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import torch
import gc
from diffusers.utils.torch_utils import randn_tensor
import comfy.model_management as mm
from ..utils.rope_utils import get_rotary_pos_embed
from ..utils.latent_preview import prepare_callback
class HyVideoFlowEditSamplerNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("HYVIDEOMODEL",),
"source_embeds": ("HYVIDEMBEDS", ),
"target_embeds": ("HYVIDEMBEDS", ),
"samples": ("LATENT", {"tooltip": "init Latents to use for video2video process"} ),
"steps": ("INT", {"default": 30, "min": 1}),
"skip_steps": ("INT", {"default": 4, "min": 0}),
"drift_steps": ("INT", {"default": 0, "min": 0}),
"source_guidance_scale": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 30.0, "step": 0.01}),
"target_guidance_scale": ("FLOAT", {"default": 12.0, "min": 0.0, "max": 30.0, "step": 0.01}),
"flow_shift": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 30.0, "step": 0.01}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"force_offload": ("BOOLEAN", {"default": True}),
},
}
RETURN_TYPES = ("LATENT",)
RETURN_NAMES = ("samples",)
FUNCTION = "process"
CATEGORY = "hunyuanloom"
def process(self,
model,
source_embeds,
target_embeds,
flow_shift,
steps,
skip_steps,
drift_steps,
source_guidance_scale,
target_guidance_scale,
seed,
samples,
force_offload):
model = model.model
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
dtype = model["dtype"]
transformer = model["pipe"].transformer
pipeline = model["pipe"]
generator = torch.Generator(device=torch.device("cpu")).manual_seed(seed)
latents = samples["samples"] if samples is not None else None
batch_size, num_channels_latents, latent_num_frames, latent_height, latent_width = latents.shape
height = latent_height * pipeline.vae_scale_factor
width = latent_width * pipeline.vae_scale_factor
num_frames = (latent_num_frames - 1) * 4 + 1
if width <= 0 or height <= 0 or num_frames <= 0:
raise ValueError(
f"`height` and `width` and `video_length` must be positive integers, got height={height}, width={width}, video_length={num_frames}"
)
if (num_frames - 1) % 4 != 0:
raise ValueError(
f"`video_length-1` must be a multiple of 4, got {num_frames}"
)
freqs_cos, freqs_sin = get_rotary_pos_embed(transformer, num_frames, height, width)
pipeline.scheduler.shift = flow_shift
if model["block_swap_args"] is not None:
for name, param in transformer.named_parameters():
#print(name, param.data.device)
if "single" not in name and "double" not in name:
param.data = param.data.to(device)
transformer.block_swap(
model["block_swap_args"]["double_blocks_to_swap"] - 1 ,
model["block_swap_args"]["single_blocks_to_swap"] - 1,
offload_txt_in = model["block_swap_args"]["offload_txt_in"],
offload_img_in = model["block_swap_args"]["offload_img_in"],
)
elif model["manual_offloading"]:
transformer.to(device)
mm.soft_empty_cache()
gc.collect()
try:
torch.cuda.reset_peak_memory_stats(device)
except:
pass
pipeline.scheduler.set_timesteps(steps, device=device)
timesteps = pipeline.scheduler.timesteps
timesteps = torch.cat([timesteps, torch.tensor([0]).to(timesteps.device)]).to(timesteps.device)
latent_video_length = (num_frames - 1) // 4 + 1
# 5. Prepare latent variables
num_channels_latents = transformer.config.in_channels
latents = latents.to(device)
shape = (
1,
num_channels_latents,
latent_video_length,
int(height) // pipeline.vae_scale_factor,
int(width) // pipeline.vae_scale_factor,
)
noise = randn_tensor(shape, generator=generator, device=device, dtype=torch.float32)
frames_needed = noise.shape[1]
current_frames = latents.shape[1]
if frames_needed > current_frames:
repeat_factor = frames_needed - current_frames
additional_frame = torch.randn((latents.size(0), repeat_factor, latents.size(2), latents.size(3), latents.size(4)), dtype=latents.dtype, device=latents.device)
latents = torch.cat((additional_frame, latents), dim=1)
self.additional_frames = repeat_factor
elif frames_needed < current_frames:
latents = latents[:, :frames_needed, :, :, :]
# 7. Denoising loop
self._num_timesteps = len(timesteps)
callback = prepare_callback(transformer, steps)
x_init = latents.clone()
from comfy.utils import ProgressBar
from tqdm import tqdm
comfy_pbar = ProgressBar(len(timesteps))
N = len(timesteps)
x_tgt = latents
with tqdm(total=len(timesteps)) as progress_bar:
for idx, (t, t_prev) in enumerate(zip(timesteps[:-1], timesteps[1:])):
if idx < skip_steps:
continue
t_expand = t.repeat(x_init.shape[0])
source_guidance_expand = (
torch.tensor(
[source_guidance_scale] * x_init.shape[0],
dtype=torch.float32,
device=device,
).to(pipeline.base_dtype)
* 1000.0
if source_guidance_scale is not None
else None
)
target_guidance_expand = (
torch.tensor(
[target_guidance_scale] * x_init.shape[0],
dtype=torch.float32,
device=device,
).to(pipeline.base_dtype)
* 1000.0
if target_guidance_scale is not None
else None
)
# predict the noise residual
with torch.autocast(
device_type="cuda", dtype=pipeline.base_dtype, enabled=True
):
noise = torch.randn(x_init.shape, generator=generator).to(x_init.device)
sigma = t / 1000.0
sigma_prev = t_prev / 1000.0
zt_src = (1-sigma)*x_init + sigma*noise
if idx < N-drift_steps:
zt_tgt = x_tgt + zt_src - x_init
vt_src = transformer( # For an input image (129, 192, 336) (1, 256, 256)
zt_src, # [2, 16, 33, 24, 42]
t_expand, # [2]
text_states=source_embeds["prompt_embeds"], # [2, 256, 4096]
text_mask=source_embeds["attention_mask"], # [2, 256]
text_states_2=source_embeds["prompt_embeds_2"], # [2, 768]
freqs_cos=freqs_cos, # [seqlen, head_dim]
freqs_sin=freqs_sin, # [seqlen, head_dim]
guidance=source_guidance_expand,
stg_block_idx=-1,
stg_mode=None,
return_dict=True,
)["x"]
else:
if idx == N - drift_steps:
x_tgt = x_tgt + zt_src - x_init
zt_tgt = x_tgt
vt_src = 0
vt_tgt = transformer( # For an input image (129, 192, 336) (1, 256, 256)
zt_tgt, # [2, 16, 33, 24, 42]
t_expand, # [2]
text_states=target_embeds["prompt_embeds"], # [2, 256, 4096]
text_mask=target_embeds["attention_mask"], # [2, 256]
text_states_2=target_embeds["prompt_embeds_2"], # [2, 768]
freqs_cos=freqs_cos, # [seqlen, head_dim]
freqs_sin=freqs_sin, # [seqlen, head_dim]
guidance=target_guidance_expand,
stg_block_idx=-1,
stg_mode=None,
return_dict=True,
)["x"]
v_delta = vt_tgt - vt_src
x_tgt = x_tgt.to(torch.float32)
v_delta = v_delta.to(torch.float32)
x_tgt = x_tgt + (sigma_prev - sigma) * v_delta
x_tgt = x_tgt.to(torch.bfloat16)
# compute the previous noisy sample x_t -> x_t-1
#latents = pipeline.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
progress_bar.update()
if callback is not None:
callback(idx, latents.detach()[-1].permute(1,0,2,3), None, steps)
else:
comfy_pbar.update(1)
try:
torch.cuda.reset_peak_memory_stats(device)
except:
pass
if force_offload:
if model["manual_offloading"]:
transformer.to(offload_device)
mm.soft_empty_cache()
gc.collect()
return ({
"samples": x_tgt
},)
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