Update README.md

README.md

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----
-license: apache-2.0
----
+---
+pipeline_tag: any-to-any
+license: apache-2.0
+library_name: transformers
+---
+
+<div align='center'>
+<h1>Emu3: Next-Token Prediction is All You Need</h1h1>
+<h3></h3>
+
+[Emu3 Team, BAAI](https://www.baai.ac.cn/english.html)
+
+| [Project Page](https://emu.baai.ac.cn) | [Paper](https://huggingface.co/papers/2409.18869) | [🤗HF Models](https://huggingface.co/collections/BAAI/emu3-66f4e64f70850ff358a2e60f) |  [github](https://github.com/baaivision/Emu3)
+ | [Demo](https://huggingface.co/spaces/BAAI/Emu3) |
+
+
+</div>
+
+<div align='center'>
+<img src="https://github.com/baaivision/Emu3/blob/main/assets/arch.png?raw=True" class="interpolation-image" alt="arch." height="80%" width="70%" />
+</div>
+
+We introduce **Emu3**, a new suite of state-of-the-art multimodal models trained solely with **<i>next-token prediction</i>**! By tokenizing images, text, and videos into a discrete space, we train a single transformer from scratch on a mixture of multimodal sequences.
+
+### Emu3 excels in both generation and perception
+**Emu3** outperforms several well-established task-specific models in both generation and perception tasks, surpassing flagship open models such as SDXL, LLaVA-1.6 and OpenSora-1.2, while eliminating the need for diffusion or compositional architectures.
+
+<div align='center'>
+<img src="https://github.com/baaivision/Emu3/blob/main/assets/comparison.png?raw=True" class="interpolation-image" alt="comparison." height="80%" width="80%" />
+</div>
+
+### Highlights
+
+- **Emu3** is capable of generating high-quality images following the text input, by simply predicting the next vision token. The model naturally supports flexible resolutions and styles.
+- **Emu3** shows strong vision-language understanding capabilities to see the physical world and provides coherent text responses. Notably, this capability is achieved without depending on a CLIP and a pretrained LLM.
+- **Emu3** simply generates a video causally by predicting the next token in a video sequence, unlike the video diffusion model as in Sora. With a video in context, Emu3 can also naturally extend the video and predict what will happen next. 
+
+
+
+#### Quickstart
+
+```python
+from PIL import Image
+from transformers import AutoTokenizer, AutoModel, AutoImageProcessor, AutoModelForCausalLM
+from transformers.generation.configuration_utils import GenerationConfig
+from transformers.generation import LogitsProcessorList, PrefixConstrainedLogitsProcessor, UnbatchedClassifierFreeGuidanceLogitsProcessor
+import torch
+
+import sys
+sys.path.append(PATH_TO_BAAI_Emu3-Gen_MODEL)
+from processing_emu3 import Emu3Processor
+
+# model path
+EMU_HUB = "BAAI/Emu3-Gen"
+VQ_HUB = "BAAI/Emu3-VisionTokenizer"
+
+# prepare model and processor
+model = AutoModelForCausalLM.from_pretrained(
+    EMU_HUB,
+    device_map="cuda:0",
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2",
+    trust_remote_code=True,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(EMU_HUB, trust_remote_code=True)
+image_processor = AutoImageProcessor.from_pretrained(VQ_HUB, trust_remote_code=True)
+image_tokenizer = AutoModel.from_pretrained(VQ_HUB, device_map="cuda:0", trust_remote_code=True).eval()
+processor = Emu3Processor(image_processor, image_tokenizer, tokenizer)
+
+# prepare input
+POSITIVE_PROMPT = " masterpiece, film grained, best quality."
+NEGATIVE_PROMPT = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry."
+
+classifier_free_guidance = 3.0
+prompt = "a portrait of young girl."
+prompt += POSITIVE_PROMPT
+
+kwargs = dict(
+    mode='G',
+    ratio="1:1",
+    image_area=model.config.image_area,
+    return_tensors="pt",
+)
+pos_inputs = processor(text=prompt, **kwargs)
+neg_inputs = processor(text=NEGATIVE_PROMPT, **kwargs)
+
+# prepare hyper parameters
+GENERATION_CONFIG = GenerationConfig(
+    use_cache=True,
+    eos_token_id=model.config.eos_token_id,
+    pad_token_id=model.config.pad_token_id,
+    max_new_tokens=40960,
+    do_sample=True,
+    top_k=2048,
+)
+
+h, w = pos_inputs.image_size[0]
+constrained_fn = processor.build_prefix_constrained_fn(h, w)
+logits_processor = LogitsProcessorList([
+    UnbatchedClassifierFreeGuidanceLogitsProcessor(
+        classifier_free_guidance,
+        model,
+        unconditional_ids=neg_inputs.input_ids.to("cuda:0"),
+    ),
+    PrefixConstrainedLogitsProcessor(
+        constrained_fn ,
+        num_beams=1,
+    ),
+])
+
+# generate
+outputs = model.generate(
+    pos_inputs.input_ids.to("cuda:0"),
+    GENERATION_CONFIG,
+    logits_processor=logits_processor
+)
+
+mm_list = processor.decode(outputs[0])
+for idx, im in enumerate(mm_list):
+    if not isinstance(im, Image.Image):
+        continue
+    im.save(f"result_{idx}.png")
+
+```