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https://git.datalinker.icu/kijai/ComfyUI-CogVideoXWrapper.git
synced 2025-12-08 20:34:23 +08:00
Better sampling preview and support VHS live latent preview
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kijai
parent
76f7930d07
commit
90e8367f5e
@ -1,79 +0,0 @@
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import io
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import torch
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from PIL import Image
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import struct
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import numpy as np
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from comfy.cli_args import args, LatentPreviewMethod
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from comfy.taesd.taesd import TAESD
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import comfy.model_management
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import folder_paths
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import comfy.utils
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import logging
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MAX_PREVIEW_RESOLUTION = args.preview_size
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def preview_to_image(latent_image):
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latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1) # change scale from -1..1 to 0..1
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.mul(0xFF) # to 0..255
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).to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device))
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return Image.fromarray(latents_ubyte.numpy())
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class LatentPreviewer:
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def decode_latent_to_preview(self, x0):
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pass
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def decode_latent_to_preview_image(self, preview_format, x0):
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preview_image = self.decode_latent_to_preview(x0)
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return ("GIF", preview_image, MAX_PREVIEW_RESOLUTION)
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class Latent2RGBPreviewer(LatentPreviewer):
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def __init__(self):
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latent_rgb_factors = [[0.11945946736445662, 0.09919175788574555, -0.004832707433877734], [-0.0011977028264356232, 0.05496505130267682, 0.021321622433638193], [-0.014088548986590666, -0.008701477861945644, -0.020991313281459367], [0.03063921972519621, 0.12186477097625073, 0.0139593690235148], [0.0927403067854673, 0.030293187650929136, 0.05083134241694003], [0.0379112441305742, 0.04935199882777209, 0.058562766246777774], [0.017749911959153715, 0.008839453404921545, 0.036005638019226294], [0.10610119248526109, 0.02339855688237826, 0.057154257614084596], [0.1273639464837117, -0.010959856130713416, 0.043268631260428896], [-0.01873510946881321, 0.08220930648486932, 0.10613256772247093], [0.008429116376722327, 0.07623856561000408, 0.09295712117576727], [0.12938137079617007, 0.12360403483892413, 0.04478930933220116], [0.04565908794779364, 0.041064156741596365, -0.017695041535528512], [0.00019003240570281826, -0.013965147883381978, 0.05329669529635849], [0.08082391586738358, 0.11548306825496074, -0.021464170006615893], [-0.01517932393230994, -0.0057985555313003236, 0.07216646476618871]]
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self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu").transpose(0, 1)
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self.latent_rgb_factors_bias = None
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# if latent_rgb_factors_bias is not None:
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# self.latent_rgb_factors_bias = torch.tensor(latent_rgb_factors_bias, device="cpu")
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def decode_latent_to_preview(self, x0):
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self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device)
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if self.latent_rgb_factors_bias is not None:
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self.latent_rgb_factors_bias = self.latent_rgb_factors_bias.to(dtype=x0.dtype, device=x0.device)
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latent_image = torch.nn.functional.linear(x0[0].permute(1, 2, 0), self.latent_rgb_factors,
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bias=self.latent_rgb_factors_bias)
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return preview_to_image(latent_image)
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def get_previewer():
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previewer = None
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method = args.preview_method
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if method != LatentPreviewMethod.NoPreviews:
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# TODO previewer method
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if method == LatentPreviewMethod.Auto:
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method = LatentPreviewMethod.Latent2RGB
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if previewer is None:
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previewer = Latent2RGBPreviewer()
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return previewer
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def prepare_callback(model, steps, x0_output_dict=None):
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preview_format = "JPEG"
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if preview_format not in ["JPEG", "PNG"]:
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preview_format = "JPEG"
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previewer = get_previewer()
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pbar = comfy.utils.ProgressBar(steps)
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def callback(step, x0, x, total_steps):
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if x0_output_dict is not None:
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x0_output_dict["x0"] = x0
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preview_bytes = None
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if previewer:
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preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0)
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pbar.update_absolute(step + 1, total_steps, preview_bytes)
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return callback
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@ -111,6 +111,34 @@ def retrieve_timesteps(
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timesteps = scheduler.timesteps
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return timesteps, num_inference_steps
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class CogVideoXLatentFormat():
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latent_channels = 16
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latent_dimensions = 3
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scale_factor = 0.7
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taesd_decoder_name = None
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latent_rgb_factors = [[0.03197404301362048, 0.04091260743347359, 0.0015679806301828524],
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[0.005517101026578029, 0.0052348639043457755, -0.005613441650464035],
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[0.0012485338264583965, -0.016096744206117782, 0.025023940031635054],
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[0.01760126794276171, 0.0036818415416642893, -0.0006019202528157255],
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[0.000444954842288864, 0.006102128982092191, 0.0008457999272962447],
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[-0.010531904354560697, -0.0032275501924977175, -0.00886595780267917],
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[-0.0001454543946122991, 0.010199210750845965, -0.00012702234832386188],
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[0.02078497279904325, -0.001669617778939972, 0.006712703698951264],
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[0.005529571599763264, 0.009733929789086743, 0.001887302765339838],
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[0.012138415094654218, 0.024684961927224837, 0.037211249767461915],
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[0.0010364484570000384, 0.01983636315929172, 0.009864602025627755],
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[0.006802862648143341, -0.0010509255113510681, -0.007026003345126021],
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[0.0003532208468418043, 0.005351971582801936, -0.01845912126717106],
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[-0.009045079994694397, -0.01127941143183089, 0.0042294057970470806],
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[0.002548289972720752, 0.025224244654428216, -0.0006086130121693347],
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[-0.011135669222532816, 0.0018181308593668505, 0.02794541485349922]]
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latent_rgb_factors_bias = [ -0.023, 0.0, -0.017]
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class CogVideoXModelPlaceholder():
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def __init__(self):
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self.latent_format = CogVideoXLatentFormat
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class CogVideoXPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
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r"""
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Pipeline for text-to-video generation using CogVideoX.
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@ -598,8 +626,12 @@ class CogVideoXPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
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disable_enhance()
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# 11. Denoising loop
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from .latent_preview import prepare_callback
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callback = prepare_callback(self.transformer, num_inference_steps)
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#from .latent_preview import prepare_callback
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#callback = prepare_callback(self.transformer, num_inference_steps)
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from latent_preview import prepare_callback
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self.model = CogVideoXModelPlaceholder()
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self.load_device = device
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callback = prepare_callback(self, num_inference_steps)
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comfy_pbar = ProgressBar(len(timesteps))
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with self.progress_bar(total=len(timesteps)) as progress_bar:
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@ -815,8 +847,11 @@ class CogVideoXPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
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if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
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progress_bar.update()
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if callback is not None:
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callback(i, (latents - noise_pred * (t / 1000)).detach()[0], None, num_inference_steps)
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if callback is not None:
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alpha_prod_t = self.scheduler.alphas_cumprod[t]
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beta_prod_t = 1 - alpha_prod_t
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callback_tensor = (alpha_prod_t**0.5) * latent_model_input[0][:, :16, :, :] - (beta_prod_t**0.5) * noise_pred.detach()[0]
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callback(i, callback_tensor * 5, None, num_inference_steps)
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else:
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comfy_pbar.update(1)
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