import os import gradio as gr import json import logging import torch from PIL import Image import spaces from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL import copy import random import time from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images from huggingface_hub import HfFileSystem, ModelCard from huggingface_hub import login hf_token = os.environ.get("HF_TOKEN") login(token=hf_token) # Load LoRAs from JSON file with open('loras.json', 'r') as f: loras = json.load(f) # Initialize the base model dtype = torch.bfloat16 device = "cuda" if torch.cuda.is_available() else "cpu" base_model = "John6666/real-flux-10b-schnell-fp8-flux" taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device) good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device) pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device) MAX_SEED = 2**32-1 pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe) class calculateDuration: def __init__(self, activity_name=""): self.activity_name = activity_name def __enter__(self): self.start_time = time.time() return self def __exit__(self, exc_type, exc_value, traceback): self.end_time = time.time() self.elapsed_time = self.end_time - self.start_time if self.activity_name: print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds") else: print(f"Elapsed time: {self.elapsed_time:.6f} seconds") def update_selection(evt: gr.SelectData, width, height, default_scale, lora_scale): selected_lora = loras[evt.index] new_placeholder = f"Type a prompt for {selected_lora['title']}" lora_repo = selected_lora["repo"] updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨" if "aspect" in selected_lora: if selected_lora["aspect"] == "portrait": width = 768 height = 1024 elif selected_lora["aspect"] == "landscape": width = 1024 height = 768 return ( #gr.update(placeholder=new_placeholder), prompt, updated_text, evt.index, width, height, lora_scale, ) @spaces.GPU(duration=70) def generate_image(prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, progress): pipe.to("cuda") generator = torch.Generator(device="cuda").manual_seed(seed) with calculateDuration("Generating image"): # Generate image for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images( prompt=prompt_mash, num_inference_steps=steps, guidance_scale=cfg_scale, width=width, height=height, generator=generator, joint_attention_kwargs={"scale": lora_scale}, output_type="pil", good_vae=good_vae, ): yield img def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)): if selected_index is None: raise gr.Error("You must select a LoRA before proceeding.") selected_lora = loras[selected_index] lora_path = selected_lora["repo"] trigger_word = selected_lora["trigger_word"] if(trigger_word): if "trigger_position" in selected_lora: if selected_lora["trigger_position"] == "prepend": prompt_mash = f"{trigger_word} {prompt}" else: prompt_mash = f"{prompt} {trigger_word}" else: prompt_mash = f"{trigger_word} {prompt}" else: prompt_mash = prompt with calculateDuration("Unloading LoRA"): pipe.unload_lora_weights() # Load LoRA weights with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"): if "weights" in selected_lora: pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"]) else: pipe.load_lora_weights(lora_path) # Set random seed for reproducibility with calculateDuration("Randomizing seed"): if randomize_seed: seed = random.randint(0, MAX_SEED) image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress) # Consume the generator to get the final image final_image = None step_counter = 0 for image in image_generator: step_counter+=1 final_image = image yield image, seed, gr.update(value=progress, visible=True) yield final_image, seed, gr.update(value=progress, visible=False) run_lora.zerogpu = True css = ''' #gen_btn{height: 100%} #title{text-align: center} #title h1{font-size: 3em; display:inline-flex; align-items:center} #title img{width: 100px; margin-right: 0.5em} #gallery .grid-wrap{height: 10vh} #lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%} .card_internal{display: flex;height: 100px;margin-top: .5em} .card_internal img{margin-right: 1em} .styler{--form-gap-width: 0px !important} #progress{height:30px} #progress .generating{display:none} .progress-container {width: 100%;height: 30px;background-color: #f0f0f0;border-radius: 15px;overflow: hidden;margin-bottom: 20px} ''' with gr.Blocks(theme=gr.themes.Soft(), css=css) as app: title = gr.HTML( """

LoRA SOONfactory

""", elem_id="title", ) # Info blob stating what the app is running info_blob = gr.HTML( """
Img. Manufactory Running On: Our 'MytHSTic Color SOON®' Fast (4-8 step) FLUX Schnell-Base Model (at AlekseyCalvin/Mythstic_Color_Soonr_Flux). Now testing HST-triggered historic photo LoRAs (#s2-8,11,12,14,16)for training-eval & merging.
""" ) # Info blob stating what the app is running info_blob = gr.HTML( """
Prephrase prompts w/: 1: RCA style || 2-thru-12: HST style analog film photo; HST autochrome photograph || 13: HST style in Peterhof || 14: LEN Vladimir Lenin || 15: SOTS style || 16: crisp photo || 17: TOK hybrid || 18: 2004 photo || 19: TOK portra || 20: flmft Kodachrome || 21: HST Austin Osman Spare style || 22: polaroid photo || 23: pficonics || 24: wh3r3sw4ld0 || 25: retrofuturism || 26: vintage cover ||
""" ) selected_index = gr.State(None) with gr.Row(): with gr.Column(scale=3): prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Select LoRa/Style & type prompt!") with gr.Column(scale=1, elem_id="gen_column"): generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn") with gr.Row(): with gr.Column(scale=3): selected_info = gr.Markdown("") gallery = gr.Gallery( [(item["image"], item["title"]) for item in loras], label="LoRA Inventory", allow_preview=False, columns=3, elem_id="gallery" ) with gr.Column(scale=4): result = gr.Image(label="Generated Image") with gr.Row(): with gr.Accordion("Advanced Settings", open=True): with gr.Column(): with gr.Row(): cfg_scale = gr.Slider(label="CFG Scale", minimum=0, maximum=20, step=.5, value=0) steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=5) with gr.Row(): width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=768) height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=768) with gr.Row(): randomize_seed = gr.Checkbox(True, label="Randomize seed") seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True) default_scale = gr.Checkbox(True, label="Use default LoRA scale") lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=3.0, step=0.01, value=0.95) gallery.select( update_selection, inputs=[width, height, default_scale, lora_scale], outputs=[prompt, selected_info, selected_index, width, height, lora_scale] ) gr.on( triggers=[generate_button.click, prompt.submit], fn=run_lora, inputs=[prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale], outputs=[result, seed] ) app.queue(default_concurrency_limit=2).launch(show_error=True) app.launch()