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import io
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 ("GIF", preview_image, MAX_PREVIEW_RESOLUTION)
class Latent2RGBPreviewer(LatentPreviewer):
def __init__(self):
#latent_rgb_factors = [[0.05389399697934166, 0.025018778505575393, -0.009193515248318657], [0.02318250640590553, -0.026987363837713156, 0.040172639061236956], [0.046035451343323666, -0.02039565868920197, 0.01275569344290342], [-0.015559161155025095, 0.051403973219861246, 0.03179031307996347], [-0.02766167769640129, 0.03749545161530447, 0.003335141009473408], [0.05824598730479011, 0.021744367381243884, -0.01578925627951616], [0.05260929401500947, 0.0560165014956886, -0.027477296572565126], [0.018513891242931686, 0.041961785217662514, 0.004490763489747966], [0.024063060899760215, 0.065082853069653, 0.044343437673514896], [0.05250992323006226, 0.04361117432588933, 0.01030076055524387], [0.0038921710021782366, -0.025299228133723792, 0.019370764014574535], [-0.00011950534333568519, 0.06549370069727675, -0.03436712163379723], [-0.026020578032683626, -0.013341758571090847, -0.009119046570271953], [0.024412451175602937, 0.030135064560817174, -0.008355486384198006], [0.04002209845752687, -0.017341304390739463, 0.02818338690302971], [-0.032575108695213684, -0.009588338926775117, -0.03077312160940468]]
#latent_rgb_factors = [[0.1236769792512748, 0.11775175335219157, -0.17700629766423637], [-0.08504104329270078, 0.026605813147523694, -0.006843165704926019], [-0.17093308616366876, 0.027991854696200386, 0.14179146288816308], [-0.17179555328757623, 0.09844317368603078, 0.14470997015982784], [-0.16975067171668484, -0.10739852629856643, -0.1894254942909962], [-0.19315259266769888, -0.011029760569485209, -0.08519702054654255], [-0.08399895091432583, -0.0964246452052032, -0.033622359523655665], [0.08148916330842498, 0.027500645903400067, -0.06593099749891196], [0.0456603103902293, -0.17844808072462398, 0.04204775167149785], [0.001751626383204502, -0.030567890189647867, -0.022078082809772193], [0.05110631095056278, -0.0709677393548804, 0.08963683539504264], [0.010515800868829, -0.18382052841762514, -0.08554553339721907]]
latent_rgb_factors =[
[-0.0069, -0.0045, 0.0018],
[ 0.0154, -0.0692, -0.0274],
[ 0.0333, 0.0019, 0.0206],
[-0.1390, 0.0628, 0.1678],
[-0.0725, 0.0134, -0.1898],
[ 0.0074, -0.0270, -0.0209],
[-0.0176, -0.0277, -0.0221],
[ 0.5294, 0.5204, 0.3852],
[-0.0326, -0.0446, -0.0143],
[-0.0659, 0.0153, -0.0153],
[ 0.0185, -0.0217, 0.0014],
[-0.0396, -0.0495, -0.0281]
]
self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu").transpose(0, 1)
self.latent_rgb_factors_bias = [-0.0940, -0.1418, -0.1453]
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 = torch.tensor(self.latent_rgb_factors_bias, device="cpu").to(dtype=x0.dtype, device=x0.device)
latent_image = torch.nn.functional.linear(x0[0].permute(1, 2, 0), self.latent_rgb_factors,
bias=self.latent_rgb_factors_bias)
return preview_to_image(latent_image)
def get_previewer():
previewer = None
method = args.preview_method
if method != LatentPreviewMethod.NoPreviews:
# TODO previewer method
if method == LatentPreviewMethod.Auto:
method = LatentPreviewMethod.Latent2RGB
if previewer is None:
previewer = Latent2RGBPreviewer()
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()
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
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