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from hidiffusion import apply_hidiffusion, remove_hidiffusion |
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from diffusers import DiffusionPipeline, DDIMScheduler, AutoencoderKL |
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import gradio as gr |
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import torch |
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import spaces |
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model = "stabilityai/stable-diffusion-xl-base-1.0" |
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vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) |
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scheduler = DDIMScheduler.from_pretrained(model, subfolder="scheduler") |
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pipe = DiffusionPipeline.from_pretrained(model, vae=vae, scheduler=scheduler, torch_dtype=torch.float16, use_safetensors=True, variant="fp16").to("cuda") |
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model_15 = "runwayml/stable-diffusion-v1-5" |
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scheduler_15 = DDIMScheduler.from_pretrained(model_15, subfolder="scheduler") |
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pipe_15 = DiffusionPipeline.from_pretrained(model_15, vae=vae, scheduler=scheduler_15, torch_dtype=torch.float16, use_safetensors=True, variant="fp16").to("cuda") |
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pipe.enable_vae_tiling() |
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apply_hidiffusion(pipe) |
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@spaces.GPU |
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def run_hidiffusion(prompt, negative_prompt, progress=gr.Progress(track_tqdm=True)): |
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return pipe(prompt, guidance_scale=7.5, height=2048, width=2048, eta=1.0, negative_prompt=negative_prompt, num_inference_steps=25).images[0] |
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@spaces.GPU |
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def run_hidiffusion_15(prompt, negative_prompt, progress=gr.Progress(track_tqdm=True)): |
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return pipe_15(prompt, guidance_scale=7.5, height=1024, width=1024, eta=1.0, negative_prompt=negative_prompt, num_inference_steps=25).images[0] |
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with gr.Blocks() as demo: |
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gr.Markdown("# HiDiffusion Demo") |
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gr.Markdown("Make Stable Diffusion generated higher resolution images than what it was trained for") |
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with gr.Tab("SDXL in 2048x2048"): |
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with gr.Row(): |
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prompt = gr.Textbox(label="Prompt") |
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negative_prompt = gr.Textbox(label="Negative Prompt") |
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btn = gr.Button("Run") |
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with gr.Tab("SD1.5 in 1024x1024"): |
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with gr.Row(): |
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prompt_15 = gr.Textbox(label="Prompt") |
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negative_prompt_15 = gr.Textbox(label="Negative Prompt") |
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btn_15 = gr.Button("Run") |
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output = gr.Image() |
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btn.click(fn=run_hidiffusion, inputs=[prompt, negative_prompt], outputs=[output]) |
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btn_15.click(fn=run_hidiffusion, inputs=[prompt_15, negative_prompt_15], outputs=[output]) |
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demo.launch() |
