117 lines
4.3 KiB
Python
117 lines
4.3 KiB
Python
from .InvSR.inference_invsr import get_configs
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from .InvSR.sampler_invsr import InvSamplerSR, BaseSampler
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import torch
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from comfy.utils import ProgressBar
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def split_tensor_into_batches(tensor, batch_size):
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"""
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Split a tensor into smaller batches of specified size
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Args:
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tensor (torch.Tensor): Input tensor of shape (N, C, H, W)
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batch_size (int): Desired batch size for splitting
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Returns:
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list: List of tensors, each with batch_size (except possibly the last one)
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"""
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# Get original batch size
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original_batch_size = tensor.size(0)
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# Calculate number of full batches and remaining samples
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num_full_batches = original_batch_size // batch_size
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remaining_samples = original_batch_size % batch_size
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# Split tensor into chunks
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batches = []
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# Handle full batches
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for i in range(num_full_batches):
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start_idx = i * batch_size
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end_idx = start_idx + batch_size
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batch = tensor[start_idx:end_idx]
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batches.append(batch)
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# Handle remaining samples if any
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if remaining_samples > 0:
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last_batch = tensor[-remaining_samples:]
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batches.append(last_batch)
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return batches
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class Namespace:
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def __init__(self, **kwargs):
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for key, value in kwargs.items():
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setattr(self, key, value)
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def __repr__(self):
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items = [f"{key}={repr(value)}" for key, value in vars(self).items()]
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return f"Namespace({', '.join(items)})"
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class LoadInvSRModels:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"sd_model": (['stabilityai/sd-turbo'],),
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"invsr_model": (['noise_predictor_sd_turbo_v5.pth'],),
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"tiled_vae": ("BOOLEAN", {"default": True}),
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},
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}
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RETURN_TYPES = ("INVSR_PIPE",)
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RETURN_NAMES = ("invsr_pipe",)
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FUNCTION = "loadmodel"
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CATEGORY = "INVSR"
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def loadmodel(self, sd_model, invsr_model, tiled_vae):
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args = Namespace(bs=1, chopping_bs=8, timesteps=None, num_steps=1, cfg_path='custom_nodes/ComfyUI_InvSR/InvSR/configs/sample-sd-turbo.yaml', sd_path='models/diffusers', started_ckpt_path='custom_nodes/ComfyUI_InvSR/weights/noise_predictor_sd_turbo_v5.pth', tiled_vae=tiled_vae, color_fix='', chopping_size=128)
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configs = get_configs(args)
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base_sampler = BaseSampler(configs)
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return (base_sampler,)
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class InvSRSampler:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"invsr_pipe": ("INVSR_PIPE",),
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"images": ("IMAGE",),
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"num_steps": ("INT",{"default": 1, "min": 1, "max": 5}),
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"batch_size": ("INT",{"default": 1}),
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"chopping_batch_size": ("INT",{"default": 8}),
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"chopping_size": ([128, 256, 512],{"default": 128}),
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"color_fix": (['none', 'wavelet', 'ycbcr'], {"default": "none"}),
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"seed": ("INT", {"default": 123, "min": 0, "max": 2**32 - 1, "step": 1}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("image",)
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FUNCTION = "process"
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CATEGORY = "INVSR"
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def process(self, invsr_pipe, images, num_steps, batch_size, chopping_batch_size, chopping_size, color_fix, seed):
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base_sampler = invsr_pipe
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if color_fix == "none":
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color_fix = ""
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args = Namespace(bs=batch_size, chopping_bs=chopping_batch_size, timesteps=None, num_steps=num_steps, cfg_path='custom_nodes/ComfyUI_InvSR/InvSR/configs/sample-sd-turbo.yaml', sd_path='models/diffusers', started_ckpt_path='custom_nodes/ComfyUI_InvSR/weights/noise_predictor_sd_turbo_v5.pth', tiled_vae=base_sampler.configs.tiled_vae, color_fix=color_fix, chopping_size=chopping_size)
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configs = get_configs(args)
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base_sampler.configs = get_configs(args, log=True)
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base_sampler.setup_seed(seed)
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sampler = InvSamplerSR(base_sampler)
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images_bchw = images.permute(0,3,1,2)
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batches = split_tensor_into_batches(images_bchw, batch_size)
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results = []
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pbar = ProgressBar(len(batches))
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for batch in batches:
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result = sampler.inference(image_bchw=batch)
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results.append(torch.from_numpy(result))
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pbar.update(1)
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return (torch.cat(results, dim=0),) |