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import gradio as gr
from image_generator import ImageGenerator
import os
header = """Hi! This HuggingFace Space is a demo for the homework from the [10th lesson](https://course.fast.ai/Lessons/lesson10.html) of the fast.ai course. You can pick some of the examples below and click the "Generate Image" Button.
The code demonstrates:
* how to use an existing image as a starting point for the output image generation, in addition to the prompt
* how to use negative prompt
* how to capture latents through the generation
* how to mix prompt embeddings
"""
ig = ImageGenerator()
print(ig)
ig.load_models()
ig.load_scheduler()
def call(prompt, secondary_prompt, mix_ratio, negative_prompt, steps, init_image ):
print(f"{prompt=} {secondary_prompt=} {mix_ratio=} {negative_prompt=} {steps=} {init_image=} ")
generated_image, latents = ig.generate(
prompt=prompt,
secondary_prompt=secondary_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
def update_noisy_image_visibility(init_image):
if init_image is None:
print("update_noisy_image_visibility: hide noisy image")
return gr.Image(type="pil", label="Starting Image with Added Noise", visible=False)
else:
print("update_noisy_image_visibility: show noisy image")
return gr.Image(type="pil", label="Starting Image with Added Noise", visible=True)
def run_inference(prompt="", secondary_prompt="", mix_ratio=0.5, negative_prompt="", guidance=7.5, steps=10, init_image=None, progress=gr.Progress()): #, mix_ratio, negative_prompt, steps, starting_image, load_set_btn,
print(f"{prompt=} {secondary_prompt=} {mix_ratio=} {negative_prompt=} {steps=} {init_image=} ")
generated_image, latents = ig.generate(
prompt=prompt,
secondary_prompt=secondary_prompt,
prompt_mix_ratio=mix_ratio,
negative_prompt=negative_prompt,
guidance=guidance,
steps=steps,
init_image=init_image,
latent_callback_mod=1,
progress_tqdm=progress.tqdm )
if init_image is not None:
noisy_latent = latents[1]
else:
noisy_latent = None
return generated_image, noisy_latent, ig.image_grid(latents)
with gr.Blocks() as demo:
with gr.Row():
gr.Markdown(value=header)
with gr.Row():
with gr.Column(scale=1):
prompt = gr.Textbox(value="a cute dog", label="Prompt", info="primary prompt used to generate an image")
secondary_prompt = gr.Textbox(value=None, label="Secondary Prompt", info="secondary prompt to mix with the primary embeddings")
mix_ratio = gr.Slider(0, 1, value=0.5, label="Mix Ratio", info="mix ratio between primary and secondary prompt. 0 = primary only. 1 = secondary only")
negative_prompt = gr.Textbox(value=None, label="Negative Prompt", info="remove certain aspect from the picture")
guidance = gr.Slider(0, 14, value=7.5, label="Guidance", info="how closely the model should follow the prompt (higher the closer)")
steps = gr.Slider(10, 50, value=10, step=1, label="Generation Steps", info="How many steps are used to generate the picture")
init_image = gr.Image(type="pil", value=None, label="Starting Image",) # info="starting image from this image as opposed to random noise"
generate_image_btn = gr.Button("Generate Image")
with gr.Column(scale=1):
output_image = gr.Image(type="pil", label="Generated Image",)
noisy_image = gr.Image(type="pil", label="Starting Image with Added Noise", visible=False)
noisy_image.change(fn=update_noisy_image_visibility, inputs=init_image, outputs=noisy_image)
latent_images = gr.Image(type="pil", label="Latents through the denoising process", visible=True)
with gr.Row():
# broken images should be fixed soon https://github.com/gradio-app/gradio/issues/5067
gr.Examples(
examples=[
# simple prompt
["a cute dog", "", "", "", 7.5, 10, None],
# negative prompt
["a beautiful tree", "", "", "green", 7.5, 10, None],
# with base image
["a painting of Paris at night in the style of Pierre Auguste Renoir", "", "", "", 7.5, 50, os.path.join( os.path.dirname(__file__), "examples/ex4.jpg")],
# with prompt
["a sloth", "a jaguar", 0.5, "", 7.5, 30, None],
],
inputs=[prompt, secondary_prompt, mix_ratio, negative_prompt, guidance, steps, init_image],
outputs=[output_image, noisy_image, latent_images],
fn=run_inference,
cache_examples=False)
generate_image_btn.click(
fn=run_inference,
inputs=[prompt, secondary_prompt, mix_ratio, negative_prompt, guidance, steps, init_image],
outputs=[output_image, noisy_image, latent_images])
demo.launch(allowed_paths=["./"]) #allowed_paths=["/tmp/"] |