Update README.md

README.md

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 ---
 license: apache-2.0
+base_model:
+- BAAI/Emu3-Gen
 library_name: transformers
+tags:
+- merge
 ---
 
-<div align='center'>
-<h1>Emu3: Next-Token Prediction is All You Need</h1h1>
-<h3></h3>
+This is an interpolated upscale of [BAAI/Emu3-Gen](https://huggingface.co/BAAI/Emu3-Gen) from 8B to 11.5B.
+For each layer in [7,8,9,10,11,12,13,14,15,16,22,23,24], the weights were lerp'd between the previous layer and the current and inserted between the two.
 
-[Emu3 Team, BAAI](https://www.baai.ac.cn/english.html)
-
-| [Project Page](https://emu.baai.ac.cn) | [Paper](https://baai-solution.ks3-cn-beijing.ksyuncs.com/emu3/Emu3-tech-report.pdf?KSSAccessKeyId=AKLTgew6Kdg6RsK92QSfB2KLA&Expires=2591406552&Signature=6BvwfLVqvfww26Bhwvk3mG0FrL8%3D) | [🤗HF Models](https://huggingface.co/collections/BAAI/emu3-66f4e64f70850ff358a2e60f) |  [github](https://github.com/baaivision/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")
-
-```