|
import spaces |
|
import base64 |
|
from io import BytesIO |
|
|
|
import gradio as gr |
|
import PIL.Image |
|
import torch |
|
|
|
from diffusers import StableDiffusionPipeline, AutoencoderKL, AutoencoderTiny |
|
from peft import PeftModel |
|
|
|
device = "cuda" |
|
weight_type = torch.float16 |
|
|
|
pipe = StableDiffusionPipeline.from_pretrained("IDKiro/sdxs-512-dreamshaper") |
|
pipe.unet = PeftModel.from_pretrained(pipe.unet, "IDKiro/sdxs-512-dreamshaper-anime") |
|
pipe.unet.merge_and_unload() |
|
pipe.to(device, dtype=weight_type) |
|
|
|
vae_tiny = AutoencoderTiny.from_pretrained( |
|
"IDKiro/sdxs-512-dreamshaper", subfolder="vae" |
|
) |
|
vae_tiny.to(device, dtype=weight_type) |
|
|
|
vae_large = AutoencoderKL.from_pretrained( |
|
"IDKiro/sdxs-512-dreamshaper", subfolder="vae_large" |
|
) |
|
vae_tiny.to(device, dtype=weight_type) |
|
|
|
|
|
def pil_image_to_data_url(img, format="PNG"): |
|
buffered = BytesIO() |
|
img.save(buffered, format=format) |
|
img_str = base64.b64encode(buffered.getvalue()).decode() |
|
return f"data:image/{format.lower()};base64,{img_str}" |
|
|
|
|
|
@spaces.GPU |
|
def run( |
|
prompt: str, |
|
device_type="GPU", |
|
vae_type=None, |
|
param_dtype="torch.float16", |
|
) -> PIL.Image.Image: |
|
if vae_type == "tiny vae": |
|
pipe.vae = vae_tiny |
|
elif vae_type == "large vae": |
|
pipe.vae = vae_large |
|
|
|
if device_type == "CPU": |
|
device = "cpu" |
|
param_dtype = "torch.float32" |
|
else: |
|
device = "cuda" |
|
|
|
pipe.to( |
|
torch_device=device, |
|
torch_dtype=torch.float16 if param_dtype == "torch.float16" else torch.float32, |
|
) |
|
|
|
result = pipe( |
|
prompt=prompt, |
|
guidance_scale=0.0, |
|
num_inference_steps=1, |
|
output_type="pil", |
|
).images[0] |
|
|
|
result_url = pil_image_to_data_url(result) |
|
|
|
return (result, result_url) |
|
|
|
|
|
examples = [ |
|
"A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece", |
|
] |
|
|
|
with gr.Blocks(css="style.css") as demo: |
|
gr.Markdown("# SDXS-512-DreamShaper-Anime") |
|
gr.Markdown("[SDXS: Real-Time One-Step Latent Diffusion Models with Image Conditions](https://arxiv.org/abs/2403.16627) | [GitHub](https://github.com/IDKiro/sdxs)") |
|
with gr.Group(): |
|
with gr.Row(): |
|
with gr.Column(min_width=685): |
|
with gr.Row(): |
|
prompt = gr.Text( |
|
label="Prompt", |
|
show_label=False, |
|
max_lines=1, |
|
placeholder="Enter your prompt", |
|
container=False, |
|
) |
|
run_button = gr.Button("Run", scale=0) |
|
|
|
device_choices = ["GPU", "CPU"] |
|
device_type = gr.Radio( |
|
device_choices, |
|
label="Device", |
|
value=device_choices[0], |
|
interactive=True, |
|
info="Thanks to the community for the GPU!", |
|
) |
|
|
|
vae_choices = ["tiny vae", "large vae"] |
|
vae_type = gr.Radio( |
|
vae_choices, |
|
label="Image Decoder Type", |
|
value=vae_choices[0], |
|
interactive=True, |
|
info="To save GPU memory, use tiny vae. For better quality, use large vae.", |
|
) |
|
|
|
dtype_choices = ["torch.float16", "torch.float32"] |
|
param_dtype = gr.Radio( |
|
dtype_choices, |
|
label="torch.weight_type", |
|
value=dtype_choices[0], |
|
interactive=True, |
|
info="To save GPU memory, use torch.float16. For better quality, use torch.float32.", |
|
) |
|
|
|
download_output = gr.Button( |
|
"Download output", elem_id="download_output" |
|
) |
|
|
|
with gr.Column(min_width=512): |
|
result = gr.Image( |
|
label="Result", |
|
height=512, |
|
width=512, |
|
elem_id="output_image", |
|
show_label=False, |
|
show_download_button=True, |
|
) |
|
|
|
gr.Examples(examples=examples, inputs=prompt, outputs=result, fn=run) |
|
|
|
demo.load(None, None, None) |
|
|
|
inputs = [prompt, device_type, vae_type, param_dtype] |
|
outputs = [result, download_output] |
|
prompt.submit(fn=run, inputs=inputs, outputs=outputs) |
|
run_button.click(fn=run, inputs=inputs, outputs=outputs) |
|
|
|
if __name__ == "__main__": |
|
demo.queue().launch(debug=True) |
|
|
