refactor gui.py

app.py

@@ -184,6 +184,7 @@ import diffusers
 
 diffusers.utils.logging.set_verbosity(40)
 import warnings
+from gui import GuiSD
 
 warnings.filterwarnings(
     action="ignore",
@@ -204,407 +205,12 @@ warnings.filterwarnings(
 logger.setLevel(logging.DEBUG)
 
 
-class GuiSD:
-    def __init__(self, stream=True):
-        self.model = None
-
-        print("Loading model...")
-        self.model = Model_Diffusers(
-            base_model_id="cagliostrolab/animagine-xl-3.1",
-            task_name="txt2img",
-            vae_model=None,
-            type_model_precision=torch.float16,
-            retain_task_model_in_cache=False,
-        )
-
-    def load_new_model(
-            self,
-            model_name,
-            vae_model,
-            task,
-            progress=gr.Progress(track_tqdm=True)):
-        """
-        :param model_name:
-        :param vae_model:
-        :param task:
-        :param progress:
-        """
-        yield f"Loading model: {model_name}"
-
-        vae_model = vae_model if vae_model != "None" else None
-
-        if model_name in model_list:
-            model_is_xl = "xl" in model_name.lower()
-            sdxl_in_vae = vae_model and "sdxl" in vae_model.lower()
-            model_type = "SDXL" if model_is_xl else "SD 1.5"
-            incompatible_vae = (model_is_xl and vae_model and not sdxl_in_vae) or (not model_is_xl and sdxl_in_vae)
-
-            if incompatible_vae:
-                vae_model = None
-
-        self.model.load_pipe(
-            model_name,
-            task_name=task_stablepy[task],
-            vae_model=vae_model if vae_model != "None" else None,
-            type_model_precision=torch.float16,
-            retain_task_model_in_cache=False,
-        )
-        yield f"Model loaded: {model_name}"
-
-    @spaces.GPU
-    def generate_pipeline(
-            self,
-            prompt,
-            neg_prompt,
-            num_images,
-            steps,
-            cfg,
-            clip_skip,
-            seed,
-            lora1,
-            lora_scale1,
-            lora2,
-            lora_scale2,
-            lora3,
-            lora_scale3,
-            lora4,
-            lora_scale4,
-            lora5,
-            lora_scale5,
-            sampler,
-            img_height,
-            img_width,
-            model_name,
-            vae_model,
-            task,
-            image_control,
-            preprocessor_name,
-            preprocess_resolution,
-            image_resolution,
-            style_prompt,  # list []
-            style_json_file,
-            image_mask,
-            strength,
-            low_threshold,
-            high_threshold,
-            value_threshold,
-            distance_threshold,
-            controlnet_output_scaling_in_unet,
-            controlnet_start_threshold,
-            controlnet_stop_threshold,
-            textual_inversion,
-            syntax_weights,
-            upscaler_model_path,
-            upscaler_increases_size,
-            esrgan_tile,
-            esrgan_tile_overlap,
-            hires_steps,
-            hires_denoising_strength,
-            hires_sampler,
-            hires_prompt,
-            hires_negative_prompt,
-            hires_before_adetailer,
-            hires_after_adetailer,
-            loop_generation,
-            leave_progress_bar,
-            disable_progress_bar,
-            image_previews,
-            display_images,
-            save_generated_images,
-            image_storage_location,
-            retain_compel_previous_load,
-            retain_detailfix_model_previous_load,
-            retain_hires_model_previous_load,
-            t2i_adapter_preprocessor,
-            t2i_adapter_conditioning_scale,
-            t2i_adapter_conditioning_factor,
-            xformers_memory_efficient_attention,
-            freeu,
-            generator_in_cpu,
-            adetailer_inpaint_only,
-            adetailer_verbose,
-            adetailer_sampler,
-            adetailer_active_a,
-            prompt_ad_a,
-            negative_prompt_ad_a,
-            strength_ad_a,
-            face_detector_ad_a,
-            person_detector_ad_a,
-            hand_detector_ad_a,
-            mask_dilation_a,
-            mask_blur_a,
-            mask_padding_a,
-            adetailer_active_b,
-            prompt_ad_b,
-            negative_prompt_ad_b,
-            strength_ad_b,
-            face_detector_ad_b,
-            person_detector_ad_b,
-            hand_detector_ad_b,
-            mask_dilation_b,
-            mask_blur_b,
-            mask_padding_b,
-            retain_task_cache_gui,
-            image_ip1,
-            mask_ip1,
-            model_ip1,
-            mode_ip1,
-            scale_ip1,
-            image_ip2,
-            mask_ip2,
-            model_ip2,
-            mode_ip2,
-            scale_ip2):
-        vae_model = vae_model if vae_model != "None" else None
-        loras_list: list = [lora1, lora2, lora3, lora4, lora5]
-        vae_msg: str = f"VAE: {vae_model}" if vae_model else ""
-        msg_lora: list = []
-
-        if model_name in model_list:
-            model_is_xl = "xl" in model_name.lower()
-            sdxl_in_vae = vae_model and "sdxl" in vae_model.lower()
-            model_type = "SDXL" if model_is_xl else "SD 1.5"
-            incompatible_vae = ((model_is_xl and
-                                 vae_model and
-                                 not sdxl_in_vae) or
-                                (not model_is_xl and
-                                 sdxl_in_vae))
-
-            if incompatible_vae:
-                msg_inc_vae = (
-                    f"The selected VAE is for a {'SD 1.5' if model_is_xl else 'SDXL'} model, but you"
-                    f" are using a {model_type} model. The default VAE "
-                    "will be used."
-                )
-                gr.Info(msg_inc_vae)
-                vae_msg = msg_inc_vae
-                vae_model = None
-
-            for la in loras_list:
-                if la is None or la == "None" or la not in lora_model_list:
-                    continue
-
-                print(la)
-                lora_type = ("animetarot" in la.lower() or "Hyper-SD15-8steps".lower() in la.lower())
-                if (model_is_xl and lora_type) or (not model_is_xl and not lora_type):
-                    msg_inc_lora = f"The LoRA {la} is for {'SD 1.5' if model_is_xl else 'SDXL'}, but you are using {model_type}."
-                    gr.Info(msg_inc_lora)
-                    msg_lora.append(msg_inc_lora)
-
-        task = task_stablepy[task]
-
-        params_ip_img: list = []
-        params_ip_msk: list = []
-        params_ip_model: list = []
-        params_ip_mode: list = []
-        params_ip_scale: list = []
-
-        all_adapters = [
-            (image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1),
-            (image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2),
-        ]
-
-        for (imgip,
-             mskip,
-             modelip,
-             modeip,
-             scaleip) in all_adapters:
-            if imgip:
-                params_ip_img.append(imgip)
-                if mskip:
-                    params_ip_msk.append(mskip)
-                params_ip_model.append(modelip)
-                params_ip_mode.append(modeip)
-                params_ip_scale.append(scaleip)
-
-        # First load
-        model_precision = torch.float16
-        if not self.model:
-            from modelstream import Model_Diffusers2
-
-            print("Loading model...")
-            self.model = Model_Diffusers2(
-                base_model_id=model_name,
-                task_name=task,
-                vae_model=vae_model if vae_model != "None" else None,
-                type_model_precision=model_precision,
-                retain_task_model_in_cache=retain_task_cache_gui,
-            )
-
-        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'"
-            )
-
-        if task == "inpaint" and not image_mask:
-            raise ValueError("No mask image found: Specify one in 'Image Mask'")
-
-        if upscaler_model_path in [None, "Lanczos", "Nearest"]:
-            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
-                )
-
-            upscaler_model = f"./upscalers/{url_upscaler.split('/')[-1]}"
-
-        logging.getLogger("ultralytics").setLevel(logging.INFO if adetailer_verbose else logging.ERROR)
-
-        print("Config model:", model_name, vae_model, loras_list)
-
-        self.model.load_pipe(
-            model_name,
-            task_name=task,
-            vae_model=vae_model if vae_model != "None" else None,
-            type_model_precision=model_precision,
-            retain_task_model_in_cache=retain_task_cache_gui,
-        )
-
-        if textual_inversion and self.model.class_name == "StableDiffusionXLPipeline":
-            print("No Textual inversion for SDXL")
-
-        adetailer_params_A = {
-            "face_detector_ad": face_detector_ad_a,
-            "person_detector_ad": person_detector_ad_a,
-            "hand_detector_ad": hand_detector_ad_a,
-            "prompt": prompt_ad_a,
-            "negative_prompt": negative_prompt_ad_a,
-            "strength": strength_ad_a,
-            # "image_list_task" : None,
-            "mask_dilation": mask_dilation_a,
-            "mask_blur": mask_blur_a,
-            "mask_padding": mask_padding_a,
-            "inpaint_only": adetailer_inpaint_only,
-            "sampler": adetailer_sampler,
-        }
-
-        adetailer_params_B = {
-            "face_detector_ad": face_detector_ad_b,
-            "person_detector_ad": person_detector_ad_b,
-            "hand_detector_ad": hand_detector_ad_b,
-            "prompt": prompt_ad_b,
-            "negative_prompt": negative_prompt_ad_b,
-            "strength": strength_ad_b,
-            # "image_list_task" : None,
-            "mask_dilation": mask_dilation_b,
-            "mask_blur": mask_blur_b,
-            "mask_padding": mask_padding_b,
-        }
-        pipe_params = {
-            "prompt": prompt,
-            "negative_prompt": neg_prompt,
-            "img_height": img_height,
-            "img_width": img_width,
-            "num_images": num_images,
-            "num_steps": steps,
-            "guidance_scale": cfg,
-            "clip_skip": clip_skip,
-            "seed": seed,
-            "image": image_control,
-            "preprocessor_name": preprocessor_name,
-            "preprocess_resolution": preprocess_resolution,
-            "image_resolution": image_resolution,
-            "style_prompt": style_prompt if style_prompt else "",
-            "style_json_file": "",
-            "image_mask": image_mask,  # only for Inpaint
-            "strength": strength,  # only for Inpaint or ...
-            "low_threshold": low_threshold,
-            "high_threshold": high_threshold,
-            "value_threshold": value_threshold,
-            "distance_threshold": distance_threshold,
-            "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,
-            "textual_inversion": embed_list if textual_inversion and self.model.class_name != "StableDiffusionXLPipeline" else [],
-            "syntax_weights": syntax_weights,  # "Classic"
-            "sampler": sampler,
-            "xformers_memory_efficient_attention": xformers_memory_efficient_attention,
-            "gui_active": True,
-            "loop_generation": loop_generation,
-            "controlnet_conditioning_scale": float(controlnet_output_scaling_in_unet),
-            "control_guidance_start": float(controlnet_start_threshold),
-            "control_guidance_end": float(controlnet_stop_threshold),
-            "generator_in_cpu": generator_in_cpu,
-            "FreeU": freeu,
-            "adetailer_A": adetailer_active_a,
-            "adetailer_A_params": adetailer_params_A,
-            "adetailer_B": adetailer_active_b,
-            "adetailer_B_params": adetailer_params_B,
-            "leave_progress_bar": leave_progress_bar,
-            "disable_progress_bar": disable_progress_bar,
-            "image_previews": image_previews,
-            "display_images": display_images,
-            "save_generated_images": save_generated_images,
-            "image_storage_location": image_storage_location,
-            "retain_compel_previous_load": retain_compel_previous_load,
-            "retain_detailfix_model_previous_load": retain_detailfix_model_previous_load,
-            "retain_hires_model_previous_load": retain_hires_model_previous_load,
-            "t2i_adapter_preprocessor": t2i_adapter_preprocessor,
-            "t2i_adapter_conditioning_scale": float(t2i_adapter_conditioning_scale),
-            "t2i_adapter_conditioning_factor": float(t2i_adapter_conditioning_factor),
-            "upscaler_model_path": upscaler_model,
-            "upscaler_increases_size": upscaler_increases_size,
-            "esrgan_tile": esrgan_tile,
-            "esrgan_tile_overlap": esrgan_tile_overlap,
-            "hires_steps": hires_steps,
-            "hires_denoising_strength": hires_denoising_strength,
-            "hires_prompt": hires_prompt,
-            "hires_negative_prompt": hires_negative_prompt,
-            "hires_sampler": hires_sampler,
-            "hires_before_adetailer": hires_before_adetailer,
-            "hires_after_adetailer": hires_after_adetailer,
-            "ip_adapter_image": params_ip_img,
-            "ip_adapter_mask": params_ip_msk,
-            "ip_adapter_model": params_ip_model,
-            "ip_adapter_mode": params_ip_mode,
-            "ip_adapter_scale": params_ip_scale,
-        }
-
-        # print(pipe_params)
-
-        random_number = random.randint(1, 100)
-        if random_number < 25 and num_images < 3:
-            if (not upscaler_model and
-                    steps < 45 and
-                    task in ["txt2img", "img2img"] and
-                    not adetailer_active_a and
-                    not adetailer_active_b):
-                num_images *= 2
-                pipe_params["num_images"] = num_images
-                gr.Info("Num images x 2 🎉")
-
-        # Maybe fix lora issue: 'Cannot copy out of meta tensor; no data!''
-        self.model.pipe.to("cuda:0" if torch.cuda.is_available() else "cpu")
-
-        info_state = f"PROCESSING"
-        for img, seed, data in self.model(**pipe_params):
-            info_state += "."
-            if data:
-                info_state = f"COMPLETED. Seeds: {str(seed)}"
-                if vae_msg:
-                    info_state = info_state + "<br>" + vae_msg
-                if msg_lora:
-                    info_state = info_state + "<br>" + "<br>".join(msg_lora)
-            yield img, info_state
-
-
-sd_gen = GuiSD()
+# init GuiSD
+sd_gen = GuiSD(
+    model_list=model_list,
+    task_stablepy=task_stablepy,
+    lora_model_list=lora_model_list
+)
 
 with open("app.css", "r") as f:
     CSS: str = f.read()
@@ -649,7 +255,7 @@ with gr.Blocks(css=CSS) as app:
                 model_name_gui = gr.Dropdown(
                     label="Model",
                     choices=model_list,
-                    value=model_list[-6] or model_list[0],
+                    value="models/animaPencilXL_v500.safetensors" or model_list[0],
                     allow_custom_value=True
                 )
                 prompt_gui = gr.Textbox(
@@ -1104,6 +710,7 @@ with gr.Blocks(css=CSS) as app:
                             value=choices_task[0]
                         )
 
+
                     task_gui.change(
                         change_preprocessor_choices,
                         [task_gui],

gui.py

@@ -0,0 +1,432 @@
+import spaces
+import os
+from stablepy import Model_Diffusers
+from stablepy.diffusers_vanilla.model import scheduler_names
+from stablepy.diffusers_vanilla.style_prompt_config import STYLE_NAMES
+import torch
+import re
+import shutil
+import random
+import spaces
+import gradio as gr
+from PIL import Image
+import IPython.display
+import time, json
+from IPython.utils import capture
+import logging
+from utils.string_utils import extract_parameters
+from stablepy import logger
+
+logging.getLogger("diffusers").setLevel(logging.ERROR)
+import diffusers
+
+diffusers.utils.logging.set_verbosity(40)
+import warnings
+
+
+class GuiSD:
+    def __init__(self,
+                 model_list,
+                 task_stablepy,
+                 lora_model_list,
+                 stream=True):
+        self.model = None
+
+        print("Loading model...")
+        self.model = Model_Diffusers(
+            base_model_id="cagliostrolab/animagine-xl-3.1",
+            task_name="txt2img",
+            vae_model=None,
+            type_model_precision=torch.float16,
+            retain_task_model_in_cache=False,
+        )
+        self.model_list = model_list
+        self.task_stablepy = task_stablepy
+        self.lora_model_list = lora_model_list
+        self.stream = stream
+
+    def load_new_model(
+            self,
+            model_name,
+            vae_model,
+            task,
+            progress=gr.Progress(track_tqdm=True)):
+        """
+        :param model_name:
+        :param vae_model:
+        :param task:
+        :param progress:
+        """
+        yield f"Loading model: {model_name}"
+
+        vae_model = vae_model if vae_model != "None" else None
+
+        if model_name in self.model_list:
+            model_is_xl = "xl" in model_name.lower()
+            sdxl_in_vae = vae_model and "sdxl" in vae_model.lower()
+            model_type = "SDXL" if model_is_xl else "SD 1.5"
+            incompatible_vae = (model_is_xl and vae_model and not sdxl_in_vae) or (not model_is_xl and sdxl_in_vae)
+
+            if incompatible_vae:
+                vae_model = None
+
+        self.model.load_pipe(
+            model_name,
+            task_name=self.task_stablepy[task],
+            vae_model=vae_model if vae_model != "None" else None,
+            type_model_precision=torch.float16,
+            retain_task_model_in_cache=False,
+        )
+        yield f"Model loaded: {model_name}"
+
+    @spaces.GPU
+    def generate_pipeline(
+            self,
+            prompt,
+            neg_prompt,
+            num_images,
+            steps,
+            cfg,
+            clip_skip,
+            seed,
+            lora1,
+            lora_scale1,
+            lora2,
+            lora_scale2,
+            lora3,
+            lora_scale3,
+            lora4,
+            lora_scale4,
+            lora5,
+            lora_scale5,
+            sampler,
+            img_height,
+            img_width,
+            model_name,
+            vae_model,
+            task,
+            image_control,
+            preprocessor_name,
+            preprocess_resolution,
+            image_resolution,
+            style_prompt,  # list []
+            style_json_file,
+            image_mask,
+            strength,
+            low_threshold,
+            high_threshold,
+            value_threshold,
+            distance_threshold,
+            controlnet_output_scaling_in_unet,
+            controlnet_start_threshold,
+            controlnet_stop_threshold,
+            textual_inversion,
+            syntax_weights,
+            upscaler_model_path,
+            upscaler_increases_size,
+            esrgan_tile,
+            esrgan_tile_overlap,
+            hires_steps,
+            hires_denoising_strength,
+            hires_sampler,
+            hires_prompt,
+            hires_negative_prompt,
+            hires_before_adetailer,
+            hires_after_adetailer,
+            loop_generation,
+            leave_progress_bar,
+            disable_progress_bar,
+            image_previews,
+            display_images,
+            save_generated_images,
+            image_storage_location,
+            retain_compel_previous_load,
+            retain_detailfix_model_previous_load,
+            retain_hires_model_previous_load,
+            t2i_adapter_preprocessor,
+            t2i_adapter_conditioning_scale,
+            t2i_adapter_conditioning_factor,
+            xformers_memory_efficient_attention,
+            freeu,
+            generator_in_cpu,
+            adetailer_inpaint_only,
+            adetailer_verbose,
+            adetailer_sampler,
+            adetailer_active_a,
+            prompt_ad_a,
+            negative_prompt_ad_a,
+            strength_ad_a,
+            face_detector_ad_a,
+            person_detector_ad_a,
+            hand_detector_ad_a,
+            mask_dilation_a,
+            mask_blur_a,
+            mask_padding_a,
+            adetailer_active_b,
+            prompt_ad_b,
+            negative_prompt_ad_b,
+            strength_ad_b,
+            face_detector_ad_b,
+            person_detector_ad_b,
+            hand_detector_ad_b,
+            mask_dilation_b,
+            mask_blur_b,
+            mask_padding_b,
+            retain_task_cache_gui,
+            image_ip1,
+            mask_ip1,
+            model_ip1,
+            mode_ip1,
+            scale_ip1,
+            image_ip2,
+            mask_ip2,
+            model_ip2,
+            mode_ip2,
+            scale_ip2):
+        vae_model = vae_model if vae_model != "None" else None
+        loras_list: list = [lora1, lora2, lora3, lora4, lora5]
+        vae_msg: str = f"VAE: {vae_model}" if vae_model else ""
+        msg_lora: list = []
+
+        if model_name in self.model_list:
+            model_is_xl = "xl" in model_name.lower()
+            sdxl_in_vae = vae_model and "sdxl" in vae_model.lower()
+            model_type = "SDXL" if model_is_xl else "SD 1.5"
+            incompatible_vae = ((model_is_xl and
+                                 vae_model and
+                                 not sdxl_in_vae) or
+                                (not model_is_xl and
+                                 sdxl_in_vae))
+
+            if incompatible_vae:
+                msg_inc_vae = (
+                    f"The selected VAE is for a {'SD 1.5' if model_is_xl else 'SDXL'} model, but you"
+                    f" are using a {model_type} model. The default VAE "
+                    "will be used."
+                )
+                gr.Info(msg_inc_vae)
+                vae_msg = msg_inc_vae
+                vae_model = None
+
+            for la in loras_list:
+                if la is None or la == "None" or la not in self.lora_model_list:
+                    continue
+
+                print(la)
+                lora_type = ("animetarot" in la.lower() or "Hyper-SD15-8steps".lower() in la.lower())
+                if (model_is_xl and lora_type) or (not model_is_xl and not lora_type):
+                    msg_inc_lora = f"The LoRA {la} is for {'SD 1.5' if model_is_xl else 'SDXL'}, but you are using {model_type}."
+                    gr.Info(msg_inc_lora)
+                    msg_lora.append(msg_inc_lora)
+
+        task = self.task_stablepy[task]
+
+        params_ip_img: list = []
+        params_ip_msk: list = []
+        params_ip_model: list = []
+        params_ip_mode: list = []
+        params_ip_scale: list = []
+
+        all_adapters = [
+            (image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1),
+            (image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2),
+        ]
+
+        for (imgip,
+             mskip,
+             modelip,
+             modeip,
+             scaleip) in all_adapters:
+            if imgip:
+                params_ip_img.append(imgip)
+                if mskip:
+                    params_ip_msk.append(mskip)
+                params_ip_model.append(modelip)
+                params_ip_mode.append(modeip)
+                params_ip_scale.append(scaleip)
+
+        # First load
+        model_precision = torch.float16
+        if not self.model:
+            from modelstream import Model_Diffusers2
+
+            print("Loading model...")
+            self.model = Model_Diffusers2(
+                base_model_id=model_name,
+                task_name=task,
+                vae_model=vae_model if vae_model != "None" else None,
+                type_model_precision=model_precision,
+                retain_task_model_in_cache=retain_task_cache_gui,
+            )
+
+        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'"
+            )
+
+        if task == "inpaint" and not image_mask:
+            raise ValueError("No mask image found: Specify one in 'Image Mask'")
+
+        if upscaler_model_path in [None, "Lanczos", "Nearest"]:
+            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
+                )
+
+            upscaler_model = f"./upscalers/{url_upscaler.split('/')[-1]}"
+
+        logging.getLogger("ultralytics").setLevel(logging.INFO if adetailer_verbose else logging.ERROR)
+
+        print("Config model:", model_name, vae_model, loras_list)
+
+        self.model.load_pipe(
+            model_name,
+            task_name=task,
+            vae_model=vae_model if vae_model != "None" else None,
+            type_model_precision=model_precision,
+            retain_task_model_in_cache=retain_task_cache_gui,
+        )
+
+        if textual_inversion and self.model.class_name == "StableDiffusionXLPipeline":
+            print("No Textual inversion for SDXL")
+
+        adetailer_params_A: dict = {
+            "face_detector_ad": face_detector_ad_a,
+            "person_detector_ad": person_detector_ad_a,
+            "hand_detector_ad": hand_detector_ad_a,
+            "prompt": prompt_ad_a,
+            "negative_prompt": negative_prompt_ad_a,
+            "strength": strength_ad_a,
+            # "image_list_task" : None,
+            "mask_dilation": mask_dilation_a,
+            "mask_blur": mask_blur_a,
+            "mask_padding": mask_padding_a,
+            "inpaint_only": adetailer_inpaint_only,
+            "sampler": adetailer_sampler,
+        }
+
+        adetailer_params_B: dict = {
+            "face_detector_ad": face_detector_ad_b,
+            "person_detector_ad": person_detector_ad_b,
+            "hand_detector_ad": hand_detector_ad_b,
+            "prompt": prompt_ad_b,
+            "negative_prompt": negative_prompt_ad_b,
+            "strength": strength_ad_b,
+            # "image_list_task" : None,
+            "mask_dilation": mask_dilation_b,
+            "mask_blur": mask_blur_b,
+            "mask_padding": mask_padding_b,
+        }
+        pipe_params: dict = {
+            "prompt": prompt,
+            "negative_prompt": neg_prompt,
+            "img_height": img_height,
+            "img_width": img_width,
+            "num_images": num_images,
+            "num_steps": steps,
+            "guidance_scale": cfg,
+            "clip_skip": clip_skip,
+            "seed": seed,
+            "image": image_control,
+            "preprocessor_name": preprocessor_name,
+            "preprocess_resolution": preprocess_resolution,
+            "image_resolution": image_resolution,
+            "style_prompt": style_prompt if style_prompt else "",
+            "style_json_file": "",
+            "image_mask": image_mask,  # only for Inpaint
+            "strength": strength,  # only for Inpaint or ...
+            "low_threshold": low_threshold,
+            "high_threshold": high_threshold,
+            "value_threshold": value_threshold,
+            "distance_threshold": distance_threshold,
+            "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,
+            "textual_inversion": embed_list if textual_inversion and self.model.class_name != "StableDiffusionXLPipeline" else [],
+            "syntax_weights": syntax_weights,  # "Classic"
+            "sampler": sampler,
+            "xformers_memory_efficient_attention": xformers_memory_efficient_attention,
+            "gui_active": True,
+            "loop_generation": loop_generation,
+            "controlnet_conditioning_scale": float(controlnet_output_scaling_in_unet),
+            "control_guidance_start": float(controlnet_start_threshold),
+            "control_guidance_end": float(controlnet_stop_threshold),
+            "generator_in_cpu": generator_in_cpu,
+            "FreeU": freeu,
+            "adetailer_A": adetailer_active_a,
+            "adetailer_A_params": adetailer_params_A,
+            "adetailer_B": adetailer_active_b,
+            "adetailer_B_params": adetailer_params_B,
+            "leave_progress_bar": leave_progress_bar,
+            "disable_progress_bar": disable_progress_bar,
+            "image_previews": image_previews,
+            "display_images": display_images,
+            "save_generated_images": save_generated_images,
+            "image_storage_location": image_storage_location,
+            "retain_compel_previous_load": retain_compel_previous_load,
+            "retain_detailfix_model_previous_load": retain_detailfix_model_previous_load,
+            "retain_hires_model_previous_load": retain_hires_model_previous_load,
+            "t2i_adapter_preprocessor": t2i_adapter_preprocessor,
+            "t2i_adapter_conditioning_scale": float(t2i_adapter_conditioning_scale),
+            "t2i_adapter_conditioning_factor": float(t2i_adapter_conditioning_factor),
+            "upscaler_model_path": upscaler_model,
+            "upscaler_increases_size": upscaler_increases_size,
+            "esrgan_tile": esrgan_tile,
+            "esrgan_tile_overlap": esrgan_tile_overlap,
+            "hires_steps": hires_steps,
+            "hires_denoising_strength": hires_denoising_strength,
+            "hires_prompt": hires_prompt,
+            "hires_negative_prompt": hires_negative_prompt,
+            "hires_sampler": hires_sampler,
+            "hires_before_adetailer": hires_before_adetailer,
+            "hires_after_adetailer": hires_after_adetailer,
+            "ip_adapter_image": params_ip_img,
+            "ip_adapter_mask": params_ip_msk,
+            "ip_adapter_model": params_ip_model,
+            "ip_adapter_mode": params_ip_mode,
+            "ip_adapter_scale": params_ip_scale,
+        }
+
+        # print(pipe_params)
+
+        random_number = random.randint(1, 100)
+        if random_number < 25 and num_images < 3:
+            if (not upscaler_model and
+                    steps < 45 and
+                    task in ["txt2img", "img2img"] and
+                    not adetailer_active_a and
+                    not adetailer_active_b):
+                num_images *= 2
+                pipe_params["num_images"] = num_images
+                gr.Info("Num images x 2 🎉")
+
+        # Maybe fix lora issue: 'Cannot copy out of meta tensor; no data!''
+        self.model.pipe.to("cuda:0" if torch.cuda.is_available() else "cpu")
+
+        info_state = f"PROCESSING"
+        for img, seed, data in self.model(**pipe_params):
+            info_state += "."
+            if data:
+                info_state = f"COMPLETED. Seeds: {str(seed)}"
+                if vae_msg:
+                    info_state = info_state + "<br>" + vae_msg
+                if msg_lora:
+                    info_state = info_state + "<br>" + "<br>".join(msg_lora)
+            yield img, info_state

service/gemini_service.py

@@ -0,0 +1,11 @@
+def prompt_gemini(prompt: str):
+    """
+    :param prompt:
+    :return:
+    """
+    import os
+    gemini_api_key: str = os.environ.get("GEMINI_API_KEY")
+    if not gemini_api_key:
+        print("\033[91mYou need an API key to download Gemini models.\033[0m")
+
+    return prompt

utils/model_utils.py

@@ -20,4 +20,4 @@ def get_model_list(directory_path):
             # model_list.append((name_without_extension, file_path))
             model_list.append(file_path)
             print('\033[34mFILE: ' + file_path + '\033[0m')
-    return model_list
+    return model_list