Upload 13 files

app -nonyieldlast.py

@@ -0,0 +1,696 @@
+import spaces
+import gradio as gr
+import json
+import logging
+import torch
+from PIL import Image
+from diffusers import DiffusionPipeline
+from diffusers import FluxControlNetPipeline, FluxControlNetModel, FluxMultiControlNetModel
+from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
+import copy
+import random
+import time
+
+
+from mod import (models, clear_cache, get_repo_safetensors, is_repo_name, is_repo_exists,
+                 description_ui, num_loras, compose_lora_json, is_valid_lora, fuse_loras,
+                 get_trigger_word, enhance_prompt, deselect_lora, num_cns, set_control_union_image,
+                 get_control_union_mode, set_control_union_mode, get_control_params)
+from flux import (search_civitai_lora, select_civitai_lora, search_civitai_lora_json,
+                  download_my_lora, get_all_lora_tupled_list, apply_lora_prompt,
+                  update_loras, get_t2i_model_info)
+from tagger.tagger import predict_tags_wd, compose_prompt_to_copy
+from tagger.fl2flux import predict_tags_fl2_flux
+
+
+dtype = torch.bfloat16
+#dtype = torch.float8_e4m3fn
+# Initialize the base model
+base_model = models[0]
+controlnet_model_union_repo = 'InstantX/FLUX.1-dev-Controlnet-Union'
+#controlnet_model_union_repo = 'InstantX/FLUX.1-dev-Controlnet-Union-alpha'
+pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype)
+controlnet_union = None
+controlnet = None
+last_model = models[0]
+last_cn_on = False
+
+# https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union
+# https://huggingface.co/spaces/jiuface/FLUX.1-dev-Controlnet-Union
+def change_base_model(repo_id: str, cn_on: bool, progress=gr.Progress(track_tqdm=True)):
+    global pipe
+    global controlnet_union
+    global controlnet
+    global last_model
+    global last_cn_on
+    global dtype
+    try:
+        if (repo_id == last_model and cn_on is last_cn_on) or not is_repo_name(repo_id) or not is_repo_exists(repo_id): return gr.update(visible=True)
+        if cn_on:
+            #progress(0, desc=f"Loading model: {repo_id} / Loading ControlNet: {controlnet_model_union_repo}")
+            print(f"Loading model: {repo_id} / Loading ControlNet: {controlnet_model_union_repo}")
+            clear_cache()
+            controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union_repo, torch_dtype=dtype)
+            controlnet = FluxMultiControlNetModel([controlnet_union])
+            pipe = FluxControlNetPipeline.from_pretrained(repo_id, controlnet=controlnet, torch_dtype=dtype)
+            last_model = repo_id
+            last_cn_on = cn_on
+            #progress(1, desc=f"Model loaded: {repo_id} / ControlNet Loaded: {controlnet_model_union_repo}")
+            print(f"Model loaded: {repo_id} / ControlNet Loaded: {controlnet_model_union_repo}")
+        else:
+            #progress(0, desc=f"Loading model: {repo_id}")
+            print(f"Loading model: {repo_id}")
+            clear_cache()
+            pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=dtype)
+            last_model = repo_id
+            last_cn_on = cn_on
+            #progress(1, desc=f"Model loaded: {repo_id}")
+            print(f"Model loaded: {repo_id}")
+    except Exception as e:
+        print(f"Model load Error: {e}")
+        raise gr.Error(f"Model load Error: {e}") from e
+    return gr.update(visible=True)
+
+change_base_model.zerogpu = True
+
+# Load LoRAs from JSON file
+with open('loras.json', 'r') as f:
+    loras = json.load(f)
+
+MAX_SEED = 2**32-1
+
+class calculateDuration:
+    def __init__(self, activity_name=""):
+        self.activity_name = activity_name
+
+    def __enter__(self):
+        self.start_time = time.time()
+        return self
+    
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.end_time = time.time()
+        self.elapsed_time = self.end_time - self.start_time
+        if self.activity_name:
+            print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
+        else:
+            print(f"Elapsed time: {self.elapsed_time:.6f} seconds")
+
+def update_selection(evt: gr.SelectData, width, height):
+    selected_lora = loras[evt.index]
+    new_placeholder = f"Type a prompt for {selected_lora['title']}"
+    lora_repo = selected_lora["repo"]
+    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨"
+    if "aspect" in selected_lora:
+        if selected_lora["aspect"] == "portrait":
+            width = 768
+            height = 1024
+        elif selected_lora["aspect"] == "landscape":
+            width = 1024
+            height = 768
+        else:
+            width = 1024
+            height = 1024
+    return (
+        gr.update(placeholder=new_placeholder),
+        updated_text,
+        evt.index,
+        width,
+        height,
+    )
+
+@spaces.GPU(duration=70)
+def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, cn_on, progress=gr.Progress(track_tqdm=True)):
+    global pipe
+    global controlnet
+    global controlnet_union
+    try:
+        pipe.to("cuda")
+        generator = torch.Generator(device="cuda").manual_seed(seed)
+        
+        with calculateDuration("Generating image"):
+            # Generate image
+            modes, images, scales = get_control_params()
+            if not cn_on or len(modes) == 0:
+                progress(0, desc="Start Inference.")
+                image = pipe(
+                    prompt=prompt_mash,
+                    num_inference_steps=steps,
+                    guidance_scale=cfg_scale,
+                    width=width,
+                    height=height,
+                    generator=generator,
+                    joint_attention_kwargs={"scale": lora_scale},
+                ).images[0]
+            else:
+                progress(0, desc="Start Inference with ControlNet.")
+                if controlnet is not None: controlnet.to("cuda")
+                if controlnet_union is not None: controlnet_union.to("cuda")
+                image = pipe(
+                    prompt=prompt_mash,
+                    control_image=images,
+                    control_mode=modes,
+                    num_inference_steps=steps,
+                    guidance_scale=cfg_scale,
+                    width=width,
+                    height=height,
+                    controlnet_conditioning_scale=scales,
+                    generator=generator,
+                    joint_attention_kwargs={"scale": lora_scale},
+                ).images[0]
+    except Exception as e:
+        print(e)
+        raise gr.Error(f"Inference Error {e}") from e
+    return image
+
+def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height,
+              lora_scale, lora_json, cn_on, progress=gr.Progress(track_tqdm=True)):
+    global pipe
+    if selected_index is None and not is_valid_lora(lora_json):
+        gr.Info("LoRA isn't selected.")
+    #    raise gr.Error("You must select a LoRA before proceeding.")
+    progress(0, desc="Preparing Inference.")
+
+    prompt_mash = prompt
+    if is_valid_lora(lora_json):
+        with calculateDuration("Loading LoRA weights"):
+            fuse_loras(pipe, lora_json)
+            trigger_word = get_trigger_word(lora_json)
+            prompt_mash = f"{prompt} {trigger_word}"
+    if selected_index is not None:
+        selected_lora = loras[selected_index]
+        lora_path = selected_lora["repo"]
+        trigger_word = selected_lora["trigger_word"]
+        if(trigger_word):
+            if "trigger_position" in selected_lora:
+                if selected_lora["trigger_position"] == "prepend":
+                    prompt_mash = f"{trigger_word} {prompt}"
+                else:
+                    prompt_mash = f"{prompt} {trigger_word}"
+            else:
+                prompt_mash = f"{trigger_word} {prompt}"
+        else:
+            prompt_mash = prompt
+        # Load LoRA weights
+        with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
+            if "weights" in selected_lora:
+                pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"])
+            else:
+                pipe.load_lora_weights(lora_path)
+        
+    # Set random seed for reproducibility
+    with calculateDuration("Randomizing seed"):
+        if randomize_seed:
+            seed = random.randint(0, MAX_SEED)
+    
+    progress(0, desc="Running Inference.")
+
+    image = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, cn_on, progress)
+    if is_valid_lora(lora_json):
+        pipe.unfuse_lora()
+        pipe.unload_lora_weights()
+    if selected_index is not None: pipe.unload_lora_weights()
+    pipe.to("cpu")
+    if controlnet is not None: controlnet.to("cpu")
+    if controlnet_union is not None: controlnet_union.to("cpu")
+    clear_cache()
+    return image, seed
+
+def get_huggingface_safetensors(link):
+  split_link = link.split("/")
+  if(len(split_link) == 2):
+            model_card = ModelCard.load(link)
+            base_model = model_card.data.get("base_model")
+            print(base_model)
+            if((base_model != "black-forest-labs/FLUX.1-dev") and (base_model != "black-forest-labs/FLUX.1-schnell")):
+                raise Exception("Not a FLUX LoRA!")
+            image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
+            trigger_word = model_card.data.get("instance_prompt", "")
+            image_url = f"https://huggingface.co/{link}/resolve/main/{image_path}" if image_path else None
+            fs = HfFileSystem()
+            try:
+                list_of_files = fs.ls(link, detail=False)
+                for file in list_of_files:
+                    if(file.endswith(".safetensors")):
+                        safetensors_name = file.split("/")[-1]
+                    if (not image_url and file.lower().endswith((".jpg", ".jpeg", ".png", ".webp"))):
+                      image_elements = file.split("/")
+                      image_url = f"https://huggingface.co/{link}/resolve/main/{image_elements[-1]}"
+            except Exception as e:
+              print(e)
+              gr.Warning(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
+              raise Exception(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
+            return split_link[1], link, safetensors_name, trigger_word, image_url
+
+def check_custom_model(link):
+    if(link.startswith("https://")):
+        if(link.startswith("https://huggingface.co") or link.startswith("https://www.huggingface.co")):
+            link_split = link.split("huggingface.co/")
+            return get_huggingface_safetensors(link_split[1])
+    else: 
+        return get_huggingface_safetensors(link)
+
+def add_custom_lora(custom_lora):
+    global loras
+    if(custom_lora):
+        try:
+            title, repo, path, trigger_word, image = check_custom_model(custom_lora)
+            print(f"Loaded custom LoRA: {repo}")
+            card = f'''
+            <div class="custom_lora_card">
+              <span>Loaded custom LoRA:</span>
+              <div class="card_internal">
+                <img src="{image}" />
+                <div>
+                    <h3>{title}</h3>
+                    <small>{"Using: <code><b>"+trigger_word+"</code></b> as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}<br></small>
+                </div>
+              </div>
+            </div>
+            '''
+            existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None)
+            if(not existing_item_index):
+                new_item = {
+                    "image": image,
+                    "title": title,
+                    "repo": repo,
+                    "weights": path,
+                    "trigger_word": trigger_word
+                }
+                print(new_item)
+                existing_item_index = len(loras)
+                loras.append(new_item)
+        
+            return gr.update(visible=True, value=card), gr.update(visible=True), gr.Gallery(selected_index=None), f"Custom: {path}", existing_item_index, trigger_word
+        except Exception as e:
+            gr.Warning(f"Invalid LoRA: either you entered an invalid link, or a non-FLUX LoRA")
+            return gr.update(visible=True, value=f"Invalid LoRA: either you entered an invalid link, a non-FLUX LoRA"), gr.update(visible=True), gr.update(), "", None, ""
+    else:
+        return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
+
+def remove_custom_lora():
+    return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
+
+run_lora.zerogpu = True
+
+css = '''
+#gen_btn{height: 100%}
+#title{text-align: center}
+#title h1{font-size: 3em; display:inline-flex; align-items:center}
+#title img{width: 100px; margin-right: 0.5em}
+#gallery .grid-wrap{height: 10vh}
+#lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%}
+.card_internal{display: flex;height: 100px;margin-top: .5em}
+.card_internal img{margin-right: 1em}
+.styler{--form-gap-width: 0px !important}
+#progress{height:30px}
+#progress .generating{display:none}
+.progress-container {width: 100%;height: 30px;background-color: #f0f0f0;border-radius: 15px;overflow: hidden;margin-bottom: 20px}
+.progress-bar {height: 100%;background-color: #4f46e5;width: calc(var(--current) / var(--total) * 100%);transition: width 0.5s ease-in-out}
+#model-info {text-align: center; !important}
+'''
+with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=css, delete_cache=(60, 3600)) as app:
+    with gr.Tab("FLUX LoRA the Explorer"):
+        title = gr.HTML(
+            """<h1><img src="https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer/resolve/main/flux_lora.png" alt="LoRA">FLUX LoRA the Explorer Mod</h1>""",
+            elem_id="title",
+        )
+        selected_index = gr.State(None)
+        with gr.Row():
+            with gr.Column(scale=3):
+                with gr.Group():
+                    with gr.Accordion("Generate Prompt from Image", open=False):
+                        tagger_image = gr.Image(label="Input image", type="pil", sources=["upload", "clipboard"], height=256)
+                        with gr.Accordion(label="Advanced options", open=False):
+                            tagger_general_threshold = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.01, interactive=True)
+                            tagger_character_threshold = gr.Slider(label="Character threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.01, interactive=True)
+                            neg_prompt = gr.Text(label="Negative Prompt", lines=1, max_lines=8, placeholder="", visible=False)
+                            v2_character = gr.Textbox(label="Character", placeholder="hatsune miku", scale=2, visible=False)
+                            v2_series = gr.Textbox(label="Series", placeholder="vocaloid", scale=2, visible=False)
+                            v2_copy = gr.Button(value="Copy to clipboard", size="sm", interactive=False, visible=False)
+                        tagger_algorithms = gr.CheckboxGroup(["Use WD Tagger", "Use Florence-2-Flux"], label="Algorithms", value=["Use WD Tagger"])
+                        tagger_generate_from_image = gr.Button(value="Generate Prompt from Image")
+                    prompt = gr.Textbox(label="Prompt", lines=1, max_lines=8, placeholder="Type a prompt")
+                    prompt_enhance = gr.Button(value="Enhance your prompt", variant="secondary")
+            with gr.Column(scale=1, elem_id="gen_column"):
+                generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn")
+        with gr.Row():
+            with gr.Column():
+                selected_info = gr.Markdown("")
+                gallery = gr.Gallery(
+                    [(item["image"], item["title"]) for item in loras],
+                    label="LoRA Gallery",
+                    allow_preview=False,
+                    columns=3,
+                    elem_id="gallery"
+                )
+                with gr.Group():
+                    custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path", placeholder="multimodalart/vintage-ads-flux")
+                    gr.Markdown("[Check the list of FLUX LoRas](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", elem_id="lora_list")
+                custom_lora_info = gr.HTML(visible=False)
+                custom_lora_button = gr.Button("Remove custom LoRA", visible=False)
+                deselect_lora_button = gr.Button("Deselect LoRA", variant="secondary")
+            with gr.Column():
+                result = gr.Image(label="Generated Image", format="png", show_share_button=False)
+        with gr.Group():
+            model_name = gr.Dropdown(label="Base Model", info="You can enter a huggingface model repo_id to want to use.", choices=models, value=models[0], allow_custom_value=True)
+            model_info = gr.Markdown(elem_id="model-info")
+        with gr.Row():
+            with gr.Accordion("Advanced Settings", open=False):
+                with gr.Column():
+                    with gr.Row():
+                        cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
+                        steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
+                    with gr.Row():
+                        width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
+                        height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
+                    with gr.Row():
+                        randomize_seed = gr.Checkbox(True, label="Randomize seed")
+                        seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
+                        lora_scale = gr.Slider(label="LoRA Scale", minimum=-3, maximum=3, step=0.01, value=0.95)
+                    with gr.Accordion("External LoRA", open=True):
+                        with gr.Column():
+                            lora_repo_json = gr.JSON(value=[{}] * num_loras, visible=False)
+                            lora_repo = [None] * num_loras
+                            lora_weights = [None] * num_loras
+                            lora_trigger = [None] * num_loras
+                            lora_wt = [None] * num_loras
+                            lora_info = [None] * num_loras
+                            lora_copy = [None] * num_loras
+                            lora_md = [None] * num_loras
+                            lora_num = [None] * num_loras
+                            with gr.Row():
+                                for i in range(num_loras):
+                                    with gr.Column():
+                                        lora_repo[i] = gr.Dropdown(label=f"LoRA {int(i+1)} Repo", choices=get_all_lora_tupled_list(), info="Input LoRA Repo ID", value="", allow_custom_value=True)
+                                        with gr.Row():
+                                            lora_weights[i] = gr.Dropdown(label=f"LoRA {int(i+1)} Filename", choices=[], info="Optional", value="", allow_custom_value=True)
+                                            lora_trigger[i] = gr.Textbox(label=f"LoRA {int(i+1)} Trigger Prompt", lines=1, max_lines=4, value="")
+                                            lora_wt[i] = gr.Slider(label=f"LoRA {int(i+1)} Scale", minimum=-3, maximum=3, step=0.01, value=1.00)
+                                        with gr.Row():
+                                            lora_info[i] = gr.Textbox(label="", info="Example of prompt:", value="", show_copy_button=True, interactive=False, visible=False)
+                                            lora_copy[i] = gr.Button(value="Copy example to prompt", visible=False)
+                                            lora_md[i] = gr.Markdown(value="", visible=False)
+                                            lora_num[i] = gr.Number(i, visible=False)
+                            with gr.Accordion("From URL", open=True, visible=True):
+                                with gr.Row():
+                                    lora_search_civitai_query = gr.Textbox(label="Query", placeholder="flux", lines=1)
+                                    lora_search_civitai_submit = gr.Button("Search on Civitai")
+                                    lora_search_civitai_basemodel = gr.CheckboxGroup(label="Search LoRA for", choices=["Flux.1 D", "Flux.1 S"], value=["Flux.1 D", "Flux.1 S"])
+                                with gr.Row():
+                                    lora_search_civitai_json = gr.JSON(value={}, visible=False)
+                                    lora_search_civitai_desc = gr.Markdown(value="", visible=False)
+                                lora_search_civitai_result = gr.Dropdown(label="Search Results", choices=[("", "")], value="", allow_custom_value=True, visible=False)
+                                lora_download_url = gr.Textbox(label="URL", placeholder="http://...my_lora_url.safetensors", lines=1)
+                                with gr.Row():
+                                    lora_download = [None] * num_loras
+                                    for i in range(num_loras):
+                                        lora_download[i] = gr.Button(f"Get and set LoRA to {int(i+1)}")
+                    with gr.Accordion("ControlNet (🚧Under construction...🚧)", open=False, visible=False):
+                        with gr.Column():
+                            cn_on = gr.Checkbox(False, label="Use ControlNet")
+                            cn_mode = [None] * num_cns
+                            cn_scale = [None] * num_cns
+                            cn_image = [None] * num_cns
+                            cn_image_ref = [None] * num_cns
+                            cn_res = [None] * num_cns
+                            cn_num = [None] * num_cns
+                            with gr.Row():
+                                for i in range(num_cns):
+                                    with gr.Column():
+                                        with gr.Row():
+                                            cn_mode[i] = gr.Dropdown(label=f"ControlNet {int(i+1)} Mode", choices=get_control_union_mode(), value=get_control_union_mode()[0], allow_custom_value=False)
+                                            cn_scale[i] = gr.Slider(label=f"ControlNet {int(i+1)} Weight", minimum=0.0, maximum=1.0, step=0.01, value=0.75)
+                                            cn_res[i] = gr.Slider(label=f"ControlNet {int(i+1)} Preprocess resolution", minimum=128, maximum=512, value=384, step=1)
+                                            cn_num[i] = gr.Number(i, visible=False)
+                                        with gr.Row():
+                                            cn_image_ref[i] = gr.Image(label="Image Reference", type="pil", format="png", height=256, sources=["upload", "clipboard"], show_share_button=False)
+                                            cn_image[i] = gr.Image(label="Control Image", type="pil", format="png", height=256, show_share_button=False, interactive=False)
+
+    gallery.select(
+        update_selection,
+        inputs=[width, height],
+        outputs=[prompt, selected_info, selected_index, width, height],
+        queue=False,
+        show_api=False,
+    )
+    custom_lora.input(
+        add_custom_lora,
+        inputs=[custom_lora],
+        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, prompt],
+        queue=False,
+        show_api=False,
+    )
+    custom_lora_button.click(
+        remove_custom_lora,
+        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora],
+        queue=False,
+        show_api=False,
+    )
+    gr.on(
+        triggers=[generate_button.click, prompt.submit],
+        fn=change_base_model,
+        inputs=[model_name, cn_on],
+        outputs=[result],
+        queue=True,
+        show_api=False,
+        trigger_mode="once",
+    ).success(
+        fn=run_lora,
+        inputs=[prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height,
+                 lora_scale, lora_repo_json, cn_on], 
+        outputs=[result, seed],
+        queue=True,
+        show_api=True,
+    )
+
+    deselect_lora_button.click(deselect_lora, None, [prompt, selected_info, selected_index, width, height], queue=False, show_api=False)
+    """gr.on(
+        triggers=[model_name.change, cn_on.change],
+        fn=change_base_model,
+        inputs=[model_name, cn_on],
+        outputs=[result],
+        queue=True,
+        show_api=False,
+        trigger_mode="once",
+    ).then(get_t2i_model_info, [model_name], [model_info], queue=True, show_api=False)"""
+    gr.on(
+        triggers=[model_name.change, cn_on.change],
+        fn=get_t2i_model_info,
+        inputs=[model_name], 
+        outputs=[model_info],
+        queue=False,
+        show_api=False,
+        trigger_mode="once",
+    ).then(change_base_model, [model_name, cn_on], [result], queue=True, show_api=False)
+    prompt_enhance.click(enhance_prompt, [prompt], [prompt], queue=False, show_api=False)
+
+    gr.on(
+        triggers=[lora_search_civitai_submit.click, lora_search_civitai_query.submit],
+        fn=search_civitai_lora,
+        inputs=[lora_search_civitai_query, lora_search_civitai_basemodel],
+        outputs=[lora_search_civitai_result, lora_search_civitai_desc, lora_search_civitai_submit, lora_search_civitai_query],
+        scroll_to_output=True,
+        queue=True,
+        show_api=False,
+    )
+    lora_search_civitai_json.change(search_civitai_lora_json, [lora_search_civitai_query, lora_search_civitai_basemodel], [lora_search_civitai_json], queue=True, show_api=True)  # fn for api
+    lora_search_civitai_result.change(select_civitai_lora, [lora_search_civitai_result], [lora_download_url, lora_search_civitai_desc], scroll_to_output=True, queue=False, show_api=False)
+
+    for i, l in enumerate(lora_repo):
+        deselect_lora_button.click(lambda: ("", 1.0), None, [lora_repo[i], lora_wt[i]], queue=False, show_api=False)
+        gr.on(
+            triggers=[lora_download[i].click],
+            fn=download_my_lora,
+            inputs=[lora_download_url, lora_repo[i]],
+            outputs=[lora_repo[i]],
+            scroll_to_output=True,
+            queue=True,
+            show_api=False,
+        )
+        gr.on(
+            triggers=[lora_repo[i].change, lora_wt[i].change],
+            fn=update_loras,
+            inputs=[prompt, lora_repo[i], lora_wt[i]],
+            outputs=[prompt, lora_repo[i], lora_wt[i], lora_info[i], lora_md[i]],
+            queue=False,
+            trigger_mode="once",
+            show_api=False,
+        ).success(get_repo_safetensors, [lora_repo[i]], [lora_weights[i]], queue=False, show_api=False
+        ).success(apply_lora_prompt, [lora_info[i]], [lora_trigger[i]], queue=False, show_api=False
+        ).success(compose_lora_json, [lora_repo_json, lora_num[i], lora_repo[i], lora_wt[i], lora_weights[i], lora_trigger[i]], [lora_repo_json], queue=False, show_api=False)
+        
+    for i, m in enumerate(cn_mode):
+        gr.on(
+            triggers=[cn_mode[i].change, cn_scale[i].change],
+            fn=set_control_union_mode,
+            inputs=[cn_num[i], cn_mode[i], cn_scale[i]],
+            outputs=[cn_on],
+            queue=True,
+            show_api=False,
+        ).success(set_control_union_image, [cn_num[i], cn_mode[i], cn_image_ref[i], height, width, cn_res[i]], [cn_image[i]], queue=False, show_api=False)
+        cn_image_ref[i].upload(set_control_union_image, [cn_num[i], cn_mode[i], cn_image_ref[i], height, width, cn_res[i]], [cn_image[i]], queue=False, show_api=False)
+
+    tagger_generate_from_image.click(lambda: ("", "", ""), None, [v2_series, v2_character, prompt], queue=False, show_api=False,
+    ).success(
+        predict_tags_wd,
+        [tagger_image, prompt, tagger_algorithms, tagger_general_threshold, tagger_character_threshold],
+        [v2_series, v2_character, prompt, v2_copy],
+        show_api=False,
+    ).success(predict_tags_fl2_flux, [tagger_image, prompt, tagger_algorithms], [prompt], show_api=False,
+    ).success(compose_prompt_to_copy, [v2_character, v2_series, prompt], [prompt], queue=False, show_api=False)
+
+    with gr.Tab("FLUX Prompt Generator"):
+        from prompt import (PromptGenerator, HuggingFaceInferenceNode, florence_caption,
+            ARTFORM, PHOTO_TYPE, ROLES, HAIRSTYLES, LIGHTING, COMPOSITION, POSE, BACKGROUND,
+            PHOTOGRAPHY_STYLES, DEVICE, PHOTOGRAPHER, ARTIST, DIGITAL_ARTFORM, PLACE,
+            FEMALE_DEFAULT_TAGS, MALE_DEFAULT_TAGS, FEMALE_BODY_TYPES, MALE_BODY_TYPES,
+            FEMALE_CLOTHING, MALE_CLOTHING, FEMALE_ADDITIONAL_DETAILS, MALE_ADDITIONAL_DETAILS, pg_title)
+
+        prompt_generator = PromptGenerator()
+        huggingface_node = HuggingFaceInferenceNode()
+
+        gr.HTML(pg_title)
+
+        with gr.Row():
+            with gr.Column(scale=2):
+                with gr.Accordion("Basic Settings"):
+                    pg_custom = gr.Textbox(label="Custom Input Prompt (optional)")
+                    pg_subject = gr.Textbox(label="Subject (optional)")
+                    pg_gender = gr.Radio(["female", "male"], label="Gender", value="female")
+                    
+                    # Add the radio button for global option selection
+                    pg_global_option = gr.Radio(
+                        ["Disabled", "Random", "No Figure Rand"],
+                        label="Set all options to:",
+                        value="Disabled"
+                    )
+                
+                with gr.Accordion("Artform and Photo Type", open=False):
+                    pg_artform = gr.Dropdown(["disabled", "random"] + ARTFORM, label="Artform", value="disabled")
+                    pg_photo_type = gr.Dropdown(["disabled", "random"] + PHOTO_TYPE, label="Photo Type", value="disabled")
+            
+                with gr.Accordion("Character Details", open=False):
+                    pg_body_types = gr.Dropdown(["disabled", "random"] + FEMALE_BODY_TYPES + MALE_BODY_TYPES, label="Body Types", value="disabled")
+                    pg_default_tags = gr.Dropdown(["disabled", "random"] + FEMALE_DEFAULT_TAGS + MALE_DEFAULT_TAGS, label="Default Tags", value="disabled")
+                    pg_roles = gr.Dropdown(["disabled", "random"] + ROLES, label="Roles", value="disabled")
+                    pg_hairstyles = gr.Dropdown(["disabled", "random"] + HAIRSTYLES, label="Hairstyles", value="disabled")
+                    pg_clothing = gr.Dropdown(["disabled", "random"] + FEMALE_CLOTHING + MALE_CLOTHING, label="Clothing", value="disabled")
+            
+                with gr.Accordion("Scene Details", open=False):
+                    pg_place = gr.Dropdown(["disabled", "random"] + PLACE, label="Place", value="disabled")
+                    pg_lighting = gr.Dropdown(["disabled", "random"] + LIGHTING, label="Lighting", value="disabled")
+                    pg_composition = gr.Dropdown(["disabled", "random"] + COMPOSITION, label="Composition", value="disabled")
+                    pg_pose = gr.Dropdown(["disabled", "random"] + POSE, label="Pose", value="disabled")
+                    pg_background = gr.Dropdown(["disabled", "random"] + BACKGROUND, label="Background", value="disabled")
+            
+                with gr.Accordion("Style and Artist", open=False):
+                    pg_additional_details = gr.Dropdown(["disabled", "random"] + FEMALE_ADDITIONAL_DETAILS + MALE_ADDITIONAL_DETAILS, label="Additional Details", value="disabled")
+                    pg_photography_styles = gr.Dropdown(["disabled", "random"] + PHOTOGRAPHY_STYLES, label="Photography Styles", value="disabled")
+                    pg_device = gr.Dropdown(["disabled", "random"] + DEVICE, label="Device", value="disabled")
+                    pg_photographer = gr.Dropdown(["disabled", "random"] + PHOTOGRAPHER, label="Photographer", value="disabled")
+                    pg_artist = gr.Dropdown(["disabled", "random"] + ARTIST, label="Artist", value="disabled")
+                    pg_digital_artform = gr.Dropdown(["disabled", "random"] + DIGITAL_ARTFORM, label="Digital Artform", value="disabled")
+                
+                pg_generate_button = gr.Button("Generate Prompt")
+
+            with gr.Column(scale=2):
+                with gr.Accordion("Image and Caption", open=False):
+                    pg_input_image = gr.Image(label="Input Image (optional)")
+                    pg_caption_output = gr.Textbox(label="Generated Caption", lines=3)
+                    pg_create_caption_button = gr.Button("Create Caption")
+                    pg_add_caption_button = gr.Button("Add Caption to Prompt")
+
+                with gr.Accordion("Prompt Generation", open=True):
+                    pg_output = gr.Textbox(label="Generated Prompt / Input Text", lines=4)
+                    pg_t5xxl_output = gr.Textbox(label="T5XXL Output", visible=True)
+                    pg_clip_l_output = gr.Textbox(label="CLIP L Output", visible=True)
+                    pg_clip_g_output = gr.Textbox(label="CLIP G Output", visible=True)
+            
+            with gr.Column(scale=2):
+                with gr.Accordion("Prompt Generation with LLM", open=False):
+                    pg_happy_talk = gr.Checkbox(label="Happy Talk", value=True)
+                    pg_compress = gr.Checkbox(label="Compress", value=True)
+                    pg_compression_level = gr.Radio(["soft", "medium", "hard"], label="Compression Level", value="hard")
+                    pg_poster = gr.Checkbox(label="Poster", value=False)
+                    pg_custom_base_prompt = gr.Textbox(label="Custom Base Prompt", lines=5)
+                pg_generate_text_button = gr.Button("Generate Prompt with LLM (Llama 3.1 70B)")
+                pg_text_output = gr.Textbox(label="Generated Text", lines=10)
+
+    description_ui()
+
+    def create_caption(image):
+        if image is not None:
+            return florence_caption(image)
+        return ""
+
+    pg_create_caption_button.click(
+        create_caption,
+        inputs=[pg_input_image],
+        outputs=[pg_caption_output]
+    )
+
+    def generate_prompt_with_dynamic_seed(*args):
+        # Generate a new random seed
+        dynamic_seed = random.randint(0, 1000000)
+        
+        # Call the generate_prompt function with the dynamic seed
+        result = prompt_generator.generate_prompt(dynamic_seed, *args)
+        
+        # Return the result along with the used seed
+        return [dynamic_seed] + list(result)
+
+    pg_generate_button.click(
+        generate_prompt_with_dynamic_seed,
+        inputs=[pg_custom, pg_subject, pg_gender, pg_artform, pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles,
+                pg_additional_details, pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform,
+                pg_place, pg_lighting, pg_clothing, pg_composition, pg_pose, pg_background, pg_input_image],
+        outputs=[gr.Number(label="Used Seed", visible=False), pg_output, gr.Number(visible=False), pg_t5xxl_output, pg_clip_l_output, pg_clip_g_output]
+    ) #
+
+    pg_add_caption_button.click(
+        prompt_generator.add_caption_to_prompt,
+        inputs=[pg_output, pg_caption_output],
+        outputs=[pg_output]
+    )
+
+    pg_generate_text_button.click(
+        huggingface_node.generate,
+        inputs=[pg_output, pg_happy_talk, pg_compress, pg_compression_level, pg_poster, pg_custom_base_prompt],
+        outputs=pg_text_output
+    )
+
+    def update_all_options(choice):
+        updates = {}
+        if choice == "Disabled":
+            for dropdown in [
+                pg_artform, pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles, pg_clothing,
+                pg_place, pg_lighting, pg_composition, pg_pose, pg_background, pg_additional_details,
+                pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform
+            ]:
+                updates[dropdown] = gr.update(value="disabled")
+        elif choice == "Random":
+            for dropdown in [
+                pg_artform, pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles, pg_clothing,
+                pg_place, pg_lighting, pg_composition, pg_pose, pg_background, pg_additional_details,
+                pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform
+            ]:
+                updates[dropdown] = gr.update(value="random")
+        else:  # No Figure Random
+            for dropdown in [pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles, pg_clothing, pg_pose, pg_additional_details]:
+                updates[dropdown] = gr.update(value="disabled")
+            for dropdown in [pg_artform, pg_place, pg_lighting, pg_composition, pg_background, pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform]:
+                updates[dropdown] = gr.update(value="random")
+        return updates
+    
+    pg_global_option.change(
+        update_all_options,
+        inputs=[pg_global_option],
+        outputs=[
+            pg_artform, pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles, pg_clothing,
+            pg_place, pg_lighting, pg_composition, pg_pose, pg_background, pg_additional_details,
+            pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform
+        ]
+    )
+
+app.queue()
+app.launch()

app.py

@@ -1,44 +1,54 @@
 import spaces
 import gradio as gr
 import json
-import logging
 import torch
-from PIL import Image
-from diffusers import DiffusionPipeline
+from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL
+from live_preview_helpers import flux_pipe_call_that_returns_an_iterable_of_images
 from diffusers import FluxControlNetPipeline, FluxControlNetModel, FluxMultiControlNetModel
-from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
-import copy
+from huggingface_hub import HfFileSystem, ModelCard
 import random
 import time
 
-
 from mod import (models, clear_cache, get_repo_safetensors, is_repo_name, is_repo_exists,
                  description_ui, num_loras, compose_lora_json, is_valid_lora, fuse_loras,
                  get_trigger_word, enhance_prompt, deselect_lora, num_cns, set_control_union_image,
-                 get_control_union_mode, set_control_union_mode, get_control_params)
+                 get_control_union_mode, set_control_union_mode, get_control_params, translate_to_en)
 from flux import (search_civitai_lora, select_civitai_lora, search_civitai_lora_json,
                   download_my_lora, get_all_lora_tupled_list, apply_lora_prompt,
                   update_loras, get_t2i_model_info)
 from tagger.tagger import predict_tags_wd, compose_prompt_to_copy
 from tagger.fl2flux import predict_tags_fl2_flux
 
+# Load LoRAs from JSON file
+with open('loras.json', 'r') as f:
+    loras = json.load(f)
 
-dtype = torch.bfloat16
-#dtype = torch.float8_e4m3fn
 # Initialize the base model
 base_model = models[0]
 controlnet_model_union_repo = 'InstantX/FLUX.1-dev-Controlnet-Union'
 #controlnet_model_union_repo = 'InstantX/FLUX.1-dev-Controlnet-Union-alpha'
-pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype)
+dtype = torch.bfloat16
+#dtype = torch.float8_e4m3fn
+#device = "cuda" if torch.cuda.is_available() else "cpu"
+taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype)
+good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype)
+pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1)
 controlnet_union = None
 controlnet = None
 last_model = models[0]
 last_cn_on = False
+#controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union_repo, torch_dtype=dtype)
+#controlnet = FluxMultiControlNetModel([controlnet_union])
+
+MAX_SEED = 2**32-1
 
 # https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union
 # https://huggingface.co/spaces/jiuface/FLUX.1-dev-Controlnet-Union
-def change_base_model(repo_id: str, cn_on: bool, progress=gr.Progress(track_tqdm=True)):
+#@spaces.GPU()
+def change_base_model(repo_id: str, cn_on: bool, progress=gr.Progress(track_tqdm=True)): # , progress=gr.Progress(track_tqdm=True) # gradio.exceptions.Error: 'Model load Error: too many values to unpack (expected 2)'
     global pipe
+    global taef1
+    global good_vae
     global controlnet_union
     global controlnet
     global last_model
@@ -46,25 +56,29 @@ def change_base_model(repo_id: str, cn_on: bool, progress=gr.Progress(track_tqdm
     global dtype
     try:
         if (repo_id == last_model and cn_on is last_cn_on) or not is_repo_name(repo_id) or not is_repo_exists(repo_id): return gr.update(visible=True)
+        pipe.to("cpu")
+        good_vae.to("cpu")
+        taef1.to("cpu")
+        if controlnet is not None: controlnet.to("cpu")
+        if controlnet_union is not None: controlnet_union.to("cpu")
+        clear_cache()
         if cn_on:
-            #progress(0, desc=f"Loading model: {repo_id} / Loading ControlNet: {controlnet_model_union_repo}")
+            progress(0, desc=f"Loading model: {repo_id} / Loading ControlNet: {controlnet_model_union_repo}")
             print(f"Loading model: {repo_id} / Loading ControlNet: {controlnet_model_union_repo}")
-            clear_cache()
             controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union_repo, torch_dtype=dtype)
             controlnet = FluxMultiControlNetModel([controlnet_union])
             pipe = FluxControlNetPipeline.from_pretrained(repo_id, controlnet=controlnet, torch_dtype=dtype)
             last_model = repo_id
             last_cn_on = cn_on
-            #progress(1, desc=f"Model loaded: {repo_id} / ControlNet Loaded: {controlnet_model_union_repo}")
+            progress(1, desc=f"Model loaded: {repo_id} / ControlNet Loaded: {controlnet_model_union_repo}")
             print(f"Model loaded: {repo_id} / ControlNet Loaded: {controlnet_model_union_repo}")
         else:
-            #progress(0, desc=f"Loading model: {repo_id}")
+            progress(0, desc=f"Loading model: {repo_id}")
             print(f"Loading model: {repo_id}")
-            clear_cache()
             pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=dtype)
             last_model = repo_id
             last_cn_on = cn_on
-            #progress(1, desc=f"Model loaded: {repo_id}")
+            progress(1, desc=f"Model loaded: {repo_id}")
             print(f"Model loaded: {repo_id}")
     except Exception as e:
         print(f"Model load Error: {e}")
@@ -73,12 +87,6 @@ def change_base_model(repo_id: str, cn_on: bool, progress=gr.Progress(track_tqdm
 
 change_base_model.zerogpu = True
 
-# Load LoRAs from JSON file
-with open('loras.json', 'r') as f:
-    loras = json.load(f)
-
-MAX_SEED = 2**32-1
-
 class calculateDuration:
     def __init__(self, activity_name=""):
         self.activity_name = activity_name
@@ -121,18 +129,24 @@ def update_selection(evt: gr.SelectData, width, height):
 @spaces.GPU(duration=70)
 def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, cn_on, progress=gr.Progress(track_tqdm=True)):
     global pipe
+    global taef1
+    global good_vae
     global controlnet
     global controlnet_union
     try:
-        pipe.to("cuda")
+        good_vae.to("cuda")
+        taef1.to("cuda")
         generator = torch.Generator(device="cuda").manual_seed(seed)
         
         with calculateDuration("Generating image"):
             # Generate image
             modes, images, scales = get_control_params()
             if not cn_on or len(modes) == 0:
+                pipe.vae = taef1
+                pipe.to("cuda")
+                pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
                 progress(0, desc="Start Inference.")
-                image = pipe(
+                for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
                     prompt=prompt_mash,
                     num_inference_steps=steps,
                     guidance_scale=cfg_scale,
@@ -140,12 +154,18 @@ def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scal
                     height=height,
                     generator=generator,
                     joint_attention_kwargs={"scale": lora_scale},
-                ).images[0]
+                    output_type="pil",
+                    good_vae=good_vae,
+                ):
+                    yield img
             else:
-                progress(0, desc="Start Inference with ControlNet.")
                 if controlnet is not None: controlnet.to("cuda")
                 if controlnet_union is not None: controlnet_union.to("cuda")
-                image = pipe(
+                pipe.vae = good_vae
+                pipe.controlnet = controlnet
+                pipe.to("cuda")
+                progress(0, desc="Start Inference with ControlNet.")
+                for img in pipe(
                     prompt=prompt_mash,
                     control_image=images,
                     control_mode=modes,
@@ -156,23 +176,35 @@ def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scal
                     controlnet_conditioning_scale=scales,
                     generator=generator,
                     joint_attention_kwargs={"scale": lora_scale},
-                ).images[0]
+                ).images:
+                    yield img
     except Exception as e:
         print(e)
-        raise gr.Error(f"Inference Error {e}") from e
-    return image
+        raise gr.Error(f"Inference Error: {e}") from e
 
 def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height,
-              lora_scale, lora_json, cn_on, progress=gr.Progress(track_tqdm=True)):
+              lora_scale, lora_json, cn_on, translate_on, progress=gr.Progress(track_tqdm=True)):
     global pipe
     if selected_index is None and not is_valid_lora(lora_json):
         gr.Info("LoRA isn't selected.")
     #    raise gr.Error("You must select a LoRA before proceeding.")
     progress(0, desc="Preparing Inference.")
 
+    with calculateDuration("Unloading LoRA"):
+        try:
+            pipe.unfuse_lora()
+            pipe.unload_lora_weights()
+        except Exception as e:
+            print(e)
+    
+    clear_cache() #
+
+    if translate_on: prompt = translate_to_en(prompt)
+
     prompt_mash = prompt
     if is_valid_lora(lora_json):
-        with calculateDuration("Loading LoRA weights"):
+        # Load External LoRA weights
+        with calculateDuration("Loading External LoRA weights"):
             fuse_loras(pipe, lora_json)
             trigger_word = get_trigger_word(lora_json)
             prompt_mash = f"{prompt} {trigger_word}"
@@ -203,17 +235,17 @@ def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, wid
             seed = random.randint(0, MAX_SEED)
     
     progress(0, desc="Running Inference.")
-
-    image = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, cn_on, progress)
-    if is_valid_lora(lora_json):
-        pipe.unfuse_lora()
-        pipe.unload_lora_weights()
-    if selected_index is not None: pipe.unload_lora_weights()
-    pipe.to("cpu")
-    if controlnet is not None: controlnet.to("cpu")
-    if controlnet_union is not None: controlnet_union.to("cpu")
-    clear_cache()
-    return image, seed
+    image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, cn_on, progress)
+    # Consume the generator to get the final image
+    final_image = None
+    step_counter = 0
+    for image in image_generator:
+        step_counter+=1
+        final_image = image
+        progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
+        yield image, seed, gr.update(value=progress_bar, visible=True)
+        
+    yield final_image, seed, gr.update(value=progress_bar, visible=False)
 
 def get_huggingface_safetensors(link):
   split_link = link.split("/")
@@ -306,7 +338,7 @@ css = '''
 #progress .generating{display:none}
 .progress-container {width: 100%;height: 30px;background-color: #f0f0f0;border-radius: 15px;overflow: hidden;margin-bottom: 20px}
 .progress-bar {height: 100%;background-color: #4f46e5;width: calc(var(--current) / var(--total) * 100%);transition: width 0.5s ease-in-out}
-#model-info {text-align: center; !important}
+.info {text-align:center; display:inline-flex; align-items:center !important}
 '''
 with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=css, delete_cache=(60, 3600)) as app:
     with gr.Tab("FLUX LoRA the Explorer"):
@@ -329,8 +361,10 @@ with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=css, delete_cache
                             v2_copy = gr.Button(value="Copy to clipboard", size="sm", interactive=False, visible=False)
                         tagger_algorithms = gr.CheckboxGroup(["Use WD Tagger", "Use Florence-2-Flux"], label="Algorithms", value=["Use WD Tagger"])
                         tagger_generate_from_image = gr.Button(value="Generate Prompt from Image")
-                    prompt = gr.Textbox(label="Prompt", lines=1, max_lines=8, placeholder="Type a prompt")
-                    prompt_enhance = gr.Button(value="Enhance your prompt", variant="secondary")
+                    prompt = gr.Textbox(label="Prompt", lines=1, max_lines=8, placeholder="Type a prompt", show_copy_button=True)
+                    with gr.Row():
+                        prompt_enhance = gr.Button(value="Enhance your prompt", variant="secondary")
+                        auto_trans = gr.Checkbox(label="Auto translate to English", value=True, elem_classes="info")
             with gr.Column(scale=1, elem_id="gen_column"):
                 generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn")
         with gr.Row():
@@ -350,19 +384,20 @@ with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=css, delete_cache
                 custom_lora_button = gr.Button("Remove custom LoRA", visible=False)
                 deselect_lora_button = gr.Button("Deselect LoRA", variant="secondary")
             with gr.Column():
+                progress_bar = gr.Markdown(elem_id="progress",visible=False)
                 result = gr.Image(label="Generated Image", format="png", show_share_button=False)
         with gr.Group():
             model_name = gr.Dropdown(label="Base Model", info="You can enter a huggingface model repo_id to want to use.", choices=models, value=models[0], allow_custom_value=True)
-            model_info = gr.Markdown(elem_id="model-info")
+            model_info = gr.Markdown(elem_classes="info")
         with gr.Row():
             with gr.Accordion("Advanced Settings", open=False):
                 with gr.Column():
-                    with gr.Row():
-                        cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
-                        steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
                     with gr.Row():
                         width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
                         height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
+                    with gr.Row():
+                        cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
+                        steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
                     with gr.Row():
                         randomize_seed = gr.Checkbox(True, label="Randomize seed")
                         seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
@@ -417,8 +452,8 @@ with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=css, delete_cache
                             with gr.Row():
                                 for i in range(num_cns):
                                     with gr.Column():
+                                        cn_mode[i] = gr.Radio(label=f"ControlNet {int(i+1)} Mode", choices=get_control_union_mode(), value=get_control_union_mode()[0])
                                         with gr.Row():
-                                            cn_mode[i] = gr.Dropdown(label=f"ControlNet {int(i+1)} Mode", choices=get_control_union_mode(), value=get_control_union_mode()[0], allow_custom_value=False)
                                             cn_scale[i] = gr.Slider(label=f"ControlNet {int(i+1)} Weight", minimum=0.0, maximum=1.0, step=0.01, value=0.75)
                                             cn_res[i] = gr.Slider(label=f"ControlNet {int(i+1)} Preprocess resolution", minimum=128, maximum=512, value=384, step=1)
                                             cn_num[i] = gr.Number(i, visible=False)
@@ -432,6 +467,7 @@ with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=css, delete_cache
         outputs=[prompt, selected_info, selected_index, width, height],
         queue=False,
         show_api=False,
+        trigger_mode="once",
     )
     custom_lora.input(
         add_custom_lora,
@@ -457,22 +493,13 @@ with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=css, delete_cache
     ).success(
         fn=run_lora,
         inputs=[prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height,
-                 lora_scale, lora_repo_json, cn_on], 
-        outputs=[result, seed],
+                 lora_scale, lora_repo_json, cn_on, auto_trans], 
+        outputs=[result, seed, progress_bar],
         queue=True,
         show_api=True,
     )
 
     deselect_lora_button.click(deselect_lora, None, [prompt, selected_info, selected_index, width, height], queue=False, show_api=False)
-    """gr.on(
-        triggers=[model_name.change, cn_on.change],
-        fn=change_base_model,
-        inputs=[model_name, cn_on],
-        outputs=[result],
-        queue=True,
-        show_api=False,
-        trigger_mode="once",
-    ).then(get_t2i_model_info, [model_name], [model_info], queue=True, show_api=False)"""
     gr.on(
         triggers=[model_name.change, cn_on.change],
         fn=get_t2i_model_info,

mod.py

@@ -74,6 +74,17 @@ def is_repo_exists(repo_id):
         return True # for safe
 
 
+from translatepy import Translator
+translator = Translator()
+def translate_to_en(input: str):
+    try:
+        output = str(translator.translate(input, 'English'))
+    except Exception as e:
+        output = input
+        print(e)
+    return output
+
+
 def clear_cache():
     try:
         torch.cuda.empty_cache()
@@ -349,7 +360,7 @@ enhancer_flux = load_prompt_enhancer()
 
 @spaces.GPU(duration=30)
 def enhance_prompt(input_prompt):
-    result = enhancer_flux("enhance prompt: " + input_prompt, max_length = 256)
+    result = enhancer_flux("enhance prompt: " + translate_to_en(input_prompt), max_length = 256)
     enhanced_text = result[0]['generated_text']
     return enhanced_text
 

modutils.py

@@ -27,6 +27,17 @@ def list_sub(a, b):
     return [e for e in a if e not in b]
 
 
+from translatepy import Translator
+translator = Translator()
+def translate_to_en(input: str):
+    try:
+        output = str(translator.translate(input, 'English'))
+    except Exception as e:
+        output = input
+        print(e)
+    return output
+
+
 def get_local_model_list(dir_path):
     model_list = []
     valid_extensions = ('.ckpt', '.pt', '.pth', '.safetensors', '.bin')

requirements.txt

@@ -14,4 +14,5 @@ numpy
 opencv-python
 deepspeed
 mediapipe
-openai==1.37.0
+openai==1.37.0
+translatepy

tagger/tagger.py

@@ -1,11 +1,8 @@
+import spaces
 from PIL import Image
 import torch
 import gradio as gr
-import spaces
-from transformers import (
-    AutoImageProcessor,
-    AutoModelForImageClassification,
-)
+from transformers import AutoImageProcessor, AutoModelForImageClassification
 from pathlib import Path
 
 
@@ -14,9 +11,13 @@ WD_MODEL_NAME = WD_MODEL_NAMES[0]
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 default_device = device
-wd_model = AutoModelForImageClassification.from_pretrained(WD_MODEL_NAME, trust_remote_code=True).to(default_device).eval()
-wd_processor = AutoImageProcessor.from_pretrained(WD_MODEL_NAME, trust_remote_code=True)
 
+try:
+    wd_model = AutoModelForImageClassification.from_pretrained(WD_MODEL_NAME, trust_remote_code=True).to(default_device).eval()
+    wd_processor = AutoImageProcessor.from_pretrained(WD_MODEL_NAME, trust_remote_code=True)
+except Exception as e:
+    print(e)
+    wd_model = wd_processor = None
 
 def _people_tag(noun: str, minimum: int = 1, maximum: int = 5):
     return (
@@ -186,18 +187,16 @@ def convert_danbooru_to_e621_prompt(input_prompt: str = "", prompt_type: str = "
     return output_prompt
 
 
+from translatepy import Translator
+translator = Translator()
 def translate_prompt(prompt: str = ""):
-    def translate_to_english(prompt):
-        import httpcore
-        setattr(httpcore, 'SyncHTTPTransport', 'AsyncHTTPProxy')
-        from googletrans import Translator
-        translator = Translator()
+    def translate_to_english(input: str):
         try:
-            translated_prompt = translator.translate(prompt, src='auto', dest='en').text
-            return translated_prompt
+            output = str(translator.translate(input, 'English'))
         except Exception as e:
+            output = input
             print(e)
-            return prompt
+        return output
 
     def is_japanese(s):
         import unicodedata
@@ -220,17 +219,13 @@ def translate_prompt(prompt: str = ""):
 
 
 def translate_prompt_to_ja(prompt: str = ""):
-    def translate_to_japanese(prompt):
-        import httpcore
-        setattr(httpcore, 'SyncHTTPTransport', 'AsyncHTTPProxy')
-        from googletrans import Translator
-        translator = Translator()
+    def translate_to_japanese(input: str):
         try:
-            translated_prompt = translator.translate(prompt, src='en', dest='ja').text
-            return translated_prompt
+            output = str(translator.translate(input, 'Japanese'))
         except Exception as e:
+            output = input
             print(e)
-            return prompt
+        return output
 
     def is_japanese(s):
         import unicodedata