samedii / perceptor Goto Github PK
View Code? Open in Web Editor NEWModular image generation library
License: Other
Modular image generation library
License: Other
Other checkpoints are already added but a larger model was trained with a larger resolution too
https://github.com/mlfoundations/open_clip
Port pytorch model https://github.com/jianzhangcs/panini
FileNotFoundError: [Errno 2] No such file or directory: 'perceptor/losses/clip/vectors/textoff.json'
https://github.com/huggingface/diffusers
from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
model_id = "google/ddpm-celebahq-256"
# load model and scheduler
ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference
Three other models from this repo have already been added (super resolution, text2image, and text2image finetuned on no watermarks)
https://github.com/CompVis/latent-diffusion
Choose between
https://github.com/crowsonkb/cloob-training
and
https://github.com/ml-jku/cloob
Might be merged into diffusers
huggingface/diffusers#532
https://github.com/neonsecret/stable-diffusion
https://github.com/TheLastBen/fast-stable-diffusion
Only inference x2.4
https://github.com/facebookincubator/AITemplate/tree/main/examples/05_stable_diffusion
https://huggingface.co/docs/transformers/model_doc/flava
from PIL import Image
import requests
from transformers import FlavaProcessor, FlavaModel
model = FlavaModel.from_pretrained("facebook/flava-full")
processor = FlavaProcessor.from_pretrained("facebook/flava-full")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(text=["a photo of a cat"], images=image, return_tensors="pt", padding=True)
outputs = model(**inputs)
logits_per_image = outputs.contrastive_logits_per_image # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
https://github.com/Sense-GVT/DeCLIP
FILIP and DeCLIP may as well be added at the same time.
We get numerical errors when alpha/sigma is close to zero otherwise
Additionally
LANCZOS is supposed to be good for downsampling
Compare thresholding implementation with majesty diffusion
https://github.com/multimodalart/majesty-diffusion
https://github.com/huggingface/transformers
Allows returning last hidden layer that is used in stable diffusion.
I noticed the use of the spherical loss in the BLIP files. What is the gain you get from this loss?
Add CLIP model from here
https://github.com/openai/glide-text2im
that has been trained to handle noisy images.
256x256 model already added. Should add 512 and 1024 checkpoints for imagenet and ffhq.
https://huggingface.co/nousr/conditioned-prior
https://github.com/laion-ai/deep-image-diffusion-prior
Allows converting text embeddings to image embeddings
Port models from this jax repo https://github.com/google-research/vision_transformer#lit-models
Added in v1.3.0
https://github.com/mlfoundations/open_clip
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.