Support JoyAI-Image-Edit (#1393)

* auto intergrate joyimage model

* joyimage pipeline

* train

* ready

* styling

* joyai-image docs

* update readme

* pr review
This commit is contained in:
Hong Zhang
2026-04-15 16:57:11 +08:00
committed by GitHub
parent 8f18e24597
commit 079e51c9f3
21 changed files with 2040 additions and 123 deletions

View File

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import torch
from typing import Optional
class JoyAIImageTextEncoder(torch.nn.Module):
def __init__(self):
super().__init__()
from transformers import Qwen3VLConfig, Qwen3VLForConditionalGeneration
config = Qwen3VLConfig(
text_config={
"attention_bias": False,
"attention_dropout": 0.0,
"bos_token_id": 151643,
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 12288,
"max_position_embeddings": 262144,
"model_type": "qwen3_vl_text",
"num_attention_heads": 32,
"num_hidden_layers": 36,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-6,
"rope_scaling": {
"mrope_interleaved": True,
"mrope_section": [24, 20, 20],
"rope_type": "default",
},
"rope_theta": 5000000,
"use_cache": True,
"vocab_size": 151936,
},
vision_config={
"deepstack_visual_indexes": [8, 16, 24],
"depth": 27,
"hidden_act": "gelu_pytorch_tanh",
"hidden_size": 1152,
"in_channels": 3,
"initializer_range": 0.02,
"intermediate_size": 4304,
"model_type": "qwen3_vl",
"num_heads": 16,
"num_position_embeddings": 2304,
"out_hidden_size": 4096,
"patch_size": 16,
"spatial_merge_size": 2,
"temporal_patch_size": 2,
},
image_token_id=151655,
video_token_id=151656,
vision_start_token_id=151652,
vision_end_token_id=151653,
tie_word_embeddings=False,
)
self.model = Qwen3VLForConditionalGeneration(config)
self.config = config
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
pixel_values: Optional[torch.Tensor] = None,
image_grid_thw: Optional[torch.LongTensor] = None,
**kwargs,
):
pre_norm_output = [None]
def hook_fn(module, args, kwargs_output=None):
pre_norm_output[0] = args[0]
self.model.model.language_model.norm.register_forward_hook(hook_fn)
_ = self.model(
input_ids=input_ids,
pixel_values=pixel_values,
image_grid_thw=image_grid_thw,
attention_mask=attention_mask,
output_hidden_states=True,
**kwargs,
)
return pre_norm_output[0]