File size: 4,856 Bytes
ad90256 1721be2 ad90256 751b1a3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | import gradio as gr
import random
import os
from deep_translator import GoogleTranslator
from huggingface_hub import InferenceClient
# Project by Nymbo
MODEL_ID = "black-forest-labs/FLUX.1-dev"
API_TOKEN = os.getenv("HF_READ_TOKEN")
timeout = 100
# Function to query the API and return the generated image
def query(prompt, is_negative=False, steps=35, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024, oauth_token: gr.OAuthToken | None = None):
if prompt == "" or prompt is None:
return None
key = random.randint(0, 999)
# Translate the prompt from Russian to English if necessary
if any('Ѐ' <= ch <= 'ӿ' for ch in prompt):
try:
prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
except Exception as e:
print(f"Translation skipped: {e}")
print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')
# Add some extra flair to the prompt
prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
print(f'\033[1mGeneration {key}:\033[0m {prompt}')
seed = int(seed) if seed != -1 else random.randint(1, 1000000000)
token = oauth_token.token if oauth_token is not None else API_TOKEN
print("token source:", "visitor" if oauth_token is not None else "space secret")
if not token:
raise gr.Error("Please sign in with Hugging Face to generate images.")
client = InferenceClient(token=token, timeout=timeout)
try:
image = client.text_to_image(
prompt,
model=MODEL_ID,
negative_prompt=is_negative or None,
num_inference_steps=int(steps),
guidance_scale=cfg_scale,
width=int(width),
height=int(height),
seed=seed,
)
except Exception as e:
print(f"Error: Failed to get image: {e}")
raise gr.Error(f"Image generation failed: {e}")
print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
return image
# CSS to style the app
css = """
#app-container {
max-width: 800px;
margin-left: auto;
margin-right: auto;
}
"""
# Build the Gradio UI with Blocks
with gr.Blocks() as app:
# Add a title to the app
gr.HTML("<center><h1>FLUX.1-Dev</h1></center>")
gr.LoginButton()
# Container for all the UI elements
with gr.Column(elem_id="app-container"):
# Add a text input for the main prompt
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")
# Accordion for advanced settings
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")
with gr.Row():
width = gr.Slider(label="Width", value=1024, minimum=64, maximum=1216, step=32)
height = gr.Slider(label="Height", value=1024, minimum=64, maximum=1216, step=32)
steps = gr.Slider(label="Sampling steps", value=35, minimum=1, maximum=100, step=1)
cfg = gr.Slider(label="CFG Scale", value=7, minimum=1, maximum=20, step=1)
strength = gr.Slider(label="Strength", value=0.7, minimum=0, maximum=1, step=0.001)
seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1) # Setting the seed to -1 will make it random
method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
# Add a button to trigger the image generation
with gr.Row():
text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
# Image output area to display the generated image
with gr.Row():
image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
# Bind the button to the query function with the added width and height inputs
text_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height], outputs=image_output)
# Launch the Gradio app
app.launch(theme='Nymbo/Nymbo_Theme', css=css) |