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import gradio as gr |
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from random import randint |
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from all_models import models |
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from externalmod import gr_Interface_load, randomize_seed |
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import asyncio |
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import os |
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from threading import RLock |
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lock = RLock() |
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HF_TOKEN = os.environ.get("HF_TOKEN") if os.environ.get("HF_TOKEN") else None |
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def load_fn(models): |
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global models_load |
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models_load = {} |
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for model in models: |
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if model not in models_load.keys(): |
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try: |
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m = gr_Interface_load(f'models/{model}', hf_token=HF_TOKEN) |
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except Exception as error: |
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print(error) |
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m = gr.Interface(lambda: None, ['text'], ['image']) |
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models_load.update({model: m}) |
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load_fn(models) |
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num_models = 9 |
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default_models = models[:num_models] |
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inference_timeout = 600 |
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MAX_SEED=666666666 |
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starting_seed = randint(666666000, 666666666) |
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def extend_choices(choices): |
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return choices[:num_models] + (num_models - len(choices[:num_models])) * ['NA'] |
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def update_imgbox(choices): |
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choices_plus = extend_choices(choices[:num_models]) |
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return [gr.Image(None, label=m, visible=(m!='NA')) for m in choices_plus] |
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async def infer(model_str, prompt, seed=1, timeout=inference_timeout): |
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from pathlib import Path |
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kwargs = {} |
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noise = "" |
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kwargs["seed"] = seed |
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task = asyncio.create_task(asyncio.to_thread(models_load[model_str].fn, |
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prompt=f'{prompt} {noise}', **kwargs, token=HF_TOKEN)) |
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await asyncio.sleep(0) |
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try: |
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result = await asyncio.wait_for(task, timeout=timeout) |
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except (Exception, asyncio.TimeoutError) as e: |
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print(e) |
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print(f"Task timed out: {model_str}") |
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if not task.done(): task.cancel() |
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result = None |
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if task.done() and result is not None: |
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with lock: |
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png_path = "image.png" |
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result.save(png_path) |
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image = str(Path(png_path).resolve()) |
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return image |
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return None |
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def gen_fnseed(model_str, prompt, seed=1): |
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if model_str == 'NA': |
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return None |
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try: |
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loop = asyncio.new_event_loop() |
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result = loop.run_until_complete(infer(model_str, prompt, seed, inference_timeout)) |
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except (Exception, asyncio.CancelledError) as e: |
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print(e) |
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print(f"Task aborted: {model_str}") |
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result = None |
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with lock: |
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image = "https://huggingface.co/spaces/Yntec/ToyWorld/resolve/main/error.png" |
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result = image |
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finally: |
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loop.close() |
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return result |
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with gr.Blocks(theme='Yntec/HaleyCH_Theme_Winter_Green') as demo: |
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with gr.Tab('🤗 December 2024 is the date this space was launched 🤗'): |
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txt_input = gr.Textbox(label='Your prompt:', lines=4) |
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gen_button = gr.Button('Generate up to 9 images in up to 3 minutes total') |
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with gr.Row(): |
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seed = gr.Slider(label="Use a seed to replicate the same image later (maximum 666666666)", minimum=0, maximum=MAX_SEED, step=1, value=starting_seed, scale=3) |
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seed_rand = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary", scale=1) |
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seed_rand.click(randomize_seed, None, [seed], queue=False) |
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gen_button.click(lambda s: gr.update(interactive = True), None) |
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gr.HTML( |
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""" |
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<div style="text-align: center; max-width: 1200px; margin: 0 auto;"> |
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<div> |
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<body> |
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<div class="center"><p style="margin-bottom: 10px; color: #000000;">Scroll down to see more images and select models.</p> |
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</div> |
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</body> |
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</div> |
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</div> |
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""" |
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) |
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with gr.Row(): |
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output = [gr.Image(label = m, min_width=480) for m in default_models] |
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current_models = [gr.Textbox(m, visible = False) for m in default_models] |
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for m, o in zip(current_models, output): |
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gen_event = gr.on(triggers=[gen_button.click, txt_input.submit], fn=gen_fnseed, |
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inputs=[m, txt_input, seed], outputs=[o], concurrency_limit=None, queue=False) |
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with gr.Accordion('Model selection'): |
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model_choice = gr.CheckboxGroup(models, label = 'Untick the models you will not be using', value=default_models, interactive=True) |
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model_choice.change(update_imgbox, model_choice, output) |
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model_choice.change(extend_choices, model_choice, current_models) |
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with gr.Row(): |
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gr.HTML( |
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""" |
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<div class="footer"> |
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<p> For more than a hundred times more models (that's not a typo) check out <a href="https://huggingface.co/spaces/Yntec/ToyWorld">ToyWorld</a>!</a> |
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</p> |
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""" |
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) |
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demo.queue(default_concurrency_limit=200, max_size=200) |
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demo.launch(show_api=False, max_threads=400) |
