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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 = "cpu" # Linux & Windows
weight_type = torch.float32 # torch.float16 works as well, but pictures seem to be a bit worse
pipe = StableDiffusionPipeline.from_pretrained("IDKiro/sdxs-512-dreamshaper", torch_dtype=weight_type)
pipe.unet = PeftModel.from_pretrained(pipe.unet, "IDKiro/sdxs-512-dreamshaper-anime")
pipe.to(torch_device=device, torch_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}"
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 = [
"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
]
with gr.Blocks(css="style.css") as demo:
gr.Markdown("# SDXS-512-DreamShaper-Anime (only CPU now)")
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[1],
interactive=False,
info='Only CPU now.')
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)
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