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--- |
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library_name: peft |
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base_model: segmind/tiny-sd |
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pipeline_tag: text-to-image |
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--- |
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# Usage |
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```python |
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from peft import PeftModel |
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from diffusers import LCMScheduler, AutoPipelineForText2Image |
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model_id = "segmind/tiny-sd" |
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adapter_id = "akameswa/lcm-lora-tiny-sd" |
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pipe = AutoPipelineForText2Image.from_pretrained(model_id) |
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pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) |
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pipe.to("cuda") |
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PeftModel.from_pretrained(pipe.unet, adapter_id) |
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prompt = "a dog wearing a knitted hat on the floor" |
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image = pipe(prompt=prompt, num_inference_steps=4, guidance_scale=1.0).images[0] |
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display(image) |
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``` |
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# Saving complete model |
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```python |
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pipe.fuse_lora(lora_scale=1.0) |
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pipe.unload_lora_weights() |
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for param in pipe.unet.parameters(): |
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param.data = param.data.contiguous() |
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pipe.save_pretrained("./lcm-tiny-sd") |
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``` |
