import os import gradio as gr import json import logging import torch from os import path from PIL import Image import spaces from diffusers import DiffusionPipeline, AutoPipelineForText2Image from diffusers import StableDiffusion3Pipeline, FlowMatchEulerDiscreteScheduler, SD3Transformer2DModel # pip install diffusers>=0.31.0 from transformers import CLIPModel, CLIPProcessor, CLIPTextModel, CLIPTokenizer, CLIPConfig, T5EncoderModel, T5Tokenizer import copy import random import time from huggingface_hub import HfFileSystem, ModelCard from huggingface_hub import login, hf_hub_download import safetensors.torch from safetensors.torch import load_file hf_token = os.environ.get("HF_TOKEN") login(token=hf_token) cache_path = path.join(path.dirname(path.abspath(__file__)), "models") os.environ["TRANSFORMERS_CACHE"] = cache_path os.environ["HF_HUB_CACHE"] = cache_path os.environ["HF_HOME"] = cache_path 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("ariG23498/sd-3.5-merged", torch_dtype=torch.bfloat16) #clipmodel = 'norm' #if clipmodel == "long": # model_id = "zer0int/LongCLIP-GmP-ViT-L-14" # config = CLIPConfig.from_pretrained(model_id) # maxtokens = 248 #if clipmodel == "norm": # model_id = "zer0int/CLIP-GmP-ViT-L-14" # config = CLIPConfig.from_pretrained(model_id) # maxtokens = 77 #clip_model = CLIPModel.from_pretrained(model_id, torch_dtype=torch.bfloat16, config=config, ignore_mismatched_sizes=True).to("cuda") #clip_processor = CLIPProcessor.from_pretrained(model_id, padding="max_length", max_length=maxtokens, ignore_mismatched_sizes=True, return_tensors="pt", truncation=True) #pipe.tokenizer = clip_processor.tokenizer #pipe.text_encoder = clip_model.text_model #pipe.tokenizer_max_length = maxtokens #pipe.text_encoder.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"]) 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 = 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( """

LoRAStabled HSTorY S.D.3.5 LoRAs

""", elem_id="title", ) # Info blob stating what the app is running info_blob = gr.HTML( """
SOON®'s curated LoRa Gallery & Art Manufactory.|Now testing HST-triggered historic photo-trained LoRAs for Stable Diffusion 3.5.
""" ) # 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=.1, value=1.0) steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=8) 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()