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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) | |
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() |