:books: add readme

README.md

@@ -1,3 +1,143 @@
 ---
+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="200">
+
+## 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).