Upload 6 files

app.py

@@ -4,7 +4,7 @@ import numpy as np
 
 # DiffuseCraft
 from dc import (infer, _infer, pass_result, get_diffusers_model_list, get_samplers, save_image_history,
-    get_vaes, enable_diffusers_model_detail, extract_exif_data, esrgan_upscale, UPSCALER_KEYS,
+    get_vaes, enable_diffusers_model_detail, extract_exif_data, process_upscale, UPSCALER_KEYS, FACE_RESTORATION_MODELS,
     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,
@@ -217,7 +217,7 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
                         image_mask = gr.Image(label="Image Mask", type="filepath", height=384, sources=["upload", "clipboard"], show_share_button=False, elem_classes="image")
                     with gr.Row():
                         strength = gr.Slider(minimum=0.01, maximum=1.0, step=0.01, value=0.55, label="Strength",
-                                            info="This option adjusts the level of changes for img2img and inpainting.")
+                                            info="This option adjusts the level of changes for img2img, repaint and inpaint.")
                         image_resolution = gr.Slider(minimum=64, maximum=2048, step=64, value=1024, label="Image Resolution",
                                                     info="The maximum proportional size of the generated image based on the uploaded image.")
                     with gr.Row():
@@ -289,13 +289,13 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
                         cm_btn_send_ip1.click(send_img, [img_source, img_result], [image_ip1, mask_ip1], queue=False, show_api=False)
                         cm_btn_send_ip2.click(send_img, [img_source, img_result], [image_ip2, mask_ip2], queue=False, show_api=False)
 
-                with gr.Tab("Hires fix / Detailfix"):
+                with gr.Tab("Hires fix / Detailfix / Face restoration"):
                     with gr.Accordion("Hires fix", open=True):
                         with gr.Row():
                             upscaler_model_path = gr.Dropdown(label="Upscaler", choices=UPSCALER_KEYS, value=UPSCALER_KEYS[0])
                             upscaler_increases_size = gr.Slider(minimum=1.1, maximum=4., step=0.1, value=1.2, label="Upscale by")
-                            esrgan_tile = gr.Slider(minimum=0, value=0, maximum=500, step=1, label="ESRGAN Tile")
-                            esrgan_tile_overlap = gr.Slider(minimum=1, maximum=200, step=1, value=8, label="ESRGAN Tile Overlap")
+                            upscaler_tile_size = gr.Slider(minimum=0, maximum=512, step=16, value=0, label="Upscaler Tile Size", info="0 = no tiling")
+                            upscaler_tile_overlap = gr.Slider(minimum=0, maximum=48, step=1, value=8, label="Upscaler Tile Overlap")
                         with gr.Row():
                             hires_steps = gr.Slider(minimum=0, value=30, maximum=100, step=1, label="Hires Steps")
                             hires_denoising_strength = gr.Slider(minimum=0.1, maximum=1.0, step=0.01, value=0.55, label="Hires Denoising Strength")
@@ -342,7 +342,13 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
                                 mask_dilation_b = gr.Number(label="Mask dilation:", value=4, minimum=1)
                                 mask_blur_b = gr.Number(label="Mask blur:", value=4, minimum=1)
                                 mask_padding_b = gr.Number(label="Mask padding:", value=32, minimum=1)
-                                    
+                    with gr.Accordion("Face restoration", open=True, visible=True):
+                        face_rest_options = [None] + FACE_RESTORATION_MODELS
+                        with gr.Row():
+                            face_restoration_model = gr.Dropdown(label="Face restoration model", choices=face_rest_options, value=face_rest_options[0])
+                            face_restoration_visibility = gr.Slider(minimum=0., maximum=1., step=0.001, value=1., label="Visibility")
+                            face_restoration_weight = gr.Slider(minimum=0., maximum=1., step=0.001, value=.5, label="Weight", info="(0 = maximum effect, 1 = minimum effect)")
+
                 with gr.Tab("Translation Settings"):
                     chatbot = gr.Chatbot(render_markdown=False, visible=False) # component for auto-translation
                     chat_model = gr.Dropdown(choices=get_dolphin_models(), value=get_dolphin_models()[0][1], allow_custom_value=True, label="Model")
@@ -396,13 +402,13 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
                 value_threshold, distance_threshold, recolor_gamma_correction, tile_blur_sigma,
                 image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1,
                 image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2,
-                upscaler_model_path, upscaler_increases_size, esrgan_tile, esrgan_tile_overlap, hires_steps, hires_denoising_strength,
+                upscaler_model_path, upscaler_increases_size, upscaler_tile_size, upscaler_tile_overlap, hires_steps, hires_denoising_strength,
                 hires_sampler, hires_schedule_type, hires_guidance_scale, hires_prompt, hires_negative_prompt,
                 adetailer_inpaint_only, adetailer_verbose, adetailer_sampler, adetailer_active_a,
                 prompt_ad_a, negative_prompt_ad_a, strength_ad_a, face_detector_ad_a, person_detector_ad_a, hand_detector_ad_a,
                 mask_dilation_a, mask_blur_a, mask_padding_a, adetailer_active_b, prompt_ad_b, negative_prompt_ad_b, strength_ad_b,
                 face_detector_ad_b, person_detector_ad_b, hand_detector_ad_b, mask_dilation_b, mask_blur_b, mask_padding_b,
-                active_textual_inversion, gpu_duration, auto_trans, recom_prompt],
+                active_textual_inversion, face_restoration_model, face_restoration_visibility, face_restoration_weight, gpu_duration, auto_trans, recom_prompt],
         outputs=[result],
         queue=True,
         show_progress="full",
@@ -424,13 +430,13 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
                 value_threshold, distance_threshold, recolor_gamma_correction, tile_blur_sigma,
                 image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1,
                 image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2,
-                upscaler_model_path, upscaler_increases_size, esrgan_tile, esrgan_tile_overlap, hires_steps, hires_denoising_strength,
+                upscaler_model_path, upscaler_increases_size, upscaler_tile_size, upscaler_tile_overlap, hires_steps, hires_denoising_strength,
                 hires_sampler, hires_schedule_type, hires_guidance_scale, hires_prompt, hires_negative_prompt,
                 adetailer_inpaint_only, adetailer_verbose, adetailer_sampler, adetailer_active_a,
                 prompt_ad_a, negative_prompt_ad_a, strength_ad_a, face_detector_ad_a, person_detector_ad_a, hand_detector_ad_a,
                 mask_dilation_a, mask_blur_a, mask_padding_a, adetailer_active_b, prompt_ad_b, negative_prompt_ad_b, strength_ad_b,
                 face_detector_ad_b, person_detector_ad_b, hand_detector_ad_b, mask_dilation_b, mask_blur_b, mask_padding_b,
-                active_textual_inversion, gpu_duration, auto_trans, recom_prompt],
+                active_textual_inversion, face_restoration_model, face_restoration_visibility, face_restoration_weight, gpu_duration, auto_trans, recom_prompt],
         outputs=[result],
         queue=False,
         show_api=True,
@@ -462,13 +468,13 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
                 value_threshold, distance_threshold, recolor_gamma_correction, tile_blur_sigma,
                 image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1,
                 image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2,
-                upscaler_model_path, upscaler_increases_size, esrgan_tile, esrgan_tile_overlap, hires_steps, hires_denoising_strength,
+                upscaler_model_path, upscaler_increases_size, upscaler_tile_size, upscaler_tile_overlap, hires_steps, hires_denoising_strength,
                 hires_sampler, hires_schedule_type, hires_guidance_scale, hires_prompt, hires_negative_prompt,
                 adetailer_inpaint_only, adetailer_verbose, adetailer_sampler, adetailer_active_a,
                 prompt_ad_a, negative_prompt_ad_a, strength_ad_a, face_detector_ad_a, person_detector_ad_a, hand_detector_ad_a,
                 mask_dilation_a, mask_blur_a, mask_padding_a, adetailer_active_b, prompt_ad_b, negative_prompt_ad_b, strength_ad_b,
                 face_detector_ad_b, person_detector_ad_b, hand_detector_ad_b, mask_dilation_b, mask_blur_b, mask_padding_b,
-                active_textual_inversion, gpu_duration, auto_trans, recom_prompt],
+                active_textual_inversion, face_restoration_model, face_restoration_visibility, face_restoration_weight, gpu_duration, auto_trans, recom_prompt],
         outputs=[result],
         queue=True,
         show_progress="full",
@@ -651,16 +657,15 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
     with gr.Tab("Upscaler"):
         with gr.Row():
             with gr.Column():
+                USCALER_TAB_KEYS = [name for name in UPSCALER_KEYS[9:]]
                 image_up_tab = gr.Image(label="Image", type="pil", sources=["upload"])
-                upscaler_tab = gr.Dropdown(label="Upscaler", choices=UPSCALER_KEYS[9:], value=UPSCALER_KEYS[11])
+                upscaler_tab = gr.Dropdown(label="Upscaler", choices=USCALER_TAB_KEYS, value=USCALER_TAB_KEYS[5])
                 upscaler_size_tab = gr.Slider(minimum=1., maximum=4., step=0.1, value=1.1, label="Upscale by")
                 generate_button_up_tab = gr.Button(value="START UPSCALE", variant="primary")
-
             with gr.Column():
                 result_up_tab = gr.Image(label="Result", type="pil", interactive=False, format="png")
-
                 generate_button_up_tab.click(
-                    fn=esrgan_upscale,
+                    fn=process_upscale,
                     inputs=[image_up_tab, upscaler_tab, upscaler_size_tab],
                     outputs=[result_up_tab],
                 )

constants.py

@@ -4,6 +4,7 @@ from stablepy import (
     scheduler_names,
     SD15_TASKS,
     SDXL_TASKS,
+    ALL_BUILTIN_UPSCALERS,
 )
 
 # - **Download Models**
@@ -43,12 +44,19 @@ LOAD_DIFFUSERS_FORMAT_MODEL = [
     'John6666/ntr-mix-illustrious-xl-noob-xl-ntrmix35-sdxl',
     'John6666/ntr-mix-illustrious-xl-noob-xl-v777-sdxl',
     'John6666/ntr-mix-illustrious-xl-noob-xl-v777forlora-sdxl',
+    'John6666/ntr-mix-illustrious-xl-noob-xl-xi-sdxl',
+    'John6666/mistoon-anime-v10illustrious-sdxl',
+    'John6666/hassaku-xl-illustrious-v10-sdxl',
+    'John6666/hassaku-xl-illustrious-v10style-sdxl',
     'John6666/haruki-mix-illustrious-v10-sdxl',
     'John6666/noobreal-v10-sdxl',
     'John6666/complicated-noobai-merge-vprediction-sdxl',
+    'Laxhar/noobai-XL-Vpred-0.75s',
+    'Laxhar/noobai-XL-Vpred-0.75',
     'Laxhar/noobai-XL-Vpred-0.65s',
     'Laxhar/noobai-XL-Vpred-0.65',
     'Laxhar/noobai-XL-Vpred-0.6',
+    'John6666/cat-tower-noobai-xl-checkpoint-v14vpred-sdxl',
     'John6666/noobai-xl-nai-xl-vpred05version-sdxl',
     'John6666/noobai-fusion2-vpred-itercomp-v1-sdxl',
     'John6666/noobai-xl-nai-xl-vpredtestversion-sdxl',
@@ -58,6 +66,7 @@ LOAD_DIFFUSERS_FORMAT_MODEL = [
     'John6666/illustrious-pencil-xl-v200-sdxl',
     'John6666/obsession-illustriousxl-v21-sdxl',
     'John6666/obsession-illustriousxl-v30-sdxl',
+    'John6666/obsession-illustriousxl-v31-sdxl',
     'John6666/wai-nsfw-illustrious-v70-sdxl',
     'John6666/illustrious-pony-mix-v3-sdxl',
     'John6666/nova-anime-xl-illustriousv10-sdxl',
@@ -81,6 +90,7 @@ LOAD_DIFFUSERS_FORMAT_MODEL = [
     'John6666/ras-real-anime-screencap-v1-sdxl',
     'John6666/duchaiten-pony-xl-no-score-v60-sdxl',
     'John6666/mistoon-anime-ponyalpha-sdxl',
+    'John6666/mistoon-xl-copper-v20fast-sdxl',
     'John6666/ebara-mfcg-pony-mix-v12-sdxl',
     'John6666/t-ponynai3-v51-sdxl',
     'John6666/t-ponynai3-v65-sdxl',
@@ -156,6 +166,7 @@ DIRECTORY_MODELS = 'models'
 DIRECTORY_LORAS = 'loras'
 DIRECTORY_VAES = 'vaes'
 DIRECTORY_EMBEDS = 'embedings'
+DIRECTORY_UPSCALERS = 'upscalers'
 
 CACHE_HF = "/home/user/.cache/huggingface/hub/"
 STORAGE_ROOT = "/home/user/"
@@ -184,27 +195,21 @@ TASK_STABLEPY = {
     'optical pattern ControlNet': 'pattern',
     'recolor ControlNet': 'recolor',
     'tile ControlNet': 'tile',
+    'repaint ControlNet': 'repaint',
 }
 
 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",
+    **{bu: bu for bu in ALL_BUILTIN_UPSCALERS if bu not in ["HAT x4", "DAT x4", "DAT x3", "DAT x2", "SwinIR 4x"]},
+    # "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",
+    # "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",
@@ -219,6 +224,7 @@ UPSCALER_KEYS = list(UPSCALER_DICT_GUI.keys())
 DIFFUSERS_CONTROLNET_MODEL = [
     "Automatic",
 
+    "brad-twinkl/controlnet-union-sdxl-1.0-promax",
     "xinsir/controlnet-union-sdxl-1.0",
     "xinsir/anime-painter",
     "Eugeoter/noob-sdxl-controlnet-canny",
@@ -241,7 +247,6 @@ DIFFUSERS_CONTROLNET_MODEL = [
     "r3gm/controlnet-recolor-sdxl-fp16",
     "r3gm/controlnet-openpose-twins-sdxl-1.0-fp16",
     "r3gm/controlnet-qr-pattern-sdxl-fp16",
-    "brad-twinkl/controlnet-union-sdxl-1.0-promax",
     "Yakonrus/SDXL_Controlnet_Tile_Realistic_v2",
     "TheMistoAI/MistoLine",
     "briaai/BRIA-2.3-ControlNet-Recoloring",

dc.py

@@ -6,8 +6,10 @@ from stablepy import (
     SCHEDULE_PREDICTION_TYPE_OPTIONS,
     check_scheduler_compatibility,
     TASK_AND_PREPROCESSORS,
+    FACE_RESTORATION_MODELS,
 )
 from constants import (
+    DIRECTORY_UPSCALERS,
     TASK_STABLEPY,
     TASK_MODEL_LIST,
     UPSCALER_DICT_GUI,
@@ -92,6 +94,10 @@ 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
 
+directories = [DIRECTORY_MODELS, DIRECTORY_LORAS, DIRECTORY_VAES, DIRECTORY_EMBEDS, DIRECTORY_UPSCALERS]
+for directory in directories:
+    os.makedirs(directory, exist_ok=True)
+
 # Download stuffs
 for url in [url.strip() for url in download_model.split(',')]:
     if not os.path.exists(f"./models/{url.split('/')[-1]}"):
@@ -138,6 +144,7 @@ flux_pipe = FluxPipeline.from_pretrained(
 components = flux_pipe.components
 components.pop("transformer", None)
 delete_model(flux_repo)
+# components = None
 
 ## BEGIN MOD
 class GuiSD:
@@ -310,8 +317,8 @@ class GuiSD:
         syntax_weights,
         upscaler_model_path,
         upscaler_increases_size,
-        esrgan_tile,
-        esrgan_tile_overlap,
+        upscaler_tile_size,
+        upscaler_tile_overlap,
         hires_steps,
         hires_denoising_strength,
         hires_sampler,
@@ -375,6 +382,9 @@ class GuiSD:
         mode_ip2,
         scale_ip2,
         pag_scale,
+        face_restoration_model,
+        face_restoration_visibility,
+        face_restoration_weight,
     ):
         info_state = html_template_message("Navigating latent space...")
         yield info_state, gr.update(), gr.update()
@@ -424,18 +434,15 @@ class GuiSD:
         if task == "inpaint" and not image_mask:
             raise ValueError("No mask image found: Specify one in 'Image Mask'")
 
-        if upscaler_model_path in UPSCALER_KEYS[:9]:
+        if "https://" not in str(UPSCALER_DICT_GUI[upscaler_model_path]):
             upscaler_model = upscaler_model_path
         else:
-            directory_upscalers = 'upscalers'
-            os.makedirs(directory_upscalers, exist_ok=True)
-
             url_upscaler = UPSCALER_DICT_GUI[upscaler_model_path]
 
-            if not os.path.exists(f"./upscalers/{url_upscaler.split('/')[-1]}"):
-                download_things(directory_upscalers, url_upscaler, HF_TOKEN)
+            if not os.path.exists(f"./{DIRECTORY_UPSCALERS}/{url_upscaler.split('/')[-1]}"):
+                download_things(DIRECTORY_UPSCALERS, url_upscaler, HF_TOKEN)
 
-            upscaler_model = f"./upscalers/{url_upscaler.split('/')[-1]}"
+            upscaler_model = f"./{DIRECTORY_UPSCALERS}/{url_upscaler.split('/')[-1]}"
 
         logging.getLogger("ultralytics").setLevel(logging.INFO if adetailer_verbose else logging.ERROR)
 
@@ -539,8 +546,8 @@ class GuiSD:
             "t2i_adapter_conditioning_factor": float(t2i_adapter_conditioning_factor),
             "upscaler_model_path": upscaler_model,
             "upscaler_increases_size": upscaler_increases_size,
-            "esrgan_tile": esrgan_tile,
-            "esrgan_tile_overlap": esrgan_tile_overlap,
+            "upscaler_tile_size": upscaler_tile_size,
+            "upscaler_tile_overlap": upscaler_tile_overlap,
             "hires_steps": hires_steps,
             "hires_denoising_strength": hires_denoising_strength,
             "hires_prompt": hires_prompt,
@@ -555,6 +562,9 @@ class GuiSD:
             "ip_adapter_model": params_ip_model,
             "ip_adapter_mode": params_ip_mode,
             "ip_adapter_scale": params_ip_scale,
+            "face_restoration_model": face_restoration_model,
+            "face_restoration_visibility": face_restoration_visibility,
+            "face_restoration_weight": face_restoration_weight,
         }
 
         # kwargs for diffusers pipeline
@@ -705,22 +715,26 @@ def sd_gen_generate_pipeline(*args):
 
 
 @spaces.GPU(duration=15)
-def esrgan_upscale(image, upscaler_name, upscaler_size):
+def process_upscale(image, upscaler_name, upscaler_size):
     if image is None: return None
 
     from stablepy.diffusers_vanilla.utils import save_pil_image_with_metadata
-    from stablepy import UpscalerESRGAN
+    from stablepy import load_upscaler_model
 
+    image = image.convert("RGB")
     exif_image = extract_exif_data(image)
 
-    url_upscaler = UPSCALER_DICT_GUI[upscaler_name]
-    directory_upscalers = 'upscalers'
-    os.makedirs(directory_upscalers, exist_ok=True)
-    if not os.path.exists(f"./upscalers/{url_upscaler.split('/')[-1]}"):
-        download_things(directory_upscalers, url_upscaler, HF_TOKEN)
+    name_upscaler = UPSCALER_DICT_GUI[upscaler_name]
+
+    if "https://" in str(name_upscaler):
+
+        if not os.path.exists(f"./{DIRECTORY_UPSCALERS}/{name_upscaler.split('/')[-1]}"):
+            download_things(DIRECTORY_UPSCALERS, name_upscaler, HF_TOKEN)
+
+        name_upscaler = f"./{DIRECTORY_UPSCALERS}/{name_upscaler.split('/')[-1]}"
 
-    scaler_beta = UpscalerESRGAN(0, 0)
-    image_up = scaler_beta.upscale(image, upscaler_size, f"./upscalers/{url_upscaler.split('/')[-1]}")
+    scaler_beta = load_upscaler_model(model=name_upscaler, tile=0, tile_overlap=8, device="cuda", half=True)
+    image_up = scaler_beta.upscale(image, upscaler_size, True)
 
     image_path = save_pil_image_with_metadata(image_up, f'{os.getcwd()}/up_images', exif_image)
 
@@ -760,13 +774,14 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
           value_threshold=0.1, distance_threshold=0.1, recolor_gamma_correction=1., tile_blur_sigma=9,
           image_ip1_dict=None, mask_ip1=None, model_ip1="plus_face", mode_ip1="original", scale_ip1=0.7,
           image_ip2_dict=None, mask_ip2=None, model_ip2="base", mode_ip2="style", scale_ip2=0.7,
-          upscaler_model_path=None, upscaler_increases_size=1.0, esrgan_tile=5, esrgan_tile_overlap=8, hires_steps=30, hires_denoising_strength=0.55,
+          upscaler_model_path=None, upscaler_increases_size=1.0, upscaler_tile_size=0, upscaler_tile_overlap=8, hires_steps=30, hires_denoising_strength=0.55,
           hires_sampler="Use same sampler", hires_schedule_type="Use same schedule type", hires_guidance_scale=-1, hires_prompt="", hires_negative_prompt="",
           adetailer_inpaint_only=True, adetailer_verbose=False, adetailer_sampler="Use same sampler", adetailer_active_a=False,
           prompt_ad_a="", negative_prompt_ad_a="", strength_ad_a=0.35, face_detector_ad_a=True, person_detector_ad_a=True, hand_detector_ad_a=False,
           mask_dilation_a=4, mask_blur_a=4, mask_padding_a=32, adetailer_active_b=False, prompt_ad_b="", negative_prompt_ad_b="", strength_ad_b=0.35,
           face_detector_ad_b=True, person_detector_ad_b=True, hand_detector_ad_b=False, mask_dilation_b=4, mask_blur_b=4, mask_padding_b=32,
-          active_textual_inversion=False, gpu_duration=59, translate=False, recom_prompt=True, progress=gr.Progress(track_tqdm=True)):
+          active_textual_inversion=False, face_restoration_model=None, face_restoration_visibility=1., face_restoration_weight=.5,
+          gpu_duration=59, translate=False, recom_prompt=True, progress=gr.Progress(track_tqdm=True)):
     MAX_SEED = np.iinfo(np.int32).max
 
     image_mask = image_control_dict['layers'][0] if isinstance(image_control_dict, dict) and not image_mask else image_mask
@@ -833,7 +848,7 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
         height, width, model_name, vae, task, image_control, preprocessor_name, preprocess_resolution, image_resolution,
         style_prompt, style_json, image_mask, strength, low_threshold, high_threshold, value_threshold, distance_threshold,
         recolor_gamma_correction, tile_blur_sigma, control_net_output_scaling, control_net_start_threshold, control_net_stop_threshold,
-        active_textual_inversion, prompt_syntax, upscaler_model_path, upscaler_increases_size, esrgan_tile, esrgan_tile_overlap,
+        active_textual_inversion, prompt_syntax, upscaler_model_path, upscaler_increases_size, upscaler_tile_size, upscaler_tile_overlap,
         hires_steps, hires_denoising_strength, hires_sampler, hires_prompt, hires_negative_prompt, hires_before_adetailer, hires_after_adetailer,
         hires_schedule_type, hires_guidance_scale, controlnet_model, loop_generation, leave_progress_bar, disable_progress_bar, image_previews,
         display_images, save_generated_images, filename_pattern, image_storage_location, retain_compel_previous_load, retain_detailfix_model_previous_load,
@@ -842,7 +857,8 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
         strength_ad_a, face_detector_ad_a, person_detector_ad_a, hand_detector_ad_a, mask_dilation_a, mask_blur_a, mask_padding_a,
         adetailer_active_b, prompt_ad_b, negative_prompt_ad_b, strength_ad_b, face_detector_ad_b, person_detector_ad_b, hand_detector_ad_b,
         mask_dilation_b, mask_blur_b, mask_padding_b, retain_task_cache, guidance_rescale, image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1,
-        image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2, pag_scale, load_lora_cpu, verbose_info, gpu_duration
+        image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2, pag_scale, face_restoration_model, face_restoration_visibility, face_restoration_weight,
+        load_lora_cpu, verbose_info, gpu_duration
     ):
         images = stream_images if isinstance(stream_images, list) else images
     progress(1, desc="Inference completed.")
@@ -862,13 +878,14 @@ def _infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidanc
            value_threshold=0.1, distance_threshold=0.1, recolor_gamma_correction=1., tile_blur_sigma=9,
            image_ip1_dict=None, mask_ip1=None, model_ip1="plus_face", mode_ip1="original", scale_ip1=0.7,
            image_ip2_dict=None, mask_ip2=None, model_ip2="base", mode_ip2="style", scale_ip2=0.7,
-           upscaler_model_path=None, upscaler_increases_size=1.0, esrgan_tile=5, esrgan_tile_overlap=8, hires_steps=30, hires_denoising_strength=0.55,
+           upscaler_model_path=None, upscaler_increases_size=1.0, upscaler_tile_size=0, upscaler_tile_overlap=8, hires_steps=30, hires_denoising_strength=0.55,
            hires_sampler="Use same sampler", hires_schedule_type="Use same schedule type", hires_guidance_scale=-1, hires_prompt="", hires_negative_prompt="",
            adetailer_inpaint_only=True, adetailer_verbose=False, adetailer_sampler="Use same sampler", adetailer_active_a=False,
            prompt_ad_a="", negative_prompt_ad_a="", strength_ad_a=0.35, face_detector_ad_a=True, person_detector_ad_a=True, hand_detector_ad_a=False,
            mask_dilation_a=4, mask_blur_a=4, mask_padding_a=32, adetailer_active_b=False, prompt_ad_b="", negative_prompt_ad_b="", strength_ad_b=0.35,
            face_detector_ad_b=True, person_detector_ad_b=True, hand_detector_ad_b=False, mask_dilation_b=4, mask_blur_b=4, mask_padding_b=32,
-           active_textual_inversion=False, gpu_duration=59, translate=False, recom_prompt=True, progress=gr.Progress(track_tqdm=True)):
+           active_textual_inversion=False, face_restoration_model=None, face_restoration_visibility=1., face_restoration_weight=.5,
+           gpu_duration=59, translate=False, recom_prompt=True, progress=gr.Progress(track_tqdm=True)):
     return gr.update()
 
 

llmdolphin.py

@@ -37,6 +37,7 @@ llm_models = {
     "MN-12B-Lyra-v4-Q4_K_M.gguf": ["bartowski/MN-12B-Lyra-v4-GGUF", MessagesFormatterType.CHATML],
     "Lyra4-Gutenberg-12B.Q4_K_M.gguf": ["mradermacher/Lyra4-Gutenberg-12B-GGUF", MessagesFormatterType.CHATML],
     "Llama-3.1-8B-EZO-1.1-it.Q5_K_M.gguf": ["mradermacher/Llama-3.1-8B-EZO-1.1-it-GGUF", MessagesFormatterType.MISTRAL],
+    "Magnolia-v2-12B.i1-Q4_K_M.gguf": ["mradermacher/Magnolia-v2-12B-i1-GGUF", MessagesFormatterType.MISTRAL],
     "MN-12B-Starcannon-v1.i1-Q4_K_M.gguf": ["mradermacher/MN-12B-Starcannon-v1-i1-GGUF", MessagesFormatterType.MISTRAL],
     "MN-12B-Starcannon-v2.i1-Q4_K_M.gguf": ["mradermacher/MN-12B-Starcannon-v2-i1-GGUF", MessagesFormatterType.CHATML],
     "MN-12B-Starcannon-v3.i1-Q4_K_M.gguf": ["mradermacher/MN-12B-Starcannon-v3-i1-GGUF", MessagesFormatterType.CHATML],
@@ -84,6 +85,59 @@ 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],
+    "Qwen2.5-7B-Instruct-kto.Q5_K_M.gguf": ["mradermacher/Qwen2.5-7B-Instruct-kto-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Poppy_Porpoise-v0.7-L3-8B.i1-Q5_K_M.gguf": ["mradermacher/Poppy_Porpoise-v0.7-L3-8B-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "NeuralStar_Story-9b.i1-Q4_K_M.gguf": ["mradermacher/NeuralStar_Story-9b-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "MT5-Gen3-MAG-gemma-2-9B.Q4_K_M.gguf": ["mradermacher/MT5-Gen3-MAG-gemma-2-9B-GGUF", MessagesFormatterType.ALPACA],
+    "MT5-Gen3-MAGBMU-gemma-2-9B.Q4_K_M.gguf": ["mradermacher/MT5-Gen3-MAGBMU-gemma-2-9B-GGUF", MessagesFormatterType.ALPACA],
+    "MT4-Gen3-MM-gemma-2-Rv0.4MT4g2-9B.Q4_K_M.gguf": ["mradermacher/MT4-Gen3-MM-gemma-2-Rv0.4MT4g2-9B-GGUF", MessagesFormatterType.ALPACA],
+    "L3-8B-Poppy-Sunspice-experiment-c.i1-Q5_K_M.gguf": ["mradermacher/L3-8B-Poppy-Sunspice-experiment-c-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Gemma-Evo-10B.i1-Q4_K_M.gguf": ["mradermacher/Gemma-Evo-10B-i1-GGUF", MessagesFormatterType.ALPACA],
+    "Gemma2-9B-test-novelistwo.Q4_K_M.gguf": ["mradermacher/Gemma2-9B-test-novelistwo-GGUF", MessagesFormatterType.ALPACA],
+    "Gemma-2-9b-baymax.i1-Q4_K_M.gguf": ["mradermacher/Gemma-2-9b-baymax-i1-GGUF", MessagesFormatterType.ALPACA],
+    "eule-qwen2.5instruct-7b-111224.i1-Q5_K_M.gguf": ["mradermacher/eule-qwen2.5instruct-7b-111224-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Creative-7B-nerd.i1-Q5_K_M.gguf": ["mradermacher/Creative-7B-nerd-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "FuseChat-Llama-3.1-8B-Instruct-Q5_K_M.gguf": ["bartowski/FuseChat-Llama-3.1-8B-Instruct-GGUF", MessagesFormatterType.LLAMA_3],
+    "Sailor2-8B-Chat-Uncensored.i1-Q4_K_M.gguf": ["mradermacher/Sailor2-8B-Chat-Uncensored-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "oh-dcft-v3.1-llama-3.1-8b.i1-Q5_K_M.gguf": ["mradermacher/oh-dcft-v3.1-llama-3.1-8b-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "oh-dcft-v3.1-claude-3-5-haiku-20241022.i1-Q5_K_M.gguf": ["mradermacher/oh-dcft-v3.1-claude-3-5-haiku-20241022-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "NovaSpark.i1-Q5_K_M.gguf": ["mradermacher/NovaSpark-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Novaeus-Promptist-7B-Instruct.i1-Q5_K_M.gguf": ["mradermacher/Novaeus-Promptist-7B-Instruct-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "suzume-llama-3-8B-multilingual.i1-Q5_K_M.gguf": ["mradermacher/suzume-llama-3-8B-multilingual-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "suzume-llama-3-8B-japanese.i1-Q4_K_M.gguf": ["mradermacher/suzume-llama-3-8B-japanese-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "nepoticide-12B-Unslop-Unleashed-Mell-RPMax.i1-Q4_K_M.gguf": ["mradermacher/nepoticide-12B-Unslop-Unleashed-Mell-RPMax-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "NemoMix-12B-DellaV1b.i1-Q4_K_M.gguf": ["mradermacher/NemoMix-12B-DellaV1b-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "NekoMix-12B.i1-Q4_K_M.gguf": ["mradermacher/NekoMix-12B-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Moriyasu_Qwen2_JP_7B.Q5_K_M.gguf": ["mradermacher/Moriyasu_Qwen2_JP_7B-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "MN-Funhouse-12B.i1-Q4_K_M.gguf": ["mradermacher/MN-Funhouse-12B-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Mistralv0.2-Erosumikav2-Franken-Sonya-10.5B.i1-Q4_K_M.gguf": ["mradermacher/Mistralv0.2-Erosumikav2-Franken-Sonya-10.5B-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Mistral-Reddit-12B.i1-Q4_K_M.gguf": ["mradermacher/Mistral-Reddit-12B-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "MistralMusic.Q5_K_M.gguf": ["mradermacher/MistralMusic-GGUF", MessagesFormatterType.MISTRAL],
+    "miscii-Virtuoso-Small.i1-Q4_K_M.gguf": ["mradermacher/miscii-Virtuoso-Small-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Mermaid-Dolphin-Mixtral-2x7b.i1-Q4_K_M.gguf": ["mradermacher/Mermaid-Dolphin-Mixtral-2x7b-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Merged-Tasty-Unsloth-Llama-3.1-8B-v4.Q5_K_M.gguf": ["mradermacher/Merged-Tasty-Unsloth-Llama-3.1-8B-v4-GGUF", MessagesFormatterType.LLAMA_3],
+    "MedWest-7B.i1-Q5_K_M.gguf": ["mradermacher/MedWest-7B-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Matter-0.1-7B-boost-DPO-preview.i1-Q5_K_M.gguf": ["mradermacher/Matter-0.1-7B-boost-DPO-preview-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Magnolia-v3-12B.i1-Q4_K_M.gguf": ["mradermacher/Magnolia-v3-12B-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Llama-TI-8B.i1-Q5_K_M.gguf": ["mradermacher/Llama-TI-8B-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Llama3-Chinese-8B-Instruct-Agent-v1.Q5_K_M.gguf": ["mradermacher/Llama3-Chinese-8B-Instruct-Agent-v1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Llama-3-8B-Instruct-DPO-v0.3.i1-Q5_K_M.gguf": ["mradermacher/Llama-3-8B-Instruct-DPO-v0.3-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Llama3.1-Reddit-Writer-8B.i1-Q5_K_M.gguf": ["mradermacher/Llama3.1-Reddit-Writer-8B-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Llama-3.1-8B-Tortoise.i1-Q5_K_M.gguf": ["mradermacher/Llama-3.1-8B-Tortoise-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "LightChatAssistant-2x7B.i1-Q4_K_M.gguf": ["mradermacher/LightChatAssistant-2x7B-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Lamarck-14B-v0.3.i1-Q4_K_M.gguf": ["mradermacher/Lamarck-14B-v0.3-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "lamarck-14b-prose-model_stock.Q4_K_M.gguf": ["mradermacher/lamarck-14b-prose-model_stock-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "L3-ColdBrew-DualCore.i1-Q5_K_M.gguf": ["mradermacher/L3-ColdBrew-DualCore-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "L3.1-Tiv-10B.i1-Q4_K_M.gguf": ["mradermacher/L3.1-Tiv-10B-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "L3.1-Sekplus-10B.i1-Q4_K_M.gguf": ["mradermacher/L3.1-Sekplus-10B-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "KRONOS-8B-V1-P3.Q5_K_M.gguf": ["mradermacher/KRONOS-8B-V1-P3-GGUF", MessagesFormatterType.LLAMA_3],
+    "Gemma2-9B-test-novelist.Q4_K_M.gguf": ["mradermacher/Gemma2-9B-test-novelist-GGUF", MessagesFormatterType.ALPACA],
+    "G2-Nowing-9B.i1-Q4_K_M.gguf": ["mradermacher/G2-Nowing-9B-i1-GGUF", MessagesFormatterType.ALPACA],
+    "G2-Nowing-9B-32K-YS.i1-Q4_K_M.gguf": ["mradermacher/G2-Nowing-9B-32K-YS-i1-GGUF", MessagesFormatterType.ALPACA],
+    "D_AU-Tiefighter-Plus-OrcaMaid-V3-13B-32k-slerp.i1-Q4_K_M.gguf": ["mradermacher/D_AU-Tiefighter-Plus-OrcaMaid-V3-13B-32k-slerp-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Baldur-KTO.i1-Q5_K_M.gguf": ["mradermacher/Baldur-KTO-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "FuseChat-Qwen-2.5-7B-Instruct-Q5_K_M.gguf": ["bartowski/FuseChat-Qwen-2.5-7B-Instruct-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "FuseChat-Gemma-2-9B-Instruct-Q4_K_M.gguf": ["bartowski/FuseChat-Gemma-2-9B-Instruct-GGUF", MessagesFormatterType.ALPACA],
     "Josiefied-abliteratedV4-Qwen2.5-14B-Inst-BaseMerge-TIES.Q4_K_M.gguf": ["mradermacher/Josiefied-abliteratedV4-Qwen2.5-14B-Inst-BaseMerge-TIES-GGUF", MessagesFormatterType.OPEN_CHAT],
     "InfinityLake-2x7B.i1-Q4_K_M.gguf": ["mradermacher/InfinityLake-2x7B-i1-GGUF", MessagesFormatterType.MISTRAL],
     "InfinityKuno-2x7B.i1-Q4_K_M.gguf": ["mradermacher/InfinityKuno-2x7B-i1-GGUF", MessagesFormatterType.MISTRAL],

requirements.txt

@@ -1,4 +1,4 @@
-git+https://github.com/R3gm/stablepy.git@a9fe2dc  # -b refactor_sampler_fix
+git+https://github.com/R3gm/stablepy.git@5e66972  # -b refactor_sampler_fix
 accelerate
 diffusers
 invisible_watermark

utils.py

@@ -300,7 +300,7 @@ def get_model_type(repo_id: str):
     default = "SD 1.5"
     try:
         if os.path.exists(repo_id):
-            tag = checkpoint_model_type(repo_id)
+            tag, _, _ = checkpoint_model_type(repo_id)
             return DIFFUSECRAFT_CHECKPOINT_NAME[tag]
         else:
             model = api.model_info(repo_id=repo_id, timeout=5.0)