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ComfyUI-CogVideoXWrapper/nodes.py
kijai a5908e556f Update nodes.py
2024-09-19 17:50:14 +03:00

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import os
import torch
import folder_paths
import comfy.model_management as mm
from comfy.utils import ProgressBar, load_torch_file
from diffusers.schedulers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler, DDIMScheduler, PNDMScheduler, DPMSolverMultistepScheduler, EulerDiscreteScheduler, EulerAncestralDiscreteScheduler
from diffusers.models import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel
from .pipeline_cogvideox import CogVideoXPipeline
from contextlib import nullcontext
from .cogvideox_fun.transformer_3d import CogVideoXTransformer3DModel as CogVideoXTransformer3DModelFun
from .cogvideox_fun.autoencoder_magvit import AutoencoderKLCogVideoX as AutoencoderKLCogVideoXFun
from .cogvideox_fun.utils import get_image_to_video_latent, get_video_to_video_latent, ASPECT_RATIO_512, get_closest_ratio, to_pil
from .cogvideox_fun.pipeline_cogvideox_inpaint import CogVideoX_Fun_Pipeline_Inpaint
from PIL import Image
import numpy as np
import logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
log = logging.getLogger(__name__)
class DownloadAndLoadCogVideoModel:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": (
[
"THUDM/CogVideoX-2b",
"THUDM/CogVideoX-5b",
"THUDM/CogVideoX-5b-I2V",
"bertjiazheng/KoolCogVideoX-5b",
"kijai/CogVideoX-Fun-2b",
"kijai/CogVideoX-Fun-5b",
],
),
},
"optional": {
"precision": (["fp16", "fp32", "bf16"],
{"default": "bf16", "tooltip": "official recommendation is that 2b model should be fp16, 5b model should be bf16"}
),
"fp8_transformer": (['disabled', 'enabled', 'fastmode'], {"default": 'disabled', "tooltip": "enabled casts the transformer to torch.float8_e4m3fn, fastmode is only for latest nvidia GPUs"}),
"compile": (["disabled","onediff","torch"], {"tooltip": "compile the model for faster inference, these are advanced options only available on Linux, see readme for more info"}),
"enable_sequential_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "significantly reducing memory usage and slows down the inference"}),
}
}
RETURN_TYPES = ("COGVIDEOPIPE",)
RETURN_NAMES = ("cogvideo_pipe", )
FUNCTION = "loadmodel"
CATEGORY = "CogVideoWrapper"
def loadmodel(self, model, precision, fp8_transformer="disabled", compile="disabled", enable_sequential_cpu_offload=False):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
mm.soft_empty_cache()
dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
if "Fun" in model:
repo_id = "kijai/CogVideoX-Fun-pruned"
download_path = os.path.join(folder_paths.models_dir, "CogVideo")
if "2b" in model:
base_path = os.path.join(folder_paths.models_dir, "CogVideoX_Fun", "CogVideoX-Fun-2b-InP") # location of the official model
if not os.path.exists(base_path):
base_path = os.path.join(download_path, "CogVideoX-Fun-2b-InP")
elif "5b" in model:
base_path = os.path.join(folder_paths.models_dir, "CogVideoX_Fun", "CogVideoX-Fun-5b-InP") # location of the official model
if not os.path.exists(base_path):
base_path = os.path.join(download_path, "CogVideoX-Fun-5b-InP")
elif "2b" in model:
base_path = os.path.join(folder_paths.models_dir, "CogVideo", "CogVideo2B")
download_path = base_path
repo_id = model
elif "5b" in model:
base_path = os.path.join(folder_paths.models_dir, "CogVideo", (model.split("/")[-1]))
download_path = base_path
repo_id = model
if not os.path.exists(base_path):
log.info(f"Downloading model to: {base_path}")
from huggingface_hub import snapshot_download
snapshot_download(
repo_id=repo_id,
ignore_patterns=["*text_encoder*", "*tokenizer*"],
local_dir=download_path,
local_dir_use_symlinks=False,
)
if "Fun" in model:
transformer = CogVideoXTransformer3DModelFun.from_pretrained(base_path, subfolder="transformer")
else:
transformer = CogVideoXTransformer3DModel.from_pretrained(base_path, subfolder="transformer")
transformer = transformer.to(dtype).to(offload_device)
if fp8_transformer == "enabled" or fp8_transformer == "fastmode":
if "2b" in model:
for name, param in transformer.named_parameters():
if name != "pos_embedding":
param.data = param.data.to(torch.float8_e4m3fn)
elif "I2V" in model:
for name, param in transformer.named_parameters():
if "patch_embed" not in name:
param.data = param.data.to(torch.float8_e4m3fn)
else:
transformer.to(torch.float8_e4m3fn)
if fp8_transformer == "fastmode":
from .fp8_optimization import convert_fp8_linear
convert_fp8_linear(transformer, dtype)
scheduler = CogVideoXDDIMScheduler.from_pretrained(base_path, subfolder="scheduler")
if "Fun" in model:
vae = AutoencoderKLCogVideoXFun.from_pretrained(base_path, subfolder="vae").to(dtype).to(offload_device)
pipe = CogVideoX_Fun_Pipeline_Inpaint(vae, transformer, scheduler)
else:
vae = AutoencoderKLCogVideoX.from_pretrained(base_path, subfolder="vae").to(dtype).to(offload_device)
pipe = CogVideoXPipeline(vae, transformer, scheduler)
if enable_sequential_cpu_offload:
pipe.enable_sequential_cpu_offload()
if compile == "torch":
torch._dynamo.config.suppress_errors = True
pipe.transformer.to(memory_format=torch.channels_last)
pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune", fullgraph=True)
elif compile == "onediff":
from onediffx import compile_pipe
os.environ['NEXFORT_FX_FORCE_TRITON_SDPA'] = '1'
pipe = compile_pipe(
pipe,
backend="nexfort",
options= {"mode": "max-optimize:max-autotune:max-autotune", "memory_format": "channels_last", "options": {"inductor.optimize_linear_epilogue": False, "triton.fuse_attention_allow_fp16_reduction": False}},
ignores=["vae"],
fuse_qkv_projections=True,
)
pipeline = {
"pipe": pipe,
"dtype": dtype,
"base_path": base_path,
"onediff": True if compile == "onediff" else False,
"cpu_offloading": enable_sequential_cpu_offload
}
return (pipeline,)
class CogVideoEncodePrompt:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"pipeline": ("COGVIDEOPIPE",),
"prompt": ("STRING", {"default": "", "multiline": True} ),
"negative_prompt": ("STRING", {"default": "", "multiline": True} ),
}
}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
RETURN_NAMES = ("positive", "negative")
FUNCTION = "process"
CATEGORY = "CogVideoWrapper"
def process(self, pipeline, prompt, negative_prompt):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
pipe = pipeline["pipe"]
dtype = pipeline["dtype"]
pipe.text_encoder.to(device)
pipe.transformer.to(offload_device)
positive, negative = pipe.encode_prompt(
prompt=prompt,
negative_prompt=negative_prompt,
do_classifier_free_guidance=True,
num_videos_per_prompt=1,
max_sequence_length=226,
device=device,
dtype=dtype,
)
pipe.text_encoder.to(offload_device)
return (positive, negative)
# Inject clip_l and t5xxl w/ individual strength adjustments for ComfyUI's DualCLIPLoader node for CogVideoX. Use CLIPSave node from any SDXL model then load in a custom clip_l model.
# For some reason seems to give a lot more movement and consistency on new CogVideoXFun img2vid? set 'type' to flux / DualClipLoader.
class CogVideoDualTextEncode_311:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"clip": ("CLIP",),
"clip_l": ("STRING", {"default": "", "multiline": True}),
"t5xxl": ("STRING", {"default": "", "multiline": True}),
"clip_l_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), # excessive max for testing, have found intesting results up to 20 max?
"t5xxl_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), # setting this to 0.0001 or level as high as 18 seems to work.
}
}
RETURN_TYPES = ("CONDITIONING",)
RETURN_NAMES = ("conditioning",)
FUNCTION = "process"
CATEGORY = "CogVideoWrapper"
def process(self, clip, clip_l, t5xxl, clip_l_strength, t5xxl_strength):
load_device = mm.text_encoder_device()
offload_device = mm.text_encoder_offload_device()
# setup tokenizer for clip_l and t5xxl
clip.tokenizer.t5xxl.pad_to_max_length = True
clip.tokenizer.t5xxl.max_length = 226
clip.cond_stage_model.to(load_device)
# tokenize clip_l and t5xxl
tokens_l = clip.tokenize(clip_l, return_word_ids=True)
tokens_t5 = clip.tokenize(t5xxl, return_word_ids=True)
# encode the tokens separately
embeds_l = clip.encode_from_tokens(tokens_l, return_pooled=False, return_dict=False)
embeds_t5 = clip.encode_from_tokens(tokens_t5, return_pooled=False, return_dict=False)
# apply strength adjustments to each embedding
if embeds_l.dim() == 3:
embeds_l *= clip_l_strength
if embeds_t5.dim() == 3:
embeds_t5 *= t5xxl_strength
# combine the embeddings by summing them
combined_embeds = embeds_l + embeds_t5
# offload the model to save memory
clip.cond_stage_model.to(offload_device)
return (combined_embeds,)
class CogVideoTextEncode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"clip": ("CLIP",),
"prompt": ("STRING", {"default": "", "multiline": True} ),
},
"optional": {
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"force_offload": ("BOOLEAN", {"default": True}),
}
}
RETURN_TYPES = ("CONDITIONING",)
RETURN_NAMES = ("conditioning",)
FUNCTION = "process"
CATEGORY = "CogVideoWrapper"
def process(self, clip, prompt, strength=1.0, force_offload=True):
load_device = mm.text_encoder_device()
offload_device = mm.text_encoder_offload_device()
clip.tokenizer.t5xxl.pad_to_max_length = True
clip.tokenizer.t5xxl.max_length = 226
clip.cond_stage_model.to(load_device)
tokens = clip.tokenize(prompt, return_word_ids=True)
embeds = clip.encode_from_tokens(tokens, return_pooled=False, return_dict=False)
embeds *= strength
if force_offload:
clip.cond_stage_model.to(offload_device)
return (embeds, )
class CogVideoTextEncodeCombine:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"conditioning_1": ("CONDITIONING",),
"conditioning_2": ("CONDITIONING",),
"combination_mode": (["average", "weighted_average", "concatenate"], {"default": "weighted_average"}),
"weighted_average_ratio": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step": 0.01}),
},
}
RETURN_TYPES = ("CONDITIONING",)
RETURN_NAMES = ("conditioning",)
FUNCTION = "process"
CATEGORY = "CogVideoWrapper"
def process(self, conditioning_1, conditioning_2, combination_mode, weighted_average_ratio):
if conditioning_1.shape != conditioning_2.shape:
raise ValueError("conditioning_1 and conditioning_2 must have the same shape")
if combination_mode == "average":
embeds = (conditioning_1 + conditioning_2) / 2
elif combination_mode == "weighted_average":
embeds = conditioning_1 * (1 - weighted_average_ratio) + conditioning_2 * weighted_average_ratio
elif combination_mode == "concatenate":
embeds = torch.cat((conditioning_1, conditioning_2), dim=-2)
else:
raise ValueError("Invalid combination mode")
return (embeds, )
class CogVideoImageEncode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"pipeline": ("COGVIDEOPIPE",),
"image": ("IMAGE", ),
},
"optional": {
"chunk_size": ("INT", {"default": 16, "min": 1}),
"enable_vae_slicing": ("BOOLEAN", {"default": True, "tooltip": "VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes."}),
"mask": ("MASK", ),
},
}
RETURN_TYPES = ("LATENT",)
RETURN_NAMES = ("samples",)
FUNCTION = "encode"
CATEGORY = "CogVideoWrapper"
def encode(self, pipeline, image, chunk_size=8, enable_vae_slicing=True, mask=None):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
generator = torch.Generator(device=device).manual_seed(0)
B, H, W, C = image.shape
vae = pipeline["pipe"].vae
if enable_vae_slicing:
vae.enable_slicing()
else:
vae.disable_slicing()
if not pipeline["cpu_offloading"]:
vae.to(device)
input_image = image.clone()
if mask is not None:
pipeline["pipe"].original_mask = mask
# print(mask.shape)
# mask = mask.repeat(B, 1, 1) # Shape: [B, H, W]
# mask = mask.unsqueeze(-1).repeat(1, 1, 1, C)
# print(mask.shape)
# input_image = input_image * (1 -mask)
else:
pipeline["pipe"].original_mask = None
input_image = input_image * 2.0 - 1.0
input_image = input_image.to(vae.dtype).to(device)
input_image = input_image.unsqueeze(0).permute(0, 4, 1, 2, 3) # B, C, T, H, W
B, C, T, H, W = input_image.shape
latents_list = []
# Loop through the temporal dimension in chunks of 16
for i in range(0, T, chunk_size):
# Get the chunk of 16 frames (or remaining frames if less than 16 are left)
end_index = min(i + chunk_size, T)
image_chunk = input_image[:, :, i:end_index, :, :] # Shape: [B, C, chunk_size, H, W]
# Encode the chunk of images
latents = vae.encode(image_chunk)
sample_mode = "sample"
if hasattr(latents, "latent_dist") and sample_mode == "sample":
latents = latents.latent_dist.sample(generator)
elif hasattr(latents, "latent_dist") and sample_mode == "argmax":
latents = latents.latent_dist.mode()
elif hasattr(latents, "latents"):
latents = latents.latents
latents = vae.config.scaling_factor * latents
latents = latents.permute(0, 2, 1, 3, 4) # B, T_chunk, C, H, W
latents_list.append(latents)
# Concatenate all the chunks along the temporal dimension
final_latents = torch.cat(latents_list, dim=1)
print("final latents: ", final_latents.shape)
if not pipeline["cpu_offloading"]:
vae.to(offload_device)
return ({"samples": final_latents}, )
class CogVideoSampler:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"pipeline": ("COGVIDEOPIPE",),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"height": ("INT", {"default": 480, "min": 128, "max": 2048, "step": 8}),
"width": ("INT", {"default": 720, "min": 128, "max": 2048, "step": 8}),
"num_frames": ("INT", {"default": 49, "min": 16, "max": 1024, "step": 1}),
"steps": ("INT", {"default": 50, "min": 1}),
"cfg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 30.0, "step": 0.01}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"scheduler": (["DDIM", "DPM", "DDIM_tiled"], {"tooltip": "5B likes DPM, but it doesn't support temporal tiling"}),
"t_tile_length": ("INT", {"default": 16, "min": 2, "max": 128, "step": 1, "tooltip": "Length of temporal tiling, use same alue as num_frames to disable, disabled automatically for DPM"}),
"t_tile_overlap": ("INT", {"default": 8, "min": 2, "max": 128, "step": 1, "tooltip": "Overlap of temporal tiling"}),
},
"optional": {
"samples": ("LATENT", ),
"denoise_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"image_cond_latents": ("LATENT", ),
}
}
RETURN_TYPES = ("COGVIDEOPIPE", "LATENT",)
RETURN_NAMES = ("cogvideo_pipe", "samples",)
FUNCTION = "process"
CATEGORY = "CogVideoWrapper"
def process(self, pipeline, positive, negative, steps, cfg, seed, height, width, num_frames, scheduler, t_tile_length, t_tile_overlap, samples=None,
denoise_strength=1.0, image_cond_latents=None):
mm.soft_empty_cache()
base_path = pipeline["base_path"]
assert "Fun" not in base_path, "'Fun' models not supported in 'CogVideoSampler', use the 'CogVideoXFunSampler'"
assert t_tile_length > t_tile_overlap, "t_tile_length must be greater than t_tile_overlap"
assert t_tile_length <= num_frames, "t_tile_length must be equal or less than num_frames"
t_tile_length = t_tile_length // 4
t_tile_overlap = t_tile_overlap // 4
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
pipe = pipeline["pipe"]
dtype = pipeline["dtype"]
if not pipeline["cpu_offloading"]:
pipe.transformer.to(device)
generator = torch.Generator(device=device).manual_seed(seed)
if scheduler == "DDIM" or scheduler == "DDIM_tiled":
pipe.scheduler = CogVideoXDDIMScheduler.from_pretrained(base_path, subfolder="scheduler")
elif scheduler == "DPM":
pipe.scheduler = CogVideoXDPMScheduler.from_pretrained(base_path, subfolder="scheduler")
if negative.shape[1] < positive.shape[1]:
target_length = positive.shape[1]
padding = torch.zeros((negative.shape[0], target_length - negative.shape[1], negative.shape[2]), device=negative.device)
negative = torch.cat((negative, padding), dim=1)
autocastcondition = not pipeline["onediff"]
autocast_context = torch.autocast(mm.get_autocast_device(device)) if autocastcondition else nullcontext()
with autocast_context:
latents = pipeline["pipe"](
num_inference_steps=steps,
height = height,
width = width,
num_frames = num_frames,
t_tile_length = t_tile_length,
t_tile_overlap = t_tile_overlap,
guidance_scale=cfg,
latents=samples["samples"] if samples is not None else None,
image_cond_latents=image_cond_latents["samples"] if image_cond_latents is not None else None,
denoise_strength=denoise_strength,
prompt_embeds=positive.to(dtype).to(device),
negative_prompt_embeds=negative.to(dtype).to(device),
generator=generator,
device=device,
scheduler_name=scheduler
)
if not pipeline["cpu_offloading"]:
pipe.transformer.to(offload_device)
mm.soft_empty_cache()
print(latents.shape)
return (pipeline, {"samples": latents})
class CogVideoDecode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"pipeline": ("COGVIDEOPIPE",),
"samples": ("LATENT", ),
"enable_vae_tiling": ("BOOLEAN", {"default": False, "tooltip": "Drastically reduces memory use but may introduce seams"}),
},
"optional": {
"tile_sample_min_height": ("INT", {"default": 96, "min": 16, "max": 2048, "step": 8}),
"tile_sample_min_width": ("INT", {"default": 96, "min": 16, "max": 2048, "step": 8}),
"tile_overlap_factor_height": ("FLOAT", {"default": 0.083, "min": 0.0, "max": 1.0, "step": 0.001}),
"tile_overlap_factor_width": ("FLOAT", {"default": 0.083, "min": 0.0, "max": 1.0, "step": 0.001}),
"enable_vae_slicing": ("BOOLEAN", {"default": True, "tooltip": "VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes."}),
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("images",)
FUNCTION = "decode"
CATEGORY = "CogVideoWrapper"
def decode(self, pipeline, samples, enable_vae_tiling, tile_sample_min_height, tile_sample_min_width, tile_overlap_factor_height, tile_overlap_factor_width, enable_vae_slicing=True):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
latents = samples["samples"]
vae = pipeline["pipe"].vae
if enable_vae_slicing:
vae.enable_slicing()
else:
vae.disable_slicing()
if not pipeline["cpu_offloading"]:
vae.to(device)
if enable_vae_tiling:
vae.enable_tiling(
tile_sample_min_height=tile_sample_min_height,
tile_sample_min_width=tile_sample_min_width,
tile_overlap_factor_height=tile_overlap_factor_height,
tile_overlap_factor_width=tile_overlap_factor_width,
)
else:
vae.disable_tiling()
latents = latents.to(vae.dtype)
latents = latents.permute(0, 2, 1, 3, 4) # [batch_size, num_channels, num_frames, height, width]
latents = 1 / vae.config.scaling_factor * latents
frames = vae.decode(latents).sample
if not pipeline["cpu_offloading"]:
vae.to(offload_device)
mm.soft_empty_cache()
video = pipeline["pipe"].video_processor.postprocess_video(video=frames, output_type="pt")
video = video[0].permute(0, 2, 3, 1).cpu().float()
print(video.min(), video.max())
return (video,)
class CogVideoXFunSampler:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"pipeline": ("COGVIDEOPIPE",),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"video_length": ("INT", {"default": 49, "min": 5, "max": 49, "step": 4}),
"base_resolution": (
[
256,
320,
384,
448,
512,
768,
960,
1024,
], {"default": 768}
),
"seed": ("INT", {"default": 43, "min": 0, "max": 0xffffffffffffffff}),
"steps": ("INT", {"default": 50, "min": 1, "max": 200, "step": 1}),
"cfg": ("FLOAT", {"default": 6.0, "min": 1.0, "max": 20.0, "step": 0.01}),
"scheduler": (
[
"Euler",
"Euler A",
"DPM++",
"PNDM",
"DDIM",
"CogVideoXDDIM",
"CogVideoXDPMScheduler",
],
{
"default": 'DDIM'
}
)
},
"optional":{
"start_img": ("IMAGE",),
"end_img": ("IMAGE",),
"opt_empty_latent": ("LATENT",),
},
}
RETURN_TYPES = ("COGVIDEOPIPE", "LATENT",)
RETURN_NAMES = ("cogvideo_pipe", "samples",)
FUNCTION = "process"
CATEGORY = "CogVideoWrapper"
def process(self, pipeline, positive, negative, video_length, base_resolution, seed, steps, cfg, scheduler,
start_img=None, end_img=None, opt_empty_latent=None):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
pipe = pipeline["pipe"]
dtype = pipeline["dtype"]
base_path = pipeline["base_path"]
assert "Fun" in base_path, "'Unfun' models not supported in 'CogVideoXFunSampler', use the 'CogVideoSampler'"
pipe.enable_model_cpu_offload(device=device)
mm.soft_empty_cache()
aspect_ratio_sample_size = {key : [x / 512 * base_resolution for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()}
if start_img is not None:
start_img = [to_pil(_start_img) for _start_img in start_img] if start_img is not None else None
end_img = [to_pil(_end_img) for _end_img in end_img] if end_img is not None else None
# Count most suitable height and width
original_width, original_height = start_img[0].size if type(start_img) is list else Image.open(start_img).size
else:
original_width = opt_empty_latent["samples"][0].shape[-1] * 8
original_height = opt_empty_latent["samples"][0].shape[-2] * 8
closest_size, closest_ratio = get_closest_ratio(original_height, original_width, ratios=aspect_ratio_sample_size)
height, width = [int(x / 16) * 16 for x in closest_size]
print(f"Closest size: {width}x{height}")
# Load Sampler
if scheduler == "DPM++":
noise_scheduler = DPMSolverMultistepScheduler.from_pretrained(base_path, subfolder= 'scheduler')
elif scheduler == "Euler":
noise_scheduler = EulerDiscreteScheduler.from_pretrained(base_path, subfolder= 'scheduler')
elif scheduler == "Euler A":
noise_scheduler = EulerAncestralDiscreteScheduler.from_pretrained(base_path, subfolder= 'scheduler')
elif scheduler == "PNDM":
noise_scheduler = PNDMScheduler.from_pretrained(base_path, subfolder= 'scheduler')
elif scheduler == "DDIM":
noise_scheduler = DDIMScheduler.from_pretrained(base_path, subfolder= 'scheduler')
elif scheduler == "CogVideoXDDIM":
noise_scheduler = CogVideoXDDIMScheduler.from_pretrained(base_path, subfolder= 'scheduler')
elif scheduler == "CogVideoXDPMScheduler":
noise_scheduler = CogVideoXDPMScheduler.from_pretrained(base_path, subfolder= 'scheduler')
pipe.scheduler = noise_scheduler
#if not pipeline["cpu_offloading"]:
# pipe.transformer.to(device)
generator= torch.Generator(device=device).manual_seed(seed)
autocastcondition = not pipeline["onediff"]
autocast_context = torch.autocast(mm.get_autocast_device(device)) if autocastcondition else nullcontext()
with autocast_context:
video_length = int((video_length - 1) // pipe.vae.config.temporal_compression_ratio * pipe.vae.config.temporal_compression_ratio) + 1 if video_length != 1 else 1
input_video, input_video_mask, clip_image = get_image_to_video_latent(start_img, end_img, video_length=video_length, sample_size=(height, width))
latents = pipe(
prompt_embeds=positive.to(dtype).to(device),
negative_prompt_embeds=negative.to(dtype).to(device),
num_frames = video_length,
height = height,
width = width,
generator = generator,
guidance_scale = cfg,
num_inference_steps = steps,
video = input_video,
mask_video = input_video_mask,
comfyui_progressbar = True,
)
#if not pipeline["cpu_offloading"]:
# pipe.transformer.to(offload_device)
mm.soft_empty_cache()
print(latents.shape)
return (pipeline, {"samples": latents})
class CogVideoXFunVid2VidSampler:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"pipeline": ("COGVIDEOPIPE",),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"video_length": ("INT", {"default": 49, "min": 5, "max": 49, "step": 4}),
"base_resolution": (
[
256,
320,
384,
448,
512,
768,
960,
1024,
], {"default": 768}
),
"seed": ("INT", {"default": 43, "min": 0, "max": 0xffffffffffffffff}),
"steps": ("INT", {"default": 50, "min": 1, "max": 200, "step": 1}),
"cfg": ("FLOAT", {"default": 6.0, "min": 1.0, "max": 20.0, "step": 0.01}),
"scheduler": (
[
"Euler",
"Euler A",
"DPM++",
"PNDM",
"DDIM",
"CogVideoXDDIM",
"CogVideoXDPMScheduler",
],
{
"default": 'DDIM'
}
),
"denoise_strength": ("FLOAT", {"default": 0.70, "min": 0.05, "max": 1.00, "step": 0.01}),
"validation_video": ("IMAGE",),
}
}
RETURN_TYPES = ("COGVIDEOPIPE", "LATENT",)
RETURN_NAMES = ("cogvideo_pipe", "samples",)
FUNCTION = "process"
CATEGORY = "CogVideoWrapper"
def process(self, pipeline, positive, negative, video_length, base_resolution, seed, steps, cfg, denoise_strength, scheduler, validation_video):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
pipe = pipeline["pipe"]
dtype = pipeline["dtype"]
pipe.enable_model_cpu_offload(device=device)
mm.soft_empty_cache()
# Count most suitable height and width
aspect_ratio_sample_size = {key : [x / 512 * base_resolution for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()}
validation_video = np.array(validation_video.cpu().numpy() * 255, np.uint8)
original_width, original_height = Image.fromarray(validation_video[0]).size
closest_size, closest_ratio = get_closest_ratio(original_height, original_width, ratios=aspect_ratio_sample_size)
height, width = [int(x / 16) * 16 for x in closest_size]
base_path = pipeline["base_path"]
# Load Sampler
if scheduler == "DPM++":
noise_scheduler = DPMSolverMultistepScheduler.from_pretrained(base_path, subfolder= 'scheduler')
elif scheduler == "Euler":
noise_scheduler = EulerDiscreteScheduler.from_pretrained(base_path, subfolder= 'scheduler')
elif scheduler == "Euler A":
noise_scheduler = EulerAncestralDiscreteScheduler.from_pretrained(base_path, subfolder= 'scheduler')
elif scheduler == "PNDM":
noise_scheduler = PNDMScheduler.from_pretrained(base_path, subfolder= 'scheduler')
elif scheduler == "DDIM":
noise_scheduler = DDIMScheduler.from_pretrained(base_path, subfolder= 'scheduler')
elif scheduler == "CogVideoXDDIM":
noise_scheduler = CogVideoXDDIMScheduler.from_pretrained(base_path, subfolder= 'scheduler')
elif scheduler == "CogVideoXDPMScheduler":
noise_scheduler = CogVideoXDPMScheduler.from_pretrained(base_path, subfolder= 'scheduler')
pipe.scheduler = noise_scheduler
generator= torch.Generator(device).manual_seed(seed)
autocastcondition = not pipeline["onediff"]
autocast_context = torch.autocast(mm.get_autocast_device(device)) if autocastcondition else nullcontext()
with autocast_context:
video_length = int((video_length - 1) // pipe.vae.config.temporal_compression_ratio * pipe.vae.config.temporal_compression_ratio) + 1 if video_length != 1 else 1
input_video, input_video_mask, clip_image = get_video_to_video_latent(validation_video, video_length=video_length, sample_size=(height, width))
# for _lora_path, _lora_weight in zip(cogvideoxfun_model.get("loras", []), cogvideoxfun_model.get("strength_model", [])):
# pipeline = merge_lora(pipeline, _lora_path, _lora_weight)
latents = pipe(
prompt_embeds=positive.to(dtype).to(device),
negative_prompt_embeds=negative.to(dtype).to(device),
num_frames = video_length,
height = height,
width = width,
generator = generator,
guidance_scale = cfg,
num_inference_steps = steps,
video = input_video,
mask_video = input_video_mask,
strength = float(denoise_strength),
comfyui_progressbar = True,
)
# for _lora_path, _lora_weight in zip(cogvideoxfun_model.get("loras", []), cogvideoxfun_model.get("strength_model", [])):
# pipeline = unmerge_lora(pipeline, _lora_path, _lora_weight)
return (pipeline, {"samples": latents})
NODE_CLASS_MAPPINGS = {
"DownloadAndLoadCogVideoModel": DownloadAndLoadCogVideoModel,
"CogVideoSampler": CogVideoSampler,
"CogVideoDecode": CogVideoDecode,
"CogVideoTextEncode": CogVideoTextEncode,
"CogVideoDualTextEncode_311": CogVideoDualTextEncode_311,
"CogVideoImageEncode": CogVideoImageEncode,
"CogVideoXFunSampler": CogVideoXFunSampler,
"CogVideoXFunVid2VidSampler": CogVideoXFunVid2VidSampler,
"CogVideoTextEncodeCombine": CogVideoTextEncodeCombine
}
NODE_DISPLAY_NAME_MAPPINGS = {
"DownloadAndLoadCogVideoModel": "(Down)load CogVideo Model",
"CogVideoSampler": "CogVideo Sampler",
"CogVideoDecode": "CogVideo Decode",
"CogVideoTextEncode": "CogVideo TextEncode",
"CogVideoDualTextEncode_311": "CogVideo DualTextEncode",
"CogVideoImageEncode": "CogVideo ImageEncode",
"CogVideoXFunSampler": "CogVideoXFun Sampler",
"CogVideoXFunVid2VidSampler": "CogVideoXFun Vid2Vid Sampler",
"CogVideoTextEncodeCombine": "CogVideo TextEncode Combine"
}
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