Spaces:
Running
on
L40S
Running
on
L40S
File size: 4,224 Bytes
4450790 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 |
import torch
from PIL import Image
import struct
import numpy as np
from comfy.cli_args import args, LatentPreviewMethod
from comfy.taesd.taesd import TAESD
import comfy.model_management
import folder_paths
import comfy.utils
import logging
MAX_PREVIEW_RESOLUTION = args.preview_size
def preview_to_image(latent_image):
latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1) # change scale from -1..1 to 0..1
.mul(0xFF) # to 0..255
).to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device))
return Image.fromarray(latents_ubyte.numpy())
class LatentPreviewer:
def decode_latent_to_preview(self, x0):
pass
def decode_latent_to_preview_image(self, preview_format, x0):
preview_image = self.decode_latent_to_preview(x0)
return ("JPEG", preview_image, MAX_PREVIEW_RESOLUTION)
class TAESDPreviewerImpl(LatentPreviewer):
def __init__(self, taesd):
self.taesd = taesd
def decode_latent_to_preview(self, x0):
x_sample = self.taesd.decode(x0[:1])[0].movedim(0, 2)
return preview_to_image(x_sample)
class Latent2RGBPreviewer(LatentPreviewer):
def __init__(self, latent_rgb_factors, latent_rgb_factors_bias=None):
self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu").transpose(0, 1)
self.latent_rgb_factors_bias = None
if latent_rgb_factors_bias is not None:
self.latent_rgb_factors_bias = torch.tensor(latent_rgb_factors_bias, device="cpu")
def decode_latent_to_preview(self, x0):
self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device)
if self.latent_rgb_factors_bias is not None:
self.latent_rgb_factors_bias = self.latent_rgb_factors_bias.to(dtype=x0.dtype, device=x0.device)
if x0.ndim == 5:
x0 = x0[0, :, 0]
else:
x0 = x0[0]
latent_image = torch.nn.functional.linear(x0.movedim(0, -1), self.latent_rgb_factors, bias=self.latent_rgb_factors_bias)
# latent_image = x0[0].permute(1, 2, 0) @ self.latent_rgb_factors
return preview_to_image(latent_image)
def get_previewer(device, latent_format):
previewer = None
method = args.preview_method
if method != LatentPreviewMethod.NoPreviews:
# TODO previewer methods
taesd_decoder_path = None
if latent_format.taesd_decoder_name is not None:
taesd_decoder_path = next(
(fn for fn in folder_paths.get_filename_list("vae_approx")
if fn.startswith(latent_format.taesd_decoder_name)),
""
)
taesd_decoder_path = folder_paths.get_full_path("vae_approx", taesd_decoder_path)
if method == LatentPreviewMethod.Auto:
method = LatentPreviewMethod.Latent2RGB
if method == LatentPreviewMethod.TAESD:
if taesd_decoder_path:
taesd = TAESD(None, taesd_decoder_path, latent_channels=latent_format.latent_channels).to(device)
previewer = TAESDPreviewerImpl(taesd)
else:
logging.warning("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name))
if previewer is None:
if latent_format.latent_rgb_factors is not None:
previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors, latent_format.latent_rgb_factors_bias)
return previewer
def prepare_callback(model, steps, x0_output_dict=None):
preview_format = "JPEG"
if preview_format not in ["JPEG", "PNG"]:
preview_format = "JPEG"
previewer = get_previewer(model.load_device, model.model.latent_format)
pbar = comfy.utils.ProgressBar(steps)
def callback(step, x0, x, total_steps):
if x0_output_dict is not None:
x0_output_dict["x0"] = x0
preview_bytes = None
if previewer:
preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0)
pbar.update_absolute(step + 1, total_steps, preview_bytes)
return callback
|