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from PIL import Image
class NexusGenAutoregressiveModel(torch.nn.Module):
def __init__(self, max_length=1024, max_pixels=262640):
super(NexusGenAutoregressiveModel, self).__init__()
from .nexus_gen_ar_model import Qwen2_5_VLForConditionalGeneration
from transformers import Qwen2_5_VLConfig
self.max_length = max_length
self.max_pixels = max_pixels
model_config = Qwen2_5_VLConfig(**{
"_name_or_path": "DiffSynth-Studio/Nexus-GenV2",
"architectures": [
"Qwen2_5_VLForConditionalGeneration"
],
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "configuration_qwen2_5_vl.Qwen2_5_VLConfig",
"AutoModel": "modeling_qwen2_5_vl.Qwen2_5_VLModel",
"AutoModelForCausalLM": "modeling_qwen2_5_vl.Qwen2_5_VLForConditionalGeneration"
},
"bos_token_id": 151643,
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 3584,
"image_token_id": 151655,
"initializer_range": 0.02,
"intermediate_size": 18944,
"max_position_embeddings": 128000,
"max_window_layers": 28,
"model_type": "qwen2_5_vl",
"num_attention_heads": 28,
"num_hidden_layers": 28,
"num_key_value_heads": 4,
"pad_token_id": 151643,
"rms_norm_eps": 1e-06,
"rope_scaling": {
"mrope_section": [
16,
24,
24
],
"rope_type": "default",
"type": "default"
},
"rope_theta": 1000000.0,
"sliding_window": 32768,
"tie_word_embeddings": False,
"torch_dtype": "bfloat16",
"transformers_version": "4.49.0",
"use_cache": False,
"use_sliding_window": False,
"video_token_id": 151656,
"vision_config": {
"hidden_size": 1280,
"in_chans": 3,
"model_type": "qwen2_5_vl",
"spatial_patch_size": 14,
"tokens_per_second": 2,
"torch_dtype": "bfloat16"
},
"vision_end_token_id": 151653,
"vision_start_token_id": 151652,
"vision_token_id": 151654,
"vocab_size": 152064
})
self.model = Qwen2_5_VLForConditionalGeneration(model_config)
self.processor = None
def load_processor(self, path):
from .nexus_gen_ar_model import Qwen2_5_VLProcessor
self.processor = Qwen2_5_VLProcessor.from_pretrained(path)
@staticmethod
def state_dict_converter():
return NexusGenAutoregressiveModelStateDictConverter()
def bound_image(self, image, max_pixels=262640):
from qwen_vl_utils import smart_resize
resized_height, resized_width = smart_resize(
image.height,
image.width,
max_pixels=max_pixels,
)
return image.resize((resized_width, resized_height))
def get_editing_msg(self, instruction):
if '<image>' not in instruction:
instruction = '<image> ' + instruction
messages = [{"role":"user", "content":instruction}, {"role":"assistant", "content":"Here is the image: <image>"}]
return messages
def get_generation_msg(self, instruction):
instruction = "Generate an image according to the following description: {}".format(instruction)
messages = [{"role":"user", "content":instruction}, {"role":"assistant", "content":"Here is an image based on the description: <image>"}]
return messages
def forward(self, instruction, ref_image=None, num_img_tokens=81):
"""
Generate target embeddings for the given instruction and reference image.
"""
if ref_image is not None:
messages = self.get_editing_msg(instruction)
images = [self.bound_image(ref_image)] + [Image.new(mode='RGB', size=(252, 252), color=(255, 255, 255))]
output_image_embeddings = self.get_target_embeddings(images, messages, self.processor, self.model, num_img_tokens)
else:
messages = self.get_generation_msg(instruction)
images = [Image.new(mode='RGB', size=(252, 252), color=(255, 255, 255))]
output_image_embeddings = self.get_target_embeddings(images, messages, self.processor, self.model, num_img_tokens)
return output_image_embeddings
def get_target_embeddings(self, images, messages, processor, model, num_img_tokens=81):
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
text = text.replace('<image>', '<|vision_start|><|image_pad|><|vision_end|>')
inputs = processor(
text=[text],
images=images,
padding=True,
return_tensors="pt",
)
inputs = inputs.to(model.device)
input_embeds = model.model.embed_tokens(inputs['input_ids'])
image_embeds = model.visual(inputs['pixel_values'], grid_thw=inputs['image_grid_thw'])
ground_truth_image_embeds = image_embeds[-num_img_tokens:]
input_image_embeds = image_embeds[:-num_img_tokens]
image_mask = inputs['input_ids'] == model.config.image_token_id
indices = image_mask.cumsum(dim=1)
input_image_mask = torch.logical_and(indices <= (image_embeds.shape[0] - ground_truth_image_embeds.shape[0]), image_mask)
gt_image_mask = torch.logical_and(image_mask, ~input_image_mask)
input_image_mask = input_image_mask.unsqueeze(-1).expand_as(input_embeds)
input_embeds = input_embeds.masked_scatter(input_image_mask, input_image_embeds)
image_prefill_embeds = model.image_prefill_embeds(
torch.arange(81, device=model.device).long()
)
input_embeds = input_embeds.masked_scatter(gt_image_mask.unsqueeze(-1).expand_as(input_embeds), image_prefill_embeds)
position_ids, _ = model.get_rope_index(
inputs['input_ids'],
inputs['image_grid_thw'],
attention_mask=inputs['attention_mask'])
position_ids = position_ids.contiguous()
outputs = model(inputs_embeds=input_embeds, position_ids=position_ids, attention_mask=inputs['attention_mask'], return_dict=True)
output_image_embeddings = outputs.image_embeddings[:, :-1, :]
output_image_embeddings = output_image_embeddings[gt_image_mask[:, 1:]]
return output_image_embeddings, input_image_embeds, inputs['image_grid_thw']
class NexusGenAutoregressiveModelStateDictConverter:
def __init__(self):
pass
def from_civitai(self, state_dict):
state_dict = {"model." + key: value for key, value in state_dict.items()}
return state_dict
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