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| import gradio as gr | |
| from PIL import Image | |
| import torch | |
| from diffusers import StableDiffusionPipeline | |
| from free_lunch_utils import register_free_upblock2d, register_free_crossattn_upblock2d | |
| # if sd_options == 'SD1.5': | |
| # model = "runwayml/stable-diffusion-v1-5" | |
| # elif sd_options == 'SD2.1': | |
| # model = "stabilityai/stable-diffusion-2-1" | |
| # else: | |
| # model = "CompVis/stable-diffusion-v1-4" | |
| torch.manual_seed(42) | |
| model_id = "CompVis/stable-diffusion-v1-4" | |
| # pip_sd = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) | |
| # pip_sd = pip_sd.to("cuda") | |
| # pip_freeu = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) | |
| # pip_freeu = pip_freeu.to("cuda") | |
| # # -------- freeu block registration | |
| # register_free_upblock2d(pip_freeu, b1=1.2, b2=1.4, s1=0.9, s2=0.2) | |
| # register_free_crossattn_upblock2d(pip_freeu, b1=1.2, b2=1.4, s1=0.9, s2=0.2) | |
| # # -------- freeu block registration | |
| model_id = "CompVis/stable-diffusion-v1-4" | |
| pip_1_4 = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) | |
| pip_1_4 = pip_1_4.to("cuda") | |
| model_id = "runwayml/stable-diffusion-v1-5" | |
| pip_1_5 = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) | |
| pip_1_5 = pip_1_5.to("cuda") | |
| model_id = "stabilityai/stable-diffusion-2-1" | |
| pip_2_1 = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) | |
| pip_2_1 = pip_2_1.to("cuda") | |
| def infer(prompt, sd_options, seed, b1, b2, s1, s2): | |
| if sd_options == 'SD1.5': | |
| pip = pip_1_5 | |
| elif sd_options == 'SD2.1': | |
| pip = pip_2_1 | |
| else: | |
| pip = pip_1_4 | |
| register_free_upblock2d(pip, b1=1.0, b2=1.0, s1=1.0, s2=1.0) | |
| register_free_crossattn_upblock2d(pip, b1=1.0, b2=1.0, s1=1.0, s2=1.0) | |
| torch.manual_seed(seed) | |
| print("Generating SD:") | |
| sd_image = pip(prompt, num_inference_steps=25).images[0] | |
| register_free_upblock2d(pip, b1=b1, b2=b2, s1=s1, s2=s1) | |
| register_free_crossattn_upblock2d(pip, b1=b1, b2=b2, s1=s1, s2=s1) | |
| torch.manual_seed(seed) | |
| print("Generating FreeU:") | |
| freeu_image = pip(prompt, num_inference_steps=25).images[0] | |
| # First SD, then freeu | |
| images = [sd_image, freeu_image] | |
| return images | |
| examples = [ | |
| [ | |
| "A small cabin on top of a snowy mountain in the style of Disney, artstation", | |
| ], | |
| [ | |
| "a monkey doing yoga on the beach", | |
| ], | |
| [ | |
| "half human half cat, a human cat hybrid", | |
| ], | |
| [ | |
| "a hedgehog using a calculator", | |
| ], | |
| [ | |
| "kanye west | diffuse lighting | fantasy | intricate elegant highly detailed lifelike photorealistic digital painting | artstation", | |
| ], | |
| [ | |
| "astronaut pig", | |
| ], | |
| [ | |
| "two people shouting at each other", | |
| ], | |
| [ | |
| "A linked in profile picture of Elon Musk", | |
| ], | |
| [ | |
| "A man looking out of a rainy window", | |
| ], | |
| [ | |
| "close up, iron man, eating breakfast in a cabin, symmetrical balance, hyper-realistic --ar 16:9 --style raw" | |
| ], | |
| [ | |
| 'A high tech solarpunk utopia in the Amazon rainforest', | |
| ], | |
| [ | |
| 'A pikachu fine dining with a view to the Eiffel Tower', | |
| ], | |
| [ | |
| 'A mecha robot in a favela in expressionist style', | |
| ], | |
| [ | |
| 'an insect robot preparing a delicious meal', | |
| ], | |
| ] | |
| css = """ | |
| h1 { | |
| text-align: center; | |
| } | |
| #component-0 { | |
| max-width: 730px; | |
| margin: auto; | |
| } | |
| """ | |
| block = gr.Blocks(css='style.css') | |
| options = ['SD1.4', 'SD1.5', 'SD2.1'] | |
| with block: | |
| gr.Markdown("SD vs. FreeU.") | |
| with gr.Group(): | |
| with gr.Row(): | |
| sd_options = gr.Dropdown(["SD1.4", "SD1.5", "SD2.1"], label="SD options") | |
| # if sd_options == 'SD1.5': | |
| # sd = 1.5 | |
| # elif sd_options == 'SD2.1': | |
| # sd = 2.1 | |
| # else: | |
| # sd = 1.4 | |
| # pip = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) | |
| # pip = pip.to("cuda") | |
| with gr.Row(): | |
| with gr.Column(): | |
| text = gr.Textbox( | |
| label="Enter your prompt", | |
| show_label=False, | |
| max_lines=1, | |
| placeholder="Enter your prompt", | |
| container=False, | |
| ) | |
| btn = gr.Button("Generate image", scale=0) | |
| seed = gr.Slider(label='seed', | |
| minimum=0, | |
| maximum=1000, | |
| step=1, | |
| value=42) | |
| with gr.Group(): | |
| with gr.Row(): | |
| with gr.Accordion('FreeU Parameters: b', open=True): | |
| b1 = gr.Slider(label='b1: backbone factor of the first stage block of decoder', | |
| minimum=1, | |
| maximum=1.6, | |
| step=0.1, | |
| value=1) | |
| b2 = gr.Slider(label='b2: backbone factor of the second stage block of decoder', | |
| minimum=1, | |
| maximum=1.6, | |
| step=0.1, | |
| value=1) | |
| with gr.Accordion('FreeU Parameters: s', open=True): | |
| s1 = gr.Slider(label='s1: skip factor of the first stage block of decoder', | |
| minimum=0, | |
| maximum=1, | |
| step=0.1, | |
| value=1) | |
| s2 = gr.Slider(label='s2: skip factor of the second stage block of decoder', | |
| minimum=0, | |
| maximum=1, | |
| step=0.1, | |
| value=1) | |
| with gr.Row(): | |
| with gr.Group(): | |
| # btn = gr.Button("Generate image", scale=0) | |
| with gr.Row(): | |
| with gr.Column() as c1: | |
| image_1 = gr.Image(interactive=False) | |
| image_1_label = gr.Markdown("SD") | |
| with gr.Group(): | |
| # btn = gr.Button("Generate image", scale=0) | |
| with gr.Row(): | |
| with gr.Column() as c2: | |
| image_2 = gr.Image(interactive=False) | |
| image_2_label = gr.Markdown("FreeU") | |
| ex = gr.Examples(examples=examples, fn=infer, inputs=[text, sd_options, seed, b1, b2, s1, s2], outputs=[image_1, image_2], cache_examples=False) | |
| ex.dataset.headers = [""] | |
| text.submit(infer, inputs=[text, sd_options, seed, b1, b2, s1, s2], outputs=[image_1, image_2]) | |
| btn.click(infer, inputs=[text, sd_options, seed, b1, b2, s1, s2], outputs=[image_1, image_2]) | |
| block.launch() | |
| # block.queue(default_enabled=False).launch(share=False) | |