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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-2.2"
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-2.2 - 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 2.2 Beta")
gr.HTML('''
<p style="margin-bottom: 10px; font-size: 94%">
This is a demo for
<a href="https://huggingface.co/briaai/BRIA-2.2" target="_blank">BRIA 2.2 text-to-image </a>.
BRIA 2.2 improve the realism of BRIA 2.0 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 red colored sports car")
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-2.2 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) |