BRIA-2.3

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BRIA-2.3 / app.py

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	import gradio as gr
	import os
	hf_token = os.environ.get("HF_TOKEN")
	import spaces
	from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
	import torch
	import time

	class Dummy():
	pass

	resolutions = ["1024 1024","1280 768","1344 768","768 1344","768 1280"]

	# Ng
	default_negative_prompt= "Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers"

	# Load pipeline
	model_id = "briaai/BRIA-2.3"
	scheduler = EulerAncestralDiscreteScheduler(
	beta_start=0.00085,
	beta_end=0.012,
	beta_schedule="scaled_linear",
	num_train_timesteps=1000,
	steps_offset=1
	)
	pipe = StableDiffusionXLPipeline.from_pretrained(model_id, torch_dtype=torch.float16,scheduler=scheduler).to("cuda")
	pipe.force_zeros_for_empty_prompt = False

	# print("Optimizing BRIA-2.3 - this could take a while")
	# t=time.time()
	# pipe.unet = torch.compile(
	# pipe.unet, mode="reduce-overhead", fullgraph=True # 600 secs compilation
	# )
	# with torch.no_grad():
	# outputs = pipe(
	# prompt="an apple",
	# num_inference_steps=30,
	# )

	# # This will avoid future compilations on different shapes
	# unet_compiled = torch._dynamo.run(pipe.unet)
	# unet_compiled.config=pipe.unet.config
	# unet_compiled.add_embedding = Dummy()
	# unet_compiled.add_embedding.linear_1 = Dummy()
	# unet_compiled.add_embedding.linear_1.in_features = pipe.unet.add_embedding.linear_1.in_features
	# pipe.unet = unet_compiled

	# print(f"Optimizing finished successfully after {time.time()-t} secs")

	@spaces.GPU(enable_queue=True)
	def infer(prompt,negative_prompt,seed,resolution):
	print(f"""
	—/n
	{prompt}
	""")

	# generator = torch.Generator("cuda").manual_seed(555)
	t=time.time()

	if seed=="-1":
	generator=None
	else:
	try:
	seed=int(seed)
	generator = torch.Generator("cuda").manual_seed(seed)
	except:
	generator=None

	w,h = resolution.split()
	w,h = int(w),int(h)
	image = pipe(prompt,num_inference_steps=30, negative_prompt=negative_prompt,generator=generator,width=w,height=h).images[0]
	print(f'gen time is {time.time()-t} secs')

	# Future
	# Add amound of steps
	# if nsfw:
	# raise gr.Error("Generated image is NSFW")

	return image

	css = """
	#col-container{
	margin: 0 auto;
	max-width: 580px;
	}
	"""
	with gr.Blocks(css=css) as demo:
	with gr.Column(elem_id="col-container"):
	gr.Markdown("## BRIA 2.3")
	gr.HTML('''
	<p style="margin-bottom: 10px; font-size: 94%">
	This is a demo for
	<a href="https://huggingface.co/briaai/BRIA-2.3" target="_blank">BRIA 2.3 text-to-image </a>.
	BRIA 2.3 improve the generation of humans and illustrations compared to BRIA 2.2 while still trained on licensed data, and so provide full legal liability coverage for copyright and privacy infringement.
	</p>
	''')
	with gr.Group():
	with gr.Column():
	prompt_in = gr.Textbox(label="Prompt", value="A smiling man with wavy brown hair and a trimmed beard")
	resolution = gr.Dropdown(value=resolutions[0], show_label=True, label="Resolution", choices=resolutions)
	seed = gr.Textbox(label="Seed", value=-1)
	negative_prompt = gr.Textbox(label="Negative Prompt", value=default_negative_prompt)
	submit_btn = gr.Button("Generate")
	result = gr.Image(label="BRIA-2.3 Result")

	# gr.Examples(
	# examples = [
	# "Dragon, digital art, by Greg Rutkowski",
	# "Armored knight holding sword",
	# "A flat roof villa near a river with black walls and huge windows",
	# "A calm and peaceful office",
	# "Pirate guinea pig"
	# ],
	# fn = infer,
	# inputs = [
	# prompt_in
	# ],
	# outputs = [
	# result
	# ]
	# )

	submit_btn.click(
	fn = infer,
	inputs = [
	prompt_in,
	negative_prompt,
	seed,
	resolution
	],
	outputs = [
	result
	]
	)

	demo.queue().launch(show_api=False)