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 fastapi import FastAPI app = FastAPI() #----------Start of theme---------- theme = gr.themes.Soft( primary_hue="zinc", secondary_hue="stone", font=[gr.themes.GoogleFont('Kavivanar'), gr.themes.GoogleFont('Kavivanar'), 'system-ui', 'sans-serif'], font_mono=[gr.themes.GoogleFont('Source Code Pro'), gr.themes.GoogleFont('Inconsolata'), gr.themes.GoogleFont('Inconsolata'), 'monospace'], ).set( body_background_fill='*primary_100', body_text_color='secondary_600', body_text_color_subdued='*primary_500', body_text_weight='500', background_fill_primary='*primary_100', background_fill_secondary='*secondary_200', color_accent='*primary_300', border_color_accent_subdued='*primary_400', border_color_primary='*primary_400', block_background_fill='*primary_300', block_border_width='*panel_border_width', block_info_text_color='*primary_700', block_info_text_size='*text_md', panel_background_fill='*primary_200', accordion_text_color='*primary_600', table_text_color='*primary_600', input_background_fill='*primary_50', input_background_fill_focus='*primary_100', button_primary_background_fill='*primary_500', button_primary_background_fill_hover='*primary_400', button_primary_text_color='*primary_50', button_primary_text_color_hover='*primary_100', button_cancel_background_fill='*primary_500', button_cancel_background_fill_hover='*primary_400' ) #----------End of theme---------- API_TOKEN = os.getenv("HF_READ_TOKEN") headers = {"Authorization": f"Bearer {API_TOKEN}"} timeout = 100 article_text = """

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""" def query(lora_id, prompt, steps=28, cfg_scale=3.5, randomize_seed=True, seed=-1, width=1024, height=1024): if prompt == "" or prompt == None: return None if lora_id.strip() == "" or lora_id == None: lora_id = "black-forest-labs/FLUX.1-dev" key = random.randint(0, 999) API_URL = "https://api-inference.huggingface.co/models/"+ lora_id.strip() API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")]) headers = {"Authorization": f"Bearer {API_TOKEN}"} # prompt = GoogleTranslator(source='ru', target='en').translate(prompt) # print(f'\033[1mGeneration {key} translation:\033[0m {prompt}') prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect." # print(f'\033[1mGeneration {key}:\033[0m {prompt}') # If seed is -1, generate a random seed and use it if randomize_seed: seed = random.randint(1, 4294967296) payload = { "inputs": prompt, "steps": steps, "cfg_scale": cfg_scale, "seed": seed, "parameters": { "width": width, # Pass the width to the API "height": height # Pass the height to the API } } 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 ({prompt})') return image, seed, seed except Exception as e: print(f"Error when trying to open the image: {e}") return None examples = [ "a tiny astronaut hatching from an egg on the moon", "a cat holding a sign that says hello world", "an anime illustration of a wiener schnitzel", ] css = """ #app-container { max-width: 896px; margin-left: auto; margin-right: auto; } """ with gr.Blocks(theme=theme, css=css) as app: gr.HTML("

FLUX.1-Dev with LoRA support

") 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(): custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path (optional)", placeholder="multimodalart/vintage-ads-flux") with gr.Row(): with gr.Accordion("Advanced Settings", open=False): with gr.Row(): width = gr.Slider(label="Width", value=1024, minimum=64, maximum=1216, step=8) height = gr.Slider(label="Height", value=1024, minimum=64, maximum=1216, step=8) seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=4294967296, step=1) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) with gr.Row(): steps = gr.Slider(label="Sampling steps", value=28, minimum=1, maximum=100, step=1) cfg = gr.Slider(label="CFG Scale", value=3.5, 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"]) with gr.Row(): 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") with gr.Row(): seed_output = gr.Textbox(label="Seed Used", show_copy_button = True, elem_id="seed-output") gr.Markdown(article_text) gr.Examples( examples = examples, inputs = [text_prompt], ) text_button.click(query, inputs=[custom_lora, text_prompt, steps, cfg, randomize_seed, seed, width, height], outputs=[image_output,seed_output, seed]) app.launch(show_api=False, share=False)