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Merge branch 'comfyanonymous:master' into offloader-maifee
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README.md

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@@ -67,6 +67,8 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
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- [HiDream](https://comfyanonymous.github.io/ComfyUI_examples/hidream/)
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- [Qwen Image](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/)
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- [Hunyuan Image 2.1](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_image/)
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- [Flux 2](https://comfyanonymous.github.io/ComfyUI_examples/flux2/)
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- [Z Image](https://comfyanonymous.github.io/ComfyUI_examples/z_image/)
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- Image Editing Models
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- [Omnigen 2](https://comfyanonymous.github.io/ComfyUI_examples/omnigen/)
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- [Flux Kontext](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-kontext-image-editing-model)

comfy/latent_formats.py

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@@ -6,6 +6,7 @@ class LatentFormat:
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latent_dimensions = 2
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latent_rgb_factors = None
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latent_rgb_factors_bias = None
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latent_rgb_factors_reshape = None
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taesd_decoder_name = None
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def process_in(self, latent):
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class Flux2(LatentFormat):
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latent_channels = 128
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def __init__(self):
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self.latent_rgb_factors =[
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[0.0058, 0.0113, 0.0073],
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[0.0495, 0.0443, 0.0836],
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[-0.0099, 0.0096, 0.0644],
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[0.2144, 0.3009, 0.3652],
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[0.0166, -0.0039, -0.0054],
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[0.0157, 0.0103, -0.0160],
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[-0.0398, 0.0902, -0.0235],
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[-0.0052, 0.0095, 0.0109],
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[-0.3527, -0.2712, -0.1666],
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[-0.0301, -0.0356, -0.0180],
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[-0.0107, 0.0078, 0.0013],
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[0.0746, 0.0090, -0.0941],
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[0.0156, 0.0169, 0.0070],
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[-0.0034, -0.0040, -0.0114],
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[0.0032, 0.0181, 0.0080],
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[-0.0939, -0.0008, 0.0186],
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[0.0018, 0.0043, 0.0104],
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[0.0284, 0.0056, -0.0127],
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[-0.0024, -0.0022, -0.0030],
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[0.1207, -0.0026, 0.0065],
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[0.0128, 0.0101, 0.0142],
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[0.0137, -0.0072, -0.0007],
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[0.0095, 0.0092, -0.0059],
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[0.0000, -0.0077, -0.0049],
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[-0.0465, -0.0204, -0.0312],
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[0.0095, 0.0012, -0.0066],
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[0.0290, -0.0034, 0.0025],
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[0.0220, 0.0169, -0.0048],
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[-0.0332, -0.0457, -0.0468],
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[-0.0085, 0.0389, 0.0609],
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[-0.0076, 0.0003, -0.0043],
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[-0.0111, -0.0460, -0.0614],
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]
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self.latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851]
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self.latent_rgb_factors_reshape = lambda t: t.reshape(t.shape[0], 32, 2, 2, t.shape[-2], t.shape[-1]).permute(0, 1, 4, 2, 5, 3).reshape(t.shape[0], 32, t.shape[-2] * 2, t.shape[-1] * 2)
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def process_in(self, latent):
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return latent
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