Update app.py

app.py

@@ -1,112 +1,163 @@
 import gradio as gr
-import requests
-import io
-import random
-import os
-import time
-from PIL import Image
-from deep_translator import GoogleTranslator
-import json
+from model import models
+from multit2i import (
+    load_models, infer_fn, infer_rand_fn, save_gallery,
+    change_model, warm_model, get_model_info_md, loaded_models,
+    get_positive_prefix, get_positive_suffix, get_negative_prefix, get_negative_suffix,
+    get_recom_prompt_type, set_recom_prompt_preset, get_tag_type,
+)
+from tagger.tagger import (
+    predict_tags_wd, remove_specific_prompt, convert_danbooru_to_e621_prompt,
+    insert_recom_prompt, compose_prompt_to_copy,
+)
+from tagger.fl2sd3longcap import predict_tags_fl2_sd3
+from tagger.v2 import V2_ALL_MODELS, v2_random_prompt
+from tagger.utils import (
+    V2_ASPECT_RATIO_OPTIONS, V2_RATING_OPTIONS,
+    V2_LENGTH_OPTIONS, V2_IDENTITY_OPTIONS,
+)
 
-# Project by Nymbo
 
-API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"
-API_TOKEN = os.getenv("HF_READ_TOKEN")
-headers = {"Authorization": f"Bearer {API_TOKEN}"}
-timeout = 100
-
-def query(prompt, negative_prompt, steps=30, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=512, height=512):
-    if not prompt:
-        return None
-
-    key = random.randint(0, 999)
-    
-    API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")])
-    headers = {"Authorization": f"Bearer {API_TOKEN}"}
-    
-    prompt_translated = GoogleTranslator(source='auto', target='en').translate(prompt)
-    print(f'\033[1mGeneration {key} translation:\033[0m {prompt_translated}')
-
-    full_prompt = f"{prompt_translated} | ultra detail, ultra elaboration, ultra quality, perfect."
-    print(f'\033[1mGeneration {key}:\033[0m {full_prompt}')
-    
-    # Use a random seed if seed is -1
-    seed_value = seed if seed != -1 else random.randint(1, 1000000000)
-    
-    payload = {
-        "inputs": full_prompt,
-        "negative_prompt": negative_prompt,
-        "steps": steps,
-        "cfg_scale": cfg_scale,
-        "seed": seed_value,
-        "strength": strength,
-        "width": width,
-        "height": height,
-        "sampler": sampler
-    }
-
-    response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
-    if response.status_code != 200:
-        print(f"Error: Failed to get image. Response status: {response.status_code}")
-        print(f"Response content: {response.text}")
-        if response.status_code == 503:
-            raise gr.Error(f"{response.status_code} : The model is being loaded")
-        raise gr.Error(f"{response.status_code}")
-    
-    try:
-        image_bytes = response.content
-        image = Image.open(io.BytesIO(image_bytes))
-        print(f'\033[1mGeneration {key} completed!\033[0m ({full_prompt})')
-        return image
-    except Exception as e:
-        print(f"Error when trying to open the image: {e}")
-        return None
+max_images = 8
+load_models(models)
 
 css = """
-#app-container {
-    max-width: 600px;
-    margin-left: auto;
-    margin-right: auto;
-}
+.model_info { text-align: center; }
+.output { width=112px; height=112px; !important; }
+.gallery { width=100%; min_height=768px; !important; }
 """
 
-with gr.Blocks(theme='Nymbo/Nymbo_Theme', css=css) as app:
-    gr.HTML("<center><h1>FLUX.1-Schnell</h1></center>")
-    with gr.Column(elem_id="app-container"):
-        with gr.Row():
-            with gr.Column(elem_id="prompt-container"):
-                with gr.Row():
-                    text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=2, elem_id="prompt-text-input")
+with gr.Blocks(theme="Nymbo/Nymbo_Theme", fill_width=True, css=css) as demo:
+    with gr.Column(): 
+        with gr.Group():
+            model_name = gr.Dropdown(label="Select Model", choices=list(loaded_models.keys()), value=list(loaded_models.keys())[0], allow_custom_value=True)
+            model_info = gr.Markdown(value=get_model_info_md(list(loaded_models.keys())[0]), elem_classes="model_info")
+        with gr.Group():
+            with gr.Accordion("Prompt from Image File", 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)
+                    tagger_tag_type = gr.Radio(label="Convert tags to", info="danbooru for common, e621 for Pony.", choices=["danbooru", "e621"], value="danbooru")
+                    tagger_recom_prompt = gr.Radio(label="Insert reccomended prompt", choices=["None", "Animagine", "Pony"], value="None", interactive=True)  
+                    tagger_keep_tags = gr.Radio(label="Remove tags leaving only the following", choices=["body", "dress", "all"], value="all")
+                tagger_algorithms = gr.CheckboxGroup(["Use WD Tagger", "Use Florence-2-SD3-Long-Captioner"], label="Algorithms", value=["Use WD Tagger"])
+                tagger_generate_from_image = gr.Button(value="Generate Tags from Image")
+            with gr.Row():
+                v2_character = gr.Textbox(label="Character", placeholder="hatsune miku", scale=2)
+                v2_series = gr.Textbox(label="Series", placeholder="vocaloid", scale=2)
+                random_prompt = gr.Button(value="Extend Prompt 🎲", size="sm", scale=1)
+                clear_prompt = gr.Button(value="Clear Prompt 🗑️", size="sm", scale=1)
+            prompt = gr.Text(label="Prompt", lines=2, max_lines=8, placeholder="1girl, solo, ...", show_copy_button=True)
+            neg_prompt = gr.Text(label="Negative Prompt", lines=1, max_lines=8, placeholder="", visible=False)
+            with gr.Accordion("Advanced options", open=False):
+                width = gr.Number(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
+                height = gr.Number(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
+                steps = gr.Number(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
+                cfg = gr.Number(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
+            with gr.Accordion("Recommended Prompt", open=False):
+                recom_prompt_preset = gr.Radio(label="Set Presets", choices=get_recom_prompt_type(), value="Common")
                 with gr.Row():
-                    with gr.Accordion("Advanced Settings", open=False):
-                        negative_prompt = gr.Textbox(
-                            label="Negative Prompt",
-                            placeholder="What should not be in the image",
-                            value="(deformed, distorted, disfigured), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, misspellings, typos",
-                            lines=3,
-                            elem_id="negative-prompt-text-input"
-                        )
-                        steps = gr.Slider(label="Sampling steps", value=30, minimum=1, maximum=100, step=1)
-                        cfg = gr.Slider(label="CFG Scale", value=7, minimum=1, maximum=20, step=0.5)
-                        method = gr.Radio(
-                            label="Sampling method",
-                            value="DPM++ 2M Karras",
-                            choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"]
-                        )
-                        strength = gr.Slider(label="Strength", value=0.7, minimum=0, maximum=1, step=0.01)
-                        seed = gr.Number(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1)
-                        width = gr.Number(label="Width", value=512, minimum=64, maximum=1024, step=64)
-                        height = gr.Number(label="Height", value=512, minimum=64, maximum=1024, step=64)
-
-        with gr.Row():
-            text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
+                    positive_prefix = gr.CheckboxGroup(label="Use Positive Prefix", choices=get_positive_prefix(), value=[])
+                    positive_suffix = gr.CheckboxGroup(label="Use Positive Suffix", choices=get_positive_suffix(), value=["Common"])
+                    negative_prefix = gr.CheckboxGroup(label="Use Negative Prefix", choices=get_negative_prefix(), value=[])
+                    negative_suffix = gr.CheckboxGroup(label="Use Negative Suffix", choices=get_negative_suffix(), value=["Common"])
+            with gr.Accordion("Prompt Transformer", open=False):
+                v2_rating = gr.Radio(label="Rating", choices=list(V2_RATING_OPTIONS), value="sfw")
+                v2_aspect_ratio = gr.Radio(label="Aspect ratio", info="The aspect ratio of the image.", choices=list(V2_ASPECT_RATIO_OPTIONS), value="square", visible=False)
+                v2_length = gr.Radio(label="Length", info="The total length of the tags.", choices=list(V2_LENGTH_OPTIONS), value="long")
+                v2_identity = gr.Radio(label="Keep identity", info="How strictly to keep the identity of the character or subject. If you specify the detail of subject in the prompt, you should choose `strict`. Otherwise, choose `none` or `lax`. `none` is very creative but sometimes ignores the input prompt.", choices=list(V2_IDENTITY_OPTIONS), value="lax")                    
+                v2_ban_tags = gr.Textbox(label="Ban tags", info="Tags to ban from the output.", placeholder="alternate costumen, ...", value="censored")
+                v2_tag_type = gr.Radio(label="Tag Type", info="danbooru for common, e621 for Pony.", choices=["danbooru", "e621"], value="danbooru", visible=False)
+                v2_model = gr.Dropdown(label="Model", choices=list(V2_ALL_MODELS.keys()), value=list(V2_ALL_MODELS.keys())[0])
+                v2_copy = gr.Button(value="Copy to clipboard", size="sm", interactive=False)
+        image_num = gr.Slider(label="Number of images", minimum=1, maximum=max_images, value=1, step=1, interactive=True, scale=1)
         with gr.Row():
-            image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
-        
-        text_button.click(
-            query,
-            inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height],
-            outputs=image_output
+            run_button = gr.Button("Generate Image", scale=6)
+            random_button = gr.Button("Random Model 🎲", scale=3)
+            stop_button = gr.Button('Stop', interactive=False, scale=1)
+    with gr.Column(): 
+        with gr.Group():
+            with gr.Row():
+                output = [gr.Image(label='', elem_classes="output", type="filepath", format=".png",
+                          show_download_button=True, show_share_button=False, show_label=False,
+                          interactive=False, min_width=80, visible=True) for _ in range(max_images)]
+        with gr.Group():
+            results = gr.Gallery(label="Gallery", elem_classes="gallery", interactive=False, show_download_button=True, show_share_button=False,
+                                 container=True, format="png", object_fit="cover", columns=2, rows=2)
+            image_files = gr.Files(label="Download", interactive=False)
+            clear_results = gr.Button("Clear Gallery / Download 🗑️")
+    with gr.Column(): 
+        examples = gr.Examples(
+            examples = [
+                ["souryuu asuka langley, 1girl, neon genesis evangelion, plugsuit, pilot suit, red bodysuit, sitting, crossing legs, black eye patch, cat hat, throne, symmetrical, looking down, from bottom, looking at viewer, outdoors"],
+                ["sailor moon, magical girl transformation, sparkles and ribbons, soft pastel colors, crescent moon motif, starry night sky background, shoujo manga style"],
+                ["kafuu chino, 1girl, solo"],
+                ["1girl"],
+                ["beautiful sunset"],
+            ],
+            inputs=[prompt],
+        )
+        gr.Markdown(
+            f"""This demo was created in reference to the following demos.<br>
+    [Nymbo/Flood](https://huggingface.co/spaces/Nymbo/Flood), 
+    [Yntec/ToyWorldXL](https://huggingface.co/spaces/Yntec/ToyWorldXL), 
+    [Yntec/Diffusion80XX](https://huggingface.co/spaces/Yntec/Diffusion80XX).
+                """
         )
+        gr.DuplicateButton(value="Duplicate Space")
+        gr.Markdown(f"Just a few edits to *model.py* are all it takes to complete your own collection.")
+
+    gr.on(triggers=[run_button.click, prompt.submit, random_button.click], fn=lambda: gr.update(interactive=True), inputs=None, outputs=stop_button, show_api=False)
+    model_name.change(change_model, [model_name], [model_info], queue=False, show_api=False)\
+    .success(warm_model, [model_name], None, queue=True, show_api=False)
+    for i, o in enumerate(output):
+        img_i = gr.Number(i, visible=False)
+        image_num.change(lambda i, n: gr.update(visible = (i < n)), [img_i, image_num], o, show_api=False)
+        gen_event = gr.on(triggers=[run_button.click, prompt.submit],
+         fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, l1, l2, l3, l4: infer_fn(m, t1, t2, n1, n2, n3, n4, l1, l2, l3, l4) if (i < n) else None,
+         inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg,
+                  positive_prefix, positive_suffix, negative_prefix, negative_suffix],
+         outputs=[o], queue=True, show_api=False)
+        gen_event2 = gr.on(triggers=[random_button.click],
+         fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, l1, l2, l3, l4: infer_rand_fn(m, t1, t2, n1, n2, n3, n4, l1, l2, l3, l4) if (i < n) else None,
+         inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg,
+                  positive_prefix, positive_suffix, negative_prefix, negative_suffix],
+         outputs=[o], queue=True, show_api=False)
+        o.change(save_gallery, [o, results], [results, image_files], show_api=False)
+        stop_button.click(lambda: gr.update(interactive=False), None, stop_button, cancels=[gen_event, gen_event2], show_api=False)
+
+    clear_prompt.click(lambda: None, None, [prompt], queue=False, show_api=False)
+    clear_results.click(lambda: (None, None), None, [results, image_files], queue=False, show_api=False)
+    recom_prompt_preset.change(set_recom_prompt_preset, [recom_prompt_preset],
+     [positive_prefix, positive_suffix, negative_prefix, negative_suffix], queue=False, show_api=False)
+
+    random_prompt.click(
+        v2_random_prompt, [prompt, v2_series, v2_character, v2_rating, v2_aspect_ratio, v2_length,
+          v2_identity, v2_ban_tags, v2_model], [prompt, v2_series, v2_character], show_api=False,
+    ).success(
+        get_tag_type, [positive_prefix, positive_suffix, negative_prefix, negative_suffix], [v2_tag_type], queue=False, show_api=False
+    ).success(
+        convert_danbooru_to_e621_prompt, [prompt, v2_tag_type], [prompt], 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_sd3, [tagger_image, prompt, tagger_algorithms], [prompt], show_api=False,
+    ).success(
+        remove_specific_prompt, [prompt, tagger_keep_tags], [prompt], queue=False, show_api=False,
+    ).success(
+        convert_danbooru_to_e621_prompt, [prompt, tagger_tag_type], [prompt], queue=False, show_api=False,
+    ).success(
+        insert_recom_prompt, [prompt, neg_prompt, tagger_recom_prompt], [prompt, neg_prompt], queue=False, show_api=False,
+    ).success(
+        compose_prompt_to_copy, [v2_character, v2_series, prompt], [prompt], queue=False, show_api=False,
+    )
 
-app.launch(show_api=False, share=False)
+demo.queue()
+demo.launch()