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from diffusers import DiffusionPipeline, UNet2DConditionModel, LCMScheduler
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
import spaces
import gradio as gr
import torch
import PIL
# Constants
base = "stabilityai/stable-diffusion-xl-base-1.0"
repo = "tianweiy/DMD2"
checkpoints = {
"1-Step" : ["dmd2_sdxl_1step_unet.bin", 1],
"4-Step" : ["dmd2_sdxl_4step_unet.bin", 4],
}
loaded = None
CSS = """
.gradio-container {
max-width: 690px !important;
}
"""
# Ensure model and scheduler are initialized in GPU-enabled function
unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16)
unet.load_state_dict(torch.load(hf_hub_download(repo, checkpoints)), map_location="cuda")
if torch.cuda.is_available():
pipe = DiffusionPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
# Function
@spaces.GPU(enable_queue=True)
def generate_image(prompt, ckpt):
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", prediction_type="sample" if num_inference_steps==1 else "epsilon")
loaded = num_inference_steps
results = pipe(prompt, num_inference_steps=num_inference_steps, guidance_scale=0)
return results.images[0]
# Gradio Interface
with gr.Blocks(css=CSS) as demo:
gr.HTML("<h1><center>Adobe DMD2🦖</center></h1>")
gr.HTML("<p><center><a href='https://huggingface.co/tianweiy/DMD2'>https://huggingface.co/tianweiy/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='Select inference steps',choices=['1-Step', '2-Step', '4-Step', '8-Step'], value='4-Step', interactive=True)
submit = gr.Button(scale=1, variant='primary')
img = gr.Image(label='DMD2 Generated Image')
prompt.submit(fn=generate_image,
inputs=[prompt, ckpt],
outputs=img,
)
submit.click(fn=generate_image,
inputs=[prompt, ckpt],
outputs=img,
)
demo.queue().launch() |