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import gradio as gr
import torch
from diffusers import DiffusionPipeline, UNet2DConditionModel, LCMScheduler
from huggingface_hub import hf_hub_download
import spaces
from PIL import Image
# Constants
base = "stabilityai/stable-diffusion-xl-base-1.0"
repo = "tianweiy/DMD2"
checkpoints = {
"1-Step" : ["dmd2_sdxl_1step_unet_fp16.bin", 1],
"4-Step" : ["dmd2_sdxl_4step_unet_fp16.bin", 4],
}
loaded = None
CSS = """
.gradio-container {
max-width: 690px !important;
}
"""
# Ensure model and scheduler are initialized in GPU-enabled function
if torch.cuda.is_available():
unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16)
pipe = DiffusionPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to("cuda")
# Function
@spaces.GPU()
def generate_image(prompt, ckpt="4-steps"):
global loaded
print(prompt, ckpt)
checkpoint = checkpoints[ckpt][0]
num_inference_steps = checkpoints[ckpt][1]
if loaded != num_inference_steps:
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
pipe.unet.load_state_dict(torch.load(hf_hub_download(repo, checkpoint), map_location="cuda"))
loaded = num_inference_steps
if loaded == 1:
results = pipe(prompt, num_inference_steps=num_inference_steps, guidance_scale=0, timesteps=[399])
else:
results = pipe(prompt, num_inference_steps=num_inference_steps, guidance_scale=0, timesteps=[999, 749, 499, 249])
return results.images[0]
examples = [
"a cat eating a piece of cheese",
"a ROBOT riding a BLUE horse on Mars, photorealistic",
"Ironman VS Hulk, ultrarealistic",
"a CUTE robot artist painting on an easel",
"Astronaut in a jungle, cold color palette, oil pastel, detailed, 8k",
"An alien holding sign board contain word 'Flash', futuristic, neonpunk",
"Kids going to school, Anime style"
]
# Gradio Interface
with gr.Blocks(css=CSS, theme="soft") as demo:
gr.HTML("<h1><center>Adobe DMD2🦖</center></h1>")
gr.HTML("<p><center><a href='https://huggingface.co/tianweiy/DMD2'>DMD2</a> text-to-image generation</center></p>")
with gr.Group():
with gr.Row():
prompt = gr.Textbox(label='Enter your prompt (English)', scale=8)
ckpt = gr.Dropdown(label='Steps',choices=['1-Step', '4-Step'], value='4-Step', interactive=True)
submit = gr.Button(scale=1, variant='primary')
img = gr.Image(label='DMD2 Generated Image')
gr.Examples(
examples=examples,
inputs=prompt,
outputs=img,
fn=generate_image,
cache_examples="lazy",
)
prompt.submit(fn=generate_image,
inputs=[prompt, ckpt],
outputs=img,
)
submit.click(fn=generate_image,
inputs=[prompt, ckpt],
outputs=img,
)
demo.queue().launch() |