import os import gradio as gr import json import logging import torch from PIL import Image import spaces from diffusers import DiffusionPipeline, AutoPipelineForText2Image from diffusers import StableDiffusion3Pipeline # pip install diffusers>=0.31.0 import copy import random import time from huggingface_hub import login hf_token = os.environ.get("HF_TOKEN") login(token=hf_token) torch.set_float32_matmul_precision("high") torch._inductor.config.conv_1x1_as_mm = True torch._inductor.config.coordinate_descent_tuning = True torch._inductor.config.epilogue_fusion = False torch._inductor.config.coordinate_descent_check_all_directions = True # Load LoRAs from JSON file with open('loras.json', 'r') as f: loras = json.load(f) # Initialize the base model base_model = "stabilityai/stable-diffusion-3.5-large" pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-3.5-large", torch_dtype=torch.bfloat16) pipe.transformer.to(memory_format=torch.channels_last) pipe.vae.to(memory_format=torch.channels_last) pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune", fullgraph=True) pipe.vae.decode = torch.compile(pipe.vae.decode, mode="max-autotune", fullgraph=True) MAX_SEED = 2**32-1 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): 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), updated_text, evt.index, width, height, ) @spaces.GPU(duration=70) def infer(prompt, negative_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 image = pipe( prompt=f"{prompt} {trigger_word}", negative_prompt=negative_prompt, num_inference_steps=steps, guidance_scale=cfg_scale, width=width, height=height, generator=generator, joint_attention_kwargs={"scale": lora_scale}, ).images[0] return image def run_lora(prompt, negative_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"] # 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"]) pipe.fuse_lora(lora_scale=lora_scale) else: pipe.load_lora_weights(lora_path) pipe.fuse_lora(lora_scale=lora_scale) # Set random seed for reproducibility with calculateDuration("Randomizing seed"): if randomize_seed: seed = random.randint(0, MAX_SEED) image = infer(prompt, negative_prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, progress) pipe.to("cpu") pipe.unload_lora_weights() return image, seed 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} ''' with gr.Blocks(theme=gr.themes.Soft(), css=css) as app: title = gr.HTML( """

LoRAOpenFlux LoRAsoon®

""", elem_id="title", ) # Info blob stating what the app is running info_blob = gr.HTML( """
SOON®'s curated LoRa Gallery & Art Manufactory Space.|Runs on Stable Diffusion 3.5. Now testing HST-triggerable historic photo-trained LoRAs.
""" ) # Info blob stating what the app is running info_blob = gr.HTML( """
Prephrase prompts w/: "HST style autochrome photo"
""" ) selected_index = gr.State(None) with gr.Row(): with gr.Column(scale=2): prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Select LoRa/Style & type prompt!") with gr.Column(scale=2): negative_prompt = gr.Textbox(label="Negative Prompt", lines=1, placeholder="What to exclude!") 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=4.5) steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=24) with gr.Row(): width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024) height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024) 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) lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=3.0, step=0.01, value=1.0) gallery.select( update_selection, inputs=[width, height], outputs=[prompt, selected_info, selected_index, width, height] ) gr.on( triggers=[generate_button.click, prompt.submit], fn=run_lora, inputs=[prompt, negative_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()