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| from diffusers import StableDiffusionPipeline | |
| import gradio as gr | |
| import torch | |
| models = [ | |
| "DGSpitzer/Cyberpunk-Anime-Diffusion" | |
| ] | |
| prompt_prefixes = { | |
| models[0]: "dgs illustration style " | |
| } | |
| current_model = models[0] | |
| pipe = StableDiffusionPipeline.from_pretrained(current_model, torch_dtype=torch.float16) | |
| if torch.cuda.is_available(): | |
| pipe = pipe.to("cuda") | |
| device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶" | |
| def on_model_change(model): | |
| global current_model | |
| global pipe | |
| if model != current_model: | |
| current_model = model | |
| pipe = StableDiffusionPipeline.from_pretrained(current_model, torch_dtype=torch.float16) | |
| if torch.cuda.is_available(): | |
| pipe = pipe.to("cuda") | |
| def inference(prompt, guidance, steps): | |
| prompt = prompt_prefixes[current_model] + prompt | |
| image = pipe(prompt, num_inference_steps=int(steps), guidance_scale=guidance, width=512, height=512).images[0] | |
| return image | |
| with gr.Blocks() as demo: | |
| gr.HTML( | |
| """ | |
| <div style="text-align: center; max-width: 700px; margin: 0 auto;"> | |
| <div | |
| style=" | |
| display: inline-flex; | |
| align-items: center; | |
| gap: 0.8rem; | |
| font-size: 1.75rem; | |
| " | |
| > | |
| <h1 style="font-weight: 900; margin-bottom: 7px;"> | |
| DGS Diffusion Space | |
| </h1> | |
| </div> | |
| <p style="margin-bottom: 10px; font-size: 94%"> | |
| Demo for Cyberpunk Anime Diffusion. Based of Finetuned Diffusion by anzorq <a href="https://twitter.com/hahahahohohe"> | |
| </p> | |
| </div> | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| model = gr.Dropdown(label="Model", choices=models, value=models[0]) | |
| prompt = gr.Textbox(label="Prompt", placeholder="{} is added automatically".format(prompt_prefixes[current_model])) | |
| guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15) | |
| steps = gr.Slider(label="Steps", value=27, maximum=100, minimum=2) | |
| run = gr.Button(value="Run") | |
| gr.Markdown(f"Running on: {device}") | |
| with gr.Column(): | |
| image_out = gr.Image(height=512) | |
| model.change(on_model_change, inputs=model, outputs=[]) | |
| run.click(inference, inputs=[prompt, guidance, steps], outputs=image_out) | |
| gr.Examples([ | |
| ["a beautiful perfect face girl, Anime fine details portrait of school girl in front of modern tokyo city landscape on the background deep bokeh, anime masterpiece by studio ghibli, 8k, sharp high quality anime, artstation", 7.5, 27], | |
| ["landscape with fancy car", 7.5, 27], | |
| ["portrait of liu yifei girl, soldier working in a cyberpunk city, cleavage, intricate, 8k, highly detailed, digital painting, intense, sharp focus", 7.5, 27], | |
| ["portrait of a soldier working in a cyberpunk city, cleavage, intricate, 8k, highly detailed, digital painting, intense, sharp focus", | |
| 7.5, 27], | |
| ], [prompt, guidance, steps], image_out, inference, cache_examples=torch.cuda.is_available()) | |
| gr.Markdown(''' | |
| Models and Space by [@DGSpitzer](https://huggingface.co/DGSpitzer)❤️<br> | |
| [](https://twitter.com/DGSpitzer) | |
|  | |
| ''') | |
| demo.queue() | |
| demo.launch() |