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from diffusers import DiffusionPipeline, LCMScheduler |
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import torch |
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loaded_pipe = None |
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loaded_pipe_id = None |
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def load_model(pipe_id): |
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global loaded_pipe, loaded_pipe_id |
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if loaded_pipe_id != pipe_id: |
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loaded_pipe = DiffusionPipeline.from_pretrained(pipe_id, torch_dtype=torch.float16).to("cuda") |
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loaded_pipe.scheduler = LCMScheduler.from_config(loaded_pipe.scheduler.config) |
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loaded_pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl") |
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loaded_pipe_id = pipe_id |
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return loaded_pipe |
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def generate_image(prompt, num_inference_steps, seed, guidance_scale, negative_prompt=None, pipe_id="Linaqruf/animagine-xl"): |
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global loaded_pipe |
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pipe = load_model(pipe_id) |
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generator = torch.manual_seed(seed) |
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image = pipe(prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps, generator=generator, guidance_scale=guidance_scale).images[0] |
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return image |
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