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Merge 27e35f13faa79c90dddae45eea324baffcc13ace into 374bb6cc384d2a19422c0b07d69de0a41d1f3f4d

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drhead 2025-03-06 23:16:52 +08:00 committed by GitHub
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1 changed files with 21 additions and 10 deletions
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31
modules/sd_samplers_common.py
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@ -1,6 +1,6 @@
import inspect import inspect
from collections import namedtuple from collections import namedtuple
import numpy as np from contextlib import nullcontext
import torch import torch
from PIL import Image from PIL import Image
from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared, sd_models from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared, sd_models
@ -59,15 +59,11 @@ def samples_to_images_tensor(sample, approximation=None, model=None):
return x_sample return x_sample
def single_sample_to_image(sample, approximation=None, non_blocking=False):
def single_sample_to_image(sample, approximation=None):
x_sample = samples_to_images_tensor(sample.unsqueeze(0), approximation)[0] * 0.5 + 0.5 x_sample = samples_to_images_tensor(sample.unsqueeze(0), approximation)[0] * 0.5 + 0.5
x_sample = torch.clamp(x_sample, min=0.0, max=1.0) x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) x_sample = 255. * x_sample.permute(1, 2, 0)
x_sample = x_sample.astype(np.uint8) return x_sample.to(device='cpu', dtype=torch.uint8, non_blocking=non_blocking)
return Image.fromarray(x_sample)
def decode_first_stage(model, x): def decode_first_stage(model, x):
@ -76,12 +72,27 @@ def decode_first_stage(model, x):
return samples_to_images_tensor(x, approx_index, model) return samples_to_images_tensor(x, approx_index, model)
if torch.cuda.is_available():
lp_stream = torch.cuda.Stream()
live_preview_stream_context = torch.cuda.stream(lp_stream)
else:
lp_stream = None
live_preview_stream_context = nullcontext()
def sample_to_image(samples, index=0, approximation=None): def sample_to_image(samples, index=0, approximation=None):
return single_sample_to_image(samples[index], approximation) with live_preview_stream_context:
sample = single_sample_to_image(samples[index], approximation, non_blocking=lp_stream is not None)
if lp_stream is not None:
lp_stream.synchronize()
return Image.fromarray(sample.numpy())
def samples_to_image_grid(samples, approximation=None): def samples_to_image_grid(samples, approximation=None):
return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples]) with live_preview_stream_context:
sample_tensors = [single_sample_to_image(sample, approximation, non_blocking=lp_stream is not None) for sample in samples]
if lp_stream is not None:
lp_stream.synchronize()
return images.image_grid([Image.fromarray(sample.numpy()) for sample in sample_tensors])
def images_tensor_to_samples(image, approximation=None, model=None): def images_tensor_to_samples(image, approximation=None, model=None):