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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(
"""<h1><img src="https://huggingface.co/AlekseyCalvin/StabledHSTorY_SD3.5_LoRA_V2_rank256/resolve/main/acs62v.png" alt="LoRA">Stabled LoRAs soon® on S.D.3.5 Merged</h1>""",
elem_id="title",
)
# Info blob stating what the app is running
info_blob = gr.HTML(
"""<div id="info_blob">SOON®'s curated LoRa Gallery & Art Manufactory.|Now testing HST-triggered historic photo-trained LoRAs for Stable Diffusion 3.5.</div>"""
)
# Info blob stating what the app is running
info_blob = gr.HTML(
"""<div id="info_blob">Prephrase prompts w/: "HST style autochrome photo" </div>"""
)
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()