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from typing import Any, Dict, Optional |
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import numpy as np |
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import torch |
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import torch.nn as nn |
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from leffa.pipeline import LeffaPipeline |
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def pil_to_tensor(images): |
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images = np.array(images).astype(np.float32) / 255.0 |
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images = torch.from_numpy(images.transpose(2, 0, 1)) |
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return images |
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class LeffaInference(object): |
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def __init__( |
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self, |
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model: nn.Module, |
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) -> None: |
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self.device = "cuda" if torch.cuda.is_available() else "cpu" |
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self.model = model.to(self.device) |
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self.model.eval() |
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self.pipe = LeffaPipeline(model=self.model) |
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def to_gpu(self, data: Dict[str, Any]) -> Dict[str, Any]: |
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for k, v in data.items(): |
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if isinstance(v, torch.Tensor): |
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data[k] = v.to(self.device) |
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return data |
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def __call__(self, data: Dict[str, Any], **kwargs) -> Dict[str, Any]: |
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data = self.to_gpu(data) |
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num_inference_steps = kwargs.get("num_inference_steps", 50) |
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guidance_scale = kwargs.get("guidance_scale", 2.5) |
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seed = kwargs.get("seed", 42) |
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generator = torch.Generator(self.pipe.device).manual_seed(seed) |
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images = self.pipe( |
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src_image=data["src_image"], |
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ref_image=data["ref_image"], |
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mask=data["mask"], |
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densepose=data["densepose"], |
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num_inference_steps=num_inference_steps, |
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guidance_scale=guidance_scale, |
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generator=generator, |
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)[0] |
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outputs = {} |
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outputs["src_image"] = (data["src_image"] + 1.0) / 2.0 |
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outputs["ref_image"] = (data["ref_image"] + 1.0) / 2.0 |
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outputs["generated_image"] = images |
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return outputs |
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