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from __future__ import annotations |
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import gc |
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import pathlib |
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import gradio as gr |
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import PIL.Image |
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import torch |
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from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler |
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from huggingface_hub import ModelCard |
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class InferencePipeline: |
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def __init__(self, hf_token: str | None = None): |
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self.hf_token = hf_token |
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self.pipe = None |
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self.device = torch.device( |
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'cuda:0' if torch.cuda.is_available() else 'cpu') |
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self.lora_model_id = None |
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self.base_model_id = None |
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def clear(self) -> None: |
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self.lora_model_id = None |
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self.base_model_id = None |
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del self.pipe |
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self.pipe = None |
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torch.cuda.empty_cache() |
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gc.collect() |
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@staticmethod |
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def check_if_model_is_local(lora_model_id: str) -> bool: |
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return pathlib.Path(lora_model_id).exists() |
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@staticmethod |
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def get_model_card(model_id: str, |
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hf_token: str | None = None) -> ModelCard: |
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if InferencePipeline.check_if_model_is_local(model_id): |
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card_path = (pathlib.Path(model_id) / 'README.md').as_posix() |
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else: |
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card_path = model_id |
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return ModelCard.load(card_path, token=hf_token) |
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@staticmethod |
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def get_base_model_info(lora_model_id: str, |
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hf_token: str | None = None) -> str: |
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card = InferencePipeline.get_model_card(lora_model_id, hf_token) |
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return card.data.base_model |
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def load_pipe(self, lora_model_id: str) -> None: |
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if lora_model_id == self.lora_model_id: |
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return |
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base_model_id = self.get_base_model_info(lora_model_id, self.hf_token) |
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if base_model_id != self.base_model_id: |
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if self.device.type == 'cpu': |
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pipe = DiffusionPipeline.from_pretrained( |
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base_model_id, use_auth_token=self.hf_token) |
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else: |
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pipe = DiffusionPipeline.from_pretrained( |
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base_model_id, |
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torch_dtype=torch.float16, |
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use_auth_token=self.hf_token) |
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pipe = pipe.to(self.device) |
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pipe.scheduler = DPMSolverMultistepScheduler.from_config( |
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pipe.scheduler.config) |
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self.pipe = pipe |
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self.pipe.unet.load_attn_procs( |
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lora_model_id, use_auth_token=self.hf_token) |
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self.lora_model_id = lora_model_id |
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self.base_model_id = base_model_id |
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def run( |
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self, |
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lora_model_id: str, |
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prompt: str, |
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lora_scale: float, |
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seed: int, |
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n_steps: int, |
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guidance_scale: float, |
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) -> PIL.Image.Image: |
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if not torch.cuda.is_available(): |
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raise gr.Error('CUDA is not available.') |
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self.load_pipe(lora_model_id) |
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generator = torch.Generator(device=self.device).manual_seed(seed) |
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out = self.pipe( |
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prompt, |
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num_inference_steps=n_steps, |
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guidance_scale=guidance_scale, |
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generator=generator, |
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cross_attention_kwargs={'scale': lora_scale}, |
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) |
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return out.images[0] |
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