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import os
import torch
import math
import comfy.model_management as mm
from comfy.utils import ProgressBar
import folder_paths
from .depthcrafter.unet import DiffusersUNetSpatioTemporalConditionModelDepthCrafter
from .depthcrafter.depth_crafter_ppl import DepthCrafterPipeline
class DepthCrafterNode:
def __init__(self):
self.progress_bar = None
def start_progress(self, total_steps, desc="Processing"):
self.progress_bar = ProgressBar(total_steps)
def update_progress(self, *args, **kwargs):
if self.progress_bar:
self.progress_bar.update(1)
def end_progress(self):
self.progress_bar = None
CATEGORY = "DepthCrafter"
class DownloadAndLoadDepthCrafterModel(DepthCrafterNode):
@classmethod
def INPUT_TYPES(s):
return {"required": {
"enable_model_cpu_offload": ("BOOLEAN", {"default": True}),
"enable_sequential_cpu_offload": ("BOOLEAN", {"default": False}),
}}
RETURN_TYPES = ("DEPTHCRAFTER_MODEL",)
RETURN_NAMES = ("depthcrafter_model",)
FUNCTION = "load_model"
DESCRIPTION = """
Downloads and loads the DepthCrafter model.
- enable_model_cpu_offload: If True, the model will be offloaded to the CPU. (Saves VRAM)
- enable_sequential_cpu_offload: If True, the model will be offloaded to the CPU in a sequential manner. (Saves the most VRAM but runs slowly)
Only enable one of the two at a time.
"""
def load_model(self, enable_model_cpu_offload, enable_sequential_cpu_offload):
device = mm.get_torch_device()
model_dir = os.path.join(folder_paths.models_dir, "depthcrafter")
os.makedirs(model_dir, exist_ok=True)
# Paths to models
unet_path = os.path.join(model_dir, "tencent_DepthCrafter")
pretrain_path = os.path.join(model_dir, "stabilityai_stable-video-diffusion-img2vid-xt")
depthcrafter_files_to_download = [
"config.json",
"diffusion_pytorch_model.safetensors",
]
svd_files_to_download = [
"feature_extractor/preprocessor_config.json",
"image_encoder/config.json",
"image_encoder/model.fp16.safetensors",
"scheduler/scheduler_config.json",
"unet/config.json",
"unet/diffusion_pytorch_model.fp16.safetensors",
"vae/config.json",
"vae/diffusion_pytorch_model.fp16.safetensors",
"model_index.json",
]
self.start_progress(len(svd_files_to_download) + len(depthcrafter_files_to_download))
# Check if models exist, if not download them
from huggingface_hub import hf_hub_download
if not os.path.exists(unet_path):
print(f"Downloading UNet model to: {unet_path}")
for path in depthcrafter_files_to_download:
hf_hub_download(
repo_id="tencent/DepthCrafter",
filename=path,
local_dir=unet_path,
local_dir_use_symlinks=False,
revision="c1a22b53f8abf80cd0b025adf29e637773229eca",
)
self.update_progress()
if not os.path.exists(pretrain_path):
print(f"Downloading pre-trained pipeline to: {pretrain_path}")
for path in svd_files_to_download:
hf_hub_download(
repo_id="stabilityai/stable-video-diffusion-img2vid-xt",
filename=path,
local_dir=pretrain_path,
local_dir_use_symlinks=False,
revision="9e43909513c6714f1bc78bcb44d96e733cd242aa",
)
self.update_progress()
# Load the custom UNet model
unet = DiffusersUNetSpatioTemporalConditionModelDepthCrafter.from_pretrained(
unet_path,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
)
# Load the pipeline
pipe = DepthCrafterPipeline.from_pretrained(
pretrain_path,
unet=unet,
torch_dtype=torch.float16,
variant="fp16",
use_local_files_only=True,
low_cpu_mem_usage=True,
)
# Model setup
try:
pipe.enable_xformers_memory_efficient_attention()
except Exception as e:
print(e)
print("Xformers is not enabled")
pipe.enable_attention_slicing()
if enable_model_cpu_offload:
pipe.enable_model_cpu_offload()
elif enable_sequential_cpu_offload:
pipe.enable_sequential_cpu_offload()
else:
pipe.to(device)
depthcrafter_model = {
"pipe": pipe,
"device": device,
}
self.end_progress()
return (depthcrafter_model,)
class DepthCrafter(DepthCrafterNode):
@classmethod
def INPUT_TYPES(s):
return {"required": {
"depthcrafter_model": ("DEPTHCRAFTER_MODEL", ),
"images": ("IMAGE", ),
"max_res": ("INT", {"default": 1024, "min": 0, "max": 4096, "step": 64}),
"num_inference_steps": ("INT", {"default": 5, "min": 1, "max": 100}),
"guidance_scale": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1}),
"window_size": ("INT", {"default": 110, "min": 1, "max": 200}),
"overlap": ("INT", {"default": 25, "min": 0, "max": 100}),
}}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("depth_maps",)
FUNCTION = "process"
DESCRIPTION = """
Runs the DepthCrafter model on the input images.
"""
def process(self, depthcrafter_model, images, max_res, num_inference_steps, guidance_scale, window_size, overlap):
device = depthcrafter_model['device']
pipe = depthcrafter_model['pipe']
B, H, W, C = images.shape
# Round to nearest multiple of 64
width = round(W / 64) * 64
height = round(H / 64) * 64
# Scale images if necessary
max_dim = max(height, width)
if max_dim > max_res:
scale_factor = max_res / max_dim
height = round(H * scale_factor / 64) * 64
width = round(W * scale_factor / 64) * 64
images = torch.nn.functional.interpolate(images.permute(0, 3, 1, 2), size=(height, width), mode='bilinear', align_corners=False).permute(0, 2, 3, 1)
# Permute images to [t, c, h, w]
images = images.permute(0, 3, 1, 2) # [B, C, H, W]
images = images.to(device=device, dtype=torch.float16)
images = torch.clamp(images, 0, 1)
# Calculate total num of steps
num_windows = math.ceil((B - window_size) / (window_size - overlap)) + 1
self.start_progress(num_inference_steps * num_windows)
# Run the pipeline
with torch.inference_mode():
result = pipe(
images,
height=height,
width=width,
output_type="pt",
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
window_size=window_size,
overlap=overlap,
track_time=False,
progress_callback=self.update_progress,
)
res = result.frames[0] # [B, H, W, C]
# Convert to grayscale depth map
res = res.sum(dim=1) / res.shape[1] # [t, h, w]
# Normalize depth maps
res_min = res.min()
res_max = res.max()
res = (res - res_min) / (res_max - res_min + 1e-8)
# Convert back to tensor with 3 channels
depth_maps = res.unsqueeze(-1).repeat(1, 1, 1, 3) # [t, h, w, 3]
depth_maps = depth_maps.float()
self.end_progress()
return (depth_maps,)
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