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import gradio as gr |
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import requests |
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import io |
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import random |
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import os |
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import time |
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from PIL import Image |
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from deep_translator import GoogleTranslator |
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import json |
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API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev" |
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API_TOKEN = os.getenv("HF_READ_TOKEN") |
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headers = {"Authorization": f"Bearer {API_TOKEN}"} |
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timeout = 100 |
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def query(prompt, is_negative=False, steps=30, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7): |
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if prompt == "" or prompt == None: |
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return None |
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key = random.randint(0, 999) |
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API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")]) |
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headers = {"Authorization": f"Bearer {API_TOKEN}"} |
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prompt = GoogleTranslator(source='ru', target='en').translate(prompt) |
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print(f'\033[1mGeneration {key} translation:\033[0m {prompt}') |
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prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect." |
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print(f'\033[1mGeneration {key}:\033[0m {prompt}') |
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payload = { |
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"inputs": prompt, |
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"is_negative": is_negative, |
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"steps": steps, |
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"cfg_scale": cfg_scale, |
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"seed": seed if seed != -1 else random.randint(1, 1000000000), |
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"strength": strength |
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} |
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response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout) |
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if response.status_code != 200: |
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print(f"Error: Failed to get image. Response status: {response.status_code}") |
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print(f"Response content: {response.text}") |
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if response.status_code == 503: |
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raise gr.Error(f"{response.status_code} : The model is being loaded") |
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raise gr.Error(f"{response.status_code}") |
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try: |
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image_bytes = response.content |
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image = Image.open(io.BytesIO(image_bytes)) |
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print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})') |
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return image |
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except Exception as e: |
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print(f"Error when trying to open the image: {e}") |
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return None |
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css = """ |
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#app-container { |
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max-width: 580px; |
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margin-left: auto; |
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margin-right: auto; |
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} |
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""" |
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with gr.Blocks(theme='Nymbo/Nymbo_Theme', css=css) as app: |
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with gr.Column(elem_id="app-container"): |
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with gr.Row(): |
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with gr.Column(elem_id="prompt-container"): |
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with gr.Row(): |
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text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=3, elem_id="prompt-text-input") |
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with gr.Row(): |
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with gr.Accordion("Advanced Settings", open=False): |
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negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What should not be in the image", value="[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry, text, fuzziness", lines=3, elem_id="negative-prompt-text-input") |
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steps = gr.Slider(label="Sampling steps", value=35, minimum=1, maximum=100, step=1) |
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cfg = gr.Slider(label="CFG Scale", value=7, minimum=1, maximum=20, step=1) |
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method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"]) |
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strength = gr.Slider(label="Strength", value=0.7, minimum=0, maximum=1, step=0.001) |
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seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1) |
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with gr.Row(): |
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text_button = gr.Button("Run", variant='primary', elem_id="gen-button") |
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with gr.Row(): |
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image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery") |
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text_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength], outputs=image_output) |
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app.launch(show_api=False, share=False) |
