Update app.py

app.py

@@ -1,163 +1,112 @@
 import gradio as gr
-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,
-)
+import requests
+import io
+import random
+import os
+import time
+from PIL import Image
+from deep_translator import GoogleTranslator
+import json
 
+# Project by Nymbo
 
-max_images = 8
-load_models(models)
+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
 
 css = """
-.model_info { text-align: center; }
-.output { width=112px; height=112px; !important; }
-.gallery { width=100%; min_height=768px; !important; }
+#app-container {
+    max-width: 600px;
+    margin-left: auto;
+    margin-right: auto;
+}
 """
 
-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.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.Row():
-                    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.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():
-            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).
-                """
+            text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
+        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
         )
-        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,
-    )
 
-demo.queue()
-demo.launch()
+app.launch(show_api=False, share=False)