98 lines
2.3 KiB
Python
98 lines
2.3 KiB
Python
import torch
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import numpy as np
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import os
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import time
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import random
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import string
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from ldm_patched.modules.conds import CONDRegular, CONDCrossAttn
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def generate_random_filename(extension=".txt"):
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timestamp = time.strftime("%Y%m%d-%H%M%S")
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random_string = ''.join(random.choices(string.ascii_lowercase + string.digits, k=5))
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filename = f"{timestamp}-{random_string}{extension}"
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return filename
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def cond_from_a1111_to_patched_ldm(cond):
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if isinstance(cond, torch.Tensor):
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result = dict(
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cross_attn=cond,
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model_conds=dict(
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c_crossattn=CONDCrossAttn(cond),
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)
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)
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return [result, ]
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cross_attn = cond['crossattn']
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pooled_output = cond['vector']
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result = dict(
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cross_attn=cross_attn,
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pooled_output=pooled_output,
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model_conds=dict(
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c_crossattn=CONDCrossAttn(cross_attn),
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y=CONDRegular(pooled_output)
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)
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)
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return [result, ]
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@torch.no_grad()
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@torch.inference_mode()
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def pytorch_to_numpy(x):
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return [np.clip(255. * y.cpu().numpy(), 0, 255).astype(np.uint8) for y in x]
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@torch.no_grad()
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@torch.inference_mode()
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def numpy_to_pytorch(x):
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y = x.astype(np.float32) / 255.0
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y = y[None]
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y = np.ascontiguousarray(y.copy())
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y = torch.from_numpy(y).float()
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return y
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def write_images_to_mp4(frame_list: list, filename=None, fps=6):
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from modules.paths_internal import default_output_dir
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video_folder = os.path.join(default_output_dir, 'svd')
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os.makedirs(video_folder, exist_ok=True)
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if filename is None:
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filename = generate_random_filename('.mp4')
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full_path = os.path.join(video_folder, filename)
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try:
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import av
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except ImportError:
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from launch import run_pip
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run_pip(
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"install imageio[pyav]",
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"imageio[pyav]",
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)
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import av
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options = {
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"crf": str(23)
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}
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output = av.open(full_path, "w")
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stream = output.add_stream('libx264', fps, options=options)
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stream.width = frame_list[0].shape[1]
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stream.height = frame_list[0].shape[0]
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for img in frame_list:
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frame = av.VideoFrame.from_ndarray(img)
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packet = stream.encode(frame)
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output.mux(packet)
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packet = stream.encode(None)
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output.mux(packet)
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output.close()
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return full_path
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