1st

app.py

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+import os
+import json
+import base64
+import requests
+import gradio as gr
+import matplotlib.pyplot as plt
+
+from PIL import Image
+from io import BytesIO
+from huggingface_hub import HfFileSystem, hf_hub_download
+
+
+def request_to_endpoint (request:dict, endpoint_id='4556prwagxw5co'):
+    api_key = "A6DTPK0VM02LEBYFSVJKKFZ9MTZQ4ED1CMBK0OE2"
+    url = f"https://api.runpod.ai/v2/{endpoint_id}/runsync"
+    headers = {
+        "accept": "application/json",
+        "authorization": api_key,
+        "content-type": "application/json"
+    }
+    response = requests.post(url, headers=headers, data=json.dumps(request)) 
+
+    return response
+
+# Собирает только sdxl версии
+def prepare_characters(hf_path="OnMoon/loras"):
+    fs = HfFileSystem()
+
+    files = fs.ls(hf_path, detail=False)
+    character_names = []
+    character_configs = {}
+    for file in files:
+        if file.endswith(".safetensors") and file.startswith(f"{hf_path}/sdxl_"):
+            character = file[len(f"{hf_path}/sdxl_"): -len(".safetensors")]
+            character_names.append(character)
+        elif file.endswith(".json") and file.startswith(f"{hf_path}/sdxl_"):
+            character = file[len(f"{hf_path}/sdxl_"): -len(".json")]
+            with fs.open(file, 'r', encoding='utf-8') as file_json:
+                character_configs[character] = json.load(file_json)  
+        
+    return character_names, character_configs
+
+character_names, character_configs = prepare_characters()
+
+# Invoke endpoint
+request_to_endpoint({"input": {"prompt": ""}})
+
+
+def process_input (name, scale, triggers, tech_prompt, tech_negative_prompt, prompts_count, *args):    
+    boxes = list(args)
+    prompts = []
+    negative_prompts = []
+
+    # Если хотим задать свои триггерные слова или технический промпт
+    triggers = triggers if triggers != "" else ",".join(character_configs[name]["trigger_words"])
+    tech_prompt = tech_prompt if tech_prompt != "" else character_configs[name]["tech_prompt"]
+    tech_negative_prompt = tech_negative_prompt if tech_negative_prompt != "" else character_configs[name]["tech_negative_prompt"]
+    model = character_configs[name]["model"]
+    model['loras'] = {name: scale}
+    params = character_configs[name]["params"]
+    for n in range(prompts_count):
+        prompts.append(f'{triggers}, {tech_prompt}, {boxes[2*n]}')
+        negative_prompts.append(f"{tech_negative_prompt}, {boxes[2*n + 1]}")
+    
+    request_data = {
+        "input": {
+            "mode": "inference",
+            "model": model,
+            "params": params,
+            "prompt": prompts,
+            "negative_prompt": negative_prompts,
+            "height": 1216,
+            "width": 832,
+        }
+    }
+    
+    response = request_to_endpoint(request_data)
+
+    images = []
+    for base64_string in response.json()['output']['images']:
+        img = Image.open(BytesIO(base64.b64decode(base64_string)))
+        images.append(img)
+
+    gallery = [[images[i], f"{prompts[i]}"] for i in range(prompts_count)]
+
+    return gallery
+
+
+
+
+
+
+                            ######################################################
+                            #   ____               _ _         _                 #
+                            #  / ___|_ __ __ _  __| (_) ___   / \   _ __  _ __   #
+                            # | |  _| '__/ _` |/ _` | |/ _ \ / _ \ | '_ \| '_ \  #
+                            # | |_| | | | (_| | (_| | | (_) / ___ \| |_) | |_) | #
+                            #  \____|_|  \__,_|\__,_|_|\___/_/   \_\ .__/| .__/  #
+                            #                                      |_|   |_|     #
+############################################################################################################
+with gr.Blocks() as demo:
+    with gr.Group():
+        name = gr.Radio(
+            character_names,
+            label="Select character:",
+            interactive=True,
+            visible=True,
+        )
+
+        with gr.Accordion(open=False):
+            scale = gr.Slider(0, 2.0, 0.01, label=f"{name} scale:", value=0.75, interactive=True)
+            triggers = gr.Textbox(label=f"Trigger words:")
+            tech_prompt = gr.Textbox(label=f"Technical prompt:")
+            tech_negative_prompt = gr.Textbox(label=f"Negative technical prompt:")
+
+    prompts_count = gr.State(1)
+
+    with gr.Row():
+        with gr.Column():
+            add_btn = gr.Button("Add prompt")
+        del_btn = gr.Button("Delete prompt")
+            
+    add_btn.click(lambda x: x + 1, prompts_count, prompts_count)
+    del_btn.click(lambda x: x - 1, prompts_count, prompts_count)
+
+    @gr.render(inputs=prompts_count)
+    def render_count(count):
+        boxes = []
+        for i in range(count):
+            with gr.Group():
+                prompt = gr.Textbox(key=i, label=f"Prompt {i+1}")
+                negative_prompt = gr.Textbox(key=i, label=f"Negative prompt {i+1}")
+
+            boxes.append(prompt)
+            boxes.append(negative_prompt)
+        
+        generate_btn.click(
+            process_input, 
+            [name, scale, triggers, tech_prompt, tech_negative_prompt, prompts_count]+boxes,
+            output
+        )
+
+    generate_btn = gr.Button("Generate!")
+
+    output = gr.Gallery(
+        label="Generation results:",
+        object_fit="contain", 
+        height="auto",
+    )
+
+demo.launch()
+############################################################################################################