mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2025-12-09 14:04:26 +08:00
420 lines
19 KiB
Python
420 lines
19 KiB
Python
import nodes
|
|
import node_helpers
|
|
import torch
|
|
import comfy.model_management
|
|
from typing_extensions import override
|
|
from comfy_api.latest import ComfyExtension, io
|
|
from comfy.ldm.hunyuan_video.upsampler import HunyuanVideo15SRModel
|
|
import folder_paths
|
|
|
|
class CLIPTextEncodeHunyuanDiT(io.ComfyNode):
|
|
@classmethod
|
|
def define_schema(cls):
|
|
return io.Schema(
|
|
node_id="CLIPTextEncodeHunyuanDiT",
|
|
category="advanced/conditioning",
|
|
inputs=[
|
|
io.Clip.Input("clip"),
|
|
io.String.Input("bert", multiline=True, dynamic_prompts=True),
|
|
io.String.Input("mt5xl", multiline=True, dynamic_prompts=True),
|
|
],
|
|
outputs=[
|
|
io.Conditioning.Output(),
|
|
],
|
|
)
|
|
|
|
@classmethod
|
|
def execute(cls, clip, bert, mt5xl) -> io.NodeOutput:
|
|
tokens = clip.tokenize(bert)
|
|
tokens["mt5xl"] = clip.tokenize(mt5xl)["mt5xl"]
|
|
|
|
return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
|
|
|
|
encode = execute # TODO: remove
|
|
|
|
|
|
class EmptyHunyuanLatentVideo(io.ComfyNode):
|
|
@classmethod
|
|
def define_schema(cls):
|
|
return io.Schema(
|
|
node_id="EmptyHunyuanLatentVideo",
|
|
display_name="Empty HunyuanVideo 1.0 Latent",
|
|
category="latent/video",
|
|
inputs=[
|
|
io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
|
io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
|
io.Int.Input("length", default=25, min=1, max=nodes.MAX_RESOLUTION, step=4),
|
|
io.Int.Input("batch_size", default=1, min=1, max=4096),
|
|
],
|
|
outputs=[
|
|
io.Latent.Output(),
|
|
],
|
|
)
|
|
|
|
@classmethod
|
|
def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
|
|
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
|
|
return io.NodeOutput({"samples":latent})
|
|
|
|
generate = execute # TODO: remove
|
|
|
|
|
|
class EmptyHunyuanVideo15Latent(EmptyHunyuanLatentVideo):
|
|
@classmethod
|
|
def define_schema(cls):
|
|
schema = super().define_schema()
|
|
schema.node_id = "EmptyHunyuanVideo15Latent"
|
|
schema.display_name = "Empty HunyuanVideo 1.5 Latent"
|
|
return schema
|
|
|
|
@classmethod
|
|
def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
|
|
# Using scale factor of 16 instead of 8
|
|
latent = torch.zeros([batch_size, 32, ((length - 1) // 4) + 1, height // 16, width // 16], device=comfy.model_management.intermediate_device())
|
|
return io.NodeOutput({"samples": latent})
|
|
|
|
|
|
class HunyuanVideo15ImageToVideo(io.ComfyNode):
|
|
@classmethod
|
|
def define_schema(cls):
|
|
return io.Schema(
|
|
node_id="HunyuanVideo15ImageToVideo",
|
|
category="conditioning/video_models",
|
|
inputs=[
|
|
io.Conditioning.Input("positive"),
|
|
io.Conditioning.Input("negative"),
|
|
io.Vae.Input("vae"),
|
|
io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
|
io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
|
io.Int.Input("length", default=33, min=1, max=nodes.MAX_RESOLUTION, step=4),
|
|
io.Int.Input("batch_size", default=1, min=1, max=4096),
|
|
io.Image.Input("start_image", optional=True),
|
|
io.ClipVisionOutput.Input("clip_vision_output", optional=True),
|
|
],
|
|
outputs=[
|
|
io.Conditioning.Output(display_name="positive"),
|
|
io.Conditioning.Output(display_name="negative"),
|
|
io.Latent.Output(display_name="latent"),
|
|
],
|
|
)
|
|
|
|
@classmethod
|
|
def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None) -> io.NodeOutput:
|
|
latent = torch.zeros([batch_size, 32, ((length - 1) // 4) + 1, height // 16, width // 16], device=comfy.model_management.intermediate_device())
|
|
|
|
if start_image is not None:
|
|
start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
|
|
|
|
encoded = vae.encode(start_image[:, :, :, :3])
|
|
concat_latent_image = torch.zeros((latent.shape[0], 32, latent.shape[2], latent.shape[3], latent.shape[4]), device=comfy.model_management.intermediate_device())
|
|
concat_latent_image[:, :, :encoded.shape[2], :, :] = encoded
|
|
|
|
mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype)
|
|
mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0
|
|
|
|
positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask})
|
|
negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask})
|
|
|
|
if clip_vision_output is not None:
|
|
positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
|
|
negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})
|
|
|
|
out_latent = {}
|
|
out_latent["samples"] = latent
|
|
return io.NodeOutput(positive, negative, out_latent)
|
|
|
|
|
|
class HunyuanVideo15SuperResolution(io.ComfyNode):
|
|
@classmethod
|
|
def define_schema(cls):
|
|
return io.Schema(
|
|
node_id="HunyuanVideo15SuperResolution",
|
|
inputs=[
|
|
io.Conditioning.Input("positive"),
|
|
io.Conditioning.Input("negative"),
|
|
io.Vae.Input("vae", optional=True),
|
|
io.Image.Input("start_image", optional=True),
|
|
io.ClipVisionOutput.Input("clip_vision_output", optional=True),
|
|
io.Latent.Input("latent"),
|
|
io.Float.Input("noise_augmentation", default=0.70, min=0.0, max=1.0, step=0.01),
|
|
|
|
],
|
|
outputs=[
|
|
io.Conditioning.Output(display_name="positive"),
|
|
io.Conditioning.Output(display_name="negative"),
|
|
io.Latent.Output(display_name="latent"),
|
|
],
|
|
)
|
|
|
|
@classmethod
|
|
def execute(cls, positive, negative, latent, noise_augmentation, vae=None, start_image=None, clip_vision_output=None) -> io.NodeOutput:
|
|
in_latent = latent["samples"]
|
|
in_channels = in_latent.shape[1]
|
|
cond_latent = torch.zeros([in_latent.shape[0], in_channels * 2 + 2, in_latent.shape[-3], in_latent.shape[-2], in_latent.shape[-1]], device=comfy.model_management.intermediate_device())
|
|
cond_latent[:, in_channels + 1 : 2 * in_channels + 1] = in_latent
|
|
cond_latent[:, 2 * in_channels + 1] = 1
|
|
if start_image is not None:
|
|
start_image = comfy.utils.common_upscale(start_image.movedim(-1, 1), in_latent.shape[-1] * 16, in_latent.shape[-2] * 16, "bilinear", "center").movedim(1, -1)
|
|
encoded = vae.encode(start_image[:, :, :, :3])
|
|
cond_latent[:, :in_channels, :encoded.shape[2], :, :] = encoded
|
|
cond_latent[:, in_channels + 1, 0] = 1
|
|
|
|
positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": cond_latent, "noise_augmentation": noise_augmentation})
|
|
negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": cond_latent, "noise_augmentation": noise_augmentation})
|
|
if clip_vision_output is not None:
|
|
positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
|
|
negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})
|
|
|
|
return io.NodeOutput(positive, negative, latent)
|
|
|
|
|
|
class LatentUpscaleModelLoader(io.ComfyNode):
|
|
@classmethod
|
|
def define_schema(cls):
|
|
return io.Schema(
|
|
node_id="LatentUpscaleModelLoader",
|
|
display_name="Load Latent Upscale Model",
|
|
category="loaders",
|
|
inputs=[
|
|
io.Combo.Input("model_name", options=folder_paths.get_filename_list("latent_upscale_models")),
|
|
],
|
|
outputs=[
|
|
io.LatentUpscaleModel.Output(),
|
|
],
|
|
)
|
|
|
|
@classmethod
|
|
def execute(cls, model_name) -> io.NodeOutput:
|
|
model_path = folder_paths.get_full_path_or_raise("latent_upscale_models", model_name)
|
|
sd = comfy.utils.load_torch_file(model_path, safe_load=True)
|
|
|
|
if "blocks.0.block.0.conv.weight" in sd:
|
|
config = {
|
|
"in_channels": sd["in_conv.conv.weight"].shape[1],
|
|
"out_channels": sd["out_conv.conv.weight"].shape[0],
|
|
"hidden_channels": sd["in_conv.conv.weight"].shape[0],
|
|
"num_blocks": len([k for k in sd.keys() if k.startswith("blocks.") and k.endswith(".block.0.conv.weight")]),
|
|
"global_residual": False,
|
|
}
|
|
model_type = "720p"
|
|
elif "up.0.block.0.conv1.conv.weight" in sd:
|
|
sd = {key.replace("nin_shortcut", "nin_shortcut.conv", 1): value for key, value in sd.items()}
|
|
config = {
|
|
"z_channels": sd["conv_in.conv.weight"].shape[1],
|
|
"out_channels": sd["conv_out.conv.weight"].shape[0],
|
|
"block_out_channels": tuple(sd[f"up.{i}.block.0.conv1.conv.weight"].shape[0] for i in range(len([k for k in sd.keys() if k.startswith("up.") and k.endswith(".block.0.conv1.conv.weight")]))),
|
|
}
|
|
model_type = "1080p"
|
|
|
|
model = HunyuanVideo15SRModel(model_type, config)
|
|
model.load_sd(sd)
|
|
|
|
return io.NodeOutput(model)
|
|
|
|
|
|
class HunyuanVideo15LatentUpscaleWithModel(io.ComfyNode):
|
|
@classmethod
|
|
def define_schema(cls):
|
|
return io.Schema(
|
|
node_id="HunyuanVideo15LatentUpscaleWithModel",
|
|
display_name="Hunyuan Video 15 Latent Upscale With Model",
|
|
category="latent",
|
|
inputs=[
|
|
io.LatentUpscaleModel.Input("model"),
|
|
io.Latent.Input("samples"),
|
|
io.Combo.Input("upscale_method", options=["nearest-exact", "bilinear", "area", "bicubic", "bislerp"], default="bilinear"),
|
|
io.Int.Input("width", default=1280, min=0, max=16384, step=8),
|
|
io.Int.Input("height", default=720, min=0, max=16384, step=8),
|
|
io.Combo.Input("crop", options=["disabled", "center"]),
|
|
],
|
|
outputs=[
|
|
io.Latent.Output(),
|
|
],
|
|
)
|
|
|
|
@classmethod
|
|
def execute(cls, model, samples, upscale_method, width, height, crop) -> io.NodeOutput:
|
|
if width == 0 and height == 0:
|
|
return io.NodeOutput(samples)
|
|
else:
|
|
if width == 0:
|
|
height = max(64, height)
|
|
width = max(64, round(samples["samples"].shape[-1] * height / samples["samples"].shape[-2]))
|
|
elif height == 0:
|
|
width = max(64, width)
|
|
height = max(64, round(samples["samples"].shape[-2] * width / samples["samples"].shape[-1]))
|
|
else:
|
|
width = max(64, width)
|
|
height = max(64, height)
|
|
s = comfy.utils.common_upscale(samples["samples"], width // 16, height // 16, upscale_method, crop)
|
|
s = model.resample_latent(s)
|
|
return io.NodeOutput({"samples": s.cpu().float()})
|
|
|
|
|
|
PROMPT_TEMPLATE_ENCODE_VIDEO_I2V = (
|
|
"<|start_header_id|>system<|end_header_id|>\n\n<image>\nDescribe the video by detailing the following aspects according to the reference image: "
|
|
"1. The main content and theme of the video."
|
|
"2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects."
|
|
"3. Actions, events, behaviors temporal relationships, physical movement changes of the objects."
|
|
"4. background environment, light, style and atmosphere."
|
|
"5. camera angles, movements, and transitions used in the video:<|eot_id|>\n\n"
|
|
"<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
|
|
"<|start_header_id|>assistant<|end_header_id|>\n\n"
|
|
)
|
|
|
|
class TextEncodeHunyuanVideo_ImageToVideo(io.ComfyNode):
|
|
@classmethod
|
|
def define_schema(cls):
|
|
return io.Schema(
|
|
node_id="TextEncodeHunyuanVideo_ImageToVideo",
|
|
category="advanced/conditioning",
|
|
inputs=[
|
|
io.Clip.Input("clip"),
|
|
io.ClipVisionOutput.Input("clip_vision_output"),
|
|
io.String.Input("prompt", multiline=True, dynamic_prompts=True),
|
|
io.Int.Input(
|
|
"image_interleave",
|
|
default=2,
|
|
min=1,
|
|
max=512,
|
|
tooltip="How much the image influences things vs the text prompt. Higher number means more influence from the text prompt.",
|
|
),
|
|
],
|
|
outputs=[
|
|
io.Conditioning.Output(),
|
|
],
|
|
)
|
|
|
|
@classmethod
|
|
def execute(cls, clip, clip_vision_output, prompt, image_interleave) -> io.NodeOutput:
|
|
tokens = clip.tokenize(prompt, llama_template=PROMPT_TEMPLATE_ENCODE_VIDEO_I2V, image_embeds=clip_vision_output.mm_projected, image_interleave=image_interleave)
|
|
return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
|
|
|
|
encode = execute # TODO: remove
|
|
|
|
|
|
class HunyuanImageToVideo(io.ComfyNode):
|
|
@classmethod
|
|
def define_schema(cls):
|
|
return io.Schema(
|
|
node_id="HunyuanImageToVideo",
|
|
category="conditioning/video_models",
|
|
inputs=[
|
|
io.Conditioning.Input("positive"),
|
|
io.Vae.Input("vae"),
|
|
io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
|
io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
|
|
io.Int.Input("length", default=53, min=1, max=nodes.MAX_RESOLUTION, step=4),
|
|
io.Int.Input("batch_size", default=1, min=1, max=4096),
|
|
io.Combo.Input("guidance_type", options=["v1 (concat)", "v2 (replace)", "custom"]),
|
|
io.Image.Input("start_image", optional=True),
|
|
],
|
|
outputs=[
|
|
io.Conditioning.Output(display_name="positive"),
|
|
io.Latent.Output(display_name="latent"),
|
|
],
|
|
)
|
|
|
|
@classmethod
|
|
def execute(cls, positive, vae, width, height, length, batch_size, guidance_type, start_image=None) -> io.NodeOutput:
|
|
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
|
|
out_latent = {}
|
|
|
|
if start_image is not None:
|
|
start_image = comfy.utils.common_upscale(start_image[:length, :, :, :3].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
|
|
|
|
concat_latent_image = vae.encode(start_image)
|
|
mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype)
|
|
mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0
|
|
|
|
if guidance_type == "v1 (concat)":
|
|
cond = {"concat_latent_image": concat_latent_image, "concat_mask": mask}
|
|
elif guidance_type == "v2 (replace)":
|
|
cond = {'guiding_frame_index': 0}
|
|
latent[:, :, :concat_latent_image.shape[2]] = concat_latent_image
|
|
out_latent["noise_mask"] = mask
|
|
elif guidance_type == "custom":
|
|
cond = {"ref_latent": concat_latent_image}
|
|
|
|
positive = node_helpers.conditioning_set_values(positive, cond)
|
|
|
|
out_latent["samples"] = latent
|
|
return io.NodeOutput(positive, out_latent)
|
|
|
|
encode = execute # TODO: remove
|
|
|
|
|
|
class EmptyHunyuanImageLatent(io.ComfyNode):
|
|
@classmethod
|
|
def define_schema(cls):
|
|
return io.Schema(
|
|
node_id="EmptyHunyuanImageLatent",
|
|
category="latent",
|
|
inputs=[
|
|
io.Int.Input("width", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32),
|
|
io.Int.Input("height", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32),
|
|
io.Int.Input("batch_size", default=1, min=1, max=4096),
|
|
],
|
|
outputs=[
|
|
io.Latent.Output(),
|
|
],
|
|
)
|
|
|
|
@classmethod
|
|
def execute(cls, width, height, batch_size=1) -> io.NodeOutput:
|
|
latent = torch.zeros([batch_size, 64, height // 32, width // 32], device=comfy.model_management.intermediate_device())
|
|
return io.NodeOutput({"samples":latent})
|
|
|
|
generate = execute # TODO: remove
|
|
|
|
|
|
class HunyuanRefinerLatent(io.ComfyNode):
|
|
@classmethod
|
|
def define_schema(cls):
|
|
return io.Schema(
|
|
node_id="HunyuanRefinerLatent",
|
|
inputs=[
|
|
io.Conditioning.Input("positive"),
|
|
io.Conditioning.Input("negative"),
|
|
io.Latent.Input("latent"),
|
|
io.Float.Input("noise_augmentation", default=0.10, min=0.0, max=1.0, step=0.01),
|
|
|
|
],
|
|
outputs=[
|
|
io.Conditioning.Output(display_name="positive"),
|
|
io.Conditioning.Output(display_name="negative"),
|
|
io.Latent.Output(display_name="latent"),
|
|
],
|
|
)
|
|
|
|
@classmethod
|
|
def execute(cls, positive, negative, latent, noise_augmentation) -> io.NodeOutput:
|
|
latent = latent["samples"]
|
|
positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": latent, "noise_augmentation": noise_augmentation})
|
|
negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": latent, "noise_augmentation": noise_augmentation})
|
|
out_latent = {}
|
|
out_latent["samples"] = torch.zeros([latent.shape[0], 32, latent.shape[-3], latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device())
|
|
return io.NodeOutput(positive, negative, out_latent)
|
|
|
|
|
|
class HunyuanExtension(ComfyExtension):
|
|
@override
|
|
async def get_node_list(self) -> list[type[io.ComfyNode]]:
|
|
return [
|
|
CLIPTextEncodeHunyuanDiT,
|
|
TextEncodeHunyuanVideo_ImageToVideo,
|
|
EmptyHunyuanLatentVideo,
|
|
EmptyHunyuanVideo15Latent,
|
|
HunyuanVideo15ImageToVideo,
|
|
HunyuanVideo15SuperResolution,
|
|
HunyuanVideo15LatentUpscaleWithModel,
|
|
LatentUpscaleModelLoader,
|
|
HunyuanImageToVideo,
|
|
EmptyHunyuanImageLatent,
|
|
HunyuanRefinerLatent,
|
|
]
|
|
|
|
|
|
async def comfy_entrypoint() -> HunyuanExtension:
|
|
return HunyuanExtension()
|