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import gradio as gr
import torch
# region_offset = torch.tensor(region_offset).int()
from utils import gen_image_as_per_prompt
styles = ["depthmap", "cosmicgalaxy", "concept-art", "Marc Allante", "midjourney-style", "No style"]
styleValues = ["learned_embeds_depthmap.bin",
"learned_embeds_cosmic-galaxy-characters-style.bin",
"learned_embeds_sd_concept-art.bin",
"learned_embeds_style-of-marc-allante.bin",
"learned_embeds_midjourney.bin",
""]
seed_values = [30, 24, 35, 47, 78, 42]
styles_dict = dict(zip(styles, styleValues))
seed_dict = dict(zip(styles, seed_values))
# Custom loss function
def reduce_highlight(images):
"""Calculates the mean absolute error for amber color.
Args:
images: A tensor of shape (batch_size, channels, height, width).
target_red: Target red value for amber.
target_green: Target green value for amber.
target_blue: Target blue value for amber.
Returns:
The mean absolute error.
#target_red=0.8, target_green=0.6, target_blue=0.4
"""
red_error = torch.abs(images[:, 0] - 0.12).mean()
green_error = torch.abs(images[:, 1] - 0.2).mean()
blue_error = torch.abs(images[:, 2] - 0.15).mean()
# You can adjust weights for each channel if needed
amber_error = (red_error + green_error + blue_error) / 3
return amber_error
def _inference(text, style, use_loss=False):
if use_loss:
image = gen_image_as_per_prompt(text, styles_dict[style], seed_dict[style], reduce_highlight)
else:
image = gen_image_as_per_prompt(text, styles_dict[style], seed_dict[style])
return image
title = "Stable Diffusion with different styles"
description = "In this demo, the word 'puppy' is replaced by the style that is selected"
examples = [["oil painting of a dragon in puppy style", "mosiac", True],
["Spiderman in puppy style", "midjourney", True],
["Batman in puppy style", "matrix", False],
["Mojo Jojo in puppy style", "No style", False]]
demo = gr.Interface(
_inference,
inputs=[
gr.Textbox(placeholder="Type a prompt with word 'puppy' in it..", container=False, scale=7),
gr.Radio(styles, label="Select a Style"),
gr.Checkbox(label="Use custom loss")
],
outputs=[
gr.Image(width=256, height=256, label="output")
# gr.Text(label="output")
],
title=title,
description=description,
examples=examples,
cache_examples=False
)
demo.launch(debug=True) |