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/ovi054/rmx_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": "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 } # 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("

RMX.1-Dev

") 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)