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import gradio as gr | |
import tensorflow as tf | |
from semdiffusers import SemanticEditPipeline | |
device = 'gpu' if tf.config.list_physical_devices('GPU') else 'cpu' | |
pipe = SemanticEditPipeline.from_pretrained( | |
"runwayml/stable-diffusion-v1-5", | |
).to(device) | |
def infer(prompt, seed): | |
gen = tf.random.Generator.from_seed(seed) | |
out = pipe( | |
prompt=prompt, | |
generator=gen, | |
num_images_per_prompt=1, | |
guidance_scale=7 | |
) | |
images = out.images[0] | |
out_edit = pipe( | |
prompt=prompt, | |
generator=gen, | |
num_images_per_prompt=1, | |
guidance_scale=7, | |
editing_prompt=['male person', 'female person'], # Concepts to apply | |
reverse_editing_direction=[True, False], # Direction of guidance i.e. decrease the first and increase the second concept | |
edit_warmup_steps=[10, 10], # Warmup period for each concept | |
edit_guidance_scale=[4, 4], # Guidance scale for each concept | |
edit_threshold=[0.95, 0.95], # Threshold for each concept. Threshold equals the percentile of the latent space that will be discarded. I.e. threshold=0.99 uses 1% of the latent dimensions | |
edit_momentum_scale=0.3, # Momentum scale that will be added to the latent guidance | |
edit_mom_beta=0.6, # Momentum beta | |
edit_weights=[1, 1] # Weights of the individual concepts against each other | |
) | |
images_edited = out_edit.images[0] | |
return [ | |
(images, 'Stable Diffusion'), | |
(images_edited, 'Fair Diffusion') | |
] | |
inputs = [ | |
gr.inputs.Textbox(label='Prompt'), | |
gr.inputs.Number(label='Seed', default=0, step=1) | |
] | |
outputs = gr.outputs.Image(label='Images', type='numpy', number=2) | |
title = 'Semantic Edit Pipeline' | |
description = 'Semantic Edit Pipeline implementation using SemDiffusers.' | |
article = "<h3 style='text-align: center'><a href='https://github.com/crowsonkb/semdiffusers'>SemDiffusers</a></h3>" | |
gr.Interface( | |
infer, | |
inputs, | |
outputs, | |
title=title, | |
description=description, | |
article=article, | |
theme='compact' | |
).launch(); | |