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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import os | |
| import random | |
| import gradio as gr | |
| import numpy as np | |
| import PIL.Image | |
| import spaces | |
| import torch | |
| from diffusers import StableDiffusionAttendAndExcitePipeline, StableDiffusionPipeline | |
| DESCRIPTION = """\ | |
| # Attend-and-Excite | |
| This is a demo for [Attend-and-Excite](https://arxiv.org/abs/2301.13826). | |
| Attend-and-Excite performs attention-based generative semantic guidance to mitigate subject neglect in Stable Diffusion. | |
| Select a prompt and a set of indices matching the subjects you wish to strengthen (the `Check token indices` cell can help map between a word and its index). | |
| """ | |
| if not torch.cuda.is_available(): | |
| DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" | |
| if torch.cuda.is_available(): | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| model_id = "CompVis/stable-diffusion-v1-4" | |
| ax_pipe = StableDiffusionAttendAndExcitePipeline.from_pretrained(model_id) | |
| ax_pipe.to(device) | |
| sd_pipe = StableDiffusionPipeline.from_pretrained(model_id) | |
| sd_pipe.to(device) | |
| MAX_INFERENCE_STEPS = 100 | |
| MAX_SEED = np.iinfo(np.int32).max | |
| def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| return seed | |
| def get_token_table(prompt: str) -> list[tuple[int, str]]: | |
| tokens = [ax_pipe.tokenizer.decode(t) for t in ax_pipe.tokenizer(prompt)["input_ids"]] | |
| tokens = tokens[1:-1] | |
| return list(enumerate(tokens, start=1)) | |
| def run( | |
| prompt: str, | |
| indices_to_alter_str: str, | |
| seed: int = 0, | |
| apply_attend_and_excite: bool = True, | |
| num_inference_steps: int = 50, | |
| guidance_scale: float = 7.5, | |
| scale_factor: int = 20, | |
| thresholds: dict[int, float] = { | |
| 10: 0.5, | |
| 20: 0.8, | |
| }, | |
| max_iter_to_alter: int = 25, | |
| ) -> PIL.Image.Image: | |
| if num_inference_steps > MAX_INFERENCE_STEPS: | |
| raise gr.Error(f"Number of steps cannot exceed {MAX_INFERENCE_STEPS}.") | |
| generator = torch.Generator(device=device).manual_seed(seed) | |
| if apply_attend_and_excite: | |
| try: | |
| token_indices = list(map(int, indices_to_alter_str.split(","))) | |
| except Exception: | |
| raise ValueError("Invalid token indices.") | |
| out = ax_pipe( | |
| prompt=prompt, | |
| token_indices=token_indices, | |
| guidance_scale=guidance_scale, | |
| generator=generator, | |
| num_inference_steps=num_inference_steps, | |
| max_iter_to_alter=max_iter_to_alter, | |
| thresholds=thresholds, | |
| scale_factor=scale_factor, | |
| ) | |
| else: | |
| out = sd_pipe( | |
| prompt=prompt, | |
| guidance_scale=guidance_scale, | |
| generator=generator, | |
| num_inference_steps=num_inference_steps, | |
| ) | |
| return out.images[0] | |
| def process_example( | |
| prompt: str, | |
| indices_to_alter_str: str, | |
| seed: int, | |
| apply_attend_and_excite: bool, | |
| ) -> tuple[list[tuple[int, str]], PIL.Image.Image]: | |
| token_table = get_token_table(prompt) | |
| result = run( | |
| prompt=prompt, | |
| indices_to_alter_str=indices_to_alter_str, | |
| seed=seed, | |
| apply_attend_and_excite=apply_attend_and_excite, | |
| ) | |
| return token_table, result | |
| with gr.Blocks(css="style.css") as demo: | |
| gr.Markdown(DESCRIPTION) | |
| gr.DuplicateButton( | |
| value="Duplicate Space for private use", | |
| elem_id="duplicate-button", | |
| visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| prompt = gr.Text( | |
| label="Prompt", | |
| max_lines=1, | |
| placeholder="A pod of dolphins leaping out of the water in an ocean with a ship on the background", | |
| ) | |
| with gr.Accordion(label="Check token indices", open=False): | |
| show_token_indices_button = gr.Button("Show token indices") | |
| token_indices_table = gr.Dataframe(label="Token indices", headers=["Index", "Token"], col_count=2) | |
| token_indices_str = gr.Text( | |
| label="Token indices (a comma-separated list indices of the tokens you wish to alter)", | |
| max_lines=1, | |
| placeholder="4,16", | |
| ) | |
| apply_attend_and_excite = gr.Checkbox(label="Apply Attend-and-Excite", value=True) | |
| seed = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| step=1, | |
| value=0, | |
| ) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| num_inference_steps = gr.Slider( | |
| label="Number of inference steps", | |
| minimum=1, | |
| maximum=MAX_INFERENCE_STEPS, | |
| step=1, | |
| value=50, | |
| ) | |
| guidance_scale = gr.Slider( | |
| label="Guidance scale", | |
| minimum=0, | |
| maximum=50, | |
| step=0.1, | |
| value=7.5, | |
| ) | |
| run_button = gr.Button("Generate") | |
| with gr.Column(): | |
| result = gr.Image(label="Result") | |
| with gr.Row(): | |
| examples = [ | |
| [ | |
| "A mouse and a red car", | |
| "2,6", | |
| 2098, | |
| True, | |
| ], | |
| [ | |
| "A mouse and a red car", | |
| "2,6", | |
| 2098, | |
| False, | |
| ], | |
| [ | |
| "A horse and a dog", | |
| "2,5", | |
| 123, | |
| True, | |
| ], | |
| [ | |
| "A horse and a dog", | |
| "2,5", | |
| 123, | |
| False, | |
| ], | |
| [ | |
| "A painting of an elephant with glasses", | |
| "5,7", | |
| 123, | |
| True, | |
| ], | |
| [ | |
| "A painting of an elephant with glasses", | |
| "5,7", | |
| 123, | |
| False, | |
| ], | |
| [ | |
| "A playful kitten chasing a butterfly in a wildflower meadow", | |
| "3,6,10", | |
| 123, | |
| True, | |
| ], | |
| [ | |
| "A playful kitten chasing a butterfly in a wildflower meadow", | |
| "3,6,10", | |
| 123, | |
| False, | |
| ], | |
| [ | |
| "A grizzly bear catching a salmon in a crystal clear river surrounded by a forest", | |
| "2,6,15", | |
| 123, | |
| True, | |
| ], | |
| [ | |
| "A grizzly bear catching a salmon in a crystal clear river surrounded by a forest", | |
| "2,6,15", | |
| 123, | |
| False, | |
| ], | |
| [ | |
| "A pod of dolphins leaping out of the water in an ocean with a ship on the background", | |
| "4,16", | |
| 123, | |
| True, | |
| ], | |
| [ | |
| "A pod of dolphins leaping out of the water in an ocean with a ship on the background", | |
| "4,16", | |
| 123, | |
| False, | |
| ], | |
| ] | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[ | |
| prompt, | |
| token_indices_str, | |
| seed, | |
| apply_attend_and_excite, | |
| ], | |
| outputs=[ | |
| token_indices_table, | |
| result, | |
| ], | |
| fn=process_example, | |
| cache_examples=os.getenv("CACHE_EXAMPLES") == "1", | |
| examples_per_page=20, | |
| ) | |
| show_token_indices_button.click( | |
| fn=get_token_table, | |
| inputs=prompt, | |
| outputs=token_indices_table, | |
| queue=False, | |
| api_name="get-token-table", | |
| ) | |
| gr.on( | |
| triggers=[prompt.submit, token_indices_str.submit, run_button.click], | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| api_name=False, | |
| ).then( | |
| fn=get_token_table, | |
| inputs=prompt, | |
| outputs=token_indices_table, | |
| queue=False, | |
| api_name=False, | |
| ).then( | |
| fn=run, | |
| inputs=[ | |
| prompt, | |
| token_indices_str, | |
| seed, | |
| apply_attend_and_excite, | |
| num_inference_steps, | |
| guidance_scale, | |
| ], | |
| outputs=result, | |
| api_name="run", | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=20).launch() | |