mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-12-15 01:07:03 +08:00
943b3b615d
* init * update * Update model.py * Update model.py * remove print * Fix text encoding * Prevent empty negative prompt Really doesn't work otherwise * fp16 works * I2V * Update model_base.py * Update nodes_hunyuan.py * Better latent rgb factors * Use the correct sigclip output... * Support HunyuanVideo1.5 SR model * whitespaces... * Proper latent channel count * SR model fixes This also still needs timesteps scheduling based on the noise scale, can be used with two samplers too already * vae_refiner: roll the convolution through temporal Work in progress. Roll the convolution through time using 2-latent-frame chunks and a FIFO queue for the convolution seams. * Support HunyuanVideo15 latent resampler * fix * Some cleanup Co-Authored-By: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> * Proper hyvid15 I2V channels Co-Authored-By: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> * Fix TokenRefiner for fp16 Otherwise x.sum has infs, just in case only casting if input is fp16, I don't know if necessary. * Bugfix for the HunyuanVideo15 SR model * vae_refiner: roll the convolution through temporal II Roll the convolution through time using 2-latent-frame chunks and a FIFO queue for the convolution seams. Added support for encoder, lowered to 1 latent frame to save more VRAM, made work for Hunyuan Image 3.0 (as code shared). Fixed names, cleaned up code. * Allow any number of input frames in VAE. * Better VAE encode mem estimation. * Lowvram fix. * Fix hunyuan image 2.1 refiner. * Fix mistake. * Name changes. * Rename. * Whitespace. * Fix. * Fix. --------- Co-authored-by: kijai <40791699+kijai@users.noreply.github.com> Co-authored-by: Rattus <rattus128@gmail.com>
420 lines
18 KiB
Python
420 lines
18 KiB
Python
import nodes
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import node_helpers
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import torch
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import comfy.model_management
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from typing_extensions import override
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from comfy_api.latest import ComfyExtension, io
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from comfy.ldm.hunyuan_video.upsampler import HunyuanVideo15SRModel
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import folder_paths
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class CLIPTextEncodeHunyuanDiT(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="CLIPTextEncodeHunyuanDiT",
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category="advanced/conditioning",
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inputs=[
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io.Clip.Input("clip"),
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io.String.Input("bert", multiline=True, dynamic_prompts=True),
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io.String.Input("mt5xl", multiline=True, dynamic_prompts=True),
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],
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outputs=[
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io.Conditioning.Output(),
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],
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)
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@classmethod
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def execute(cls, clip, bert, mt5xl) -> io.NodeOutput:
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tokens = clip.tokenize(bert)
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tokens["mt5xl"] = clip.tokenize(mt5xl)["mt5xl"]
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return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
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encode = execute # TODO: remove
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class EmptyHunyuanLatentVideo(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="EmptyHunyuanLatentVideo",
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category="latent/video",
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inputs=[
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io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
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io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
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io.Int.Input("length", default=25, min=1, max=nodes.MAX_RESOLUTION, step=4),
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io.Int.Input("batch_size", default=1, min=1, max=4096),
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],
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outputs=[
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io.Latent.Output(),
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],
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)
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@classmethod
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def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
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latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
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return io.NodeOutput({"samples":latent})
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generate = execute # TODO: remove
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class EmptyHunyuanVideo15Latent(EmptyHunyuanLatentVideo):
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@classmethod
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def define_schema(cls):
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schema = super().define_schema()
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schema.node_id = "EmptyHunyuanVideo15Latent"
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return schema
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@classmethod
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def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
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# Using scale factor of 16 instead of 8
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latent = torch.zeros([batch_size, 32, ((length - 1) // 4) + 1, height // 16, width // 16], device=comfy.model_management.intermediate_device())
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return io.NodeOutput({"samples": latent})
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generate = execute # TODO: remove
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class HunyuanVideo15ImageToVideo(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="HunyuanVideo15ImageToVideo",
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category="conditioning/video_models",
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inputs=[
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io.Conditioning.Input("positive"),
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io.Conditioning.Input("negative"),
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io.Vae.Input("vae"),
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io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
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io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
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io.Int.Input("length", default=33, min=1, max=nodes.MAX_RESOLUTION, step=4),
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io.Int.Input("batch_size", default=1, min=1, max=4096),
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io.Image.Input("start_image", optional=True),
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io.ClipVisionOutput.Input("clip_vision_output", optional=True),
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],
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outputs=[
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io.Conditioning.Output(display_name="positive"),
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io.Conditioning.Output(display_name="negative"),
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io.Latent.Output(display_name="latent"),
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],
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)
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@classmethod
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def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None) -> io.NodeOutput:
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latent = torch.zeros([batch_size, 32, ((length - 1) // 4) + 1, height // 16, width // 16], device=comfy.model_management.intermediate_device())
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if start_image is not None:
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start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
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encoded = vae.encode(start_image[:, :, :, :3])
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concat_latent_image = torch.zeros((latent.shape[0], 32, latent.shape[2], latent.shape[3], latent.shape[4]), device=comfy.model_management.intermediate_device())
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concat_latent_image[:, :, :encoded.shape[2], :, :] = encoded
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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)
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mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0
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positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask})
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negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask})
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if clip_vision_output is not None:
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positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
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negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})
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out_latent = {}
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out_latent["samples"] = latent
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return io.NodeOutput(positive, negative, out_latent)
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class HunyuanVideo15SuperResolution(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="HunyuanVideo15SuperResolution",
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inputs=[
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io.Conditioning.Input("positive"),
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io.Conditioning.Input("negative"),
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io.Vae.Input("vae", optional=True),
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io.Image.Input("start_image", optional=True),
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io.ClipVisionOutput.Input("clip_vision_output", optional=True),
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io.Latent.Input("latent"),
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io.Float.Input("noise_augmentation", default=0.70, min=0.0, max=1.0, step=0.01),
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],
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outputs=[
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io.Conditioning.Output(display_name="positive"),
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io.Conditioning.Output(display_name="negative"),
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io.Latent.Output(display_name="latent"),
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],
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)
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@classmethod
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def execute(cls, positive, negative, latent, noise_augmentation, vae=None, start_image=None, clip_vision_output=None) -> io.NodeOutput:
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in_latent = latent["samples"]
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in_channels = in_latent.shape[1]
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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())
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cond_latent[:, in_channels + 1 : 2 * in_channels + 1] = in_latent
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cond_latent[:, 2 * in_channels + 1] = 1
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if start_image is not None:
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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)
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encoded = vae.encode(start_image[:, :, :, :3])
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cond_latent[:, :in_channels, :encoded.shape[2], :, :] = encoded
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cond_latent[:, in_channels + 1, 0] = 1
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positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": cond_latent, "noise_augmentation": noise_augmentation})
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negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": cond_latent, "noise_augmentation": noise_augmentation})
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if clip_vision_output is not None:
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positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
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negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})
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return io.NodeOutput(positive, negative, latent)
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class LatentUpscaleModelLoader(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="LatentUpscaleModelLoader",
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display_name="Load Latent Upscale Model",
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category="loaders",
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inputs=[
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io.Combo.Input("model_name", options=folder_paths.get_filename_list("latent_upscale_models")),
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],
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outputs=[
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io.LatentUpscaleModel.Output(),
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],
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)
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@classmethod
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def execute(cls, model_name) -> io.NodeOutput:
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model_path = folder_paths.get_full_path_or_raise("upscale_models", model_name)
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sd = comfy.utils.load_torch_file(model_path, safe_load=True)
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if "blocks.0.block.0.conv.weight" in sd:
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config = {
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"in_channels": sd["in_conv.conv.weight"].shape[1],
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"out_channels": sd["out_conv.conv.weight"].shape[0],
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"hidden_channels": sd["in_conv.conv.weight"].shape[0],
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"num_blocks": len([k for k in sd.keys() if k.startswith("blocks.") and k.endswith(".block.0.conv.weight")]),
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"global_residual": False,
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}
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model_type = "720p"
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elif "up.0.block.0.conv1.conv.weight" in sd:
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sd = {key.replace("nin_shortcut", "nin_shortcut.conv", 1): value for key, value in sd.items()}
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config = {
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"z_channels": sd["conv_in.conv.weight"].shape[1],
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"out_channels": sd["conv_out.conv.weight"].shape[0],
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"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")]))),
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}
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model_type = "1080p"
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model = HunyuanVideo15SRModel(model_type, config)
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model.load_sd(sd)
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return io.NodeOutput(model)
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class HunyuanVideo15LatentUpscaleWithModel(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="HunyuanVideo15LatentUpscaleWithModel",
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display_name="Hunyuan Video 15 Latent Upscale With Model",
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category="latent",
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inputs=[
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io.LatentUpscaleModel.Input("model"),
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io.Latent.Input("samples"),
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io.Combo.Input("upscale_method", options=["nearest-exact", "bilinear", "area", "bicubic", "bislerp"], default="bilinear"),
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io.Int.Input("width", default=1280, min=0, max=16384, step=8),
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io.Int.Input("height", default=720, min=0, max=16384, step=8),
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io.Combo.Input("crop", options=["disabled", "center"]),
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],
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outputs=[
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io.Latent.Output(),
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],
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)
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@classmethod
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def execute(cls, model, samples, upscale_method, width, height, crop) -> io.NodeOutput:
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if width == 0 and height == 0:
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return io.NodeOutput(samples)
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else:
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if width == 0:
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height = max(64, height)
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width = max(64, round(samples["samples"].shape[-1] * height / samples["samples"].shape[-2]))
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elif height == 0:
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width = max(64, width)
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height = max(64, round(samples["samples"].shape[-2] * width / samples["samples"].shape[-1]))
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else:
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width = max(64, width)
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height = max(64, height)
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s = comfy.utils.common_upscale(samples["samples"], width // 16, height // 16, upscale_method, crop)
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s = model.resample_latent(s)
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return io.NodeOutput({"samples": s.cpu().float()})
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PROMPT_TEMPLATE_ENCODE_VIDEO_I2V = (
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"<|start_header_id|>system<|end_header_id|>\n\n<image>\nDescribe the video by detailing the following aspects according to the reference image: "
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"1. The main content and theme of the video."
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"2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects."
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"3. Actions, events, behaviors temporal relationships, physical movement changes of the objects."
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"4. background environment, light, style and atmosphere."
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"5. camera angles, movements, and transitions used in the video:<|eot_id|>\n\n"
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"<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
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"<|start_header_id|>assistant<|end_header_id|>\n\n"
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)
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class TextEncodeHunyuanVideo_ImageToVideo(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="TextEncodeHunyuanVideo_ImageToVideo",
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category="advanced/conditioning",
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inputs=[
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io.Clip.Input("clip"),
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io.ClipVisionOutput.Input("clip_vision_output"),
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io.String.Input("prompt", multiline=True, dynamic_prompts=True),
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io.Int.Input(
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"image_interleave",
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default=2,
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min=1,
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max=512,
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tooltip="How much the image influences things vs the text prompt. Higher number means more influence from the text prompt.",
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),
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],
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outputs=[
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io.Conditioning.Output(),
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],
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)
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@classmethod
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def execute(cls, clip, clip_vision_output, prompt, image_interleave) -> io.NodeOutput:
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tokens = clip.tokenize(prompt, llama_template=PROMPT_TEMPLATE_ENCODE_VIDEO_I2V, image_embeds=clip_vision_output.mm_projected, image_interleave=image_interleave)
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return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
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encode = execute # TODO: remove
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class HunyuanImageToVideo(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="HunyuanImageToVideo",
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category="conditioning/video_models",
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inputs=[
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io.Conditioning.Input("positive"),
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io.Vae.Input("vae"),
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io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
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io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
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io.Int.Input("length", default=53, min=1, max=nodes.MAX_RESOLUTION, step=4),
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io.Int.Input("batch_size", default=1, min=1, max=4096),
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io.Combo.Input("guidance_type", options=["v1 (concat)", "v2 (replace)", "custom"]),
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io.Image.Input("start_image", optional=True),
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],
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outputs=[
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io.Conditioning.Output(display_name="positive"),
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io.Latent.Output(display_name="latent"),
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],
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)
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@classmethod
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def execute(cls, positive, vae, width, height, length, batch_size, guidance_type, start_image=None) -> io.NodeOutput:
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latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
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out_latent = {}
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if start_image is not None:
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start_image = comfy.utils.common_upscale(start_image[:length, :, :, :3].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
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concat_latent_image = vae.encode(start_image)
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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)
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mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0
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if guidance_type == "v1 (concat)":
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cond = {"concat_latent_image": concat_latent_image, "concat_mask": mask}
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elif guidance_type == "v2 (replace)":
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cond = {'guiding_frame_index': 0}
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latent[:, :, :concat_latent_image.shape[2]] = concat_latent_image
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out_latent["noise_mask"] = mask
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elif guidance_type == "custom":
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cond = {"ref_latent": concat_latent_image}
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positive = node_helpers.conditioning_set_values(positive, cond)
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out_latent["samples"] = latent
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return io.NodeOutput(positive, out_latent)
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encode = execute # TODO: remove
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class EmptyHunyuanImageLatent(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="EmptyHunyuanImageLatent",
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category="latent",
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inputs=[
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io.Int.Input("width", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32),
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io.Int.Input("height", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32),
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io.Int.Input("batch_size", default=1, min=1, max=4096),
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],
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outputs=[
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io.Latent.Output(),
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],
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)
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@classmethod
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def execute(cls, width, height, batch_size=1) -> io.NodeOutput:
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latent = torch.zeros([batch_size, 64, height // 32, width // 32], device=comfy.model_management.intermediate_device())
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return io.NodeOutput({"samples":latent})
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generate = execute # TODO: remove
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class HunyuanRefinerLatent(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="HunyuanRefinerLatent",
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inputs=[
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io.Conditioning.Input("positive"),
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io.Conditioning.Input("negative"),
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io.Latent.Input("latent"),
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io.Float.Input("noise_augmentation", default=0.10, min=0.0, max=1.0, step=0.01),
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],
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outputs=[
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io.Conditioning.Output(display_name="positive"),
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io.Conditioning.Output(display_name="negative"),
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io.Latent.Output(display_name="latent"),
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],
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)
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@classmethod
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def execute(cls, positive, negative, latent, noise_augmentation) -> io.NodeOutput:
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latent = latent["samples"]
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|
positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": latent, "noise_augmentation": noise_augmentation})
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|
negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": latent, "noise_augmentation": noise_augmentation})
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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())
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return io.NodeOutput(positive, negative, out_latent)
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|
|
|
|
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class HunyuanExtension(ComfyExtension):
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@override
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async def get_node_list(self) -> list[type[io.ComfyNode]]:
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return [
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CLIPTextEncodeHunyuanDiT,
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|
TextEncodeHunyuanVideo_ImageToVideo,
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|
EmptyHunyuanLatentVideo,
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|
EmptyHunyuanVideo15Latent,
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|
HunyuanVideo15ImageToVideo,
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|
HunyuanVideo15SuperResolution,
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|
HunyuanVideo15LatentUpscaleWithModel,
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|
LatentUpscaleModelLoader,
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|
HunyuanImageToVideo,
|
|
EmptyHunyuanImageLatent,
|
|
HunyuanRefinerLatent,
|
|
]
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|
|
|
|
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async def comfy_entrypoint() -> HunyuanExtension:
|
|
return HunyuanExtension()
|