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import gradio as gr
from image_generator import ImageGenerator
ig = ImageGenerator(g=7.5)
print(ig)
ig.load_models()
ig.load_scheduler()
def greet(prompt, mix_prompt, mix_ratio, negative_prompt, steps, init_image ):
print(f"{prompt=} {mix_prompt=} {mix_ratio=} {negative_prompt=} {steps=} {init_image=} ")
generated_image, latents = ig.generate(
prompt=prompt,
secondary_prompt=mix_prompt,
prompt_mix_ratio=mix_ratio,
negative_prompt=negative_prompt,
steps=steps,
init_image=init_image,
latent_callback_mod=None )
if init_image is not None:
noisy_latent = latents[1]
else:
noisy_latent = None
return generated_image, noisy_latent
iface = gr.Interface(
fn=greet,
inputs=[
gr.Textbox(value="a cute dog", label="Prompt", info="primary prompt used to generate an image"),
gr.Textbox(value=None, label="Secondary Prompt", info="secondary prompt to mix with the primary embeddings"),
gr.Slider(0, 1, value=0.5, label="Mix Ratio", info="mix ratio between primary and secondary prompt. 0 = primary only. 1 = secondary only"),
gr.Textbox(value=None, label="Negative Prompt", info="remove certain aspect from the picture"),
gr.Slider(10, 50, value=30, step=1, label="Generation Steps", info="How many steps are used to generate the picture"),
gr.Image(type="pil", value=None, label="Starting Image",), # info="starting image from this image as opposed to random noise"
],
outputs=[
gr.Image(type="pil", label="Generated Image",),
gr.Image(type="pil", label="Starting Image with Added Noise",)])
iface.launch()
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