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Zero
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import gradio as gr
from diffusers import AutoPipelineForText2Image
import numpy as np
import math
import spaces
import torch
import random
theme = gr.themes.Base(
font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'],
)
pipe_xlc = AutoPipelineForText2Image.from_pretrained(
"temp-org-cc/CommonCanvas-XLC",
custom_pipeline="multimodalart/sdxl_perturbed_attention_guidance",
torch_dtype=torch.float16
)
pipe_xlnc = AutoPipelineForText2Image.from_pretrained(
"temp-org-cc/CommonCanvas-XLNC",
custom_pipeline="multimodalart/sdxl_perturbed_attention_guidance",
torch_dtype=torch.float16
)
pipe_sc = AutoPipelineForText2Image.from_pretrained(
"temp-org-cc/CommonCanvas-SC",
custom_pipeline="hyoungwoncho/sd_perturbed_attention_guidance",
torch_dtype=torch.float16
)
pipe_snc = AutoPipelineForText2Image.from_pretrained(
"temp-org-cc/CommonCanvas-SNC",
custom_pipeline="hyoungwoncho/sd_perturbed_attention_guidance",
torch_dtype=torch.float16
)
device="cuda"
pipe = pipe.to(device)
@spaces.GPU
def run_xlc(prompt, negative_prompt=None, guidance_scale=7.0, pag_scale=3.0, pag_layers=["mid"], randomize_seed=True, seed=42, progress=gr.Progress(track_tqdm=True)):
if(randomize_seed):
seed = random.randint(0, 9007199254740991)
generator = torch.Generator(device="cuda").manual_seed(seed)
image = pipe_xlc(prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, pag_scale=pag_scale, pag_applied_layers=pag_layers, generator=generator, num_inference_steps=25).images[0]
return image, seed
@spaces.GPU
def run_xlnc(prompt, negative_prompt=None, guidance_scale=7.0, pag_scale=3.0, pag_layers=["mid"], randomize_seed=True, seed=42, progress=gr.Progress(track_tqdm=True)):
if(randomize_seed):
seed = random.randint(0, 9007199254740991)
generator = torch.Generator(device="cuda").manual_seed(seed)
image = pipe_xlnc(prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, pag_scale=pag_scale, pag_applied_layers=pag_layers, generator=generator, num_inference_steps=25).images[0]
return image, seed
@spaces.GPU
def run_sc(prompt, negative_prompt=None, guidance_scale=7.0, pag_scale=3.0, pag_layers=["mid"], randomize_seed=True, seed=42, progress=gr.Progress(track_tqdm=True)):
if(randomize_seed):
seed = random.randint(0, 9007199254740991)
generator = torch.Generator(device="cuda").manual_seed(seed)
image = pipe_sc(prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, pag_scale=pag_scale, pag_applied_layers=pag_layers, generator=generator, num_inference_steps=25).images[0]
return image, seed
def run_snc(prompt, negative_prompt=None, guidance_scale=7.0, pag_scale=3.0, pag_layers=["mid"], randomize_seed=True, seed=42, progress=gr.Progress(track_tqdm=True)):
if(randomize_seed):
seed = random.randint(0, 9007199254740991)
generator = torch.Generator(device="cuda").manual_seed(seed)
image = pipe_sc(prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, pag_scale=pag_scale, pag_applied_layers=pag_layers, generator=generator, num_inference_steps=25).images[0]
return image, seed
css = '''
.gradio-container{
max-width: 768px !important;
margin: 0 auto;
}
'''
with gr.Blocks(css=css, theme=theme) as demo:
gr.Markdown('''# CommonCanvas
Demo for the CommonCanvas suite of models trained on the CommonCatalogue, a dataset with ~70M images dedicated to the Creative Commons
''')
with gr.Group():
with gr.Tab("CommonCanvas XLC"):
with gr.Row():
prompt_xlc = gr.Textbox(show_label=False, scale=4, placeholder="Your prompt")
button_xlc = gr.Button("Generate", min_width=120)
with gr.Tab("CommonCanvas XLNC"):
with gr.Row():
prompt_xlnc = gr.Textbox(show_label=False, scale=4, placeholder="Your prompt")
button_xlnc = gr.Button("Generate", min_width=120)
with gr.Tab("CommonCanvas SC"):
prompt_sc = gr.Textbox(show_label=False, scale=4, placeholder="Your prompt")
button_sc = gr.Button("Generate", min_width=120)
with gr.Tab("CommonCanvas SNC"):
prompt_snc = gr.Textbox(show_label=False, scale=4, placeholder="Your prompt")
button_snc = gr.Button("Generate", min_width=120)
output = gr.Image(label="Your result", interactive=False)
with gr.Accordion("Advanced Settings", open=False):
guidance_scale = gr.Number(label="CFG Guidance Scale", info="The guidance scale for CFG, ignored if no prompt is entered (unconditional generation)", value=7.0)
negative_prompt = gr.Textbox(label="Negative prompt", info="Is only applied for the CFG part, leave blank for unconditional generation")
pag_scale = gr.Number(label="Pag Scale", value=3.0)
pag_layers = gr.Dropdown(label="Model layers to apply Pag to", info="mid is the one used on the paper, up and down blocks seem unstable", choices=["up", "mid", "down"], multiselect=True, value="mid")
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
seed = gr.Slider(minimum=1, maximum=9007199254740991, step=1, randomize=True)
gr.Examples(fn=run, examples=[" ", "an insect robot preparing a delicious meal, anime style", "a photo of a group of friends at an amusement park"], inputs=prompt, outputs=[output, seed], cache_examples=True)
gr.on(
triggers=[
button_xlc.click,
prompt_xlc.submit
],
fn=run_xlc,
inputs=[prompt_xlc, negative_prompt, guidance_scale, pag_scale, pag_layers, randomize_seed, seed],
outputs=[output, seed],
)
gr.on(
triggers=[
button_xlnc.click,
prompt_xlnc.submit
],
fn=run_xlnc,
inputs=[prompt_xlnc, negative_prompt, guidance_scale, pag_scale, pag_layers, randomize_seed, seed],
outputs=[output, seed],
)
gr.on(
triggers=[
button_sc.click,
prompt_sc.submit
],
fn=run_sc,
inputs=[prompt_sc, negative_prompt, guidance_scale, pag_scale, pag_layers, randomize_seed, seed],
outputs=[output, seed],
)
gr.on(
triggers=[
button_snc.click,
prompt_snc.submit
],
fn=run_sc,
inputs=[prompt_snc, negative_prompt, guidance_scale, pag_scale, pag_layers, randomize_seed, seed],
outputs=[output, seed],
)
if __name__ == "__main__":
demo.launch(share=True) |