Instructions to use bigshanedogg/clip-flant5-xxl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bigshanedogg/clip-flant5-xxl with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="bigshanedogg/clip-flant5-xxl", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("bigshanedogg/clip-flant5-xxl", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bigshanedogg/clip-flant5-xxl with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bigshanedogg/clip-flant5-xxl"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bigshanedogg/clip-flant5-xxl",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bigshanedogg/clip-flant5-xxl

SGLang

How to use bigshanedogg/clip-flant5-xxl with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "bigshanedogg/clip-flant5-xxl" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bigshanedogg/clip-flant5-xxl",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "bigshanedogg/clip-flant5-xxl" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bigshanedogg/clip-flant5-xxl",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bigshanedogg/clip-flant5-xxl with Docker Model Runner:
```
docker model run hf.co/bigshanedogg/clip-flant5-xxl
```

CLIP-FlanT5-XXL (VQAScore) — `trust_remote_code` HuggingFace format

Unofficial transformers-format port of CLIP-FlanT5-XXL, the VQAScore judge from "Evaluating Text-to-Visual Generation with Image-to-Text Generation" (Lin et al., 2024). It bundles a self-contained modeling_clipflant5.py and an auto_map, so the model loads with AutoModelForSeq2SeqLM.from_pretrained(..., trust_remote_code=True) under transformers 5.X (and 4.57) with no llava / t2v_metrics dependency and no pinned old transformers.

Not an official release. Model, weights, and architecture (CLIPT5ForConditionalGeneration) are by Zhiqiu Lin et al. — original weights zhiqiulin/clip-flant5-xxl (Apache-2.0), code https://github.com/linzhiqiu/CLIP-FlanT5 and https://github.com/linzhiqiu/t2v_metrics, paper arXiv:2404.01291. This repo re-packages that checkpoint for trust_remote_code loading and is used as the paper-strict VQAScore judge for the CVTG-2K benchmark (TextCrafter, arXiv:2503.23461).

What it is

A CLIP ViT-L/14-336 vision tower + a FlanT5-XXL encoder-decoder. Image patch features are projected and spliced into the T5 encoder inputs at an <image> placeholder; the T5 decoder then scores an answer. VQAScore = the probability the decoder assigns to "Yes" for the question 'Does this figure show "{caption}"? Please answer yes or no.' (exp(-CrossEntropy) under teacher forcing) — a reference-free image↔text alignment score.

modeling_clipflant5.py consolidates the upstream clip_t5.py + vision tower + projector + preprocessing (t5_tokenizer_image_token, expand2square) into one module using only public T5/CLIP transformers API.

transformers-5.X note. generate() / prepare_inputs_for_generation are intentionally removed — they indexed the legacy tuple KV-cache (gone in 5.X), and VQAScore needs only the teacher-forced forward() + logits. Use this model for scoring, not free-form generation.

Usage (scoring)

import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model = AutoModelForSeq2SeqLM.from_pretrained("bigshanedogg/clip-flant5-xxl", trust_remote_code=True).eval()
tokenizer = AutoTokenizer.from_pretrained("bigshanedogg/clip-flant5-xxl")
# Preprocess the image with model.vision_tower.image_processor, wrap the question with
# `format_question(...)` (t5_chat), tokenize with `t5_tokenizer_image_token`, then run a teacher-forced
# forward with labels="Yes" and read exp(-CrossEntropy) — see modeling_clipflant5.py's docstring.

Licensing

CLIP-FlanT5 model / weights / code (Zhiqiu Lin et al.): Apache-2.0 — https://github.com/linzhiqiu/CLIP-FlanT5
FlanT5-XXL base (Google): Apache-2.0. OpenAI CLIP ViT-L/14-336 vision tower: MIT.
This port (modeling_clipflant5.py, packaging): Apache-2.0 — see LICENSE; attribution in NOTICE.

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Image-Text-to-Text

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Papers for bigshanedogg/clip-flant5-xxl

TextCrafter: Accurately Rendering Multiple Texts in Complex Visual Scenes

Paper • 2503.23461 • Published Mar 30, 2025 • 94

Evaluating Text-to-Visual Generation with Image-to-Text Generation

Paper • 2404.01291 • Published Apr 1, 2024 • 6

CLIP-FlanT5-XXL (VQAScore) — trust_remote_code HuggingFace format

What it is

Usage (scoring)

Licensing

Papers for bigshanedogg/clip-flant5-xxl

CLIP-FlanT5-XXL (VQAScore) — `trust_remote_code` HuggingFace format