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Running
on
Zero
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# Copyright (C) 2025 AIDC-AI
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
import torch
from torch import nn, Tensor
from ovis_image.model.layers import (
DoubleStreamBlock,
EmbedND,
LastLayer,
MLPEmbedder,
SingleStreamBlock,
timestep_embedding,
)
from ovis_image.model.args import OvisImageModelArgs
class OvisImageModel(nn.Module):
def __init__(self, model_args: OvisImageModelArgs):
super().__init__()
self.model_args = model_args
self.in_channels = model_args.in_channels
self.out_channels = model_args.out_channels
if model_args.hidden_size % model_args.num_heads != 0:
raise ValueError(
f"Hidden size {model_args.hidden_size} must be divisible by num_heads {model_args.num_heads}"
)
pe_dim = model_args.hidden_size // model_args.num_heads
if sum(model_args.axes_dim) != pe_dim:
raise ValueError(
f"Got {model_args.axes_dim} but expected positional dim {pe_dim}"
)
self.hidden_size = model_args.hidden_size
self.num_heads = model_args.num_heads
self.pe_embedder = EmbedND(
dim=pe_dim, theta=model_args.theta, axes_dim=model_args.axes_dim
)
self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
self.semantic_txt_norm = nn.RMSNorm(model_args.context_in_dim, eps=1e-6)
self.semantic_txt_in = nn.Linear(model_args.context_in_dim, self.hidden_size, bias=True)
if model_args.norm == "layernorm":
norm_layer = nn.LayerNorm
else:
norm_layer = nn.RMSNorm
DoubleBlock = DoubleStreamBlock
self.double_blocks = nn.ModuleList(
[
DoubleBlock(
self.hidden_size,
self.num_heads,
mlp_ratio=model_args.mlp_ratio,
qkv_bias=model_args.qkv_bias,
activation=model_args.activation,
norm_layer=norm_layer,
)
for _ in range(model_args.depth)
]
)
self.single_blocks = nn.ModuleList(
[
SingleStreamBlock(
self.hidden_size,
self.num_heads,
mlp_ratio=model_args.mlp_ratio,
qkv_bias=model_args.qkv_bias,
activation=model_args.activation,
norm_layer=norm_layer,
)
for _ in range(model_args.depth_single_blocks)
]
)
self.final_layer = LastLayer(
self.hidden_size,
1,
self.out_channels,
norm_layer=norm_layer,
)
def forward(
self,
img: Tensor,
img_ids: Tensor,
txt: Tensor,
txt_ids: Tensor,
timesteps: Tensor,
) -> Tensor:
if img.ndim != 3 or txt.ndim != 3:
raise ValueError("Input img and txt tensors must have 3 dimensions.")
# running on sequences img
img = self.img_in(img)
vec = self.time_in(timestep_embedding(timesteps, 256))
txt = self.semantic_txt_norm(txt)
txt = self.semantic_txt_in(txt)
ids = torch.cat((txt_ids, img_ids), dim=1)
pe = self.pe_embedder(ids)
for block in self.double_blocks:
img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
img = torch.cat((txt, img), 1)
for block in self.single_blocks:
img = block(img, vec=vec, pe=pe)
img = img[:, txt.shape[1] :, ...]
img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels)
return img
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