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https://github.com/modelscope/DiffSynth-Studio.git
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add fun-v1.1-1.3B-control-camera
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lzw478614@alibaba-inc.com
177
diffsynth/models/wan_video_camera_controller.py
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177
diffsynth/models/wan_video_camera_controller.py
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import torch
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import torch.nn as nn
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import numpy as np
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from einops import rearrange
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from packaging import version as pver
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import os
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class SimpleAdapter(nn.Module):
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def __init__(self, in_dim, out_dim, kernel_size, stride, num_residual_blocks=1):
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super(SimpleAdapter, self).__init__()
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# Pixel Unshuffle: reduce spatial dimensions by a factor of 8
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self.pixel_unshuffle = nn.PixelUnshuffle(downscale_factor=8)
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# Convolution: reduce spatial dimensions by a factor
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# of 2 (without overlap)
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self.conv = nn.Conv2d(in_dim * 64, out_dim, kernel_size=kernel_size, stride=stride, padding=0)
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# Residual blocks for feature extraction
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self.residual_blocks = nn.Sequential(
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*[ResidualBlock(out_dim) for _ in range(num_residual_blocks)]
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)
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def forward(self, x):
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# Reshape to merge the frame dimension into batch
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bs, c, f, h, w = x.size()
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x = x.permute(0, 2, 1, 3, 4).contiguous().view(bs * f, c, h, w)
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# Pixel Unshuffle operation
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x_unshuffled = self.pixel_unshuffle(x)
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# Convolution operation
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x_conv = self.conv(x_unshuffled)
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# Feature extraction with residual blocks
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out = self.residual_blocks(x_conv)
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# Reshape to restore original bf dimension
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out = out.view(bs, f, out.size(1), out.size(2), out.size(3))
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# Permute dimensions to reorder (if needed), e.g., swap channels and feature frames
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out = out.permute(0, 2, 1, 3, 4)
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return out
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class ResidualBlock(nn.Module):
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def __init__(self, dim):
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super(ResidualBlock, self).__init__()
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self.conv1 = nn.Conv2d(dim, dim, kernel_size=3, padding=1)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = nn.Conv2d(dim, dim, kernel_size=3, padding=1)
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def forward(self, x):
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residual = x
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out = self.relu(self.conv1(x))
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out = self.conv2(out)
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out += residual
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return out
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class Camera(object):
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"""Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
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"""
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def __init__(self, entry):
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fx, fy, cx, cy = entry[1:5]
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self.fx = fx
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self.fy = fy
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self.cx = cx
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self.cy = cy
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w2c_mat = np.array(entry[7:]).reshape(3, 4)
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w2c_mat_4x4 = np.eye(4)
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w2c_mat_4x4[:3, :] = w2c_mat
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self.w2c_mat = w2c_mat_4x4
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self.c2w_mat = np.linalg.inv(w2c_mat_4x4)
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def get_relative_pose(cam_params):
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"""Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
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"""
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abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params]
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abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params]
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cam_to_origin = 0
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target_cam_c2w = np.array([
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[1, 0, 0, 0],
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[0, 1, 0, -cam_to_origin],
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[0, 0, 1, 0],
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[0, 0, 0, 1]
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])
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abs2rel = target_cam_c2w @ abs_w2cs[0]
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ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]]
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ret_poses = np.array(ret_poses, dtype=np.float32)
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return ret_poses
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def custom_meshgrid(*args):
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"""Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
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"""
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# ref: https://pytorch.org/docs/stable/generated/torch.meshgrid.html?highlight=meshgrid#torch.meshgrid
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if pver.parse(torch.__version__) < pver.parse('1.10'):
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return torch.meshgrid(*args)
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else:
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return torch.meshgrid(*args, indexing='ij')
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def ray_condition(K, c2w, H, W, device):
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"""Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
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"""
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# c2w: B, V, 4, 4
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# K: B, V, 4
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B = K.shape[0]
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j, i = custom_meshgrid(
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torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype),
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torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype),
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)
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i = i.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW]
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j = j.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW]
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fx, fy, cx, cy = K.chunk(4, dim=-1) # B,V, 1
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zs = torch.ones_like(i) # [B, HxW]
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xs = (i - cx) / fx * zs
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ys = (j - cy) / fy * zs
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zs = zs.expand_as(ys)
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directions = torch.stack((xs, ys, zs), dim=-1) # B, V, HW, 3
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directions = directions / directions.norm(dim=-1, keepdim=True) # B, V, HW, 3
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rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2) # B, V, 3, HW
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rays_o = c2w[..., :3, 3] # B, V, 3
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rays_o = rays_o[:, :, None].expand_as(rays_d) # B, V, 3, HW
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# c2w @ dirctions
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rays_dxo = torch.cross(rays_o, rays_d)
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plucker = torch.cat([rays_dxo, rays_d], dim=-1)
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plucker = plucker.reshape(B, c2w.shape[1], H, W, 6) # B, V, H, W, 6
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# plucker = plucker.permute(0, 1, 4, 2, 3)
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return plucker
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def process_pose_file(pose_file_path, width=672, height=384, original_pose_width=1280, original_pose_height=720, device='cpu', return_poses=False):
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"""Modified from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
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"""
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if os.path.isfile(pose_file_path):
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with open(pose_file_path, 'r') as f:
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poses = f.readlines()
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else:
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poses = pose_file_path.splitlines()
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poses = [pose.strip().split(' ') for pose in poses[1:]]
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cam_params = [[float(x) for x in pose] for pose in poses]
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if return_poses:
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return cam_params
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else:
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cam_params = [Camera(cam_param) for cam_param in cam_params]
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sample_wh_ratio = width / height
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pose_wh_ratio = original_pose_width / original_pose_height # Assuming placeholder ratios, change as needed
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if pose_wh_ratio > sample_wh_ratio:
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resized_ori_w = height * pose_wh_ratio
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for cam_param in cam_params:
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cam_param.fx = resized_ori_w * cam_param.fx / width
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else:
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resized_ori_h = width / pose_wh_ratio
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for cam_param in cam_params:
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cam_param.fy = resized_ori_h * cam_param.fy / height
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intrinsic = np.asarray([[cam_param.fx * width,
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cam_param.fy * height,
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cam_param.cx * width,
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cam_param.cy * height]
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for cam_param in cam_params], dtype=np.float32)
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K = torch.as_tensor(intrinsic)[None] # [1, 1, 4]
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c2ws = get_relative_pose(cam_params) # Assuming this function is defined elsewhere
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c2ws = torch.as_tensor(c2ws)[None] # [1, n_frame, 4, 4]
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plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous() # V, 6, H, W
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plucker_embedding = plucker_embedding[None]
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plucker_embedding = rearrange(plucker_embedding, "b f c h w -> b f h w c")[0]
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return plucker_embedding
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@@ -5,6 +5,7 @@ import math
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from typing import Tuple, Optional
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from einops import rearrange
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from .utils import hash_state_dict_keys
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from .wan_video_camera_controller import SimpleAdapter
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try:
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import flash_attn_interface
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FLASH_ATTN_3_AVAILABLE = True
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@@ -273,6 +274,8 @@ class WanModel(torch.nn.Module):
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has_image_input: bool,
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has_image_pos_emb: bool = False,
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has_ref_conv: bool = False,
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add_control_adapter: bool = False,
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in_dim_control_adapter: int = 24,
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):
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super().__init__()
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self.dim = dim
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@@ -308,6 +311,10 @@ class WanModel(torch.nn.Module):
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self.ref_conv = nn.Conv2d(16, dim, kernel_size=(2, 2), stride=(2, 2))
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self.has_image_pos_emb = has_image_pos_emb
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self.has_ref_conv = has_ref_conv
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if add_control_adapter:
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self.control_adapter = SimpleAdapter(in_dim_control_adapter, dim, kernel_size=patch_size[1:], stride=patch_size[1:])
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else:
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self.control_adapter = None
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def patchify(self, x: torch.Tensor):
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x = self.patch_embedding(x)
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@@ -612,6 +619,42 @@ class WanModelStateDictConverter:
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"eps": 1e-6,
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"has_ref_conv": True
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}
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elif hash_state_dict_keys(state_dict) == "ac6a5aa74f4a0aab6f64eb9a72f19901":
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# 1.3B PAI control-camera v1.1
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config = {
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"has_image_input": True,
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"patch_size": [1, 2, 2],
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"in_dim": 32,
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"dim": 1536,
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"ffn_dim": 8960,
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"freq_dim": 256,
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"text_dim": 4096,
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"out_dim": 16,
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"num_heads": 12,
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"num_layers": 30,
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"eps": 1e-6,
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"has_ref_conv": False,
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"add_control_adapter": True,
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"in_dim_control_adapter": 24,
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}
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elif hash_state_dict_keys(state_dict) == "b61c605c2adbd23124d152ed28e049ae":
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# 14B PAI control-camera v1.1
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config = {
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"has_image_input": True,
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"patch_size": [1, 2, 2],
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"in_dim": 32,
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"dim": 5120,
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"ffn_dim": 13824,
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"freq_dim": 256,
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"text_dim": 4096,
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"out_dim": 16,
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"num_heads": 40,
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"num_layers": 40,
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"eps": 1e-6,
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"has_ref_conv": False,
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"add_control_adapter": True,
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"in_dim_control_adapter": 24,
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}
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else:
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config = {}
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return state_dict, config
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