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import os |
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import gradio as gr |
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import json |
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import logging |
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import torch |
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from PIL import Image |
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import spaces |
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from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL |
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import copy |
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import random |
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import time |
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from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images |
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from huggingface_hub import HfFileSystem, ModelCard |
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from huggingface_hub import login |
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hf_token = os.environ.get("HF_TOKEN") |
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login(token=hf_token) |
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with open('loras.json', 'r') as f: |
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loras = json.load(f) |
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dtype = torch.bfloat16 |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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base_model = "John6666/real-flux-10b-schnell-fp8-flux" |
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pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16) |
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taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device) |
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good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device) |
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pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device) |
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MAX_SEED = 2**32-1 |
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pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe) |
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class calculateDuration: |
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def __init__(self, activity_name=""): |
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self.activity_name = activity_name |
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def __enter__(self): |
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self.start_time = time.time() |
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return self |
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def __exit__(self, exc_type, exc_value, traceback): |
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self.end_time = time.time() |
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self.elapsed_time = self.end_time - self.start_time |
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if self.activity_name: |
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print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds") |
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else: |
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print(f"Elapsed time: {self.elapsed_time:.6f} seconds") |
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def update_selection(evt: gr.SelectData, width, height): |
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selected_lora = loras[evt.index] |
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new_placeholder = f"Type a prompt for {selected_lora['title']}" |
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lora_repo = selected_lora["repo"] |
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updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨" |
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if "aspect" in selected_lora: |
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if selected_lora["aspect"] == "portrait": |
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width = 768 |
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height = 1024 |
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elif selected_lora["aspect"] == "landscape": |
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width = 1024 |
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height = 768 |
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return ( |
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prompt, |
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updated_text, |
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evt.index, |
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width, |
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height, |
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lora_scale, |
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) |
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@spaces.GPU(duration=70) |
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def generate_image(prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, progress): |
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pipe.to("cuda") |
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generator = torch.Generator(device="cuda").manual_seed(seed) |
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with calculateDuration("Generating image"): |
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for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images( |
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prompt=prompt_mash, |
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num_inference_steps=steps, |
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guidance_scale=cfg_scale, |
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width=width, |
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height=height, |
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generator=generator, |
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joint_attention_kwargs={"scale": lora_scale}, |
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output_type="pil", |
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good_vae=good_vae, |
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): |
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yield img |
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def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)): |
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if selected_index is None: |
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raise gr.Error("You must select a LoRA before proceeding.") |
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selected_lora = loras[selected_index] |
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lora_path = selected_lora["repo"] |
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trigger_word = selected_lora["trigger_word"] |
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if(trigger_word): |
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if "trigger_position" in selected_lora: |
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if selected_lora["trigger_position"] == "prepend": |
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prompt_mash = f"{trigger_word} {prompt}" |
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else: |
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prompt_mash = f"{prompt} {trigger_word}" |
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else: |
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prompt_mash = f"{trigger_word} {prompt}" |
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else: |
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prompt_mash = prompt |
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with calculateDuration("Unloading LoRA"): |
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pipe.unload_lora_weights() |
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with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"): |
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if "weights" in selected_lora: |
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pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"]) |
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else: |
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pipe.load_lora_weights(lora_path) |
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with calculateDuration("Randomizing seed"): |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress) |
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final_image = None |
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step_counter = 0 |
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for image in image_generator: |
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step_counter+=1 |
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final_image = image |
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progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>' |
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yield image, seed, gr.update(value=progress_bar, visible=True) |
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yield final_image, seed, gr.update(value=progress_bar, visible=False) |
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run_lora.zerogpu = True |
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css = ''' |
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#gen_btn{height: 100%} |
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#title{text-align: center} |
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#title h1{font-size: 3em; display:inline-flex; align-items:center} |
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#title img{width: 100px; margin-right: 0.5em} |
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#gallery .grid-wrap{height: 10vh} |
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''' |
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with gr.Blocks(theme=gr.themes.Soft(), css=css) as app: |
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title = gr.HTML( |
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"""<h1><img src="https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer/resolve/main/flux_lora.png" alt="LoRA"> SOONfactory </h1>""", |
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elem_id="title", |
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) |
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info_blob = gr.HTML( |
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"""<div id="info_blob"> 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. </div>""" |
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) |
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info_blob = gr.HTML( |
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"""<div id="info_blob">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 || </div>""" |
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) |
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selected_index = gr.State(None) |
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with gr.Row(): |
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with gr.Column(scale=3): |
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prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Select LoRa/Style & type prompt!") |
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with gr.Column(scale=1, elem_id="gen_column"): |
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generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn") |
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with gr.Row(): |
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with gr.Column(scale=3): |
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selected_info = gr.Markdown("") |
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gallery = gr.Gallery( |
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[(item["image"], item["title"]) for item in loras], |
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label="LoRA Inventory", |
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allow_preview=False, |
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columns=3, |
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elem_id="gallery" |
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) |
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with gr.Column(scale=4): |
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result = gr.Image(label="Generated Image") |
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with gr.Row(): |
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with gr.Accordion("Advanced Settings", open=True): |
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with gr.Column(): |
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with gr.Row(): |
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cfg_scale = gr.Slider(label="CFG Scale", minimum=0, maximum=20, step=.5, value=0) |
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steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=5) |
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with gr.Row(): |
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width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=768) |
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height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=768) |
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with gr.Row(): |
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randomize_seed = gr.Checkbox(True, label="Randomize seed") |
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seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True) |
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default_scale = gr.Checkbox(True, label="Use default LoRA scale") |
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lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=3.0, step=0.01, value=0.95) |
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gallery.select( |
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update_selection, |
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inputs=[width, height, default_scale, lora_scale], |
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outputs=[prompt, selected_info, selected_index, width, height, lora_scale] |
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) |
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gr.on( |
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triggers=[generate_button.click, prompt.submit], |
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fn=run_lora, |
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inputs=[prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale], |
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outputs=[result, seed, progress_bar] |
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) |
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app.queue(default_concurrency_limit=2).launch(show_error=True) |
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app.launch() |