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import gradio as gr
import requests
import io
import random
import os
import time
from PIL import Image
import json
import replicate
# Project by Nymbo
# API_URL = "https://api-inference.huggingface.co/models/codermert/mert2_flux"
# API_TOKEN = os.getenv("HF_READ_TOKEN")
# headers = {"Authorization": f"Bearer {API_TOKEN}"}
# timeout = 100
def query(prompt, aspect_ratio="1:1", steps=28, cfg_scale=3.5, seed=-1, strength=0.95):
if seed == -1:
seed = random.randint(1, 1000000000)
input = {
"prompt": prompt,
"hf_lora": "codermert/mert2_flux",
"output_format": "jpg",
"aspect_ratio": aspect_ratio,
"num_inference_steps": steps,
"guidance_scale": cfg_scale,
"lora_scale": strength,
"seed": seed,
"disable_safety_checker": True
}
# if(seed!=-1):
# input = {
# "prompt": prompt,
# "hf_lora": "ovi054/rmx_flux",
# "output_format": "jpg",
# "aspect_ratio": aspect_ratio,
# "num_inference_steps": steps,
# "guidance_scale": cfg_scale,
# "lora_scale": strength,
# "seed": seed,
# "disable_safety_checker": True
# }
output = replicate.run(
"lucataco/flux-dev-lora:a22c463f11808638ad5e2ebd582e07a469031f48dd567366fb4c6fdab91d614d",
input=input
)
print(output)
return output[0], seed
css = """
#app-container {
max-width: 600px;
margin-left: auto;
margin-right: auto;
}
"""
# Assuming `prompt` and other flagged data inputs need to be truncated.
# def truncate_prompts(*args):
# # Truncate the prompts (args[0] is text_prompt, args[1] is negative_prompt)
# truncated_text_prompt = args[0][:200] if isinstance(args[0], str) else args[0]
# # truncated_negative_prompt = args[1][:200] if isinstance(args[1], str) else args[1]
# return (truncated_text_prompt, *args[1:])
# Return the modified list with the truncated prompts
# return (truncated_text_prompt, truncated_negative_prompt, *args[2:])
examples = [
"KAMALINEE, A charismatic speaker is captured mid-speech. She has long, tousled brown hair that’s slightly messy on top. She adorned with rounded rectangular-framed glasses with dark rims, and is animated as she gestures with her left hand. She is holding a black microphone in her right hand, speaking passionately. The woman is wearing a light grey sweater over a white t-shirt. She’s also wearing a simple black lanyard hanging around her neck. The lanyard badge has the text “Kamalinee”. Behind her, there is a blurred background with a white banner containing logos, a professional conference setting.",
"KAMALINEE, An image of a woman. She dressed in a red and navy plaid shirt with the top unbuttoned to show a white undershirt, and the sleeves rolled up to the forearms. The woman is casually leaning against a weathered blue door frame with peeling paint, adding a rustic charm to the scene. Her arms are crossed or resting in front of her, and she has a soft, contemplative expression on her face.",
"Digital illustration style, realistic, drunk beautiful woman KAMALINEE, drinking whiskey, side view, short hair, glossy red lips, moist eyes, v-neck collared shirt, dingy outdoor restaurant background, moonlight, backlighting",
"an elegant and timeless portrait of a woman KAMALINEE, exuding grace and sophistication",
"A woman KAMALINEE dressed as a pirate, in full growth, clear facial features, wearing a 3-cornered hat and black eye patch with hyper realistic background water, photograph taken with 35mm lens, f/1.8, sunlight, natural lighting",
]
HF_TOKEN = os.getenv("SECRET_TOKEN")
callback = gr.HuggingFaceDatasetSaver(HF_TOKEN, "rmx-data")
# callback.setup([gr.Textbox, gr.Textbox, gr.Slider, gr.Slider, gr.Radio, gr.Slider, gr.Slider, gr.Image],
# "flagged_data_points")
with gr.Blocks(theme='Nymbo/Nymbo_Theme', css=css) as app:
gr.HTML("<center><h1>RMX.1-Dev</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():
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")
aspect_ratio = gr.Radio(label="Aspect ratio", value="1:1", choices=["1:1", "4:5", "2:3", "3:4","9:16", "4:3", "16:9"])
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"])
strength = gr.Slider(label="Strength", value=0.95, minimum=0, maximum=1, step=0.001)
seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1)
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",interactive=False, show_download_button=True, elem_id="gallery")
with gr.Row():
seed_output = gr.Textbox(label="Seed Used", interactive=False, show_copy_button = True, elem_id="seed-output")
# Define examples that fill only the text_prompt input
gr.Examples(
examples = examples,
fn = query,
inputs = [text_prompt],
)
# text_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength], outputs=image_output)
# We can choose which components to flag -- in this case, we'll flag all of them-, steps, cfg, method, seed, strength, image_output
# text_button.click(lambda *args: callback.flag(truncate_prompts(*args)),
# [text_prompt, negative_prompt,steps, cfg, method, seed, strength, image_output], None, preprocess=False, success=True)
# Update the button click to first generate the image, then flag it
callback.setup([text_prompt, aspect_ratio, steps, cfg, seed_output, strength, image_output],
"flagged_data_points")
def truncate_prompts(*args):
truncated_text_prompt = args[0][:200] if isinstance(args[0], str) else args[0]
return (truncated_text_prompt, *args[1:])
text_button.click(
query,
inputs=[text_prompt, aspect_ratio, steps, cfg, seed, strength],
outputs=[image_output,seed_output]
).then(
lambda *args: callback.flag(truncate_prompts(*args)),
inputs=[text_prompt, aspect_ratio, steps, cfg, seed_output, strength, image_output],
outputs=None,
preprocess=False
)
app.launch(show_api=False, share=False) |