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--- |
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library_name: diffusers |
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tags: |
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- text-to-image |
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license: apache-2.0 |
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inference: false |
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--- |
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# Sub-path Linear Approximation Model (SLAM): SD1.5 |
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Paper: [https://arxiv.org/abs/2404.13903](https://arxiv.org/abs/2404.13903)</br> |
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Project Page: [https://subpath-linear-approx-model.github.io/](https://subpath-linear-approx-model.github.io/)</br> |
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The checkpoint is a distilled from [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) with our proposed Sub-path Linear Approximation Model, which reduces the number of inference steps to only between 2-4 steps. |
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## Usage |
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First, install the latest version of the Diffusers library as well as peft, accelerate and transformers. |
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```bash |
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pip install --upgrade pip |
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pip install --upgrade diffusers transformers accelerate peft |
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``` |
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We implement SLAM to be compatible with [LCMScheduler](https://huggingface.co/docs/diffusers/v0.22.3/en/api/schedulers/lcm#diffusers.LCMScheduler). You can use SLAM just like you use LCM, with guidance_scale set to 1 constantly. |
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```python |
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from diffusers import DiffusionPipeline |
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import torch |
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pipe = DiffusionPipeline.from_pretrained("alimama-creative/slam-sd1.5") |
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# To save GPU memory, torch.float16 can be used, but it may compromise image quality. |
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pipe.to(torch_device="cuda", torch_dtype=torch.float16) |
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prompt = "a painting of a majestic kingdom with towering castles, lush gardens, ice and snow world" |
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num_inference_steps = 2 |
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images = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=1, lcm_origin_steps=50, output_type="pil").images |
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``` |
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