Xuehang Cang PRO
XuehangCang
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Smart Education Fine-Tuning Knowledge Distillation Neural Network
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XuehangCang/safe_prompt:Librarian Bot: Add language metadata for dataset updated a collection 7 days ago
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posted an update about 2 months ago
reacted to aifeifei798's post with ๐๐ฅ about 1 year ago
Post
4013
๐ This program is designed to remove emojis from a given text. It uses a regular expression (regex) pattern to match and replace emojis with an empty string, effectively removing them from the text. The pattern includes a range of Unicode characters that correspond to various types of emojis, such as emoticons, symbols, and flags. By using this program, you can clean up text data by removing any emojis that may be present, which can be useful for text processing, analysis, or other applications where emojis are not desired. ๐ป
import re
def remove_emojis(text):
# Define a broader emoji pattern
emoji_pattern = re.compile(
"["
u"\U0001F600-\U0001F64F" # emoticons
u"\U0001F300-\U0001F5FF" # symbols & pictographs
u"\U0001F680-\U0001F6FF" # transport & map symbols
u"\U0001F1E0-\U0001F1FF" # flags (iOS)
u"\U00002702-\U000027B0"
u"\U000024C2-\U0001F251"
u"\U0001F900-\U0001F9FF" # supplemental symbols and pictographs
u"\U0001FA00-\U0001FA6F" # chess symbols and more emojis
u"\U0001FA70-\U0001FAFF" # more symbols and pictographs
u"\U00002600-\U000026FF" # miscellaneous symbols
u"\U00002B50-\U00002B59" # additional symbols
u"\U0000200D" # zero width joiner
u"\U0000200C" # zero width non-joiner
u"\U0000FE0F" # emoji variation selector
"]+", flags=re.UNICODE
)
return emoji_pattern.sub(r'', text)reacted to sanaka87's post with ๐ฅ over 1 year ago
Post
1836
๐ Excited to Share Our Latest Work: 3DIS & 3DIS-FLUX for Multi-Instance Layout-to-Image Generation! โค๏ธโค๏ธโค๏ธ
๐จ Daily Paper: 3DIS-FLUX: simple and efficient multi-instance generation with DiT rendering (2501.05131)
๐ Code is now open source!
๐ Project Website: https://limuloo.github.io/3DIS/
๐ GitHub Repository: https://github.com/limuloo/3DIS
๐ 3DIS Paper: https://arxiv.org/abs/2410.12669
๐ 3DIS-FLUX Tech Report: https://arxiv.org/abs/2501.05131
๐ฅ Why 3DIS & 3DIS-FLUX?
Current SOTA multi-instance generation methods are typically adapter-based, requiring additional control modules trained on pre-trained models for layout and instance attribute control. However, with the emergence of more powerful models like FLUX and SD3.5, these methods demand constant retraining and extensive resources.
โจ Our Solution: 3DIS
We introduce a decoupled approach that only requires training a low-resolution Layout-to-Depth model to convert layouts into coarse-grained scene depth maps. Leveraging community and company pre-trained models like ControlNet + SAM2, we enable training-free controllable image generation on high-resolution models such as SDXL and FLUX.
๐ Benefits of Our Decoupled Multi-Instance Generation:
1. Enhanced Control: By constructing scenes using depth maps in the first stage, the model focuses on coarse-grained scene layout, improving control over instance placement.
2. Flexibility & Preservation: The second stage employs training-free rendering methods, allowing seamless integration with various models (e.g., fine-tuned weights, LoRA) while maintaining the generative capabilities of pre-trained models.
Join us in advancing Layout-to-Image Generation! Follow and star our repository to stay updated! โญ
๐จ Daily Paper: 3DIS-FLUX: simple and efficient multi-instance generation with DiT rendering (2501.05131)
๐ Code is now open source!
๐ Project Website: https://limuloo.github.io/3DIS/
๐ GitHub Repository: https://github.com/limuloo/3DIS
๐ 3DIS Paper: https://arxiv.org/abs/2410.12669
๐ 3DIS-FLUX Tech Report: https://arxiv.org/abs/2501.05131
๐ฅ Why 3DIS & 3DIS-FLUX?
Current SOTA multi-instance generation methods are typically adapter-based, requiring additional control modules trained on pre-trained models for layout and instance attribute control. However, with the emergence of more powerful models like FLUX and SD3.5, these methods demand constant retraining and extensive resources.
โจ Our Solution: 3DIS
We introduce a decoupled approach that only requires training a low-resolution Layout-to-Depth model to convert layouts into coarse-grained scene depth maps. Leveraging community and company pre-trained models like ControlNet + SAM2, we enable training-free controllable image generation on high-resolution models such as SDXL and FLUX.
๐ Benefits of Our Decoupled Multi-Instance Generation:
1. Enhanced Control: By constructing scenes using depth maps in the first stage, the model focuses on coarse-grained scene layout, improving control over instance placement.
2. Flexibility & Preservation: The second stage employs training-free rendering methods, allowing seamless integration with various models (e.g., fine-tuned weights, LoRA) while maintaining the generative capabilities of pre-trained models.
Join us in advancing Layout-to-Image Generation! Follow and star our repository to stay updated! โญ
reacted to AdinaY's post with ๐ค๐ฅ over 1 year ago
Post
2395
Excited to see Alibaba DAMO Academy release a multimodel dataset for vision language pretraining on the hub๐ฅ
Paper: 2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining (2501.00958)
Dataset: DAMO-NLP-SG/multimodal_textbook
โจ 6.5M images + 0.8B text from 22k hours of instructional videos
โจ Covers subjects like math, physics, and chemistry
โจ Apache 2.0
Paper: 2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining (2501.00958)
Dataset: DAMO-NLP-SG/multimodal_textbook
โจ 6.5M images + 0.8B text from 22k hours of instructional videos
โจ Covers subjects like math, physics, and chemistry
โจ Apache 2.0
reacted to nroggendorff's post with โ over 1 year ago
Post
6396
hey nvidia, can you send me a gpu?
comment or react if you want ~~me~~ to get one too. ๐๐
comment or react if you want ~~me~~ to get one too. ๐๐
- 33 replies
reacted to ehristoforu's post with โค๏ธ๐ฅ about 2 years ago
Post
3139
๐ค Hello, I have great news! FluentlyXL Final is finally here, the final release of the FluentlyXL model series. We've improved the overall aesthetics, lighting, and more.
๐ฆพ Model on HF: fluently/Fluently-XL-Final
๐ฅ Model on CivitAI: https://civitai.com/models/324891
๐ Playground: fluently/Fluently-Playground
๐ฆพ Model on HF: fluently/Fluently-XL-Final
๐ฅ Model on CivitAI: https://civitai.com/models/324891
๐ Playground: fluently/Fluently-Playground
- 4 replies
you can see