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import gradio as gr
import spaces
import torch
from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
pipeline = StableDiffusionPipeline.from_pretrained("nroggendorff/zelda-diffusion").to("cuda")
img2img = StableDiffusionImg2ImgPipeline(**pipeline.components)
@spaces.GPU
def generate(prompt, negative_prompt, width, height, sample_steps, hrf):
image = pipeline(prompt=prompt, negative_prompt=negative_prompt, width=width, height=height, num_inference_steps=sample_steps).images[0]
if hrf:
return img2img(
prompt=prompt,
image=image,
strength=0.75,
width=width * 2,
height=height * 2
).images[0]
else:
return image
with gr.Blocks() as interface:
with gr.Column():
with gr.Row():
with gr.Column():
prompt = gr.Textbox(label="Prompt", info="What do you want?", value="pretty girl face, 32k HDR, studio lighting", lines=4, interactive=True)
negative_prompt = gr.Textbox(label="Negative Prompt", info="What do you want to exclude from the image?", value="watermark, jewelry, ugly, low quality", lines=4, interactive=True)
with gr.Column():
generate_button = gr.Button("Generate")
output = gr.Image()
with gr.Row():
with gr.Accordion(label="Advanced Settings", open=False):
with gr.Row():
with gr.Column():
width = gr.Slider(label="Width", info="The width in pixels of the generated image.", value=512, minimum=128, maximum=4096, step=64, interactive=True)
height = gr.Slider(label="Height", info="The height in pixels of the generated image.", value=512, minimum=128, maximum=4096, step=64, interactive=True)
hrf = gr.Checkbox(label="High-Res Fix", info="Run through img2img.", value=True, interactive=True)
with gr.Column():
sampling_steps = gr.Slider(label="Sampling Steps", info="The number of denoising steps.", value=20, minimum=4, maximum=50, step=1, interactive=True)
generate_button.click(fn=generate, inputs=[prompt, negative_prompt, width, height, sampling_steps, hrf], outputs=[output])
if __name__ == "__main__":
interface.launch() |