine-xl-1.0',
+    'digiplay/majicMIX_sombre_v2',
+    'digiplay/majicMIX_realistic_v6',
+    'digiplay/majicMIX_realistic_v7',
+    'digiplay/DreamShaper_8',
+    'digiplay/BeautifulArt_v1',
+    'digiplay/DarkSushi2.5D_v1',
+    'digiplay/darkphoenix3D_v1.1',
+    'digiplay/BeenYouLiteL11_diffusers',
+    'Yntec/RevAnimatedV2Rebirth',
+    'youknownothing/cyberrealistic_v50',
+    'youknownothing/deliberate-v6',
+    'GraydientPlatformAPI/deliberate-cyber3',
+    'GraydientPlatformAPI/picx-real',
+    'GraydientPlatformAPI/perfectworld6',
+    'emilianJR/epiCRealism',
+    'votepurchase/counterfeitV30_v30',
+    'votepurchase/ChilloutMix',
+    'Meina/MeinaMix_V11',
+    'Meina/MeinaUnreal_V5',
+    'Meina/MeinaPastel_V7',
+    'GraydientPlatformAPI/realcartoon3d-17',
+    'GraydientPlatformAPI/realcartoon-pixar11',
+    'GraydientPlatformAPI/realcartoon-real17',
+]
+DIFFUSERS_FORMAT_LORAS = [
+    "nerijs/animation2k-flux",
+    "XLabs-AI/flux-RealismLora",
+]
+DOWNLOAD_EMBEDS = [
+    'https://huggingface.co/datasets/Nerfgun3/bad_prompt/blob/main/bad_prompt_version2.pt',
+    'https://huggingface.co/embed/negative/resolve/main/EasyNegativeV2.safetensors',
+    'https://huggingface.co/embed/negative/resolve/main/bad-hands-5.pt',
+    ]
+CIVITAI_API_KEY = os.environ.get("CIVITAI_API_KEY")
+HF_TOKEN = os.environ.get("HF_READ_TOKEN")
+DIRECTORY_MODELS = 'models'
+DIRECTORY_LORAS = 'loras'
+DIRECTORY_VAES = 'vaes'
+DIRECTORY_EMBEDS = 'embedings'
+PREPROCESSOR_CONTROLNET = {
+  "openpose": [
+    "Openpose",
+    "None",
+  ],
+  "scribble": [
+    "HED",
+    "PidiNet",
+    "None",
+  ],
+  "softedge": [
+    "PidiNet",
+    "HED",
+    "HED safe",
+    "PidiNet safe",
+    "None",
+  ],
+  "segmentation": [
+    "UPerNet",
+    "None",
+  ],
+  "depth": [
+    "DPT",
+    "Midas",
+    "None",
+  ],
+  "normalbae": [
+    "NormalBae",
+    "None",
+  ],
+  "lineart": [
+    "Lineart",
+    "Lineart coarse",
+    "Lineart (anime)",
+    "None",
+    "None (anime)",
+  ],
+  "lineart_anime": [
+    "Lineart",
+    "Lineart coarse",
+    "Lineart (anime)",
+    "None",
+    "None (anime)",
+  ],
+  "shuffle": [
+    "ContentShuffle",
+    "None",
+  ],
+  "canny": [
+    "Canny",
+    "None",
+  ],
+  "mlsd": [
+    "MLSD",
+    "None",
+  ],
+  "ip2p": [
+    "ip2p"
+  ],
+  "recolor": [
+    "Recolor luminance",
+    "Recolor intensity",
+    "None",
+  ],
+  "tile": [
+    "Mild Blur",
+    "Moderate Blur",
+    "Heavy Blur",
+    "None",
+  ],
+}
+TASK_STABLEPY = {
+    'txt2img': 'txt2img',
+    'img2img': 'img2img',
+    'inpaint': 'inpaint',
+    # 'canny T2I Adapter': 'sdxl_canny_t2i',  # NO HAVE STEP CALLBACK PARAMETERS SO NOT WORKS WITH DIFFUSERS 0.29.0
+    # 'sketch  T2I Adapter': 'sdxl_sketch_t2i',
+    # 'lineart  T2I Adapter': 'sdxl_lineart_t2i',
+    # 'depth-midas  T2I Adapter': 'sdxl_depth-midas_t2i',
+    # 'openpose  T2I Adapter': 'sdxl_openpose_t2i',
+    'openpose ControlNet': 'openpose',
+    'canny ControlNet': 'canny',
+    'mlsd ControlNet': 'mlsd',
+    'scribble ControlNet': 'scribble',
+    'softedge ControlNet': 'softedge',
+    'segmentation ControlNet': 'segmentation',
+    'depth ControlNet': 'depth',
+    'normalbae ControlNet': 'normalbae',
+    'lineart ControlNet': 'lineart',
+    'lineart_anime ControlNet': 'lineart_anime',
+    'shuffle ControlNet': 'shuffle',
+    'ip2p ControlNet': 'ip2p',
+    'optical pattern ControlNet': 'pattern',
+    'recolor ControlNet': 'recolor',
+    'tile ControlNet': 'tile',
+}
+TASK_MODEL_LIST = list(TASK_STABLEPY.keys())
+UPSCALER_DICT_GUI = {
+    None: None,
+    "Lanczos": "Lanczos",
+    "Nearest": "Nearest",
+    'Latent': 'Latent',
+    'Latent (antialiased)': 'Latent (antialiased)',
+    'Latent (bicubic)': 'Latent (bicubic)',
+    'Latent (bicubic antialiased)': 'Latent (bicubic antialiased)',
+    'Latent (nearest)': 'Latent (nearest)',
+    'Latent (nearest-exact)': 'Latent (nearest-exact)',
+    "RealESRGAN_x4plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
+    "RealESRNet_x4plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth",
+    "RealESRGAN_x4plus_anime_6B": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
+    "RealESRGAN_x2plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
+    "realesr-animevideov3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
+    "realesr-general-x4v3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
+    "realesr-general-wdn-x4v3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
+    "4x-UltraSharp": "https://huggingface.co/Shandypur/ESRGAN-4x-UltraSharp/resolve/main/4x-UltraSharp.pth",
+    "4x_foolhardy_Remacri": "https://huggingface.co/FacehugmanIII/4x_foolhardy_Remacri/resolve/main/4x_foolhardy_Remacri.pth",
+    "Remacri4xExtraSmoother": "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/Remacri%204x%20ExtraSmoother.pth",
+    "AnimeSharp4x": "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/AnimeSharp%204x.pth",
+    "lollypop": "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/lollypop.pth",
+    "RealisticRescaler4x": "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/RealisticRescaler%204x.pth",
+    "NickelbackFS4x": "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/NickelbackFS%204x.pth"
+}
+UPSCALER_KEYS = list(UPSCALER_DICT_GUI.keys())
+PROMPT_W_OPTIONS = [
+    ("Compel format: (word)weight", "Compel"),
+    ("Classic format: (word:weight)", "Classic"),
+    ("Classic-original format: (word:weight)", "Classic-original"),
+    ("Classic-no_norm format: (word:weight)", "Classic-no_norm"),
+    ("Classic-ignore", "Classic-ignore"),
+    ("None", "None"),
+]
+WARNING_MSG_VAE = (
+    "Use the right VAE for your model to maintain image quality. The wrong"
+    " VAE can lead to poor results, like blurriness in the generated images."
+)
+SDXL_TASK = [k for k, v in TASK_STABLEPY.items() if v in SDXL_TASKS]
+SD_TASK = [k for k, v in TASK_STABLEPY.items() if v in SD15_TASKS]
+FLUX_TASK = list(TASK_STABLEPY.keys())[:3] + [k for k, v in TASK_STABLEPY.items() if v in FLUX_CN_UNION_MODES.keys()]
+MODEL_TYPE_TASK = {
+    "SD 1.5": SD_TASK,
+    "SDXL": SDXL_TASK,
+    "FLUX": FLUX_TASK,
+}
+MODEL_TYPE_CLASS = {
+    "diffusers:StableDiffusionPipeline": "SD 1.5",
+    "diffusers:StableDiffusionXLPipeline": "SDXL",
+    "diffusers:FluxPipeline": "FLUX",
+}
+POST_PROCESSING_SAMPLER = ["Use same sampler"] + scheduler_names[:-2]
+SUBTITLE_GUI = (
+    "### This demo uses [diffusers](https://github.com/huggingface/diffusers)"
+    " to perform different tasks in image generation."
+)
+HELP_GUI = (
+    """### Help:
+    - The current space runs on a ZERO GPU which is assigned for approximately 60 seconds; Therefore, if you submit expensive tasks, the operation may be canceled upon reaching the maximum allowed time with 'GPU TASK ABORTED'.
+    - Distorted or strange images often result from high prompt weights, so it's best to use low weights and scales, and consider using Classic variants like 'Classic-original'.
+    - For better results with Pony Diffusion, try using sampler DPM++ 1s or DPM2 with Compel or Classic prompt weights.
+    """
+)
+EXAMPLES_GUI_HELP = (
+    """### The following examples perform specific tasks:
+    1. Generation with SDXL and upscale
+    2. Generation with FLUX dev
+    3. ControlNet Canny SDXL
+    4. Optical pattern (Optical illusion) SDXL
+    5. Convert an image to a coloring drawing
+    6. ControlNet OpenPose SD 1.5 and Latent upscale
+    - Different tasks can be performed, such as img2img or using the IP adapter, to preserve a person's appearance or a specific style based on an image.
+    """
+)
+EXAMPLES_GUI = [
+    [
+        "1girl, souryuu asuka langley, neon genesis evangelion, rebuild of evangelion, lance of longinus, cat hat, plugsuit, pilot suit, red bodysuit, sitting, crossed legs, black eye patch, throne, looking down, from bottom, looking at viewer, outdoors, (masterpiece), (best quality), (ultra-detailed), very aesthetic, illustration, disheveled hair, perfect composition, moist skin, intricate details",
+        "nfsw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, unfinished, very displeasing, oldest, early, chromatic aberration, artistic error, scan, abstract",
+        28,
+        7.0,
+        -1,
+        "None",
+        0.33,
+        "Euler a",
+        1152,
+        896,
+        "cagliostrolab/animagine-xl-3.1",
+        "txt2img",
+        "image.webp",  # img conttol
+        1024,  # img resolution
+        0.35,  # strength
+        1.0,  # cn scale
+        0.0,  # cn start
+        1.0,  # cn end
+        "Classic",
+        "Nearest",
+        45,
+        False,
+    ],
+    [
+        "a digital illustration of a movie poster titled 'Finding Emo', finding nemo parody poster, featuring a depressed cartoon clownfish with black emo hair, eyeliner, and piercings, bored expression, swimming in a dark underwater scene, in the background, movie title in a dripping, grungy font, moody blue and purple color palette",
+        "",
+        24,
+        3.5,
+        -1,
+        "None",
+        0.33,
+        "Euler a",
+        1152,
+        896,
+        "black-forest-labs/FLUX.1-dev",
+        "txt2img",
+        None,  # img conttol
+        1024,  # img resolution
+        0.35,  # strength
+        1.0,  # cn scale
+        0.0,  # cn start
+        1.0,  # cn end
+        "Classic",
+        None,
+        70,
+        True,
+    ],
+    [
+        "((masterpiece)), best quality, blonde disco girl, detailed face, realistic face, realistic hair, dynamic pose, pink pvc, intergalactic disco background, pastel lights, dynamic contrast, airbrush, fine detail, 70s vibe, midriff",
+        "(worst quality:1.2), (bad quality:1.2), (poor quality:1.2), (missing fingers:1.2), bad-artist-anime, bad-artist, bad-picture-chill-75v",
+        48,
+        3.5,
+        -1,
+        "None",
+        0.33,
+        "DPM++ 2M SDE Lu",
+        1024,
+        1024,
+        "misri/epicrealismXL_v7FinalDestination",
+        "canny ControlNet",
+        "image.webp",  # img conttol
+        1024,  # img resolution
+        0.35,  # strength
+        1.0,  # cn scale
+        0.0,  # cn start
+        1.0,  # cn end
+        "Classic",
+        None,
+        44,
+        False,
+    ],
+    [
+        "cinematic scenery old city ruins",
+        "(worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), (illustration, 3d, 2d, painting, cartoons, sketch, blurry, film grain, noise), (low quality, worst quality:1.2)",
+        50,
+        4.0,
+        -1,
+        "None",
+        0.33,
+        "Euler a",
+        1024,
+        1024,
+        "misri/juggernautXL_juggernautX",
+        "optical pattern ControlNet",
+        "spiral_no_transparent.png",  # img conttol
+        1024,  # img resolution
+        0.35,  # strength
+        1.0,  # cn scale
+        0.05,  # cn start
+        0.75,  # cn end
+        "Classic",
+        None,
+        35,
+        False,
+    ],
+    [
+        "black and white, line art, coloring drawing, clean line art, black strokes, no background, white, black, free lines, black scribbles, on paper, A blend of comic book art and lineart full of black and white color, masterpiece, high-resolution, trending on Pixiv fan box, palette knife, brush strokes, two-dimensional, planar vector, T-shirt design, stickers, and T-shirt design, vector art, fantasy art, Adobe Illustrator, hand-painted, digital painting, low polygon, soft lighting, aerial view, isometric style, retro aesthetics, 8K resolution, black sketch lines, monochrome, invert color",
+        "color, red, green, yellow, colored, duplicate, blurry, abstract, disfigured, deformed, animated, toy, figure, framed, 3d, bad art, poorly drawn, extra limbs, close up, b&w, weird colors, blurry, watermark, blur haze, 2 heads, long neck, watermark, elongated body, cropped image, out of frame, draft, deformed hands, twisted fingers, double image, malformed hands, multiple heads, extra limb, ugly, poorly drawn hands, missing limb, cut-off, over satured, grain, lowères, bad anatomy, poorly drawn face, mutation, mutated, floating limbs, disconnected limbs, out of focus, long body, disgusting, extra fingers, groos proportions, missing arms, mutated hands, cloned face, missing legs, ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, extra limbs, disfigured, deformed, body out of frame, blurry, bad anatomy, blurred, watermark, grainy, signature, cut off, draft, deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, bluelish, blue",
+        20,
+        4.0,
+        -1,
+        "loras/Coloring_book_-_LineArt.safetensors",
+        1.0,
+        "DPM++ 2M SDE Karras",
+        1024,
+        1024,
+        "cagliostrolab/animagine-xl-3.1",
+        "lineart ControlNet",
+        "color_image.png",  # img conttol
+        896,  # img resolution
+        0.35,  # strength
+        1.0,  # cn scale
+        0.0,  # cn start
+        1.0,  # cn end
+        "Compel",
+        None,
+        35,
+        False,
+    ],
+    [
+        "1girl,face,curly hair,red hair,white background,",
+        "(worst quality:2),(low quality:2),(normal quality:2),lowres,watermark,",
+        38,
+        5.0,
+        -1,
+        "None",
+        0.33,
+        "DPM++ 2M SDE Karras",
+        512,
+        512,
+        "digiplay/majicMIX_realistic_v7",
+        "openpose ControlNet",
+        "image.webp",  # img conttol
+        1024,  # img resolution
+        0.35,  # strength
+        1.0,  # cn scale
+        0.0,  # cn start
+        0.9,  # cn end
+        "Compel",
+        "Latent (antialiased)",
+        46,
+        False,
+    ],
+]
+RESOURCES = (
+    """### Resources
+    - John6666's space has some great features you might find helpful [link](https://huggingface.co/spaces/John6666/DiffuseCraftMod).
+    - You can also try the image generator in Colab’s free tier, which provides free GPU [link](https://github.com/R3gm/SD_diffusers_interactive).
+    """
+)

dc.py CHANGED Viewed

@@ -1,33 +1,52 @@
 import spaces
 import os
 from stablepy import Model_Diffusers
 from stablepy.diffusers_vanilla.style_prompt_config import STYLE_NAMES
-from stablepy.diffusers_vanilla.constants import FLUX_CN_UNION_MODES
 import torch
 import re
-from huggingface_hub import HfApi
 from stablepy import (
-    CONTROLNET_MODEL_IDS,
-    VALID_TASKS,
-    T2I_PREPROCESSOR_NAME,
-    FLASH_LORA,
-    SCHEDULER_CONFIG_MAP,
     scheduler_names,
-    IP_ADAPTER_MODELS,
     IP_ADAPTERS_SD,
     IP_ADAPTERS_SDXL,
-    REPO_IMAGE_ENCODER,
-    ALL_PROMPT_WEIGHT_OPTIONS,
-    SD15_TASKS,
-    SDXL_TASKS,
 )
 import time
 from PIL import ImageFile
-#import urllib.parse
 ImageFile.LOAD_TRUNCATED_IMAGES = True
 print(os.getenv("SPACES_ZERO_GPU"))
 import gradio as gr
 import logging
 logging.getLogger("diffusers").setLevel(logging.ERROR)
@@ -38,205 +57,63 @@ warnings.filterwarnings(action="ignore", category=FutureWarning, module="diffuse
 warnings.filterwarnings(action="ignore", category=UserWarning, module="diffusers")
 warnings.filterwarnings(action="ignore", category=FutureWarning, module="transformers")
 from stablepy import logger
-logger.setLevel(logging.CRITICAL)
 from env import (
-    HF_TOKEN, hf_read_token, # to use only for private repos
     CIVITAI_API_KEY, HF_LORA_PRIVATE_REPOS1, HF_LORA_PRIVATE_REPOS2,
     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)
-PREPROCESSOR_CONTROLNET = {
-  "openpose": [
-    "Openpose",
-    "None",
-  ],
-  "scribble": [
-    "HED",
-    "PidiNet",
-    "None",
-  ],
-  "softedge": [
-    "PidiNet",
-    "HED",
-    "HED safe",
-    "PidiNet safe",
-    "None",
-  ],
-  "segmentation": [
-    "UPerNet",
-    "None",
-  ],
-  "depth": [
-    "DPT",
-    "Midas",
-    "None",
-  ],
-  "normalbae": [
-    "NormalBae",
-    "None",
-  ],
-  "lineart": [
-    "Lineart",
-    "Lineart coarse",
-    "Lineart (anime)",
-    "None",
-    "None (anime)",
-  ],
-  "lineart_anime": [
-    "Lineart",
-    "Lineart coarse",
-    "Lineart (anime)",
-    "None",
-    "None (anime)",
-  ],
-  "shuffle": [
-    "ContentShuffle",
-    "None",
-  ],
-  "canny": [
-    "Canny",
-    "None",
-  ],
-  "mlsd": [
-    "MLSD",
-    "None",
-  ],
-  "ip2p": [
-    "ip2p"
-  ],
-  "recolor": [
-    "Recolor luminance",
-    "Recolor intensity",
-    "None",
-  ],
-  "tile": [
-    "Mild Blur",
-    "Moderate Blur",
-    "Heavy Blur",
-    "None",
-  ],
-}
-TASK_STABLEPY = {
-    'txt2img': 'txt2img',
-    'img2img': 'img2img',
-    'inpaint': 'inpaint',
-    # 'canny T2I Adapter': 'sdxl_canny_t2i',  # NO HAVE STEP CALLBACK PARAMETERS SO NOT WORKS WITH DIFFUSERS 0.29.0
-    # 'sketch  T2I Adapter': 'sdxl_sketch_t2i',
-    # 'lineart  T2I Adapter': 'sdxl_lineart_t2i',
-    # 'depth-midas  T2I Adapter': 'sdxl_depth-midas_t2i',
-    # 'openpose  T2I Adapter': 'sdxl_openpose_t2i',
-    'openpose ControlNet': 'openpose',
-    'canny ControlNet': 'canny',
-    'mlsd ControlNet': 'mlsd',
-    'scribble ControlNet': 'scribble',
-    'softedge ControlNet': 'softedge',
-    'segmentation ControlNet': 'segmentation',
-    'depth ControlNet': 'depth',
-    'normalbae ControlNet': 'normalbae',
-    'lineart ControlNet': 'lineart',
-    'lineart_anime ControlNet': 'lineart_anime',
-    'shuffle ControlNet': 'shuffle',
-    'ip2p ControlNet': 'ip2p',
-    'optical pattern ControlNet': 'pattern',
-    'recolor ControlNet': 'recolor',
-    'tile ControlNet': 'tile',
-}
-TASK_MODEL_LIST = list(TASK_STABLEPY.keys())
-UPSCALER_DICT_GUI = {
-    None: None,
-    "Lanczos": "Lanczos",
-    "Nearest": "Nearest",
-    'Latent': 'Latent',
-    'Latent (antialiased)': 'Latent (antialiased)',
-    'Latent (bicubic)': 'Latent (bicubic)',
-    'Latent (bicubic antialiased)': 'Latent (bicubic antialiased)',
-    'Latent (nearest)': 'Latent (nearest)',
-    'Latent (nearest-exact)': 'Latent (nearest-exact)',
-    "RealESRGAN_x4plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
-    "RealESRNet_x4plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth",
-    "RealESRGAN_x4plus_anime_6B": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
-    "RealESRGAN_x2plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
-    "realesr-animevideov3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
-    "realesr-general-x4v3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
-    "realesr-general-wdn-x4v3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
-    "4x-UltraSharp": "https://huggingface.co/Shandypur/ESRGAN-4x-UltraSharp/resolve/main/4x-UltraSharp.pth",
-    "4x_foolhardy_Remacri": "https://huggingface.co/FacehugmanIII/4x_foolhardy_Remacri/resolve/main/4x_foolhardy_Remacri.pth",
-    "Remacri4xExtraSmoother": "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/Remacri%204x%20ExtraSmoother.pth",
-    "AnimeSharp4x": "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/AnimeSharp%204x.pth",
-    "lollypop": "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/lollypop.pth",
-    "RealisticRescaler4x": "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/RealisticRescaler%204x.pth",
-    "NickelbackFS4x": "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/NickelbackFS%204x.pth"
-}
-UPSCALER_KEYS = list(UPSCALER_DICT_GUI.keys())
-def get_model_list(directory_path):
-    model_list = []
-    valid_extensions = {'.ckpt', '.pt', '.pth', '.safetensors', '.bin'}
-    for filename in os.listdir(directory_path):
-        if os.path.splitext(filename)[1] in valid_extensions:
-            # name_without_extension = os.path.splitext(filename)[0]
-            file_path = os.path.join(directory_path, filename)
-            # model_list.append((name_without_extension, file_path))
-            model_list.append(file_path)
-            print('\033[34mFILE: ' + file_path + '\033[0m')
-    return model_list
-## BEGIN MOD
 from modutils import (to_list, list_uniq, list_sub, get_model_id_list, get_tupled_embed_list,
                       get_tupled_model_list, get_lora_model_list, download_private_repo, download_things)
 # - **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_private_repo(HF_LORA_ESSENTIAL_PRIVATE_REPO, directory_loras, True)
-download_private_repo(HF_VAE_PRIVATE_REPO, directory_vaes, False)
-load_diffusers_format_model = list_uniq(load_diffusers_format_model + get_model_id_list())
 ## END MOD
 # 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)
-model_list = get_model_list(directory_models)
 model_list = load_diffusers_format_model + model_list
 ## BEGIN MOD
 lora_model_list = get_lora_model_list()
-vae_model_list = get_model_list(directory_vaes)
 vae_model_list.insert(0, "None")
-#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)
@@ -244,99 +121,13 @@ def get_embed_list(pipeline_name):
 print('\033[33m🏁 Download and listing of valid models completed.\033[0m')
-msg_inc_vae = (
-    "Use the right VAE for your model to maintain image quality. The wrong"
-    " VAE can lead to poor results, like blurriness in the generated images."
-)
-SDXL_TASK = [k for k, v in TASK_STABLEPY.items() if v in SDXL_TASKS]
-SD_TASK = [k for k, v in TASK_STABLEPY.items() if v in SD15_TASKS]
-FLUX_TASK = list(TASK_STABLEPY.keys())[:3] + [k for k, v in TASK_STABLEPY.items() if v in FLUX_CN_UNION_MODES.keys()]
-MODEL_TYPE_TASK = {
-    "SD 1.5": SD_TASK,
-    "SDXL": SDXL_TASK,
-    "FLUX": FLUX_TASK,
-}
-MODEL_TYPE_CLASS = {
-    "diffusers:StableDiffusionPipeline": "SD 1.5",
-    "diffusers:StableDiffusionXLPipeline": "SDXL",
-    "diffusers:FluxPipeline": "FLUX",
-}
-POST_PROCESSING_SAMPLER = ["Use same sampler"] + scheduler_names[:-2]
-def extract_parameters(input_string):
-    parameters = {}
-    input_string = input_string.replace("\n", "")
-    if "Negative prompt:" not in input_string:
-        if "Steps:" in input_string:
-            input_string = input_string.replace("Steps:", "Negative prompt: Steps:")
-        else:
-            print("Invalid metadata")
-            parameters["prompt"] = input_string
-            return parameters
-    parm = input_string.split("Negative prompt:")
-    parameters["prompt"] = parm[0].strip()
-    if "Steps:" not in parm[1]:
-        print("Steps not detected")
-        parameters["neg_prompt"] = parm[1].strip()
-        return parameters
-    parm = parm[1].split("Steps:")
-    parameters["neg_prompt"] = parm[0].strip()
-    input_string = "Steps:" + parm[1]
-    # Extracting Steps
-    steps_match = re.search(r'Steps: (\d+)', input_string)
-    if steps_match:
-        parameters['Steps'] = int(steps_match.group(1))
-    # Extracting Size
-    size_match = re.search(r'Size: (\d+x\d+)', input_string)
-    if size_match:
-        parameters['Size'] = size_match.group(1)
-        width, height = map(int, parameters['Size'].split('x'))
-        parameters['width'] = width
-        parameters['height'] = height
-    # Extracting other parameters
-    other_parameters = re.findall(r'(\w+): (.*?)(?=, \w+|$)', input_string)
-    for param in other_parameters:
-        parameters[param[0]] = param[1].strip('"')
-    return parameters
-def get_model_type(repo_id: str):
-    api = HfApi(token=os.environ.get("HF_TOKEN"))  # if use private or gated model
-    default = "SD 1.5"
-    try:
-        model = api.model_info(repo_id=repo_id, timeout=5.0)
-        tags = model.tags
-        for tag in tags:
-            if tag in MODEL_TYPE_CLASS.keys(): return MODEL_TYPE_CLASS.get(tag, default)
-    except Exception:
-        return default
-    return default
 ## BEGIN MOD
 class GuiSD:
-    def __init__(self):
         self.model = None
-        print("Loading model...")
-        self.model = Model_Diffusers(
-            base_model_id="Lykon/dreamshaper-8",
-            task_name="txt2img",
-            vae_model=None,
-            type_model_precision=torch.float16,
-            retain_task_model_in_cache=False,
-            device="cpu",
-        )
-        self.model.load_beta_styles()
-        #self.model.device = torch.device("cpu") #
     def infer_short(self, model, pipe_params, progress=gr.Progress(track_tqdm=True)):
         #progress(0, desc="Start inference...")
@@ -350,28 +141,83 @@ class GuiSD:
         return img
     def load_new_model(self, model_name, vae_model, task, progress=gr.Progress(track_tqdm=True)):
-        #yield f"Loading model: {model_name}"
         vae_model = vae_model if vae_model != "None" else None
         model_type = get_model_type(model_name)
         if vae_model:
             vae_type = "SDXL" if "sdxl" in vae_model.lower() else "SD 1.5"
             if model_type != vae_type:
-                gr.Warning(msg_inc_vae)
-        self.model.device = torch.device("cpu")
-        dtype_model = torch.bfloat16 if model_type == "FLUX" else torch.float16
-        self.model.load_pipe(
-            model_name,
-            task_name=TASK_STABLEPY[task],
-            vae_model=vae_model if vae_model != "None" else None,
-            type_model_precision=dtype_model,
-            retain_task_model_in_cache=False,
-        )
-        #yield f"Model loaded: {model_name}"
     #@spaces.GPU
     @torch.inference_mode()
@@ -479,23 +325,24 @@ class GuiSD:
         mode_ip2,
         scale_ip2,
         pag_scale,
-        #progress=gr.Progress(track_tqdm=True),
     ):
-        #progress(0, desc="Preparing inference...")
         vae_model = vae_model if vae_model != "None" else None
         loras_list = [lora1, lora2, lora3, lora4, lora5]
         vae_msg = f"VAE: {vae_model}" if vae_model else ""
         msg_lora = ""
-        print("Config model:", model_name, vae_model, loras_list)
 ## BEGIN MOD
         prompt, neg_prompt = insert_model_recom_prompt(prompt, neg_prompt, model_name)
         global lora_model_list
         lora_model_list = get_lora_model_list()
 ## END MOD
         task = TASK_STABLEPY[task]
         params_ip_img = []
@@ -518,6 +365,9 @@ class GuiSD:
                 params_ip_mode.append(modeip)
                 params_ip_scale.append(scaleip)
         if task != "txt2img" and not image_control:
             raise ValueError("No control image found: To use this function, you have to upload an image in 'Image ControlNet/Inpaint/Img2img'")
@@ -589,15 +439,15 @@ class GuiSD:
             "high_threshold": high_threshold,
             "value_threshold": value_threshold,
             "distance_threshold": distance_threshold,
-            "lora_A": lora1 if lora1 != "None" and lora1 != "" else None,
             "lora_scale_A": lora_scale1,
-            "lora_B": lora2 if lora2 != "None" and lora2 != "" else None,
             "lora_scale_B": lora_scale2,
-            "lora_C": lora3 if lora3 != "None" and lora3 != "" else None,
             "lora_scale_C": lora_scale3,
-            "lora_D": lora4 if lora4 != "None" and lora4 != "" else None,
             "lora_scale_D": lora_scale4,
-            "lora_E": lora5 if lora5 != "None" and lora5 != "" else None,
             "lora_scale_E": lora_scale5,
 ## BEGIN MOD
             "textual_inversion": get_embed_list(self.model.class_name) if textual_inversion else [],
@@ -647,21 +497,61 @@ class GuiSD:
         }
         self.model.device = torch.device("cuda:0")
-        if hasattr(self.model.pipe, "transformer") and loras_list != ["None"] * 5 and loras_list != [""] * 5:
             self.model.pipe.transformer.to(self.model.device)
             print("transformer to cuda")
-        #progress(1, desc="Inference preparation completed. Starting inference...")
-        info_state = "" # for yield version
-        return self.infer_short(self.model, pipe_params), info_state
 ## END MOD
 def dynamic_gpu_duration(func, duration, *args):
     @spaces.GPU(duration=duration)
     def wrapped_func():
-        return func(*args)
     return wrapped_func()
@@ -678,7 +568,7 @@ def sd_gen_generate_pipeline(*args):
     load_lora_cpu = args[-3]
     generation_args = args[:-3]
     lora_list = [
-        None if item == "None" or item == "" else item
         for item in [args[7], args[9], args[11], args[13], args[15]]
     ]
     lora_status = [None] * 5
@@ -687,8 +577,8 @@ def sd_gen_generate_pipeline(*args):
     if load_lora_cpu:
         msg_load_lora = "Updating LoRAs in CPU (Slow but saves GPU usage)..."
-    #if lora_list != sd_gen.model.lora_memory and lora_list != [None] * 5:
-    #    yield None, msg_load_lora
     # Load lora in CPU
     if load_lora_cpu:
@@ -714,46 +604,36 @@ def sd_gen_generate_pipeline(*args):
             )
             gr.Info(f"LoRAs in cache: {lora_cache_msg}")
-        msg_request = f"Requesting {gpu_duration_arg}s. of GPU time"
         gr.Info(msg_request)
         print(msg_request)
-    # yield from sd_gen.generate_pipeline(*generation_args)
     start_time = time.time()
-    return dynamic_gpu_duration(
         sd_gen.generate_pipeline,
         gpu_duration_arg,
         *generation_args,
     )
     end_time = time.time()
     if verbose_arg:
-        execution_time = end_time - start_time
-        msg_task_complete = (
-            f"GPU task complete in: {round(execution_time, 0) + 1} seconds"
-        )
         gr.Info(msg_task_complete)
         print(msg_task_complete)
-def extract_exif_data(image):
-    if image is None: return ""
-    try:
-        metadata_keys = ['parameters', 'metadata', 'prompt', 'Comment']
-        for key in metadata_keys:
-            if key in image.info:
-                return image.info[key]
-        return str(image.info)
-    except Exception as e:
-        return f"Error extracting metadata: {str(e)}"
-@spaces.GPU(duration=20)
 def esrgan_upscale(image, upscaler_name, upscaler_size):
     if image is None: return None
@@ -775,18 +655,22 @@ def esrgan_upscale(image, upscaler_name, upscaler_size):
     return image_path
 dynamic_gpu_duration.zerogpu = True
 sd_gen_generate_pipeline.zerogpu = True
 from pathlib import Path
 from PIL import Image
 import random, json
 from modutils import (safe_float, escape_lora_basename, to_lora_key, to_lora_path,
     get_local_model_list, get_private_lora_model_lists, get_valid_lora_name,
-    get_valid_lora_path, get_valid_lora_wt, get_lora_info, CIVITAI_SORT, CIVITAI_PERIOD,
-    normalize_prompt_list, get_civitai_info, search_lora_on_civitai, translate_to_en)
-sd_gen = GuiSD()
 #@spaces.GPU
 def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
            model_name = load_diffusers_format_model[0], lora1 = None, lora1_wt = 1.0, lora2 = None, lora2_wt = 1.0,
@@ -796,12 +680,72 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
     import numpy as np
     MAX_SEED = np.iinfo(np.int32).max
     load_lora_cpu = False
     verbose_info = False
     gpu_duration = 59
     images: list[tuple[PIL.Image.Image, str | None]] = []
-    info: str = ""
     progress(0, desc="Preparing...")
     if randomize_seed:
@@ -828,7 +772,7 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
     sd_gen.load_new_model(model_name, vae, TASK_MODEL_LIST[0])
     progress(1, desc="Model loaded.")
     progress(0, desc="Starting Inference...")
-    images, info = sd_gen_generate_pipeline(prompt, negative_prompt, 1, num_inference_steps,
         guidance_scale, True, generator, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt,
         lora4, lora4_wt, lora5, lora5_wt, sampler,
         height, width, model_name, vae, TASK_MODEL_LIST[0], None, "Canny", 512, 1024,
@@ -1008,14 +952,14 @@ def update_lora_dict(path: str):
 def download_lora(dl_urls: str):
     global loras_url_to_path_dict
     dl_path = ""
-    before = get_local_model_list(directory_loras)
     urls = []
     for url in [url.strip() for url in dl_urls.split(',')]:
-        local_path = f"{directory_loras}/{url.split('/')[-1]}"
         if not Path(local_path).exists():
-            download_things(directory_loras, url, HF_TOKEN, CIVITAI_API_KEY)
             urls.append(url)
-    after = get_local_model_list(directory_loras)
     new_files = list_sub(after, before)
     i = 0
     for file in new_files:

 import spaces
 import os
 from stablepy import Model_Diffusers
+from constants import (
+    PREPROCESSOR_CONTROLNET,
+    TASK_STABLEPY,
+    TASK_MODEL_LIST,
+    UPSCALER_DICT_GUI,
+    UPSCALER_KEYS,
+    PROMPT_W_OPTIONS,
+    WARNING_MSG_VAE,
+    SDXL_TASK,
+    MODEL_TYPE_TASK,
+    POST_PROCESSING_SAMPLER,
+)
 from stablepy.diffusers_vanilla.style_prompt_config import STYLE_NAMES
 import torch
 import re
 from stablepy import (
     scheduler_names,
     IP_ADAPTERS_SD,
     IP_ADAPTERS_SDXL,
 )
 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,
+    progress_step_bar,
+    html_template_message,
+)
+from datetime import datetime
+import gradio as gr
+import logging
+import diffusers
+import warnings
+from stablepy import logger
+# import urllib.parse
 ImageFile.LOAD_TRUNCATED_IMAGES = True
+# os.environ["PYTORCH_NO_CUDA_MEMORY_CACHING"] = "1"
 print(os.getenv("SPACES_ZERO_GPU"))
+## BEGIN MOD
 import gradio as gr
 import logging
 logging.getLogger("diffusers").setLevel(logging.ERROR)
 warnings.filterwarnings(action="ignore", category=UserWarning, module="diffusers")
 warnings.filterwarnings(action="ignore", category=FutureWarning, module="transformers")
 from stablepy import logger
+logger.setLevel(logging.DEBUG)
 from env import (
+    HF_TOKEN, HF_READ_TOKEN, # to use only for private repos
     CIVITAI_API_KEY, HF_LORA_PRIVATE_REPOS1, HF_LORA_PRIVATE_REPOS2,
     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)
 from modutils import (to_list, list_uniq, list_sub, get_model_id_list, get_tupled_embed_list,
                       get_tupled_model_list, get_lora_model_list, download_private_repo, download_things)
 # - **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_private_repo(HF_LORA_ESSENTIAL_PRIVATE_REPO, DIRECTORY_LORAS, True)
+download_private_repo(HF_VAE_PRIVATE_REPO, DIRECTORY_VAES, False)
+load_diffusers_format_model = list_uniq(LOAD_DIFFUSERS_FORMAT_MODEL + get_model_id_list())
 ## END MOD
 # 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)
+model_list = get_model_list(DIRECTORY_MODELS)
 model_list = load_diffusers_format_model + model_list
 ## BEGIN MOD
 lora_model_list = get_lora_model_list()
+vae_model_list = get_model_list(DIRECTORY_VAES)
 vae_model_list.insert(0, "None")
+#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)
 print('\033[33m🏁 Download and listing of valid models completed.\033[0m')
 ## BEGIN MOD
 class GuiSD:
+    def __init__(self, stream=True):
         self.model = None
+        self.status_loading = False
+        self.sleep_loading = 4
+        self.last_load = datetime.now()
     def infer_short(self, model, pipe_params, progress=gr.Progress(track_tqdm=True)):
         #progress(0, desc="Start inference...")
         return img
     def load_new_model(self, model_name, vae_model, task, progress=gr.Progress(track_tqdm=True)):
         vae_model = vae_model if vae_model != "None" else None
         model_type = get_model_type(model_name)
+        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,
+            )
+        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:
             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,
+                    device="cpu",
+                )
+            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 <= 8:
+                        print("Waiting for the previous model's time ops...")
+                        time.sleep(8-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,
+                )
+            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()
         mode_ip2,
         scale_ip2,
         pag_scale,
     ):
+        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]
         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]
         prompt, neg_prompt = insert_model_recom_prompt(prompt, neg_prompt, model_name)
         global lora_model_list
         lora_model_list = get_lora_model_list()
 ## END MOD
+        print("Config model:", model_name, vae_model, loras_list)
         task = TASK_STABLEPY[task]
         params_ip_img = []
                 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("No control image found: To use this function, you have to upload an image in 'Image ControlNet/Inpaint/Img2img'")
             "high_threshold": high_threshold,
             "value_threshold": value_threshold,
             "distance_threshold": distance_threshold,
+            "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,
 ## BEGIN MOD
             "textual_inversion": get_embed_list(self.model.class_name) if textual_inversion else [],
         }
         self.model.device = torch.device("cuda:0")
+        if hasattr(self.model.pipe, "transformer") and loras_list != ["None"] * 5:
             self.model.pipe.transformer.to(self.model.device)
             print("transformer to cuda")
+        #return self.infer_short(self.model, pipe_params), info_state
+        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>" + metadata[0].replace("\n", "<br>") + "<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
+                if not isinstance(img, list): img = [img]
+                img = save_images(img, metadata)
+                img = [(i, None) for i in img]
 ## END MOD
+                info_state = "COMPLETE"
+            yield info_state, img, info_images
+            #return 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()
     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]]
     ]
     lora_status = [None] * 5
     if load_lora_cpu:
         msg_load_lora = "Updating LoRAs in CPU (Slow but saves GPU usage)..."
+    if lora_list != sd_gen.model.lora_memory and lora_list != [None] * 5:
+        yield msg_load_lora, gr.update(), gr.update()
     # Load lora in CPU
     if load_lora_cpu:
             )
             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(
+    #return 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 esrgan_upscale(image, upscaler_name, upscaler_size):
     if image is None: return None
     return image_path
 dynamic_gpu_duration.zerogpu = True
 sd_gen_generate_pipeline.zerogpu = True
+sd_gen = GuiSD()
 from pathlib import Path
 from PIL import Image
 import random, json
 from modutils import (safe_float, escape_lora_basename, to_lora_key, to_lora_path,
     get_local_model_list, get_private_lora_model_lists, get_valid_lora_name,
+    get_valid_lora_path, get_valid_lora_wt, get_lora_info, CIVITAI_SORT, CIVITAI_PERIOD, CIVITAI_BASEMODEL,
+    normalize_prompt_list, get_civitai_info, search_lora_on_civitai, translate_to_en, get_t2i_model_info, get_civitai_tag, save_image_history)
 #@spaces.GPU
 def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
            model_name = load_diffusers_format_model[0], lora1 = None, lora1_wt = 1.0, lora2 = None, lora2_wt = 1.0,
     import numpy as np
     MAX_SEED = np.iinfo(np.int32).max
+    image_previews = True
+    load_lora_cpu = False
+    verbose_info = False
+    gpu_duration = 59
+    images: list[tuple[PIL.Image.Image, str | None]] = []
+    progress(0, desc="Preparing...")
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator().manual_seed(seed).seed()
+    if translate:
+        prompt = translate_to_en(prompt)
+        negative_prompt = translate_to_en(prompt)
+    prompt, negative_prompt = insert_model_recom_prompt(prompt, negative_prompt, model_name)
+    progress(0.5, desc="Preparing...")
+    lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt = \
+        set_prompt_loras(prompt, model_name, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt)
+    lora1 = get_valid_lora_path(lora1)
+    lora2 = get_valid_lora_path(lora2)
+    lora3 = get_valid_lora_path(lora3)
+    lora4 = get_valid_lora_path(lora4)
+    lora5 = get_valid_lora_path(lora5)
+    progress(1, desc="Preparation completed. Starting inference...")
+    progress(0, desc="Loading model...")
+    for _ in sd_gen.load_new_model(model_name, vae, TASK_MODEL_LIST[0]):
+        pass
+    progress(1, desc="Model loaded.")
+    progress(0, desc="Starting Inference...")
+    for info_state, stream_images, info_images in sd_gen_generate_pipeline(prompt, negative_prompt, 1, num_inference_steps,
+        guidance_scale, True, generator, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt,
+        lora4, lora4_wt, lora5, lora5_wt, sampler,
+        height, width, model_name, vae, TASK_MODEL_LIST[0], None, "Canny", 512, 1024,
+        None, None, None, 0.35, 100, 200, 0.1, 0.1, 1.0, 0., 1., False, "Classic", None,
+        1.0, 100, 10, 30, 0.55, "Use same sampler", "", "",
+        False, True, 1, True, False, image_previews, False, False, "./images", False, False, False, True, 1, 0.55,
+        False, False, False, True, False, "Use same sampler", False, "", "", 0.35, True, True, False, 4, 4, 32,
+        False, "", "", 0.35, True, True, False, 4, 4, 32,
+        True, None, None, "plus_face", "original", 0.7, None, None, "base", "style", 0.7, 0.0,
+        load_lora_cpu, verbose_info, gpu_duration
+    ):
+        images = stream_images if isinstance(stream_images, list) else images
+    progress(1, desc="Inference completed.")
+    output_image = images[0][0] if images else None
+    return output_image
+#@spaces.GPU
+def __infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
+           model_name = load_diffusers_format_model[0], lora1 = None, lora1_wt = 1.0, lora2 = None, lora2_wt = 1.0,
+           lora3 = None, lora3_wt = 1.0, lora4 = None, lora4_wt = 1.0, lora5 = None, lora5_wt = 1.0,
+           sampler = "Euler a", vae = None, translate=True, progress=gr.Progress(track_tqdm=True)):
+    import PIL
+    import numpy as np
+    MAX_SEED = np.iinfo(np.int32).max
     load_lora_cpu = False
     verbose_info = False
     gpu_duration = 59
     images: list[tuple[PIL.Image.Image, str | None]] = []
+    info_state = info_images = ""
     progress(0, desc="Preparing...")
     if randomize_seed:
     sd_gen.load_new_model(model_name, vae, TASK_MODEL_LIST[0])
     progress(1, desc="Model loaded.")
     progress(0, desc="Starting Inference...")
+    info_state, images, info_images = sd_gen_generate_pipeline(prompt, negative_prompt, 1, num_inference_steps,
         guidance_scale, True, generator, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt,
         lora4, lora4_wt, lora5, lora5_wt, sampler,
         height, width, model_name, vae, TASK_MODEL_LIST[0], None, "Canny", 512, 1024,
 def download_lora(dl_urls: str):
     global loras_url_to_path_dict
     dl_path = ""
+    before = get_local_model_list(DIRECTORY_LORAS)
     urls = []
     for url in [url.strip() for url in dl_urls.split(',')]:
+        local_path = f"{DIRECTORY_LORAS}/{url.split('/')[-1]}"
         if not Path(local_path).exists():
+            download_things(DIRECTORY_LORAS, url, HF_TOKEN, CIVITAI_API_KEY)
             urls.append(url)
+    after = get_local_model_list(DIRECTORY_LORAS)
     new_files = list_sub(after, before)
     i = 0
     for file in new_files:

env.py CHANGED Viewed

@@ -2,10 +2,10 @@ import os
 CIVITAI_API_KEY = os.environ.get("CIVITAI_API_KEY")
 HF_TOKEN = os.environ.get("HF_TOKEN")
-hf_read_token = os.environ.get('HF_READ_TOKEN') # only use for private repo
 # - **List Models**
-load_diffusers_format_model = [
     'votepurchase/animagine-xl-3.1',
     'votepurchase/NSFW-GEN-ANIME-v2',
     'votepurchase/kivotos-xl-2.0',
@@ -138,11 +138,11 @@ HF_MODEL_USER_EX = ["John6666"] # sorted by a special rule
 # - **Download Models**
-download_model_list = [
 ]
 # - **Download VAEs**
-download_vae_list = [
     'https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/resolve/main/sdxl.vae.safetensors?download=true',
     'https://huggingface.co/nubby/blessed-sdxl-vae-fp16-fix/resolve/main/sdxl_vae-fp16fix-c-1.1-b-0.5.safetensors?download=true',
     "https://huggingface.co/nubby/blessed-sdxl-vae-fp16-fix/blob/main/sdxl_vae-fp16fix-blessed.safetensors",
@@ -151,29 +151,26 @@ download_vae_list = [
 ]
 # - **Download LoRAs**
-download_lora_list = [
 ]
 # Download Embeddings
-download_embeds = [
     'https://huggingface.co/datasets/Nerfgun3/bad_prompt/blob/main/bad_prompt_version2.pt',
     'https://huggingface.co/embed/negative/resolve/main/EasyNegativeV2.safetensors',
     'https://huggingface.co/embed/negative/resolve/main/bad-hands-5.pt',
 ]
-directory_models = 'models'
-os.makedirs(directory_models, exist_ok=True)
-directory_loras = 'loras'
-os.makedirs(directory_loras, exist_ok=True)
-directory_vaes = 'vaes'
-os.makedirs(directory_vaes, exist_ok=True)
-directory_embeds = 'embedings'
-os.makedirs(directory_embeds, exist_ok=True)
-directory_embeds_sdxl = 'embedings_xl'
-os.makedirs(directory_embeds_sdxl, exist_ok=True)
-directory_embeds_positive_sdxl = 'embedings_xl/positive'
-os.makedirs(directory_embeds_positive_sdxl, exist_ok=True)
 HF_LORA_PRIVATE_REPOS1 = ['John6666/loratest1', 'John6666/loratest3', 'John6666/loratest4', 'John6666/loratest6']
 HF_LORA_PRIVATE_REPOS2 = ['John6666/loratest10', 'John6666/loratest11','John6666/loratest'] # to be sorted as 1 repo

 CIVITAI_API_KEY = os.environ.get("CIVITAI_API_KEY")
 HF_TOKEN = os.environ.get("HF_TOKEN")
+HF_READ_TOKEN = os.environ.get('HF_READ_TOKEN') # only use for private repo
 # - **List Models**
+LOAD_DIFFUSERS_FORMAT_MODEL = [
     'votepurchase/animagine-xl-3.1',
     'votepurchase/NSFW-GEN-ANIME-v2',
     'votepurchase/kivotos-xl-2.0',
 # - **Download Models**
+DOWNLOAD_MODEL_LIST = [
 ]
 # - **Download VAEs**
+DOWNLOAD_VAE_LIST = [
     'https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/resolve/main/sdxl.vae.safetensors?download=true',
     'https://huggingface.co/nubby/blessed-sdxl-vae-fp16-fix/resolve/main/sdxl_vae-fp16fix-c-1.1-b-0.5.safetensors?download=true',
     "https://huggingface.co/nubby/blessed-sdxl-vae-fp16-fix/blob/main/sdxl_vae-fp16fix-blessed.safetensors",
 ]
 # - **Download LoRAs**
+DOWNLOAD_LORA_LIST = [
 ]
 # Download Embeddings
+DOWNLOAD_EMBEDS = [
     'https://huggingface.co/datasets/Nerfgun3/bad_prompt/blob/main/bad_prompt_version2.pt',
     'https://huggingface.co/embed/negative/resolve/main/EasyNegativeV2.safetensors',
     'https://huggingface.co/embed/negative/resolve/main/bad-hands-5.pt',
 ]
+DIRECTORY_MODELS = 'models'
+DIRECTORY_LORAS = 'loras'
+DIRECTORY_VAES = 'vaes'
+DIRECTORY_EMBEDS = 'embedings'
+DIRECTORY_EMBEDS_SDXL = 'embedings_xl'
+DIRECTORY_EMBEDS_POSITIVE_SDXL = 'embedings_xl/positive'
+directories = [DIRECTORY_MODELS, DIRECTORY_LORAS, DIRECTORY_VAES, DIRECTORY_EMBEDS, DIRECTORY_EMBEDS_SDXL, DIRECTORY_EMBEDS_POSITIVE_SDXL]
+for directory in directories:
+    os.makedirs(directory, exist_ok=True)
 HF_LORA_PRIVATE_REPOS1 = ['John6666/loratest1', 'John6666/loratest3', 'John6666/loratest4', 'John6666/loratest6']
 HF_LORA_PRIVATE_REPOS2 = ['John6666/loratest10', 'John6666/loratest11','John6666/loratest'] # to be sorted as 1 repo

llmdolphin.py CHANGED Viewed

@@ -1,5 +1,9 @@
 import spaces
 import gradio as gr
 from llama_cpp import Llama
 from llama_cpp_agent import LlamaCppAgent, MessagesFormatterType
 from llama_cpp_agent.providers import LlamaCppPythonProvider
@@ -7,7 +11,6 @@ from llama_cpp_agent.chat_history import BasicChatHistory
 from llama_cpp_agent.chat_history.messages import Roles
 from ja_to_danbooru.ja_to_danbooru import jatags_to_danbooru_tags
 import wrapt_timeout_decorator
-from pathlib import Path
 from llama_cpp_agent.messages_formatter import MessagesFormatter
 from formatter import mistral_v1_formatter, mistral_v2_formatter, mistral_v3_tekken_formatter
@@ -19,6 +22,7 @@ llm_models = {
     #"": ["", MessagesFormatterType.OPEN_CHAT],
     #"": ["", MessagesFormatterType.CHATML],
     #"": ["", MessagesFormatterType.PHI_3],
     "mn-12b-lyra-v2a1-q5_k_m.gguf": ["HalleyStarbun/MN-12B-Lyra-v2a1-Q5_K_M-GGUF", MessagesFormatterType.CHATML],
     "L3-8B-Tamamo-v1.i1-Q5_K_M.gguf": ["mradermacher/L3-8B-Tamamo-v1-i1-GGUF", MessagesFormatterType.LLAMA_3],
     "MN-Chinofun-12B-2.i1-Q4_K_M.gguf": ["mradermacher/MN-Chinofun-12B-2-i1-GGUF", MessagesFormatterType.MISTRAL],
@@ -68,6 +72,19 @@ llm_models = {
     "ChatWaifu_22B_v2.0_preview.Q4_K_S.gguf": ["mradermacher/ChatWaifu_22B_v2.0_preview-GGUF", MessagesFormatterType.MISTRAL],
     "ChatWaifu_v1.4.Q5_K_M.gguf": ["mradermacher/ChatWaifu_v1.4-GGUF", MessagesFormatterType.MISTRAL],
     "ChatWaifu_v1.3.1.Q4_K_M.gguf": ["mradermacher/ChatWaifu_v1.3.1-GGUF", MessagesFormatterType.MISTRAL],
     "hermes-llama3-roleplay-1000-v2.Q5_K_M.gguf": ["mradermacher/hermes-llama3-roleplay-1000-v2-GGUF", MessagesFormatterType.LLAMA_3],
     "hermes-stheno-8B-v0.1.i1-Q5_K_M.gguf": ["mradermacher/hermes-stheno-8B-v0.1-i1-GGUF", MessagesFormatterType.LLAMA_3],
     "qwen-carpmuscle-r-v0.3.Q4_K_M.gguf": ["mradermacher/qwen-carpmuscle-r-v0.3-GGUF", MessagesFormatterType.OPEN_CHAT],
@@ -832,6 +849,7 @@ llm_languages = ["English", "Japanese", "Chinese", "Korean", "Spanish", "Portugu
 llm_models_tupled_list = []
 default_llm_model_filename = list(llm_models.keys())[0]
 override_llm_format = None
 def to_list(s):
@@ -844,7 +862,6 @@ def list_uniq(l):
 @wrapt_timeout_decorator.timeout(dec_timeout=3.5)
 def to_list_ja(s):
-    import re
     s = re.sub(r'[、。]', ',', s)
     return [x.strip() for x in s.split(",") if not s == ""]
@@ -859,7 +876,6 @@ def is_japanese(s):
 def update_llm_model_tupled_list():
-    from pathlib import Path
     global llm_models_tupled_list
     llm_models_tupled_list = []
     for k, v in llm_models.items():
@@ -876,7 +892,6 @@ def update_llm_model_tupled_list():
 def download_llm_models():
-    from huggingface_hub import hf_hub_download
     global llm_models_tupled_list
     llm_models_tupled_list = []
     for k, v in llm_models.items():
@@ -890,7 +905,6 @@ def download_llm_models():
 def download_llm_model(filename):
-    from huggingface_hub import hf_hub_download
     if not filename in llm_models.keys(): return default_llm_model_filename
     try:
         hf_hub_download(repo_id = llm_models[filename][0], filename = filename, local_dir = llm_models_dir)
@@ -951,8 +965,6 @@ def get_dolphin_model_format(filename):
 def add_dolphin_models(query, format_name):
-    import re
-    from huggingface_hub import HfApi
     global llm_models
     api = HfApi()
     add_models = {}
@@ -964,20 +976,19 @@ def add_dolphin_models(query, format_name):
         if s and  "" in s: s.remove("")
         if len(s) == 1:
             repo = s[0]
-            if not api.repo_exists(repo_id = repo): return gr.update(visible=True)
             files = api.list_repo_files(repo_id = repo)
             for file in files:
                 if str(file).endswith(".gguf"): add_models[filename] = [repo, format]
         elif len(s) >= 2:
             repo = s[0]
             filename = s[1]
-            if not api.repo_exists(repo_id = repo) or not api.file_exists(repo_id = repo, filename = filename): return gr.update(visible=True)
             add_models[filename] = [repo, format]
-        else: return gr.update(visible=True)
     except Exception as e:
         print(e)
-        return gr.update(visible=True)
-    #print(add_models)
     llm_models = (llm_models | add_models).copy()
     update_llm_model_tupled_list()
     choices = get_dolphin_models()
@@ -1177,7 +1188,6 @@ Output should be enclosed in //GENBEGIN//:// and //://GENEND//. The text to be g
 def get_dolphin_sysprompt():
-    import re
     prompt = re.sub('<LANGUAGE>', dolphin_output_language, dolphin_system_prompt.get(dolphin_sysprompt_mode, ""))
     return prompt
@@ -1207,11 +1217,11 @@ def select_dolphin_language(lang: str):
 @wrapt_timeout_decorator.timeout(dec_timeout=5.0)
 def get_raw_prompt(msg: str):
-    import re
     m = re.findall(r'/GENBEGIN/(.+?)/GENEND/', msg, re.DOTALL)
     return re.sub(r'[*/:_"#\n]', ' ', ", ".join(m)).lower() if m else ""
 @spaces.GPU(duration=60)
 def dolphin_respond(
     message: str,
@@ -1225,87 +1235,92 @@ def dolphin_respond(
     repeat_penalty: float = 1.1,
     progress=gr.Progress(track_tqdm=True),
 ):
-    from pathlib import Path
-    progress(0, desc="Processing...")
-    if override_llm_format:
-        chat_template = override_llm_format
-    else:
-        chat_template = llm_models[model][1]
-    llm = Llama(
-        model_path=str(Path(f"{llm_models_dir}/{model}")),
-        flash_attn=True,
-        n_gpu_layers=81, # 81
-        n_batch=1024,
-        n_ctx=8192, #8192
-    )
-    provider = LlamaCppPythonProvider(llm)
-    agent = LlamaCppAgent(
-        provider,
-        system_prompt=f"{system_message}",
-        predefined_messages_formatter_type=chat_template if not isinstance(chat_template, MessagesFormatter) else None,
-        custom_messages_formatter=chat_template if isinstance(chat_template, MessagesFormatter) else None,
-        debug_output=False
-    )
-    settings = provider.get_provider_default_settings()
-    settings.temperature = temperature
-    settings.top_k = top_k
-    settings.top_p = top_p
-    settings.max_tokens = max_tokens
-    settings.repeat_penalty = repeat_penalty
-    settings.stream = True
-    messages = BasicChatHistory()
-    for msn in history:
-        user = {
-            'role': Roles.user,
-            'content': msn[0]
-        }
-        assistant = {
-            'role': Roles.assistant,
-            'content': msn[1]
-        }
-        messages.add_message(user)
-        messages.add_message(assistant)
-    stream = agent.get_chat_response(
-        message,
-        llm_sampling_settings=settings,
-        chat_history=messages,
-        returns_streaming_generator=True,
-        print_output=False
-    )
-    progress(0.5, desc="Processing...")
-    outputs = ""
-    for output in stream:
-        outputs += output
-        yield [(outputs, None)]
 def dolphin_parse(
     history: list[tuple[str, str]],
 ):
-    if dolphin_sysprompt_mode == "Chat with LLM" or not history or len(history) < 1:
-        return "", gr.update(visible=True), gr.update(visible=True)
     try:
         msg = history[-1][0]
         raw_prompt = get_raw_prompt(msg)
-    except Exception:
-        return "", gr.update(visible=True), gr.update(visible=True)
-    prompts = []
-    if dolphin_sysprompt_mode == "Japanese to Danbooru Dictionary" and is_japanese(raw_prompt):
-        prompts = list_uniq(jatags_to_danbooru_tags(to_list_ja(raw_prompt)) + ["nsfw", "explicit"])
-    else:
-        prompts = list_uniq(to_list(raw_prompt) + ["nsfw", "explicit"])
-    return ", ".join(prompts), gr.update(interactive=True), gr.update(interactive=True)
 @spaces.GPU(duration=60)
 def dolphin_respond_auto(
     message: str,
@@ -1319,94 +1334,100 @@ def dolphin_respond_auto(
     repeat_penalty: float = 1.1,
     progress=gr.Progress(track_tqdm=True),
 ):
-    #if not is_japanese(message): return [(None, None)]
-    from pathlib import Path
-    progress(0, desc="Processing...")
-    if override_llm_format:
-        chat_template = override_llm_format
-    else:
-        chat_template = llm_models[model][1]
-    llm = Llama(
-        model_path=str(Path(f"{llm_models_dir}/{model}")),
-        flash_attn=True,
-        n_gpu_layers=81, # 81
-        n_batch=1024,
-        n_ctx=8192, #8192
-    )
-    provider = LlamaCppPythonProvider(llm)
-    agent = LlamaCppAgent(
-        provider,
-        system_prompt=f"{system_message}",
-        predefined_messages_formatter_type=chat_template if not isinstance(chat_template, MessagesFormatter) else None,
-        custom_messages_formatter=chat_template if isinstance(chat_template, MessagesFormatter) else None,
-        debug_output=False
-    )
-    settings = provider.get_provider_default_settings()
-    settings.temperature = temperature
-    settings.top_k = top_k
-    settings.top_p = top_p
-    settings.max_tokens = max_tokens
-    settings.repeat_penalty = repeat_penalty
-    settings.stream = True
-    messages = BasicChatHistory()
-    for msn in history:
-        user = {
-            'role': Roles.user,
-            'content': msn[0]
-        }
-        assistant = {
-            'role': Roles.assistant,
-            'content': msn[1]
-        }
-        messages.add_message(user)
-        messages.add_message(assistant)
-    progress(0, desc="Translating...")
-    stream = agent.get_chat_response(
-        message,
-        llm_sampling_settings=settings,
-        chat_history=messages,
-        returns_streaming_generator=True,
-        print_output=False
-    )
-    progress(0.5, desc="Processing...")
-    outputs = ""
-    for output in stream:
-        outputs += output
-        yield [(outputs, None)]
 def dolphin_parse_simple(
     message: str,
     history: list[tuple[str, str]],
 ):
-    #if not is_japanese(message): return message
-    if dolphin_sysprompt_mode == "Chat with LLM" or not history or len(history) < 1: return message
     try:
         msg = history[-1][0]
         raw_prompt = get_raw_prompt(msg)
-    except Exception:
         return ""
-    prompts = []
-    if dolphin_sysprompt_mode == "Japanese to Danbooru Dictionary" and is_japanese(raw_prompt):
-        prompts = list_uniq(jatags_to_danbooru_tags(to_list_ja(raw_prompt)) + ["nsfw", "explicit", "rating_explicit"])
-    else:
-        prompts = list_uniq(to_list(raw_prompt) + ["nsfw", "explicit", "rating_explicit"])
-    return ", ".join(prompts)
 # https://huggingface.co/spaces/CaioXapelaum/GGUF-Playground
 import cv2
 cv2.setNumThreads(1)
 @spaces.GPU()
 def respond_playground(
     message,
@@ -1419,47 +1440,47 @@ def respond_playground(
     top_k,
     repeat_penalty,
 ):
-    if override_llm_format:
-        chat_template = override_llm_format
-    else:
-        chat_template = llm_models[model][1]
-    llm = Llama(
-        model_path=str(Path(f"{llm_models_dir}/{model}")),
-        flash_attn=True,
-        n_gpu_layers=81, # 81
-        n_batch=1024,
-        n_ctx=8192, #8192
-    )
-    provider = LlamaCppPythonProvider(llm)
-    agent = LlamaCppAgent(
-        provider,
-        system_prompt=f"{system_message}",
-        predefined_messages_formatter_type=chat_template if not isinstance(chat_template, MessagesFormatter) else None,
-        custom_messages_formatter=chat_template if isinstance(chat_template, MessagesFormatter) else None,
-        debug_output=False
-    )
-    settings = provider.get_provider_default_settings()
-    settings.temperature = temperature
-    settings.top_k = top_k
-    settings.top_p = top_p
-    settings.max_tokens = max_tokens
-    settings.repeat_penalty = repeat_penalty
-    settings.stream = True
-    messages = BasicChatHistory()
-    # Add user and assistant messages to the history
-    for msn in history:
-        user = {'role': Roles.user, 'content': msn[0]}
-        assistant = {'role': Roles.assistant, 'content': msn[1]}
-        messages.add_message(user)
-        messages.add_message(assistant)
-    # Stream the response
     try:
         stream = agent.get_chat_response(
             message,
             llm_sampling_settings=settings,
@@ -1473,4 +1494,5 @@ def respond_playground(
             outputs += output
             yield outputs
     except Exception as e:
-        yield f"Error during response generation: {str(e)}"

 import spaces
 import gradio as gr
+from pathlib import Path
+import re
+import torch
+from huggingface_hub import hf_hub_download, HfApi
 from llama_cpp import Llama
 from llama_cpp_agent import LlamaCppAgent, MessagesFormatterType
 from llama_cpp_agent.providers import LlamaCppPythonProvider
 from llama_cpp_agent.chat_history.messages import Roles
 from ja_to_danbooru.ja_to_danbooru import jatags_to_danbooru_tags
 import wrapt_timeout_decorator
 from llama_cpp_agent.messages_formatter import MessagesFormatter
 from formatter import mistral_v1_formatter, mistral_v2_formatter, mistral_v3_tekken_formatter
     #"": ["", MessagesFormatterType.OPEN_CHAT],
     #"": ["", MessagesFormatterType.CHATML],
     #"": ["", MessagesFormatterType.PHI_3],
+    #"": ["", MessagesFormatterType.GEMMA_2],
     "mn-12b-lyra-v2a1-q5_k_m.gguf": ["HalleyStarbun/MN-12B-Lyra-v2a1-Q5_K_M-GGUF", MessagesFormatterType.CHATML],
     "L3-8B-Tamamo-v1.i1-Q5_K_M.gguf": ["mradermacher/L3-8B-Tamamo-v1-i1-GGUF", MessagesFormatterType.LLAMA_3],
     "MN-Chinofun-12B-2.i1-Q4_K_M.gguf": ["mradermacher/MN-Chinofun-12B-2-i1-GGUF", MessagesFormatterType.MISTRAL],
     "ChatWaifu_22B_v2.0_preview.Q4_K_S.gguf": ["mradermacher/ChatWaifu_22B_v2.0_preview-GGUF", MessagesFormatterType.MISTRAL],
     "ChatWaifu_v1.4.Q5_K_M.gguf": ["mradermacher/ChatWaifu_v1.4-GGUF", MessagesFormatterType.MISTRAL],
     "ChatWaifu_v1.3.1.Q4_K_M.gguf": ["mradermacher/ChatWaifu_v1.3.1-GGUF", MessagesFormatterType.MISTRAL],
+    "Magnum_Dark_Madness_12b.Q4_K_S.gguf": ["mradermacher/Magnum_Dark_Madness_12b-GGUF", MessagesFormatterType.MISTRAL],
+    "Magnum_Lyra_Darkness_12b.Q4_K_M.gguf": ["mradermacher/Magnum_Lyra_Darkness_12b-GGUF", MessagesFormatterType.MISTRAL],
+    "Heart_Stolen-8B-task.i1-Q4_K_M.gguf": ["mradermacher/Heart_Stolen-8B-task-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Magnum_Backyard_Party_12b.Q4_K_M.gguf": ["mradermacher/Magnum_Backyard_Party_12b-GGUF", MessagesFormatterType.MISTRAL],
+    "Magnum_Madness-12b.Q4_K_M.gguf": ["mradermacher/Magnum_Madness-12b-GGUF", MessagesFormatterType.MISTRAL],
+    "L3.1-Moe-2x8B-v0.2.i1-Q4_K_M.gguf": ["mradermacher/L3.1-Moe-2x8B-v0.2-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Qwen2.5-14B-Wernicke-DPO.i1-Q4_K_M.gguf": ["mradermacher/Qwen2.5-14B-Wernicke-DPO-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Gemma-2-Ataraxy-v4d-9B.i1-Q4_K_M.gguf": ["mradermacher/Gemma-2-Ataraxy-v4d-9B-i1-GGUF", MessagesFormatterType.GEMMA_2],
+    "qwen2.5-14b-megamerge-pt2-q5_k_m.gguf": ["CultriX/Qwen2.5-14B-MegaMerge-pt2-Q5_K_M-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "quantqwen2-merged-16bit-q4_k_m.gguf": ["davidbzyk/QuantQwen2-merged-16bit-Q4_K_M-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Mistral-nemo-ja-rp-v0.2-Q4_K_S.gguf": ["ascktgcc/Mistral-nemo-ja-rp-v0.2-GGUF", MessagesFormatterType.MISTRAL],
+    "llama3.1-darkstorm-aspire-8b-q4_k_m.gguf": ["ZeroXClem/Llama3.1-DarkStorm-Aspire-8B-Q4_K_M-GGUF", MessagesFormatterType.LLAMA_3],
+    "llama-3-yggdrasil-astralspice-8b-q4_k_m.gguf": ["ZeroXClem/Llama-3-Yggdrasil-AstralSpice-8B-Q4_K_M-GGUF", MessagesFormatterType.LLAMA_3],
     "hermes-llama3-roleplay-1000-v2.Q5_K_M.gguf": ["mradermacher/hermes-llama3-roleplay-1000-v2-GGUF", MessagesFormatterType.LLAMA_3],
     "hermes-stheno-8B-v0.1.i1-Q5_K_M.gguf": ["mradermacher/hermes-stheno-8B-v0.1-i1-GGUF", MessagesFormatterType.LLAMA_3],
     "qwen-carpmuscle-r-v0.3.Q4_K_M.gguf": ["mradermacher/qwen-carpmuscle-r-v0.3-GGUF", MessagesFormatterType.OPEN_CHAT],
 llm_models_tupled_list = []
 default_llm_model_filename = list(llm_models.keys())[0]
 override_llm_format = None
+device = "cuda" if torch.cuda.is_available() else "cpu"
 def to_list(s):
 @wrapt_timeout_decorator.timeout(dec_timeout=3.5)
 def to_list_ja(s):
     s = re.sub(r'[、。]', ',', s)
     return [x.strip() for x in s.split(",") if not s == ""]
 def update_llm_model_tupled_list():
     global llm_models_tupled_list
     llm_models_tupled_list = []
     for k, v in llm_models.items():
 def download_llm_models():
     global llm_models_tupled_list
     llm_models_tupled_list = []
     for k, v in llm_models.items():
 def download_llm_model(filename):
     if not filename in llm_models.keys(): return default_llm_model_filename
     try:
         hf_hub_download(repo_id = llm_models[filename][0], filename = filename, local_dir = llm_models_dir)
 def add_dolphin_models(query, format_name):
     global llm_models
     api = HfApi()
     add_models = {}
         if s and  "" in s: s.remove("")
         if len(s) == 1:
             repo = s[0]
+            if not api.repo_exists(repo_id = repo): return gr.update()
             files = api.list_repo_files(repo_id = repo)
             for file in files:
                 if str(file).endswith(".gguf"): add_models[filename] = [repo, format]
         elif len(s) >= 2:
             repo = s[0]
             filename = s[1]
+            if not api.repo_exists(repo_id = repo) or not api.file_exists(repo_id = repo, filename = filename): return gr.update()
             add_models[filename] = [repo, format]
+        else: return gr.update()
     except Exception as e:
         print(e)
+        return gr.update()
     llm_models = (llm_models | add_models).copy()
     update_llm_model_tupled_list()
     choices = get_dolphin_models()
 def get_dolphin_sysprompt():
     prompt = re.sub('<LANGUAGE>', dolphin_output_language, dolphin_system_prompt.get(dolphin_sysprompt_mode, ""))
     return prompt
 @wrapt_timeout_decorator.timeout(dec_timeout=5.0)
 def get_raw_prompt(msg: str):
     m = re.findall(r'/GENBEGIN/(.+?)/GENEND/', msg, re.DOTALL)
     return re.sub(r'[*/:_"#\n]', ' ', ", ".join(m)).lower() if m else ""
+@torch.inference_mode()
 @spaces.GPU(duration=60)
 def dolphin_respond(
     message: str,
     repeat_penalty: float = 1.1,
     progress=gr.Progress(track_tqdm=True),
 ):
+    try:
+        progress(0, desc="Processing...")
+        if override_llm_format:
+            chat_template = override_llm_format
+        else:
+            chat_template = llm_models[model][1]
+        llm = Llama(
+            model_path=str(Path(f"{llm_models_dir}/{model}")),
+            flash_attn=True,
+            n_gpu_layers=81, # 81
+            n_batch=1024,
+            n_ctx=8192, #8192
+        )
+        provider = LlamaCppPythonProvider(llm)
+        agent = LlamaCppAgent(
+            provider,
+            system_prompt=f"{system_message}",
+            predefined_messages_formatter_type=chat_template if not isinstance(chat_template, MessagesFormatter) else None,
+            custom_messages_formatter=chat_template if isinstance(chat_template, MessagesFormatter) else None,
+            debug_output=False
+        )
+        settings = provider.get_provider_default_settings()
+        settings.temperature = temperature
+        settings.top_k = top_k
+        settings.top_p = top_p
+        settings.max_tokens = max_tokens
+        settings.repeat_penalty = repeat_penalty
+        settings.stream = True
+        messages = BasicChatHistory()
+        for msn in history:
+            user = {
+                'role': Roles.user,
+                'content': msn[0]
+            }
+            assistant = {
+                'role': Roles.assistant,
+                'content': msn[1]
+            }
+            messages.add_message(user)
+            messages.add_message(assistant)
+        stream = agent.get_chat_response(
+            message,
+            llm_sampling_settings=settings,
+            chat_history=messages,
+            returns_streaming_generator=True,
+            print_output=False
+        )
+        progress(0.5, desc="Processing...")
+        outputs = ""
+        for output in stream:
+            outputs += output
+            yield [(outputs, None)]
+    except Exception as e:
+        print(e)
+        yield [("", None)]
 def dolphin_parse(
     history: list[tuple[str, str]],
 ):
     try:
+        if dolphin_sysprompt_mode == "Chat with LLM" or not history or len(history) < 1:
+            return "", gr.update(), gr.update()
         msg = history[-1][0]
         raw_prompt = get_raw_prompt(msg)
+        prompts = []
+        if dolphin_sysprompt_mode == "Japanese to Danbooru Dictionary" and is_japanese(raw_prompt):
+            prompts = list_uniq(jatags_to_danbooru_tags(to_list_ja(raw_prompt)) + ["nsfw", "explicit"])
+        else:
+            prompts = list_uniq(to_list(raw_prompt) + ["nsfw", "explicit"])
+        return ", ".join(prompts), gr.update(interactive=True), gr.update(interactive=True)
+    except Exception as e:
+        print(e)
+        return "", gr.update(), gr.update()
+@torch.inference_mode()
 @spaces.GPU(duration=60)
 def dolphin_respond_auto(
     message: str,
     repeat_penalty: float = 1.1,
     progress=gr.Progress(track_tqdm=True),
 ):
+    try:
+        #if not is_japanese(message): return [(None, None)]
+        progress(0, desc="Processing...")
+        if override_llm_format:
+            chat_template = override_llm_format
+        else:
+            chat_template = llm_models[model][1]
+        llm = Llama(
+            model_path=str(Path(f"{llm_models_dir}/{model}")),
+            flash_attn=True,
+            n_gpu_layers=81, # 81
+            n_batch=1024,
+            n_ctx=8192, #8192
+        )
+        provider = LlamaCppPythonProvider(llm)
+        agent = LlamaCppAgent(
+            provider,
+            system_prompt=f"{system_message}",
+            predefined_messages_formatter_type=chat_template if not isinstance(chat_template, MessagesFormatter) else None,
+            custom_messages_formatter=chat_template if isinstance(chat_template, MessagesFormatter) else None,
+            debug_output=False
+        )
+        settings = provider.get_provider_default_settings()
+        settings.temperature = temperature
+        settings.top_k = top_k
+        settings.top_p = top_p
+        settings.max_tokens = max_tokens
+        settings.repeat_penalty = repeat_penalty
+        settings.stream = True
+        messages = BasicChatHistory()
+        for msn in history:
+            user = {
+                'role': Roles.user,
+                'content': msn[0]
+            }
+            assistant = {
+                'role': Roles.assistant,
+                'content': msn[1]
+            }
+            messages.add_message(user)
+            messages.add_message(assistant)
+        progress(0, desc="Translating...")
+        stream = agent.get_chat_response(
+            message,
+            llm_sampling_settings=settings,
+            chat_history=messages,
+            returns_streaming_generator=True,
+            print_output=False
+        )
+        progress(0.5, desc="Processing...")
+        outputs = ""
+        for output in stream:
+            outputs += output
+            yield [(outputs, None)], gr.update(), gr.update()
+    except Exception as e:
+        print(e)
+        yield [("", None)], gr.update(), gr.update()
 def dolphin_parse_simple(
     message: str,
     history: list[tuple[str, str]],
 ):
     try:
+        #if not is_japanese(message): return message
+        if dolphin_sysprompt_mode == "Chat with LLM" or not history or len(history) < 1: return message
         msg = history[-1][0]
         raw_prompt = get_raw_prompt(msg)
+        prompts = []
+        if dolphin_sysprompt_mode == "Japanese to Danbooru Dictionary" and is_japanese(raw_prompt):
+            prompts = list_uniq(jatags_to_danbooru_tags(to_list_ja(raw_prompt)) + ["nsfw", "explicit", "rating_explicit"])
+        else:
+            prompts = list_uniq(to_list(raw_prompt) + ["nsfw", "explicit", "rating_explicit"])
+        return ", ".join(prompts)
+    except Exception as e:
+        print(e)
         return ""
 # https://huggingface.co/spaces/CaioXapelaum/GGUF-Playground
 import cv2
 cv2.setNumThreads(1)
+@torch.inference_mode()
 @spaces.GPU()
 def respond_playground(
     message,
     top_k,
     repeat_penalty,
 ):
     try:
+        if override_llm_format:
+            chat_template = override_llm_format
+        else:
+            chat_template = llm_models[model][1]
+        llm = Llama(
+            model_path=str(Path(f"{llm_models_dir}/{model}")),
+            flash_attn=True,
+            n_gpu_layers=81, # 81
+            n_batch=1024,
+            n_ctx=8192, #8192
+        )
+        provider = LlamaCppPythonProvider(llm)
+        agent = LlamaCppAgent(
+            provider,
+            system_prompt=f"{system_message}",
+            predefined_messages_formatter_type=chat_template if not isinstance(chat_template, MessagesFormatter) else None,
+            custom_messages_formatter=chat_template if isinstance(chat_template, MessagesFormatter) else None,
+            debug_output=False
+        )
+        settings = provider.get_provider_default_settings()
+        settings.temperature = temperature
+        settings.top_k = top_k
+        settings.top_p = top_p
+        settings.max_tokens = max_tokens
+        settings.repeat_penalty = repeat_penalty
+        settings.stream = True
+        messages = BasicChatHistory()
+        # Add user and assistant messages to the history
+        for msn in history:
+            user = {'role': Roles.user, 'content': msn[0]}
+            assistant = {'role': Roles.assistant, 'content': msn[1]}
+            messages.add_message(user)
+            messages.add_message(assistant)
+        # Stream the response
         stream = agent.get_chat_response(
             message,
             llm_sampling_settings=settings,
             outputs += output
             yield outputs
     except Exception as e:
+        print(e)
+        yield ""

modutils.py CHANGED Viewed

@@ -5,6 +5,7 @@ import os
 import re
 from pathlib import Path
 from PIL import Image
 import shutil
 import requests
 from requests.adapters import HTTPAdapter
@@ -12,11 +13,16 @@ from urllib3.util import Retry
 import urllib.parse
 import pandas as pd
 from huggingface_hub import HfApi, HfFolder, hf_hub_download, snapshot_download
 from env import (HF_LORA_PRIVATE_REPOS1, HF_LORA_PRIVATE_REPOS2,
     HF_MODEL_USER_EX, HF_MODEL_USER_LIKES, DIFFUSERS_FORMAT_LORAS,
-    directory_loras, hf_read_token, HF_TOKEN, CIVITAI_API_KEY)
 MODEL_TYPE_DICT = {
@@ -46,7 +52,6 @@ def is_repo_name(s):
     return re.fullmatch(r'^[^/]+?/[^/]+?$', s)
-from translatepy import Translator
 translator = Translator()
 def translate_to_en(input: str):
     try:
@@ -64,6 +69,7 @@ def get_local_model_list(dir_path):
         if file.suffix in valid_extensions:
             file_path = str(Path(f"{dir_path}/{file.name}"))
             model_list.append(file_path)
     return model_list
@@ -98,21 +104,81 @@ def split_hf_url(url: str):
         print(e)
-def download_hf_file(directory, url, progress=gr.Progress(track_tqdm=True)):
-    hf_token = get_token()
     repo_id, filename, subfolder, repo_type = split_hf_url(url)
     try:
-        print(f"Downloading {url} to {directory}")
-        if subfolder is not None: path = hf_hub_download(repo_id=repo_id, filename=filename, subfolder=subfolder, repo_type=repo_type, local_dir=directory, token=hf_token)
-        else: path = hf_hub_download(repo_id=repo_id, filename=filename, repo_type=repo_type, local_dir=directory, token=hf_token)
         return path
     except Exception as e:
-        print(f"Failed to download: {e}")
         return None
-def download_things(directory, url, hf_token="", civitai_api_key=""):
     url = url.strip()
     if "drive.google.com" in url:
         original_dir = os.getcwd()
         os.chdir(directory)
@@ -123,18 +189,48 @@ def download_things(directory, url, hf_token="", civitai_api_key=""):
         # url = urllib.parse.quote(url, safe=':/')  # fix encoding
         if "/blob/" in url:
             url = url.replace("/blob/", "/resolve/")
-        download_hf_file(directory, url)
     elif "civitai.com" in url:
-        if "?" in url:
-            url = url.split("?")[0]
-        if civitai_api_key:
-            url = url + f"?token={civitai_api_key}"
-            os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
-        else:
             print("\033[91mYou need an API key to download Civitai models.\033[0m")
     else:
         os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
 def get_download_file(temp_dir, url, civitai_key="", progress=gr.Progress(track_tqdm=True)):
     if not "http" in url and is_repo_name(url) and not Path(url).exists():
@@ -173,7 +269,7 @@ def to_lora_key(path: str):
 def to_lora_path(key: str):
     if Path(key).is_file(): return key
-    path = Path(f"{directory_loras}/{escape_lora_basename(key)}.safetensors")
     return str(path)
@@ -203,25 +299,21 @@ def save_images(images: list[Image.Image], metadatas: list[str]):
         raise Exception(f"Failed to save image file:") from e
-def save_gallery_images(images, progress=gr.Progress(track_tqdm=True)):
-    from datetime import datetime, timezone, timedelta
     progress(0, desc="Updating gallery...")
-    dt_now = datetime.now(timezone(timedelta(hours=9)))
-    basename = dt_now.strftime('%Y%m%d_%H%M%S_')
-    i = 1
-    if not images: return images, gr.update(visible=False)
     output_images = []
     output_paths = []
-    for image in images:
-        filename = basename + str(i) + ".png"
-        i += 1
         oldpath = Path(image[0])
         newpath = oldpath
         try:
             if oldpath.exists():
                 newpath = oldpath.resolve().rename(Path(filename).resolve())
         except Exception as e:
-           print(e)
         finally:
             output_paths.append(str(newpath))
             output_images.append((str(newpath), str(filename)))
@@ -229,10 +321,47 @@ def save_gallery_images(images, progress=gr.Progress(track_tqdm=True)):
     return gr.update(value=output_images), gr.update(value=output_paths, visible=True)
 def download_private_repo(repo_id, dir_path, is_replace):
-    if not hf_read_token: return
     try:
-        snapshot_download(repo_id=repo_id, local_dir=dir_path, allow_patterns=['*.ckpt', '*.pt', '*.pth', '*.safetensors', '*.bin'], use_auth_token=hf_read_token)
     except Exception as e:
         print(f"Error: Failed to download {repo_id}.")
         print(e)
@@ -250,9 +379,9 @@ private_model_path_repo_dict = {} # {"local filepath": "huggingface repo_id", ..
 def get_private_model_list(repo_id, dir_path):
     global private_model_path_repo_dict
     api = HfApi()
-    if not hf_read_token: return []
     try:
-        files = api.list_repo_files(repo_id, token=hf_read_token)
     except Exception as e:
         print(f"Error: Failed to list {repo_id}.")
         print(e)
@@ -270,11 +399,11 @@ def get_private_model_list(repo_id, dir_path):
 def download_private_file(repo_id, path, is_replace):
     file = Path(path)
     newpath = Path(f'{file.parent.name}/{escape_lora_basename(file.stem)}{file.suffix}') if is_replace else file
-    if not hf_read_token or newpath.exists(): return
     filename = file.name
     dirname = file.parent.name
     try:
-        hf_hub_download(repo_id=repo_id, filename=filename, local_dir=dirname, use_auth_token=hf_read_token)
     except Exception as e:
         print(f"Error: Failed to download {filename}.")
         print(e)
@@ -404,9 +533,9 @@ def get_private_lora_model_lists():
     models1 = []
     models2 = []
     for repo in HF_LORA_PRIVATE_REPOS1:
-        models1.extend(get_private_model_list(repo, directory_loras))
     for repo in HF_LORA_PRIVATE_REPOS2:
-        models2.extend(get_private_model_list(repo, directory_loras))
     models = list_uniq(models1 + sorted(models2))
     private_lora_model_list = models.copy()
     return models
@@ -451,7 +580,7 @@ def get_civitai_info(path):
 def get_lora_model_list():
-    loras = list_uniq(get_private_lora_model_lists() + DIFFUSERS_FORMAT_LORAS + get_local_model_list(directory_loras))
     loras.insert(0, "None")
     loras.insert(0, "")
     return loras
@@ -503,14 +632,14 @@ def update_lora_dict(path):
 def download_lora(dl_urls: str):
     global loras_url_to_path_dict
     dl_path = ""
-    before = get_local_model_list(directory_loras)
     urls = []
     for url in [url.strip() for url in dl_urls.split(',')]:
-        local_path = f"{directory_loras}/{url.split('/')[-1]}"
         if not Path(local_path).exists():
-            download_things(directory_loras, url, HF_TOKEN, CIVITAI_API_KEY)
             urls.append(url)
-    after = get_local_model_list(directory_loras)
     new_files = list_sub(after, before)
     i = 0
     for file in new_files:
@@ -761,12 +890,14 @@ def update_loras(prompt, prompt_syntax, lora1, lora1_wt, lora2, lora2_wt, lora3,
      gr.update(value=tag5, label=label5, visible=on5), gr.update(visible=on5), gr.update(value=md5, visible=on5)
-def get_my_lora(link_url):
-    before = get_local_model_list(directory_loras)
     for url in [url.strip() for url in link_url.split(',')]:
-        if not Path(f"{directory_loras}/{url.split('/')[-1]}").exists():
-            download_things(directory_loras, url, HF_TOKEN, CIVITAI_API_KEY)
-    after = get_local_model_list(directory_loras)
     new_files = list_sub(after, before)
     for file in new_files:
         path = Path(file)
@@ -774,11 +905,16 @@ def get_my_lora(link_url):
             new_path = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}')
             path.resolve().rename(new_path.resolve())
             update_lora_dict(str(new_path))
     new_lora_model_list = get_lora_model_list()
     new_lora_tupled_list = get_all_lora_tupled_list()
     return gr.update(
-        choices=new_lora_tupled_list, value=new_lora_model_list[-1]
     ), gr.update(
         choices=new_lora_tupled_list
     ), gr.update(
@@ -787,6 +923,8 @@ def get_my_lora(link_url):
         choices=new_lora_tupled_list
     ), gr.update(
         choices=new_lora_tupled_list
     )
@@ -794,12 +932,12 @@ def upload_file_lora(files, progress=gr.Progress(track_tqdm=True)):
     progress(0, desc="Uploading...")
     file_paths = [file.name for file in files]
     progress(1, desc="Uploaded.")
-    return gr.update(value=file_paths, visible=True), gr.update(visible=True)
 def move_file_lora(filepaths):
     for file in filepaths:
-        path = Path(shutil.move(Path(file).resolve(), Path(f"./{directory_loras}").resolve()))
         newpath = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}')
         path.resolve().rename(newpath.resolve())
         update_lora_dict(str(newpath))
@@ -941,7 +1079,7 @@ def update_civitai_selection(evt: gr.SelectData):
         selected = civitai_last_choices[selected_index][1]
         return gr.update(value=selected)
     except Exception:
-        return gr.update(visible=True)
 def select_civitai_lora(search_result):
@@ -1425,3 +1563,78 @@ def get_model_pipeline(repo_id: str):
     else:
         return default

 import re
 from pathlib import Path
 from PIL import Image
+import numpy as np
 import shutil
 import requests
 from requests.adapters import HTTPAdapter
 import urllib.parse
 import pandas as pd
 from huggingface_hub import HfApi, HfFolder, hf_hub_download, snapshot_download
+from translatepy import Translator
+from unidecode import unidecode
+import copy
+from datetime import datetime, timezone, timedelta
+FILENAME_TIMEZONE = timezone(timedelta(hours=9)) # JST
 from env import (HF_LORA_PRIVATE_REPOS1, HF_LORA_PRIVATE_REPOS2,
     HF_MODEL_USER_EX, HF_MODEL_USER_LIKES, DIFFUSERS_FORMAT_LORAS,
+    DIRECTORY_LORAS, HF_READ_TOKEN, HF_TOKEN, CIVITAI_API_KEY)
 MODEL_TYPE_DICT = {
     return re.fullmatch(r'^[^/]+?/[^/]+?$', s)
 translator = Translator()
 def translate_to_en(input: str):
     try:
         if file.suffix in valid_extensions:
             file_path = str(Path(f"{dir_path}/{file.name}"))
             model_list.append(file_path)
+            #print('\033[34mFILE: ' + file_path + '\033[0m')
     return model_list
         print(e)
+def download_hf_file(directory, url, force_filename="", hf_token="", progress=gr.Progress(track_tqdm=True)):
     repo_id, filename, subfolder, repo_type = split_hf_url(url)
+    kwargs = {}
+    if subfolder is not None: kwargs["subfolder"] = subfolder
+    if force_filename: kwargs["force_filename"] = force_filename
     try:
+        print(f"Start downloading: {url} to {directory}")
+        path = hf_hub_download(repo_id=repo_id, filename=filename, repo_type=repo_type, local_dir=directory, token=hf_token, **kwargs)
         return path
     except Exception as e:
+        print(f"Download failed: {url} {e}")
         return None
+USER_AGENT = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:127.0) Gecko/20100101 Firefox/127.0'
+def request_json_data(url):
+    model_version_id = url.split('/')[-1]
+    if "?modelVersionId=" in model_version_id:
+        match = re.search(r'modelVersionId=(\d+)', url)
+        model_version_id = match.group(1)
+    endpoint_url = f"https://civitai.com/api/v1/model-versions/{model_version_id}"
+    params = {}
+    headers = {'User-Agent': USER_AGENT, 'content-type': 'application/json'}
+    session = requests.Session()
+    retries = Retry(total=5, backoff_factor=1, status_forcelist=[500, 502, 503, 504])
+    session.mount("https://", HTTPAdapter(max_retries=retries))
+    try:
+        result = session.get(endpoint_url, params=params, headers=headers, stream=True, timeout=(3.0, 15))
+        result.raise_for_status()
+        json_data = result.json()
+        return json_data if json_data else None
+    except Exception as e:
+        print(f"Error: {e}")
+        return None
+class ModelInformation:
+    def __init__(self, json_data):
+        self.model_version_id = json_data.get("id", "")
+        self.model_id = json_data.get("modelId", "")
+        self.download_url = json_data.get("downloadUrl", "")
+        self.model_url = f"https://civitai.com/models/{self.model_id}?modelVersionId={self.model_version_id}"
+        self.filename_url = next(
+            (v.get("name", "") for v in json_data.get("files", []) if str(self.model_version_id) in v.get("downloadUrl", "")), ""
+        )
+        self.filename_url = self.filename_url if self.filename_url else ""
+        self.description = json_data.get("description", "")
+        if self.description is None: self.description = ""
+        self.model_name = json_data.get("model", {}).get("name", "")
+        self.model_type = json_data.get("model", {}).get("type", "")
+        self.nsfw = json_data.get("model", {}).get("nsfw", False)
+        self.poi = json_data.get("model", {}).get("poi", False)
+        self.images = [img.get("url", "") for img in json_data.get("images", [])]
+        self.example_prompt = json_data.get("trainedWords", [""])[0] if json_data.get("trainedWords") else ""
+        self.original_json = copy.deepcopy(json_data)
+def retrieve_model_info(url):
+    json_data = request_json_data(url)
+    if not json_data:
+        return None
+    model_descriptor = ModelInformation(json_data)
+    return model_descriptor
+def download_things(directory, url, hf_token="", civitai_api_key="", romanize=False):
+    hf_token = get_token()
     url = url.strip()
+    downloaded_file_path = None
     if "drive.google.com" in url:
         original_dir = os.getcwd()
         os.chdir(directory)
         # url = urllib.parse.quote(url, safe=':/')  # fix encoding
         if "/blob/" in url:
             url = url.replace("/blob/", "/resolve/")
+        filename = unidecode(url.split('/')[-1]) if romanize else url.split('/')[-1]
+        download_hf_file(directory, url, filename, hf_token)
+        downloaded_file_path = os.path.join(directory, filename)
     elif "civitai.com" in url:
+        if not civitai_api_key:
             print("\033[91mYou need an API key to download Civitai models.\033[0m")
+        model_profile = retrieve_model_info(url)
+        if model_profile.download_url and model_profile.filename_url:
+            url = model_profile.download_url
+            filename = unidecode(model_profile.filename_url) if romanize else model_profile.filename_url
+        else:
+            if "?" in url:
+                url = url.split("?")[0]
+            filename = ""
+        url_dl = url + f"?token={civitai_api_key}"
+        print(f"Filename: {filename}")
+        param_filename = ""
+        if filename:
+            param_filename = f"-o '{filename}'"
+        aria2_command = (
+            f'aria2c --console-log-level=error --summary-interval=10 -c -x 16 '
+            f'-k 1M -s 16 -d "{directory}" {param_filename} "{url_dl}"'
+        )
+        os.system(aria2_command)
+        if param_filename and os.path.exists(os.path.join(directory, filename)):
+            downloaded_file_path = os.path.join(directory, filename)
     else:
         os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
+    return downloaded_file_path
 def get_download_file(temp_dir, url, civitai_key="", progress=gr.Progress(track_tqdm=True)):
     if not "http" in url and is_repo_name(url) and not Path(url).exists():
 def to_lora_path(key: str):
     if Path(key).is_file(): return key
+    path = Path(f"{DIRECTORY_LORAS}/{escape_lora_basename(key)}.safetensors")
     return str(path)
         raise Exception(f"Failed to save image file:") from e
+def save_gallery_images(images, model_name="", progress=gr.Progress(track_tqdm=True)):
     progress(0, desc="Updating gallery...")
+    basename = f"{model_name.split('/')[-1]}_{datetime.now(FILENAME_TIMEZONE).strftime('%Y%m%d_%H%M%S')}_"
+    if not images: return images, gr.update()
     output_images = []
     output_paths = []
+    for i, image in enumerate(images):
+        filename = f"{basename}{str(i + 1)}.png"
         oldpath = Path(image[0])
         newpath = oldpath
         try:
             if oldpath.exists():
                 newpath = oldpath.resolve().rename(Path(filename).resolve())
         except Exception as e:
+            print(e)
         finally:
             output_paths.append(str(newpath))
             output_images.append((str(newpath), str(filename)))
     return gr.update(value=output_images), gr.update(value=output_paths, visible=True)
+def save_gallery_history(images, files, history_gallery, history_files, progress=gr.Progress(track_tqdm=True)):
+    if not images or not files: return gr.update(), gr.update()
+    if not history_gallery: history_gallery = []
+    if not history_files: history_files = []
+    output_gallery = images + history_gallery
+    output_files = files + history_files
+    return gr.update(value=output_gallery), gr.update(value=output_files, visible=True)
+def save_image_history(image, gallery, files, model_name: str, progress=gr.Progress(track_tqdm=True)):
+    if not gallery: gallery = []
+    if not files: files = []
+    try:
+        basename = f"{model_name.split('/')[-1]}_{datetime.now(FILENAME_TIMEZONE).strftime('%Y%m%d_%H%M%S')}"
+        if image is None or not isinstance(image, (str, Image.Image, np.ndarray, tuple)): return gr.update(), gr.update()
+        filename = f"{basename}.png"
+        if isinstance(image, tuple): image = image[0]
+        if isinstance(image, str): oldpath = image
+        elif isinstance(image, Image.Image):
+            oldpath = "temp.png"
+            image.save(oldpath)
+        elif isinstance(image, np.ndarray):
+            oldpath = "temp.png"
+            Image.fromarray(image).convert('RGBA').save(oldpath)
+        oldpath = Path(oldpath)
+        newpath = oldpath
+        if oldpath.exists():
+            shutil.copy(oldpath.resolve(), Path(filename).resolve())
+            newpath = Path(filename).resolve()
+        files.insert(0, str(newpath))
+        gallery.insert(0, (str(newpath), str(filename)))
+    except Exception as e:
+        print(e)
+    finally:
+        return gr.update(value=gallery), gr.update(value=files, visible=True)
 def download_private_repo(repo_id, dir_path, is_replace):
+    if not HF_READ_TOKEN: return
     try:
+        snapshot_download(repo_id=repo_id, local_dir=dir_path, allow_patterns=['*.ckpt', '*.pt', '*.pth', '*.safetensors', '*.bin'], token=HF_READ_TOKEN)
     except Exception as e:
         print(f"Error: Failed to download {repo_id}.")
         print(e)
 def get_private_model_list(repo_id, dir_path):
     global private_model_path_repo_dict
     api = HfApi()
+    if not HF_READ_TOKEN: return []
     try:
+        files = api.list_repo_files(repo_id, token=HF_READ_TOKEN)
     except Exception as e:
         print(f"Error: Failed to list {repo_id}.")
         print(e)
 def download_private_file(repo_id, path, is_replace):
     file = Path(path)
     newpath = Path(f'{file.parent.name}/{escape_lora_basename(file.stem)}{file.suffix}') if is_replace else file
+    if not HF_READ_TOKEN or newpath.exists(): return
     filename = file.name
     dirname = file.parent.name
     try:
+        hf_hub_download(repo_id=repo_id, filename=filename, local_dir=dirname, token=HF_READ_TOKEN)
     except Exception as e:
         print(f"Error: Failed to download {filename}.")
         print(e)
     models1 = []
     models2 = []
     for repo in HF_LORA_PRIVATE_REPOS1:
+        models1.extend(get_private_model_list(repo, DIRECTORY_LORAS))
     for repo in HF_LORA_PRIVATE_REPOS2:
+        models2.extend(get_private_model_list(repo, DIRECTORY_LORAS))
     models = list_uniq(models1 + sorted(models2))
     private_lora_model_list = models.copy()
     return models
 def get_lora_model_list():
+    loras = list_uniq(get_private_lora_model_lists() + DIFFUSERS_FORMAT_LORAS + get_local_model_list(DIRECTORY_LORAS))
     loras.insert(0, "None")
     loras.insert(0, "")
     return loras
 def download_lora(dl_urls: str):
     global loras_url_to_path_dict
     dl_path = ""
+    before = get_local_model_list(DIRECTORY_LORAS)
     urls = []
     for url in [url.strip() for url in dl_urls.split(',')]:
+        local_path = f"{DIRECTORY_LORAS}/{url.split('/')[-1]}"
         if not Path(local_path).exists():
+            download_things(DIRECTORY_LORAS, url, HF_TOKEN, CIVITAI_API_KEY)
             urls.append(url)
+    after = get_local_model_list(DIRECTORY_LORAS)
     new_files = list_sub(after, before)
     i = 0
     for file in new_files:
      gr.update(value=tag5, label=label5, visible=on5), gr.update(visible=on5), gr.update(value=md5, visible=on5)
+def get_my_lora(link_url, romanize):
+    l_name = ""
+    l_path = ""
+    before = get_local_model_list(DIRECTORY_LORAS)
     for url in [url.strip() for url in link_url.split(',')]:
+        if not Path(f"{DIRECTORY_LORAS}/{url.split('/')[-1]}").exists():
+            l_name = download_things(DIRECTORY_LORAS, url, HF_TOKEN, CIVITAI_API_KEY, romanize)
+    after = get_local_model_list(DIRECTORY_LORAS)
     new_files = list_sub(after, before)
     for file in new_files:
         path = Path(file)
             new_path = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}')
             path.resolve().rename(new_path.resolve())
             update_lora_dict(str(new_path))
+            l_path = str(new_path)
     new_lora_model_list = get_lora_model_list()
     new_lora_tupled_list = get_all_lora_tupled_list()
+    msg_lora = "Downloaded"
+    if l_name:
+        msg_lora += f": <b>{l_name}</b>"
+        print(msg_lora)
     return gr.update(
+        choices=new_lora_tupled_list, value=l_path
     ), gr.update(
         choices=new_lora_tupled_list
     ), gr.update(
         choices=new_lora_tupled_list
     ), gr.update(
         choices=new_lora_tupled_list
+    ), gr.update(
+        value=msg_lora
     )
     progress(0, desc="Uploading...")
     file_paths = [file.name for file in files]
     progress(1, desc="Uploaded.")
+    return gr.update(value=file_paths, visible=True), gr.update()
 def move_file_lora(filepaths):
     for file in filepaths:
+        path = Path(shutil.move(Path(file).resolve(), Path(f"./{DIRECTORY_LORAS}").resolve()))
         newpath = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}')
         path.resolve().rename(newpath.resolve())
         update_lora_dict(str(newpath))
         selected = civitai_last_choices[selected_index][1]
         return gr.update(value=selected)
     except Exception:
+        return gr.update()
 def select_civitai_lora(search_result):
     else:
         return default
+EXAMPLES_GUI = [
+    [
+        "1girl, souryuu asuka langley, neon genesis evangelion, plugsuit, pilot suit, red bodysuit, sitting, crossing legs, black eye patch, cat hat, throne, symmetrical, looking down, from bottom, looking at viewer, outdoors, masterpiece, best quality, very aesthetic, absurdres",
+        "nsfw, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]",
+        1,
+        30,
+        7.5,
+        True,
+        -1,
+        "Euler a",
+        1152,
+        896,
+        "votepurchase/animagine-xl-3.1",
+    ],
+    [
+        "solo, princess Zelda OOT, score_9, score_8_up, score_8, medium breasts, cute, eyelashes, cute small face, long hair, crown braid, hairclip, pointy ears, soft curvy body, looking at viewer, smile, blush, white dress, medium body, (((holding the Master Sword))), standing, deep forest in the background",
+        "score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white,",
+        1,
+        30,
+        5.,
+        True,
+        -1,
+        "Euler a",
+        1024,
+        1024,
+        "votepurchase/ponyDiffusionV6XL",
+    ],
+    [
+        "1girl, oomuro sakurako, yuru yuri, official art, school uniform, anime artwork, anime style, vibrant, studio anime, highly detailed, masterpiece, best quality, very aesthetic, absurdres",
+        "photo, deformed, black and white, realism, disfigured, low contrast, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]",
+        1,
+        40,
+        7.0,
+        True,
+        -1,
+        "Euler a",
+        1024,
+        1024,
+        "Raelina/Rae-Diffusion-XL-V2",
+    ],
+    [
+        "1girl, akaza akari, yuru yuri, official art, anime artwork, anime style, vibrant, studio anime, highly detailed, masterpiece, best quality, very aesthetic, absurdres",
+        "photo, deformed, black and white, realism, disfigured, low contrast, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]",
+        1,
+        35,
+        7.0,
+        True,
+        -1,
+        "Euler a",
+        1024,
+        1024,
+        "Raelina/Raemu-XL-V4",
+    ],
+    [
+        "yoshida yuuko, machikado mazoku, 1girl, solo, demon horns,horns, school uniform, long hair, open mouth, skirt, demon girl, ahoge, shiny, shiny hair, anime artwork",
+        "nsfw, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]",
+        1,
+        50,
+        7.,
+        True,
+        -1,
+        "Euler a",
+        1024,
+        1024,
+        "cagliostrolab/animagine-xl-3.1",
+    ],
+]
+RESOURCES = (
+    """### Resources
+    - You can also try the image generator in Colab’s free tier, which provides free GPU [link](https://github.com/R3gm/SD_diffusers_interactive).
+    """
+)

requirements.txt CHANGED Viewed

@@ -1,6 +1,5 @@
 spaces
 accelerate
-spaces>=0.30.3
 diffusers
 invisible_watermark
 transformers
@@ -21,4 +20,5 @@ dartrs
 translatepy
 timm
 wrapt-timeout-decorator
-sentencepiece

 spaces
 accelerate
 diffusers
 invisible_watermark
 transformers
 translatepy
 timm
 wrapt-timeout-decorator
+sentencepiece
+unidecode

utils.py ADDED Viewed

	@@ -0,0 +1,421 @@

+import os
+import re
+import gradio as gr
+from constants import (
+    DIFFUSERS_FORMAT_LORAS,
+    CIVITAI_API_KEY,
+    HF_TOKEN,
+    MODEL_TYPE_CLASS,
+    DIRECTORY_LORAS,
+)
+from huggingface_hub import HfApi
+from diffusers import DiffusionPipeline
+from huggingface_hub import model_info as model_info_data
+from diffusers.pipelines.pipeline_loading_utils import variant_compatible_siblings
+from pathlib import PosixPath
+from unidecode import unidecode
+import urllib.parse
+import copy
+import requests
+from requests.adapters import HTTPAdapter
+from urllib3.util import Retry
+USER_AGENT = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:127.0) Gecko/20100101 Firefox/127.0'
+def request_json_data(url):
+    model_version_id = url.split('/')[-1]
+    if "?modelVersionId=" in model_version_id:
+        match = re.search(r'modelVersionId=(\d+)', url)
+        model_version_id = match.group(1)
+    endpoint_url = f"https://civitai.com/api/v1/model-versions/{model_version_id}"
+    params = {}
+    headers = {'User-Agent': USER_AGENT, 'content-type': 'application/json'}
+    session = requests.Session()
+    retries = Retry(total=5, backoff_factor=1, status_forcelist=[500, 502, 503, 504])
+    session.mount("https://", HTTPAdapter(max_retries=retries))
+    try:
+        result = session.get(endpoint_url, params=params, headers=headers, stream=True, timeout=(3.0, 15))
+        result.raise_for_status()
+        json_data = result.json()
+        return json_data if json_data else None
+    except Exception as e:
+        print(f"Error: {e}")
+        return None
+class ModelInformation:
+    def __init__(self, json_data):
+        self.model_version_id = json_data.get("id", "")
+        self.model_id = json_data.get("modelId", "")
+        self.download_url = json_data.get("downloadUrl", "")
+        self.model_url = f"https://civitai.com/models/{self.model_id}?modelVersionId={self.model_version_id}"
+        self.filename_url = next(
+            (v.get("name", "") for v in json_data.get("files", []) if str(self.model_version_id) in v.get("downloadUrl", "")), ""
+        )
+        self.filename_url = self.filename_url if self.filename_url else ""
+        self.description = json_data.get("description", "")
+        if self.description is None: self.description = ""
+        self.model_name = json_data.get("model", {}).get("name", "")
+        self.model_type = json_data.get("model", {}).get("type", "")
+        self.nsfw = json_data.get("model", {}).get("nsfw", False)
+        self.poi = json_data.get("model", {}).get("poi", False)
+        self.images = [img.get("url", "") for img in json_data.get("images", [])]
+        self.example_prompt = json_data.get("trainedWords", [""])[0] if json_data.get("trainedWords") else ""
+        self.original_json = copy.deepcopy(json_data)
+def retrieve_model_info(url):
+    json_data = request_json_data(url)
+    if not json_data:
+        return None
+    model_descriptor = ModelInformation(json_data)
+    return model_descriptor
+def download_things(directory, url, hf_token="", civitai_api_key="", romanize=False):
+    url = url.strip()
+    downloaded_file_path = None
+    if "drive.google.com" in url:
+        original_dir = os.getcwd()
+        os.chdir(directory)
+        os.system(f"gdown --fuzzy {url}")
+        os.chdir(original_dir)
+    elif "huggingface.co" in url:
+        url = url.replace("?download=true", "")
+        # url = urllib.parse.quote(url, safe=':/')  # fix encoding
+        if "/blob/" in url:
+            url = url.replace("/blob/", "/resolve/")
+        user_header = f'"Authorization: Bearer {hf_token}"'
+        filename = unidecode(url.split('/')[-1]) if romanize else url.split('/')[-1]
+        if hf_token:
+            os.system(f"aria2c --console-log-level=error --summary-interval=10 --header={user_header} -c -x 16 -k 1M -s 16 {url} -d {directory}  -o {filename}")
+        else:
+            os.system(f"aria2c --optimize-concurrent-downloads --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 {url} -d {directory}  -o {filename}")
+        downloaded_file_path = os.path.join(directory, filename)
+    elif "civitai.com" in url:
+        if not civitai_api_key:
+            print("\033[91mYou need an API key to download Civitai models.\033[0m")
+        model_profile = retrieve_model_info(url)
+        if model_profile.download_url and model_profile.filename_url:
+            url = model_profile.download_url
+            filename = unidecode(model_profile.filename_url) if romanize else model_profile.filename_url
+        else:
+            if "?" in url:
+                url = url.split("?")[0]
+            filename = ""
+        url_dl = url + f"?token={civitai_api_key}"
+        print(f"Filename: {filename}")
+        param_filename = ""
+        if filename:
+            param_filename = f"-o '{filename}'"
+        aria2_command = (
+            f'aria2c --console-log-level=error --summary-interval=10 -c -x 16 '
+            f'-k 1M -s 16 -d "{directory}" {param_filename} "{url_dl}"'
+        )
+        os.system(aria2_command)
+        if param_filename and os.path.exists(os.path.join(directory, filename)):
+            downloaded_file_path = os.path.join(directory, filename)
+        # # PLAN B
+        # # Follow the redirect to get the actual download URL
+        # curl_command = (
+        #     f'curl -L -sI --connect-timeout 5 --max-time 5 '
+        #     f'-H "Content-Type: application/json" '
+        #     f'-H "Authorization: Bearer {civitai_api_key}" "{url}"'
+        # )
+        # headers = os.popen(curl_command).read()
+        # # Look for the redirected "Location" URL
+        # location_match = re.search(r'location: (.+)', headers, re.IGNORECASE)
+        # if location_match:
+        #     redirect_url = location_match.group(1).strip()
+        #     # Extract the filename from the redirect URL's "Content-Disposition"
+        #     filename_match = re.search(r'filename%3D%22(.+?)%22', redirect_url)
+        #     if filename_match:
+        #         encoded_filename = filename_match.group(1)
+        #         # Decode the URL-encoded filename
+        #         decoded_filename = urllib.parse.unquote(encoded_filename)
+        #         filename = unidecode(decoded_filename) if romanize else decoded_filename
+        #         print(f"Filename: {filename}")
+        #         aria2_command = (
+        #             f'aria2c --console-log-level=error --summary-interval=10 -c -x 16 '
+        #             f'-k 1M -s 16 -d "{directory}" -o "{filename}" "{redirect_url}"'
+        #         )
+        #         return_code = os.system(aria2_command)
+        #         # if return_code != 0:
+        #         #     raise RuntimeError(f"Failed to download file: {filename}. Error code: {return_code}")
+        #         downloaded_file_path = os.path.join(directory, filename)
+        #         if not os.path.exists(downloaded_file_path):
+        #             downloaded_file_path = None
+        # if not downloaded_file_path:
+        #     # Old method
+        #     if "?" in url:
+        #         url = url.split("?")[0]
+        #     url = url + f"?token={civitai_api_key}"
+        #     os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
+    else:
+        os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
+    return downloaded_file_path
+def get_model_list(directory_path):
+    model_list = []
+    valid_extensions = {'.ckpt', '.pt', '.pth', '.safetensors', '.bin'}
+    for filename in os.listdir(directory_path):
+        if os.path.splitext(filename)[1] in valid_extensions:
+            # name_without_extension = os.path.splitext(filename)[0]
+            file_path = os.path.join(directory_path, filename)
+            # model_list.append((name_without_extension, file_path))
+            model_list.append(file_path)
+            print('\033[34mFILE: ' + file_path + '\033[0m')
+    return model_list
+def extract_parameters(input_string):
+    parameters = {}
+    input_string = input_string.replace("\n", "")
+    if "Negative prompt:" not in input_string:
+        if "Steps:" in input_string:
+            input_string = input_string.replace("Steps:", "Negative prompt: Steps:")
+        else:
+            print("Invalid metadata")
+            parameters["prompt"] = input_string
+            return parameters
+    parm = input_string.split("Negative prompt:")
+    parameters["prompt"] = parm[0].strip()
+    if "Steps:" not in parm[1]:
+        print("Steps not detected")
+        parameters["neg_prompt"] = parm[1].strip()
+        return parameters
+    parm = parm[1].split("Steps:")
+    parameters["neg_prompt"] = parm[0].strip()
+    input_string = "Steps:" + parm[1]
+    # Extracting Steps
+    steps_match = re.search(r'Steps: (\d+)', input_string)
+    if steps_match:
+        parameters['Steps'] = int(steps_match.group(1))
+    # Extracting Size
+    size_match = re.search(r'Size: (\d+x\d+)', input_string)
+    if size_match:
+        parameters['Size'] = size_match.group(1)
+        width, height = map(int, parameters['Size'].split('x'))
+        parameters['width'] = width
+        parameters['height'] = height
+    # Extracting other parameters
+    other_parameters = re.findall(r'(\w+): (.*?)(?=, \w+|$)', input_string)
+    for param in other_parameters:
+        parameters[param[0]] = param[1].strip('"')
+    return parameters
+def get_my_lora(link_url, romanize):
+    l_name = ""
+    for url in [url.strip() for url in link_url.split(',')]:
+        if not os.path.exists(f"./loras/{url.split('/')[-1]}"):
+            l_name = download_things(DIRECTORY_LORAS, url, HF_TOKEN, CIVITAI_API_KEY, romanize)
+    new_lora_model_list = get_model_list(DIRECTORY_LORAS)
+    new_lora_model_list.insert(0, "None")
+    new_lora_model_list = new_lora_model_list + DIFFUSERS_FORMAT_LORAS
+    msg_lora = "Downloaded"
+    if l_name:
+        msg_lora += f": <b>{l_name}</b>"
+        print(msg_lora)
+    return gr.update(
+        choices=new_lora_model_list
+    ), gr.update(
+        choices=new_lora_model_list
+    ), gr.update(
+        choices=new_lora_model_list
+    ), gr.update(
+        choices=new_lora_model_list
+    ), gr.update(
+        choices=new_lora_model_list
+    ), gr.update(
+        value=msg_lora
+    )
+def info_html(json_data, title, subtitle):
+    return f"""
+        <div style='padding: 0; border-radius: 10px;'>
+            <p style='margin: 0; font-weight: bold;'>{title}</p>
+            <details>
+                <summary>Details</summary>
+                <p style='margin: 0; font-weight: bold;'>{subtitle}</p>
+            </details>
+        </div>
+        """
+def get_model_type(repo_id: str):
+    api = HfApi(token=os.environ.get("HF_TOKEN"))  # if use private or gated model
+    default = "SD 1.5"
+    try:
+        model = api.model_info(repo_id=repo_id, timeout=5.0)
+        tags = model.tags
+        for tag in tags:
+            if tag in MODEL_TYPE_CLASS.keys(): return MODEL_TYPE_CLASS.get(tag, default)
+    except Exception:
+        return default
+    return default
+def restart_space(repo_id: str, factory_reboot: bool):
+    api = HfApi(token=os.environ.get("HF_TOKEN"))
+    try:
+        runtime = api.get_space_runtime(repo_id=repo_id)
+        if runtime.stage == "RUNNING":
+            api.restart_space(repo_id=repo_id, factory_reboot=factory_reboot)
+            print(f"Restarting space: {repo_id}")
+        else:
+            print(f"Space {repo_id} is in stage: {runtime.stage}")
+    except Exception as e:
+        print(e)
+def extract_exif_data(image):
+    if image is None: return ""
+    try:
+        metadata_keys = ['parameters', 'metadata', 'prompt', 'Comment']
+        for key in metadata_keys:
+            if key in image.info:
+                return image.info[key]
+        return str(image.info)
+    except Exception as e:
+        return f"Error extracting metadata: {str(e)}"
+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
+def download_diffuser_repo(repo_name: str, model_type: str, revision: str = "main", token=True):
+    variant = None
+    if token is True and not os.environ.get("HF_TOKEN"):
+        token = None
+    if model_type == "SDXL":
+        info = model_info_data(
+            repo_name,
+            token=token,
+            revision=revision,
+            timeout=5.0,
+        )
+        filenames = {sibling.rfilename for sibling in info.siblings}
+        model_filenames, variant_filenames = variant_compatible_siblings(
+            filenames, variant="fp16"
+        )
+        if len(variant_filenames):
+            variant = "fp16"
+    cached_folder = DiffusionPipeline.download(
+        pretrained_model_name=repo_name,
+        force_download=False,
+        token=token,
+        revision=revision,
+        # mirror="https://hf-mirror.com",
+        variant=variant,
+        use_safetensors=True,
+        trust_remote_code=False,
+        timeout=5.0,
+    )
+    if isinstance(cached_folder, PosixPath):
+        cached_folder = cached_folder.as_posix()
+    # Task model
+    # from huggingface_hub import hf_hub_download
+    # hf_hub_download(
+    #     task_model,
+    #     filename="diffusion_pytorch_model.safetensors",  # fix fp16 variant
+    # )
+    return cached_folder
+def progress_step_bar(step, total):
+    # Calculate the percentage for the progress bar width
+    percentage = min(100, ((step / total) * 100))
+    return f"""
+        <div style="position: relative; width: 100%; background-color: gray; border-radius: 5px; overflow: hidden;">
+            <div style="width: {percentage}%; height: 17px; background-color: #800080; transition: width 0.5s;"></div>
+            <div style="position: absolute; width: 100%; text-align: center; color: white; top: 0; line-height: 19px; font-size: 13px;">
+                {int(percentage)}%
+            </div>
+        </div>
+        """
+def html_template_message(msg):
+    return f"""
+        <div style="position: relative; width: 100%; background-color: gray; border-radius: 5px; overflow: hidden;">
+            <div style="width: 0%; height: 17px; background-color: #800080; transition: width 0.5s;"></div>
+            <div style="position: absolute; width: 100%; text-align: center; color: white; top: 0; line-height: 19px; font-size: 14px; font-weight: bold; text-shadow: 1px 1px 2px black;">
+                {msg}
+            </div>
+        </div>
+        """