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import gradio as gr |
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from diffusers import DiffusionPipeline |
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import torch |
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from diffusers import DDPMScheduler, UNet2DModel |
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from PIL import Image |
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import numpy as np |
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def erzeuge(prompt): |
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return pipeline(prompt).images |
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def erzeuge_komplex(prompt): |
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scheduler = DDPMScheduler.from_pretrained("google/ddpm-cat-256") |
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model = UNet2DModel.from_pretrained("google/ddpm-cat-256").to("cuda") |
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scheduler.set_timesteps(50) |
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sample_size = model.config.sample_size |
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noise = torch.randn((1, 3, sample_size, sample_size)).to("cuda") |
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input = noise |
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for t in scheduler.timesteps: |
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with torch.no_grad(): |
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noisy_residual = model(input, t).sample |
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prev_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample |
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input = prev_noisy_sample |
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image = (input / 2 + 0.5).clamp(0, 1) |
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image = image.cpu().permute(0, 2, 3, 1).numpy()[0] |
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image = Image.fromarray((image * 255).round().astype("uint8")) |
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return image |
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pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2") |
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pipeline.to("cuda") |
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with gr.Blocks() as demo: |
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with gr.Column(variant="panel"): |
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with gr.Row(variant="compact"): |
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text = gr.Textbox( |
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label="Deine Beschreibung:", |
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show_label=False, |
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max_lines=1, |
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placeholder="Bildbeschreibung", |
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).style( |
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container=False, |
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) |
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btn = gr.Button("erzeuge Bild").style(full_width=False, min_width=100) |
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gallery = gr.Gallery( |
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label="Erzeugtes Bild", show_label=False, elem_id="gallery" |
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).style(columns=[2], rows=[2], object_fit="contain", height="auto") |
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btn.click(erzeuge, inputs=[text], outputs=[gallery]) |
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text.submit(erzeuge, inputs=[text], outputs=[gallery]) |
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if __name__ == "__main__": |
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demo.launch() |
