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Browse files- README.md +4 -7
- app.py +29 -25
- externalmod.py +27 -0
README.md
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---
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title:
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emoji: 🛕🛕
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colorFrom: green
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colorTo: blue
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sdk: gradio
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sdk_version:
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app_file: app.py
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pinned:
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- Yntec/Diffusion80XX
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- Yntec/HuggingfaceDiffusion
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short_description: Compare up to 6 image models!
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: Huggingface Diffusion
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emoji: 🛕🛕
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colorFrom: green
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colorTo: blue
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sdk: gradio
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sdk_version: 5.0.1
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app_file: app.py
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pinned: true
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short_description: Compare 909+ AI Art Models 6 at a time!
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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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
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import asyncio
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import os
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from threading import RLock
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# https://huggingface.co/docs/api-inference/detailed_parameters
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# https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
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async def infer(model_str, prompt, nprompt="", height=
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from pathlib import Path
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kwargs = {}
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if height
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if width
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if steps
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if cfg
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else:
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rand = randint(1, 500)
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for i in range(rand):
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noise += " "
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task = asyncio.create_task(asyncio.to_thread(models_load[model_str].fn,
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prompt=
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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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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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raise Exception(f"Task timed out: {model_str}")
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except Exception as e:
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print(e)
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if not task.done(): task.cancel()
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result = None
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raise Exception(e
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if task.done() and result is not None and not isinstance(result, tuple):
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with lock:
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png_path = "image.png"
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result
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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_fn(model_str, prompt, nprompt="", height=
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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, nprompt,
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"""
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with gr.Blocks(theme='
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with gr.Column(scale=2):
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with gr.Group():
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txt_input = gr.Textbox(label='Your prompt:', lines=4)
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steps = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
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cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
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seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
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with gr.Row():
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gen_button = gr.Button(f'Generate up to {int(num_models)} images in up to 3 minutes total', variant='primary', scale=3)
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random_button = gr.Button(f'Random {int(num_models)} 🎲', variant='secondary', scale=1)
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with gr.Group():
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with gr.Row():
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output = [gr.Image(label=m, show_download_button=True, elem_classes="output",
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interactive=False,
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visible=True) 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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steps2 = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
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cfg2 = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
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seed2 = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
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num_images = gr.Slider(1, max_images, value=max_images, step=1, label='Number of images')
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with gr.Row():
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gen_button2 = gr.Button('Generate', variant='primary', scale=2)
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with gr.Group():
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with gr.Row():
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output2 = [gr.Image(label='', show_download_button=True, elem_classes="output",
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interactive=False,
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show_share_button=False, show_label=False) for _ in range(max_images)]
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with gr.Column(scale=2):
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gr.Markdown("Based on the [TestGen](https://huggingface.co/spaces/derwahnsinn/TestGen) Space by derwahnsinn, the [SpacIO](https://huggingface.co/spaces/RdnUser77/SpacIO_v1) Space by RdnUser77 and Omnibus's Maximum Multiplier!")
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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)
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# https://github.com/gradio-app/gradio/issues/6339
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import gradio as gr
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from all_models import models
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from externalmod import gr_Interface_load, save_image, 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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# https://huggingface.co/docs/api-inference/detailed_parameters
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# https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
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async def infer(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1, timeout=inference_timeout):
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kwargs = {}
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if height > 0: kwargs["height"] = height
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if width > 0: kwargs["width"] = width
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if steps > 0: kwargs["num_inference_steps"] = steps
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if cfg > 0: cfg = kwargs["guidance_scale"] = cfg
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if seed == -1: kwargs["seed"] = randomize_seed()
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else: kwargs["seed"] = seed
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task = asyncio.create_task(asyncio.to_thread(models_load[model_str].fn,
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prompt=prompt, negative_prompt=nprompt, **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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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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raise Exception(f"Task timed out: {model_str}") from e
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except Exception as e:
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print(e)
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if not task.done(): task.cancel()
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result = None
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raise Exception() from e
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if task.done() and result is not None and not isinstance(result, tuple):
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with lock:
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png_path = "image.png"
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image = save_image(result, png_path, model_str, prompt, nprompt, height, width, steps, cfg, seed)
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return image
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return None
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def gen_fn(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1):
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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, nprompt,
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"""
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with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', fill_width=True, css=CSS) as demo:
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gr.HTML(
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"""
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<div>
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<p> <center>For simultaneous generations without hidden queue check out <a href="https://huggingface.co/spaces/Yntec/ToyWorld">Toy World</a>! For more options like single model x6 check out <a href="https://huggingface.co/spaces/John6666/Diffusion80XX4sg">Diffusion80XX4sg</a> by John6666!</center>
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</p></div>
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"""
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)
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with gr.Tab('Huggingface Diffusion'):
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with gr.Column(scale=2):
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with gr.Group():
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txt_input = gr.Textbox(label='Your prompt:', lines=4)
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steps = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
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cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
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seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
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seed_rand = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary")
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seed_rand.click(randomize_seed, None, [seed], queue=False)
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with gr.Row():
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gen_button = gr.Button(f'Generate up to {int(num_models)} images in up to 3 minutes total', variant='primary', scale=3)
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random_button = gr.Button(f'Random {int(num_models)} 🎲', variant='secondary', scale=1)
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with gr.Group():
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with gr.Row():
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output = [gr.Image(label=m, show_download_button=True, elem_classes="output",
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interactive=False, width=112, height=112, show_share_button=False, format="png",
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visible=True) 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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steps2 = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
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cfg2 = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
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seed2 = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
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seed_rand2 = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary")
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seed_rand2.click(randomize_seed, None, [seed2], queue=False)
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num_images = gr.Slider(1, max_images, value=max_images, step=1, label='Number of images')
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with gr.Row():
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gen_button2 = gr.Button('Generate', variant='primary', scale=2)
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with gr.Group():
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with gr.Row():
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output2 = [gr.Image(label='', show_download_button=True, elem_classes="output",
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interactive=False, width=112, height=112, visible=True, format="png",
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show_share_button=False, show_label=False) for _ in range(max_images)]
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with gr.Column(scale=2):
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gr.Markdown("Based on the [TestGen](https://huggingface.co/spaces/derwahnsinn/TestGen) Space by derwahnsinn, the [SpacIO](https://huggingface.co/spaces/RdnUser77/SpacIO_v1) Space by RdnUser77 and Omnibus's Maximum Multiplier!")
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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)
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# https://github.com/gradio-app/gradio/issues/6339
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externalmod.py
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models.append(model.id)
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if len(models) == limit: break
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return models
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models.append(model.id)
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if len(models) == limit: break
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return models
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def save_image(image, savefile, modelname, prompt, nprompt, height=0, width=0, steps=0, cfg=0, seed=-1):
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from PIL import Image, PngImagePlugin
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import json
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try:
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metadata = {"prompt": prompt, "negative_prompt": nprompt, "Model": {"Model": modelname.split("/")[-1]}}
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if steps > 0: metadata["num_inference_steps"] = steps
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if cfg > 0: metadata["guidance_scale"] = cfg
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if seed != -1: metadata["seed"] = seed
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if width > 0 and height > 0: metadata["resolution"] = f"{width} x {height}"
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metadata_str = json.dumps(metadata)
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info = PngImagePlugin.PngInfo()
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info.add_text("metadata", metadata_str)
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image.save(savefile, "PNG", pnginfo=info)
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return str(Path(savefile).resolve())
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except Exception as e:
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print(f"Failed to save image file: {e}")
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raise Exception(f"Failed to save image file:") from e
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def randomize_seed():
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from random import seed, randint
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MAX_SEED = 2**32-1
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seed()
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rseed = randint(0, MAX_SEED)
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return rseed
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