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'], ) device="cuda" pipe_xlc = AutoPipelineForText2Image.from_pretrained( "temp-org-cc/CommonCanvas-XLC", custom_pipeline="multimodalart/sdxl_perturbed_attention_guidance", torch_dtype=torch.float16 ).to(device) pipe_xlnc = AutoPipelineForText2Image.from_pretrained( "temp-org-cc/CommonCanvas-XLNC", custom_pipeline="multimodalart/sdxl_perturbed_attention_guidance", torch_dtype=torch.float16 ).to(device) pipe_sc = AutoPipelineForText2Image.from_pretrained( "temp-org-cc/CommonCanvas-SC", custom_pipeline="hyoungwoncho/sd_perturbed_attention_guidance", torch_dtype=torch.float16 ).to(device) pipe_snc = AutoPipelineForText2Image.from_pretrained( "temp-org-cc/CommonCanvas-SNC", custom_pipeline="hyoungwoncho/sd_perturbed_attention_guidance", torch_dtype=torch.float16 ).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)