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Zero
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# coding=utf-8
# Copyright 2025 MMaDA Team
#
# 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 os
import inspect
import sys
os.environ["TOKENIZERS_PARALLELISM"] = "true"
from PIL import Image
from tqdm import tqdm
import numpy as np
import torch
import wandb
from models import MAGVITv2, get_mask_schedule, MMadaModelLM, MMadaConfig
from training.prompting_utils import UniversalPrompting
from training.utils import get_config, flatten_omega_conf, image_transform
from transformers import AutoTokenizer, AutoConfig, AutoModel
import torch.nn.functional as F
import argparse
from datasets import load_dataset
def resize_vocab(model, config):
print(f"Resizing token embeddings to {config.new_vocab_size}")
model.resize_token_embeddings(config.new_vocab_size)
def get_vq_model_class(model_type):
if model_type == "magvitv2":
return MAGVITv2
else:
raise ValueError(f"model_type {model_type} not supported.")
if __name__ == '__main__':
config = get_config()
resume_wandb_run = config.wandb.resume
run_id = config.wandb.get("run_id", None)
if run_id is None:
resume_wandb_run = False
run_id = wandb.util.generate_id()
config.wandb.run_id = run_id
wandb_config = {k: v for k, v in flatten_omega_conf(config, resolve=True)}
wandb.init(
project="inference",
name=config.experiment.name + '_i2i',
config=wandb_config,
)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained(config.model.mmada.pretrained_model_path, padding_side="left")
uni_prompting = UniversalPrompting(tokenizer, max_text_len=config.dataset.preprocessing.max_seq_length, special_tokens=("<|soi|>", "<|eoi|>", "<|sov|>", "<|eov|>", "<|t2i|>", "<|mmu|>", "<|t2v|>", "<|v2v|>", "<|lvg|>"),ignore_id=-100, cond_dropout_prob=config.training.cond_dropout_prob, use_reserved_token=True)
vq_model = get_vq_model_class(config.model.vq_model.type)
vq_model = vq_model.from_pretrained(config.model.vq_model.vq_model_name).to(device)
vq_model.requires_grad_(False)
vq_model.eval()
model = MMadaModelLM.from_pretrained(config.model.mmada.pretrained_model_path, trust_remote_code=True, torch_dtype=torch.bfloat16)
model.to(device)
mask_token_id = model.config.mask_token_id
# image_preprocessor = image_transform(resolution=config.dataset.params.resolution, is_train=False)
# print(f"Loading dataset directly from: {config.hf_data_dir}")
dataset = load_dataset("timbrooks/instructpix2pix-clip-filtered", split="train")
# dataset = dataset.select(range(config.num_samples))
print(f"Processing {len(dataset)} samples.")
# config.training.batch_size = config.batch_size
config.training.batch_size = 1
config.training.guidance_scale = config.guidance_scale
config.training.generation_timesteps = config.generation_timesteps
with open(config.dataset.params.validation_prompts_file, "r") as f:
validation_prompts = f.read().splitlines()
for step, sample in enumerate(tqdm(dataset)):
input_image = sample['original_image'].convert("RGB")
prompts = [sample['edit_prompt']]
image = image_transform(input_image,resolution=config.dataset.params.resolution).unsqueeze(0).to(device)
input_image_tokens = vq_model.get_code(image) + len(uni_prompting.text_tokenizer)
output_image_placeholder = torch.ones((len(prompts), config.model.mmada.num_vq_tokens),
dtype=torch.long, device=device) * mask_token_id
input_ids, attention_mask = uni_prompting(
(prompts, input_image_tokens, output_image_placeholder), 'i2i_gen'
)
if config.training.guidance_scale > 0:
uncond_input_ids, uncond_attention_mask = uni_prompting(([''] * len(prompts), input_image_tokens,
output_image_placeholder), 'i2i_gen')
else:
uncond_input_ids = None
uncond_attention_mask = None
if config.get("mask_schedule", None) is not None:
schedule = config.mask_schedule.schedule
args = config.mask_schedule.get("params", {})
mask_schedule = get_mask_schedule(schedule, **args)
else:
mask_schedule = get_mask_schedule(config.training.get("mask_schedule", "cosine"))
with torch.no_grad():
gen_token_ids = model.i2i_generate(
input_ids=input_ids,
uncond_input_ids=uncond_input_ids,
attention_mask=attention_mask,
uncond_attention_mask=uncond_attention_mask,
guidance_scale=config.training.guidance_scale,
temperature=config.training.get("generation_temperature", 1.0),
timesteps=config.training.generation_timesteps,
noise_schedule=mask_schedule,
noise_type=config.training.get("noise_type", "mask"),
seq_len=config.model.mmada.num_vq_tokens,
uni_prompting=uni_prompting,
config=config,
)
gen_token_ids = torch.clamp(gen_token_ids, max=config.model.mmada.codebook_size - 1, min=0)
images = vq_model.decode_code(gen_token_ids)
images = torch.clamp((images + 1.0) / 2.0, min=0.0, max=1.0)
images *= 255.0
images = images.permute(0, 2, 3, 1).cpu().numpy().astype(np.uint8)
pil_images = [Image.fromarray(image) for image in images]
wandb_images = []
pil_images = [Image.fromarray(image) for image in images]
generated_image = pil_images[0]
original_image = input_image
caption = f"Prompt: {prompts}"
wandb.log({
"Image-to-Image Results": [
wandb.Image(original_image, caption=f"Original (Step {step})"),
wandb.Image(generated_image, caption=f"Edited - {caption} (Step {step})")
]
}, step=step) |