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	---
	language:
	- fr

	thumbnail: https://raw.githubusercontent.com/AntoineSimoulin/gpt-fr/main/imgs/logo.png
	tags:
	- tf
	- pytorch
	- gpt2
	- text-to-image
	license: apache-2.0
	---

	<img src="https://raw.githubusercontent.com/AntoineSimoulin/gpt-fr/main/imgs/igpt-logo.png" width="400">

	## Model description

	iGPT-fr 🇫🇷 is a GPT model for French pre-trained incremental language model developped by the [Laboratoire de Linguistique Formelle (LLF)](http://www.llf.cnrs.fr/en). We adapted [GPT-fr 🇫🇷](https://huggingface.co/asi/gpt-fr-cased-base) model to generate images conditionned by text inputs.

	## Intended uses & limitations

	The model can be leveraged for image generation tasks. The model is currently under a developpment phase.

	#### How to use

	The model might be used through the 🤗 `Transformers` librairie. You will also need to install the `Taming Transformers` library for high-resolution image synthesis:

	```bash
	pip install git+https://github.com/CompVis/taming-transformers.git
	```

	```python
	from transformers import GPT2Tokenizer, GPT2LMHeadModel
	from huggingface_hub import hf_hub_download
	from omegaconf import OmegaConf
	from taming.models import vqgan
	import torch
	from PIL import Image
	import numpy as np

	# Load VQGAN model
	vqgan_ckpt = hf_hub_download(repo_id="boris/vqgan_f16_16384", filename="model.ckpt", force_download=False)
	vqgan_config = hf_hub_download(repo_id="boris/vqgan_f16_16384", filename="config.yaml", force_download=False)

	config = OmegaConf.load(vqgan_config)
	vqgan_model = vqgan.VQModel(**config.model.params)
	vqgan_model.eval().requires_grad_(False)
	vqgan_model.init_from_ckpt(vqgan_ckpt)

	# Load pretrained model
	model = GPT2LMHeadModel.from_pretrained("asi/igpt-fr-cased-base")
	model.eval()
	tokenizer = GPT2Tokenizer.from_pretrained("asi/igpt-fr-cased-base")

	# Generate a sample of text
	input_sentence = "Une carte de l'europe"
	input_ids = tokenizer.encode(input_sentence, return_tensors='pt')
	input_ids = torch.cat((input_ids, torch.tensor([[50000]])), 1) # Add image generation token

	greedy_output = model.generate(
	input_ids.to(device),
	max_length=256+input_ids.shape[1],
	do_sample=True,
	top_p=0.92,
	top_k=0)

	def custom_to_pil(x):
	x = x.detach().cpu()
	x = torch.clamp(x, -1., 1.)
	x = (x + 1.)/2.
	x = x.permute(1,2,0).numpy()
	x = (255*x).astype(np.uint8)
	x = Image.fromarray(x)
	if not x.mode == "RGB":
	x = x.convert("RGB")
	return x

	z_idx = greedy_output[0, input_ids.shape[1]:] - 50001
	z_quant = vqgan_model.quantize.get_codebook_entry(z_idx, shape=(1, 16, 16, 256))
	x_rec = vqgan_model.decode(z_quant).to('cpu')[0]
	display(custom_to_pil(x_rec))
	```

	You may also filter results based on CLIP:

	```python
	from tqdm import tqdm

	def hallucinate(prompt, num_images=64):
	input_ids = tokenizer.encode(prompt, return_tensors='pt')
	input_ids = torch.cat((input_ids, torch.tensor([[50000]])), 1).to(device) # Add image generation token

	all_images = []
	for i in tqdm(range(num_images)):
	greedy_output = model.generate(
	input_ids.to(device),
	max_length=256+input_ids.shape[1],
	do_sample=True,
	top_p=0.92,
	top_k=0)

	z_idx = greedy_output[0, input_ids.shape[1]:] - 50001
	z_quant = vqgan_model.quantize.get_codebook_entry(z_idx, shape=(1, 16, 16, 256))
	x_rec = vqgan_model.decode(z_quant).to('cpu')[0]
	all_images.append(custom_to_pil(x_rec))
	return all_images

	input_sentence = "Une carte de l'europe"
	all_images = hallucinate(input_sentence)

	from transformers import pipeline

	opus_model = "Helsinki-NLP/opus-mt-fr-en"
	opus_translator = pipeline("translation", model=opus_model)

	opus_translator(input_sentence)

	from transformers import CLIPProcessor, CLIPModel

	clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
	clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")

	def clip_top_k(prompt, images, k=8):
	prompt_fr = opus_translator(input_sentence)[0]['translation_text']
	inputs = clip_processor(text=prompt_fr, images=images, return_tensors="pt", padding=True)
	outputs = clip_model(**inputs)
	logits = outputs.logits_per_text # this is the image-text similarity score
	scores = np.array(logits[0].detach()).argsort()[-k:][::-1]
	return [images[score] for score in scores]

	filtered_images = clip_top_k(input_sentence, all_images)

	for fi in filtered_images:
	display(fi)
	```

	## Training data

	We created a dedicated corpus to train our generative model. The training corpus consists in text-image pairs. We aggregated portions from existing corpora: [Laion-5B](https://laion.ai/blog/laion-5b/) and [WIT](https://github.com/google-research-datasets/wit). The final dataset includes 10,807,534 samples.

	## Training procedure

	We pre-trained the model on the new CNRS (French National Centre for Scientific Research) [Jean Zay](http://www.idris.fr/eng/jean-zay/) supercomputer. We perform the training within a total of 140 hours of computation on Tesla V-100 hardware (TDP of 300W). The training was distributed on 8 compute nodes of 8 GPUs. We used data parallelization in order to divide each micro-batch on the computing units. We estimated the total emissions at 1161.22 kgCO2eq, using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al., (2019)](lacoste-2019).