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Merge pull request #10 from WarpedAnimation/V1.7.0

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V1.7.0
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WarpedAnimation
2025-07-16 09:04:16 -05:00
committed by GitHub
parent 2509a2683e d74e972256
commit 031ec21b5c
16 changed files with 5799 additions and 4552 deletions
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Sample_Workflows/Warped Framepack I2V or T2V Workflow.json
+717 -629
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Sample_Workflows/Warped Framepack V2V Workflow.json
+934 -902
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Sample_Workflows/Warped Hunyuan I2V Using I2V Model Workflow.json
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Sample_Workflows/Warped Hunyuan I2V Workflow.json
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Sample_Workflows/Warped Hunyuan v2v Workflow.json
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View File
@@ -1,8 +1,8 @@
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@@ -1800,19 +1608,24 @@
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Sample_Workflows/Warped Multi LORA Mixer Extended Workflow.json
+15 -12
View File
@@ -1,19 +1,19 @@
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@@ -66,9 +68,10 @@
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Sample_Workflows/Warped Reverse Video Workflow.json
+231
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],
"flags": {},
"order": 0,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "image_batch",
"type": "IMAGE",
"links": [
3
]
},
{
"name": "first_image",
"type": "IMAGE",
"links": null
},
{
"name": "last_image",
"type": "IMAGE",
"links": null
},
{
"name": "num_frames",
"type": "INT",
"links": null
},
{
"name": "width",
"type": "INT",
"links": null
},
{
"name": "height",
"type": "INT",
"links": null
},
{
"name": "fps",
"type": "FLOAT",
"links": [
5
]
}
],
"properties": {
"Node name for S&R": "WarpedBundleAllVideoImages"
},
"widgets_values": [
"",
true
],
"color": "#323",
"bgcolor": "#535"
}
],
"links": [
[
3,
10,
0,
9,
0,
"IMAGE"
],
[
4,
9,
0,
11,
0,
"IMAGE"
],
[
5,
10,
6,
11,
4,
"FLOAT"
]
],
"groups": [],
"config": {},
"extra": {
"ds": {
"scale": 1,
"offset": [
-126,
-214
]
},
"frontendVersion": "1.20.5",
"VHS_latentpreview": true,
"VHS_latentpreviewrate": 15,
"VHS_MetadataImage": true,
"VHS_KeepIntermediate": true
},
"version": 0.4
}
Sample_Workflows/Warped WAN I2V Workflow.json
+130 -141
View File
@@ -1,8 +1,8 @@
{
"id": "2fac44f1-8935-4d17-8233-fe2dcaee9ba8",
"revision": 0,
"last_node_id": 139,
"last_link_id": 295,
"last_node_id": 140,
"last_link_id": 296,
"nodes": [
{
"id": 38,
@@ -13,7 +13,7 @@
],
"size": [
376.05950927734375,
100.8628921508789
106
],
"flags": {},
"order": 0,
@@ -86,7 +86,7 @@
90.64099884033203
],
"flags": {},
"order": 10,
"order": 12,
"mode": 0,
"inputs": [
{
@@ -209,7 +209,7 @@
134.78460693359375
],
"flags": {},
"order": 6,
"order": 7,
"mode": 0,
"inputs": [
{
@@ -304,7 +304,7 @@
"flags": {
"collapsed": true
},
"order": 8,
"order": 10,
"mode": 0,
"inputs": [
{
@@ -346,7 +346,7 @@
284.9806823730469
],
"flags": {},
"order": 9,
"order": 11,
"mode": 0,
"inputs": [
{
@@ -378,48 +378,6 @@
"color": "#232",
"bgcolor": "#353"
},
{
"id": 110,
"type": "ModelSamplingSD3",
"pos": [
195.28842163085938,
-359.64794921875
],
"size": [
315,
58
],
"flags": {
"collapsed": true
},
"order": 18,
"mode": 0,
"inputs": [
{
"name": "model",
"type": "MODEL",
"link": 262
}
],
"outputs": [
{
"name": "MODEL",
"type": "MODEL",
"slot_index": 0,
"links": [
259
]
}
],
"properties": {
"cnr_id": "comfy-core",
"ver": "0.3.27",
"Node name for S&R": "ModelSamplingSD3"
},
"widgets_values": [
12
]
},
{
"id": 109,
"type": "BasicGuider",
@@ -480,7 +438,7 @@
"flags": {
"collapsed": true
},
"order": 15,
"order": 17,
"mode": 0,
"inputs": [
{
@@ -604,52 +562,6 @@
"color": "#223",
"bgcolor": "#335"
},
{
"id": 133,
"type": "WarpedTeaCache",
"pos": [
3.7849433422088623,
-360.75299072265625
],
"size": [
315,
106
],
"flags": {
"collapsed": true
},
"order": 17,
"mode": 0,
"inputs": [
{
"name": "model",
"type": "MODEL",
"link": 274
}
],
"outputs": [
{
"name": "model",
"type": "MODEL",
"slot_index": 0,
"links": [
262
]
}
],
"properties": {
"cnr_id": "ComfyUI-WarpedToolset",
"ver": "4e08ee52f8f8f1b9b672937866812d63bbb520eb",
"Node name for S&R": "WarpedTeaCache"
},
"widgets_values": [
"wan2.1_i2v_720p_14B",
0.4,
1
],
"color": "#323",
"bgcolor": "#535"
},
{
"id": 135,
"type": "WanVideoEnhanceAVideoKJ",
@@ -664,7 +576,7 @@
"flags": {
"collapsed": true
},
"order": 16,
"order": 18,
"mode": 0,
"inputs": [
{
@@ -683,9 +595,7 @@
"name": "model",
"type": "MODEL",
"slot_index": 0,
"links": [
274
]
"links": []
}
],
"properties": {
@@ -753,7 +663,7 @@
517.2369384765625
],
"flags": {},
"order": 12,
"order": 14,
"mode": 0,
"inputs": [
{
@@ -782,7 +692,7 @@
126
],
"flags": {},
"order": 11,
"order": 13,
"mode": 0,
"inputs": [
{
@@ -832,10 +742,10 @@
],
"size": [
315,
190
214
],
"flags": {},
"order": 7,
"order": 8,
"mode": 0,
"inputs": [
{
@@ -883,7 +793,8 @@
400,
720,
"lanczos",
"center"
"center",
false
],
"color": "#323",
"bgcolor": "#535"
@@ -902,7 +813,7 @@
"flags": {
"collapsed": true
},
"order": 13,
"order": 15,
"mode": 0,
"inputs": [
{
@@ -947,7 +858,7 @@
250
],
"flags": {},
"order": 14,
"order": 16,
"mode": 0,
"inputs": [
{
@@ -985,14 +896,6 @@
},
"link": 285
},
{
"name": "height",
"type": "INT",
"widget": {
"name": "height"
},
"link": null
},
{
"name": "height",
"type": "INT",
@@ -1068,7 +971,7 @@
"inputs": [],
"outputs": [
{
"name": "CLIP_VISION",
"name": "clip_vision",
"type": "CLIP_VISION",
"links": [
289
@@ -1078,8 +981,8 @@
"properties": {
"cnr_id": "ComfyUI-WarpedToolset",
"ver": "4e08ee52f8f8f1b9b672937866812d63bbb520eb",
"widget_ue_connectable": {},
"Node name for S&R": "WarpedCLIPVisionLoader"
"Node name for S&R": "WarpedCLIPVisionLoader",
"widget_ue_connectable": {}
},
"widgets_values": [
"clip_vision_h_fp8_e4m3fn.safetensors"
@@ -1106,25 +1009,118 @@
"inputs": [],
"outputs": [
{
"name": "VAE",
"name": "vae",
"type": "VAE",
"links": [
290,
291
]
},
{
"name": "vae_state_dict",
"type": "DICT",
"links": null
}
],
"properties": {
"cnr_id": "ComfyUI-WarpedToolset",
"ver": "4e08ee52f8f8f1b9b672937866812d63bbb520eb",
"widget_ue_connectable": {},
"Node name for S&R": "WarpedVAELoader"
"Node name for S&R": "WarpedVAELoader",
"widget_ue_connectable": {}
},
"widgets_values": [
"wan_2.1_vae_fp8_e4m3fn.safetensors"
],
"color": "#323",
"bgcolor": "#535"
},
{
"id": 140,
"type": "MagCache",
"pos": [
-13.26900863647461,
-361.42431640625
],
"size": [
270,
178
],
"flags": {
"collapsed": true
},
"order": 6,
"mode": 0,
"inputs": [
{
"name": "model",
"type": "MODEL",
"link": null
}
],
"outputs": [
{
"name": "model",
"type": "MODEL",
"links": [
296
]
}
],
"properties": {
"aux_id": "Zehong-Ma/ComfyUI-MagCache",
"ver": "7d4e982bf7955498afca891c7094c48a70985537",
"Node name for S&R": "MagCache"
},
"widgets_values": [
"wan2.1_i2v_720p_14B",
0.12000000000000002,
0.2,
3,
0,
-1
]
},
{
"id": 110,
"type": "ModelSamplingSD3",
"pos": [
189.9645538330078,
-359.64776611328125
],
"size": [
315,
58
],
"flags": {
"collapsed": true
},
"order": 9,
"mode": 0,
"inputs": [
{
"name": "model",
"type": "MODEL",
"link": 296
}
],
"outputs": [
{
"name": "MODEL",
"type": "MODEL",
"slot_index": 0,
"links": [
259
]
}
],
"properties": {
"cnr_id": "comfy-core",
"ver": "0.3.27",
"Node name for S&R": "ModelSamplingSD3"
},
"widgets_values": [
12
]
}
],
"links": [
@@ -1184,14 +1180,6 @@
0,
"MODEL"
],
[
262,
133,
0,
110,
0,
"MODEL"
],
[
263,
6,
@@ -1248,14 +1236,6 @@
0,
"MODEL"
],
[
274,
135,
0,
133,
0,
"MODEL"
],
[
275,
134,
@@ -1341,7 +1321,7 @@
137,
3,
134,
7,
6,
"INT"
],
[
@@ -1383,6 +1363,14 @@
134,
2,
"VAE"
],
[
296,
140,
0,
110,
0,
"MODEL"
]
],
"groups": [],
@@ -1391,10 +1379,11 @@
"ds": {
"scale": 0.7513148009015777,
"offset": [
158.34800000000385,
168.428999999993
159.67900000000375,
451.9319999999936
]
},
"frontendVersion": "1.20.5",
"node_versions": {
"comfyui-easy-use": "1.2.7",
"comfy-core": "0.3.26",
@@ -1405,7 +1394,7 @@
},
"ue_links": [],
"VHS_latentpreview": true,
"VHS_latentpreviewrate": 10,
"VHS_latentpreviewrate": 15,
"VHS_MetadataImage": true,
"VHS_KeepIntermediate": true
},
__init__.py
+24 -6
View File
@@ -6,27 +6,29 @@ NODE_CLASS_MAPPINGS = {
"WarpedHunyuanLoraMerge": WarpedHunyuanLoraMerge,
"WarpedHunyuanMultiLoraMixer": WarpedHunyuanMultiLoraMixer,
"WarpedHunyuanMultiLoraMixerExt": WarpedHunyuanMultiLoraMixerExt,
# "WarpedHunyuanMultiLoraMixerExt2": WarpedHunyuanMultiLoraMixerExt2,
"WarpedHunyuanLoraAvgMerge": WarpedHunyuanLoraAvgMerge,
"WarpedHunyuanLoraBatchMerge": WarpedHunyuanLoraBatchMerge,
"WarpedWanLoraMerge": WarpedWanLoraMerge,
"WarpedSamplerCustomAdv": WarpedSamplerCustomAdv,
"WarpedSamplerCustomAdvLatent": WarpedSamplerCustomAdvLatent,
# "WarpedAdvancedSamplerBatch": WarpedAdvancedSamplerBatch,
"WarpedFramepackSampler": WarpedFramepackSampler,
"WarpedCreateSpecialImageBatch": WarpedCreateSpecialImageBatch,
"WarpedCreateEmptyImageBatch": WarpedCreateEmptyImageBatch,
"WarpedCreateEmptyLatentBatch": WarpedCreateEmptyLatentBatch,
"WarpedBundleVideoImages": WarpedBundleVideoImages,
"WarpedBundleAllVideoImages": WarpedBundleAllVideoImages,
"WarpedGetImageFromVideo": WarpedGetImageFromVideo,
"WarpedGetTwoImagesFromVideo": WarpedGetTwoImagesFromVideo,
"WarpedImageNoiseAugmentation": WarpedImageNoiseAugmentation,
"WarpedLeapfusionHunyuanI2V": WarpedLeapfusionHunyuanI2V,
"WarpedSaveAnimatedPng": WarpedSaveAnimatedPng,
"WarpedUpscaleWithModel": WarpedUpscaleWithModel,
"WarpedLoadVideosBatch": WarpedLoadVideosBatch,
"WarpedWanImageToVideo": WarpedWanImageToVideo,
"WarpedTeaCache": WarpedTeaCache,
"WarpedWanLoadAndEditLoraBlocks": WarpedWanLoadAndEditLoraBlocks,
"WarpedImageResize": WarpedImageResize,
"WarpedImageResizeKJStyle": WarpedImageResizeKJStyle,
# "WarpedImageResize": WarpedImageResize,
"WarpedImageScaleToSide": WarpedImageScaleToSide,
"WarpedLoadLorasBatchByPrefix": WarpedLoadLorasBatchByPrefix,
"WarpedHunyuanVideoLoraLoader": WarpedHunyuanVideoLoraLoader,
@@ -36,11 +38,18 @@ NODE_CLASS_MAPPINGS = {
"WarpedHunyuanLoraConvert": WarpedHunyuanLoraConvert,
"WarpedFramepackLoraSelectBatch": WarpedFramepackLoraSelectBatch,
"WarpedFramepackMultiLoraSelect": WarpedFramepackMultiLoraSelect,
"WarpedFramepackMultiLoraSelectExt": WarpedFramepackMultiLoraSelectExt,
"WarpedDualCLIPLoader": WarpedDualCLIPLoader,
"WarpedDualEncoder": WarpedDualEncoder,
"WarpedCLIPVisionLoader": WarpedCLIPVisionLoader,
"WarpedVAELoader": WarpedVAELoader,
"WarpedLoadFramePackModel": WarpedLoadFramePackModel,
"WarpedNumericalConversion": WarpedNumericalConversion,
"WarpedLoraReSave": WarpedLoraReSave,
"WarpedCheckpointLoader": WarpedCheckpointLoader,
"WarpedReverseImageBatch": WarpedReverseImageBatch,
"WarpedMultiLoraLoader": WarpedMultiLoraLoader,
"WarpedHunyuanImageToVideo": WarpedHunyuanImageToVideo,
}
NODE_DISPLAY_NAME_MAPPINGS = {
@@ -49,27 +58,29 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"WarpedHunyuanLoraMerge": "Warped Hunyuan Lora Merge",
"WarpedHunyuanMultiLoraMixer": "Warped Hunyuan Multi Lora Mixer",
"WarpedHunyuanMultiLoraMixerExt": "Warped Hunyuan Multi Lora Mixer Ext",
# "WarpedHunyuanMultiLoraMixerExt2": "Warped Hunyuan Multi Lora Mixer Ext 2",
"WarpedHunyuanLoraAvgMerge": "Warped Hunyuan Lora Avg Merge",
"WarpedHunyuanLoraBatchMerge": "Warped Hunyuan Lora Batch Merge",
"WarpedWanLoraMerge": "Warped Wan Lora Merge",
"WarpedSamplerCustomAdv": "Warped Sampler Custom Advanced (Image)",
"WarpedSamplerCustomAdvLatent": "Warped Sampler Custom Advanced (Latent)",
# "WarpedAdvancedSamplerBatch": "Warped Advanced Sampler Batch",
"WarpedFramepackSampler": "Warped Framepack Sampler",
"WarpedCreateSpecialImageBatch": "Warped Create Special Image Batch",
"WarpedCreateEmptyImageBatch": "Warped Create Empty Image Batch",
"WarpedCreateEmptyLatentBatch": "Warped Create Empty Latent Batch",
"WarpedBundleVideoImages": "Warped Bundle Video Images",
"WarpedBundleAllVideoImages": "Warped Bundle All Video Images",
"WarpedGetImageFromVideo": "Warped Get Image From Video",
"WarpedGetTwoImagesFromVideo": "Warped Get Two Images From Video",
"WarpedImageNoiseAugmentation": "Warped Image Noise Augmentation",
"WarpedLeapfusionHunyuanI2V": "Warped Leapfusion Hunyuan I2V",
"WarpedSaveAnimatedPng": "Warped Save Animated Png",
"WarpedUpscaleWithModel": "Warped Upscale With Model",
"WarpedLoadVideosBatch": "Warped Load Videos Batch",
"WarpedWanImageToVideo": "Warped Wan Image To Video",
"WarpedTeaCache": "Warped TeaCache",
"WarpedWanLoadAndEditLoraBlocks": "Warped Wan Load And Edit Lora Blocks",
"WarpedImageResize": "Warped Image Resize",
"WarpedImageResizeKJStyle": "Warped Image Resize KJ Style",
# "WarpedImageResize": "Warped Image Resize",
"WarpedImageScaleToSide": "Warped Image Scale To Side",
"WarpedLoadLorasBatchByPrefix": "Warped Load Loras Batch By Prefix",
"WarpedHunyuanVideoLoraLoader": "Warped Hunyuan Video Lora Loader",
@@ -79,11 +90,18 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"WarpedHunyuanLoraConvert": "Warped Hunyuan Lora Convert",
"WarpedFramepackLoraSelectBatch": "Warped Framepack Lora Select Batch",
"WarpedFramepackMultiLoraSelect": "Warped Framepack Multi Lora Select",
"WarpedFramepackMultiLoraSelectExt": "Warped Framepack Multi Lora Select Ext",
"WarpedDualCLIPLoader": "Warped Dual CLIP Loader",
"WarpedDualEncoder": "Warped Dual Encoder",
"WarpedCLIPVisionLoader": "Warped Load CLIP Vision",
"WarpedVAELoader": "Warped Load VAE",
"WarpedLoadFramePackModel": "Warped Load FramePack Model",
"WarpedNumericalConversion": "Warped Numerical Conversion",
"WarpedLoraReSave": "Warped Lora Re-Save",
"WarpedCheckpointLoader": "Warped Checkpoint Loader",
"WarpedReverseImageBatch": "Warped Reverse Image Batch",
"WarpedMultiLoraLoader": "Warped Multi Lora Loader",
"WarpedHunyuanImageToVideo": "Warped Hunyuan Image To Video",
}
WEB_DIRECTORY = "./web"
diffusers_helper/k_diffusion_hunyuan.py
-1
View File
@@ -23,7 +23,6 @@ def get_flux_sigmas_from_mu(n, mu):
sigmas = flux_time_shift(sigmas, mu=mu)
return sigmas
@torch.inference_mode()
def sample_hunyuan2(
transformer,
diffusers_helper/models/hunyuan_video_packed.py
+205 -1
View File
@@ -774,6 +774,9 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin,
self.inner_dim = inner_dim
self.use_gradient_checkpointing = False
self.enable_teacache = False
self.enable_magcache = False
self.verbose_messaging = False
self.orig_forward = self.forward
if has_image_proj:
self.install_image_projection(image_proj_dim)
@@ -800,7 +803,7 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin,
self.use_gradient_checkpointing = False
print('self.use_gradient_checkpointing = False')
def initialize_teacache(self, enable_teacache=True, num_steps=25, rel_l1_thresh=0.15):
def initialize_teacache(self, enable_teacache=True, num_steps=25, rel_l1_thresh=0.15, verbose_messaging=False):
self.enable_teacache = enable_teacache
self.cnt = 0
self.num_steps = num_steps
@@ -809,6 +812,32 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin,
self.previous_modulated_input = None
self.previous_residual = None
self.teacache_rescale_func = np.poly1d([7.33226126e+02, -4.01131952e+02, 6.75869174e+01, -3.14987800e+00, 9.61237896e-02])
self.verbose_messaging = verbose_messaging
def nearest_interp(self, src_array, target_length):
src_length = len(src_array)
if target_length == 1:
return np.array([src_array[-1]])
scale = (src_length - 1) / (target_length - 1)
mapped_indices = np.round(np.arange(target_length) * scale).astype(int)
return src_array[mapped_indices]
def initialize_magcache(self, enable_magcache=True, num_steps=25, magcache_thresh=0.1, K=2, retention_ratio=0.2, verbose_messaging=False):
self.enable_magcache = enable_magcache
self.cnt = 0
self.num_steps = num_steps
self.magcache_thresh = magcache_thresh
self.K = K
self.retention_ratio = retention_ratio
self.mag_ratios = np.array([1.0]+[1.25781, 1.08594, 1.02344, 1.00781, 1.02344, 1.00781, 1.02344, 1.05469, 0.99609, 1.03906, 1.00781, 1.01562, 1.00781, 1.02344, 1.01562, 0.98047, 1.05469, 0.98047, 0.96875, 1.03125, 0.97266, 0.9375, 0.96484, 0.78516])
# Nearest interpolation when the num_steps is different from the length of mag_ratios
if len(self.mag_ratios) != num_steps:
interpolated_mag_ratios = self.nearest_interp(self.mag_ratios, num_steps)
self.mag_ratios = interpolated_mag_ratios
self.verbose_messaging = verbose_messaging
def gradient_checkpointing_method(self, block, *args):
if self.use_gradient_checkpointing:
@@ -876,6 +905,162 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin,
return hidden_states, rope_freqs
def magcache_framepack_forward(
self,
hidden_states, timestep, encoder_hidden_states, encoder_attention_mask, pooled_projections, guidance,
latent_indices=None,
clean_latents=None, clean_latent_indices=None,
clean_latents_2x=None, clean_latent_2x_indices=None,
clean_latents_4x=None, clean_latent_4x_indices=None,
image_embeddings=None,
attention_kwargs=None, return_dict=True
):
if self.verbose_messaging:
print("\nMagCache Forward Entry...")
print("self.enable_magcache: {}".format(self.enable_magcache))
print("self.cnt: {}".format(self.cnt))
print("self.accumulated_ratio: {}".format(self.accumulated_ratio))
print("self.accumulated_steps: {}".format(self.accumulated_steps))
print("self.accumulated_err: {}".format(self.accumulated_err))
if attention_kwargs is None:
attention_kwargs = {}
batch_size, num_channels, num_frames, height, width = hidden_states.shape
p, p_t = self.config['patch_size'], self.config['patch_size_t']
post_patch_num_frames = num_frames // p_t
post_patch_height = height // p
post_patch_width = width // p
original_context_length = post_patch_num_frames * post_patch_height * post_patch_width
hidden_states, rope_freqs = self.process_input_hidden_states(hidden_states, latent_indices, clean_latents, clean_latent_indices, clean_latents_2x, clean_latent_2x_indices, clean_latents_4x, clean_latent_4x_indices)
temb = self.gradient_checkpointing_method(self.time_text_embed, timestep, guidance, pooled_projections)
encoder_hidden_states = self.gradient_checkpointing_method(self.context_embedder, encoder_hidden_states, timestep, encoder_attention_mask)
if self.image_projection is not None:
assert image_embeddings is not None, 'You must use image embeddings!'
extra_encoder_hidden_states = self.gradient_checkpointing_method(self.image_projection, image_embeddings)
extra_attention_mask = torch.ones((batch_size, extra_encoder_hidden_states.shape[1]), dtype=encoder_attention_mask.dtype, device=encoder_attention_mask.device)
# must cat before (not after) encoder_hidden_states, due to attn masking
encoder_hidden_states = torch.cat([extra_encoder_hidden_states, encoder_hidden_states], dim=1)
encoder_attention_mask = torch.cat([extra_attention_mask, encoder_attention_mask], dim=1)
if batch_size == 1:
# When batch size is 1, we do not need any masks or var-len funcs since cropping is mathematically same to what we want
# If they are not same, then their impls are wrong. Ours are always the correct one.
text_len = encoder_attention_mask.sum().item()
encoder_hidden_states = encoder_hidden_states[:, :text_len]
attention_mask = None, None, None, None
else:
img_seq_len = hidden_states.shape[1]
txt_seq_len = encoder_hidden_states.shape[1]
cu_seqlens_q = get_cu_seqlens(encoder_attention_mask, img_seq_len)
cu_seqlens_kv = cu_seqlens_q
max_seqlen_q = img_seq_len + txt_seq_len
max_seqlen_kv = max_seqlen_q
attention_mask = cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv
if self.enable_magcache:
if self.cnt == 0: # initialize MagCache
self.accumulated_ratio = 1.0
self.accumulated_steps = 0
self.accumulated_err = 0
skip_forward = False
if self.cnt>=int(self.retention_ratio*self.num_steps) and self.cnt>=1: # keep first retention_ratio steps
cur_mag_ratio = self.mag_ratios[self.cnt]
self.accumulated_ratio = self.accumulated_ratio*cur_mag_ratio
cur_skip_err = np.abs(1-self.accumulated_ratio)
self.accumulated_err += cur_skip_err
self.accumulated_steps += 1
if self.accumulated_err<=self.magcache_thresh and self.accumulated_steps<=self.K and np.abs(1-cur_mag_ratio)<=0.06:
skip_forward = True
else:
self.accumulated_ratio = 1.0
self.accumulated_steps = 0
self.accumulated_err = 0
if skip_forward:
hidden_states = hidden_states + self.previous_residual
else:
ori_hidden_states = hidden_states.clone()
for block_id, block in enumerate(self.transformer_blocks):
hidden_states, encoder_hidden_states = self.gradient_checkpointing_method(
block,
hidden_states,
encoder_hidden_states,
temb,
attention_mask,
rope_freqs
)
for block_id, block in enumerate(self.single_transformer_blocks):
hidden_states, encoder_hidden_states = self.gradient_checkpointing_method(
block,
hidden_states,
encoder_hidden_states,
temb,
attention_mask,
rope_freqs
)
self.previous_residual = hidden_states - ori_hidden_states
self.cnt += 1
if self.cnt == self.num_steps:
self.cnt = 0
else:
for block_id, block in enumerate(self.transformer_blocks):
hidden_states, encoder_hidden_states = self.gradient_checkpointing_method(
block,
hidden_states,
encoder_hidden_states,
temb,
attention_mask,
rope_freqs
)
for block_id, block in enumerate(self.single_transformer_blocks):
hidden_states, encoder_hidden_states = self.gradient_checkpointing_method(
block,
hidden_states,
encoder_hidden_states,
temb,
attention_mask,
rope_freqs
)
hidden_states = self.gradient_checkpointing_method(self.norm_out, hidden_states, temb)
hidden_states = hidden_states[:, -original_context_length:, :]
if self.high_quality_fp32_output_for_inference:
hidden_states = hidden_states.to(dtype=torch.float32)
if self.proj_out.weight.dtype != torch.float32:
self.proj_out.to(dtype=torch.float32)
hidden_states = self.gradient_checkpointing_method(self.proj_out, hidden_states)
hidden_states = einops.rearrange(hidden_states, 'b (t h w) (c pt ph pw) -> b c (t pt) (h ph) (w pw)',
t=post_patch_num_frames, h=post_patch_height, w=post_patch_width,
pt=p_t, ph=p, pw=p)
if return_dict:
if self.verbose_messaging:
print("MagCache Forward Exit.")
return Transformer2DModelOutput(sample=hidden_states)
if self.verbose_messaging:
print("MagCache Forward Exit.")
return hidden_states,
def forward(
self,
hidden_states, timestep, encoder_hidden_states, encoder_attention_mask, pooled_projections, guidance,
@@ -1014,3 +1199,22 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin,
return Transformer2DModelOutput(sample=hidden_states)
return hidden_states,
# def forward(
# self,
# hidden_states, timestep, encoder_hidden_states, encoder_attention_mask, pooled_projections, guidance,
# latent_indices=None,
# clean_latents=None, clean_latent_indices=None,
# clean_latents_2x=None, clean_latent_2x_indices=None,
# clean_latents_4x=None, clean_latent_4x_indices=None,
# image_embeddings=None,
# attention_kwargs=None, return_dict=True
# ):
# if not self.enable_magcache:
# return self.orig_forward(hidden_states, timestep, encoder_hidden_states, encoder_attention_mask, pooled_projections, guidance, latent_indices=latent_indices, clean_latents=clean_latents, clean_latent_indices=clean_latent_indices,
# clean_latents_2x=clean_latents_2x, clean_latent_2x_indices=clean_latent_2x_indices, clean_latents_4x=clean_latents_4x, clean_latent_4x_indices=clean_latents_4x, image_embeddings=image_embeddings,
# attention_kwargs=attention_kwargs, return_dict=return_dict)
#
# return self.magcache_framepack_forward(hidden_states, timestep, encoder_hidden_states, encoder_attention_mask, pooled_projections, guidance, latent_indices=latent_indices, clean_latents=clean_latents, clean_latent_indices=clean_latent_indices,
# clean_latents_2x=clean_latents_2x, clean_latent_2x_indices=clean_latent_2x_indices, clean_latents_4x=clean_latents_4x, clean_latent_4x_indices=clean_latents_4x, image_embeddings=image_embeddings,
# attention_kwargs=attention_kwargs, return_dict=return_dict)
nodes.py
+2314 -1850
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File diff suppressed because it is too large Load Diff
pyproject.toml
+1 -1
View File
@@ -1,7 +1,7 @@
[project]
name = "ComfyUI-WarpedToolset"
description = "A toolset for Hunyuan Video (mainly), with some additional nodes applicable to Framepack Video and WAN Video."
version = "1.6.3"
version = "1.7.0"
license = { file = "LICENSE" }
[project.urls]
requirements.txt
+1
View File
@@ -1,3 +1,4 @@
accelerate
safetensors
peft
einops
your_metadata.json
+4
View File
@@ -0,0 +1,4 @@
{
"creator": "Warped Animation",
"website": "https://subscribestar.adult/warped-animation"
}