Post
7175
AI is not a zero-sum game. Open-source AI is the tide that lifts all boats!
Tony Assi
tonyassi
AI & ML interests
computer vision, generative models, fashion, photography, marketing, e-commerce
Recent Activity
updated
a Space
3 days ago
tonyassi/MGM-Film-Diffusion
published
a dataset
3 days ago
tonyassi/foot2
updated
a Space
3 days ago
tonyassi/MGM-Film-Diffusion
Organizations
tonyassi's activity
reacted to
clem's
post with π₯
25 days ago
reacted to
abidlabs's
post with β€οΈ
5 months ago
Post
5593
π Hi Gradio community,
I'm excited to share that Gradio 5 will launch in October with improvements across security, performance, SEO, design (see the screenshot for Gradio 4 vs. Gradio 5), and user experience, making Gradio a mature framework for web-based ML applications.
Gradio 5 is currently in beta, so if you'd like to try it out early, please refer to the instructions below:
---------- Installation -------------
Gradio 5 depends on Python 3.10 or higher, so if you are running Gradio locally, please ensure that you have Python 3.10 or higher, or download it here: https://www.python.org/downloads/
* Locally: If you are running gradio locally, simply install the release candidate with
* Spaces: If you would like to update an existing gradio Space to use Gradio 5, you can simply update the
In most cases, thatβs all you have to do to run Gradio 5.0. If you start your Gradio application, you should see your Gradio app running, with a fresh new UI.
-----------------------------
Fore more information, please see: https://github.com/gradio-app/gradio/issues/9463
I'm excited to share that Gradio 5 will launch in October with improvements across security, performance, SEO, design (see the screenshot for Gradio 4 vs. Gradio 5), and user experience, making Gradio a mature framework for web-based ML applications.
Gradio 5 is currently in beta, so if you'd like to try it out early, please refer to the instructions below:
---------- Installation -------------
Gradio 5 depends on Python 3.10 or higher, so if you are running Gradio locally, please ensure that you have Python 3.10 or higher, or download it here: https://www.python.org/downloads/
* Locally: If you are running gradio locally, simply install the release candidate with
pip install gradio --pre* Spaces: If you would like to update an existing gradio Space to use Gradio 5, you can simply update the
sdk_version to be 5.0.0b3 in the README.md file on Spaces.In most cases, thatβs all you have to do to run Gradio 5.0. If you start your Gradio application, you should see your Gradio app running, with a fresh new UI.
-----------------------------
Fore more information, please see: https://github.com/gradio-app/gradio/issues/9463
- 2 replies
reacted to
Norod78's
post with β€οΈπ€―
10 months ago
Post
I've prepared a Google Colab notebook which allows you to play with interpolating between different people using IP-Adapter SDXL Face-ID Plus.
Link to notebook:
Norod78/face_id_v2_test_code
Link to Face-ID Repo:
h94/IP-Adapter-FaceID
Link to all sorts of generated examples (Use the file tab):
Norod78/face_id_v2_test_code
#Prepare a list t of num_of_results values between 0 and 1
t_space = torch.linspace(0, 1, num_of_results)
for t in tqdm(t_space):
mix_factor = t.item()
# interpolate between the two face images
image = (image1 * (1 - mix_factor) + image2 * mix_factor).astype(np.uint8)
# interpolate between the two face embedding
faceid_embeds = torch.lerp(faceid_embeds1, faceid_embeds2, t)
#generate interpolated result
images = ip_model.generate(prompt=prompt, negative_prompt=negative_prompt, face_image=image, faceid_embeds=faceid_embeds, shortcut=v2, num_samples=2, scale=scale, s_scale=s_scale, guidance_scale=guidance_scale, width=width, height=height, num_inference_steps=steps, seed=seed)Link to notebook:
Norod78/face_id_v2_test_code
Link to Face-ID Repo:
h94/IP-Adapter-FaceID
Link to all sorts of generated examples (Use the file tab):
Norod78/face_id_v2_test_code
- 9 replies
reacted to
DmitryRyumin's
post with π
11 months ago
Post
2254
πππ New Research Alert (Avatars Collection)! ππΊπ
π Title: InstructHumans: Editing Animated 3D Human Textures with Instructions
π Description: InstructHumans is a novel framework for text-instructed editing of 3D human textures that employs a modified Score Distillation Sampling (SDS-E) method along with spatial smoothness regularization and gradient-based viewpoint sampling to achieve high-quality, consistent, and instruction-true edits.
π₯ Authors: Jiayin Zhu, Linlin Yang, Angela Yao
π Paper: InstructHumans: Editing Animated 3D Human Textures with Instructions (2404.04037)
π Web Page: https://jyzhu.top/instruct-humans
π Repository: https://github.com/viridityzhu/InstructHumans
π More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
π Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
π Keywords: #InstructHumans #3DTextureEditing #TextInstructions #ScoreDistillationSampling #SDS-E #SpatialSmoothnessRegularization #3DEditing #AvatarEditing #DeepLearning #Innovation
π Title: InstructHumans: Editing Animated 3D Human Textures with Instructions
π Description: InstructHumans is a novel framework for text-instructed editing of 3D human textures that employs a modified Score Distillation Sampling (SDS-E) method along with spatial smoothness regularization and gradient-based viewpoint sampling to achieve high-quality, consistent, and instruction-true edits.
π₯ Authors: Jiayin Zhu, Linlin Yang, Angela Yao
π Paper: InstructHumans: Editing Animated 3D Human Textures with Instructions (2404.04037)
π Web Page: https://jyzhu.top/instruct-humans
π Repository: https://github.com/viridityzhu/InstructHumans
π More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
π Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
π Keywords: #InstructHumans #3DTextureEditing #TextInstructions #ScoreDistillationSampling #SDS-E #SpatialSmoothnessRegularization #3DEditing #AvatarEditing #DeepLearning #Innovation
reacted to
wanghaofan's
post with π₯
11 months ago
Post
3557
Greeting!
We are happy to introduce our InstantStyle, which is a framework that employs straightforward yet potent techniques for achieving effective disentanglement of style and content from reference images.
Code: https://github.com/InstantStyle/InstantStyle
Paper: InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation (2404.02733)
Project Page: https://instantstyle.github.io/
We are happy to introduce our InstantStyle, which is a framework that employs straightforward yet potent techniques for achieving effective disentanglement of style and content from reference images.
Code: https://github.com/InstantStyle/InstantStyle
Paper: InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation (2404.02733)
Project Page: https://instantstyle.github.io/
huggingface_hub Python library is really amazing, the more I learn about it the more impressed I am. Awesome job π€
reacted to
Wauplin's
post with β€οΈ
11 months ago
Post
π Just released version 0.21.0 of the
Exciting updates include:
ποΈ Dataclasses everywhere for improved developer experience!
πΎ HfFileSystem optimizations!
π§©
β¨
π Translated docs in Simplified Chinese and French!
π Breaking changes: simplified API for listing models and datasets!
Check out the full release notes for more details: Wauplin/huggingface_hub#4 π€π»
huggingface_hub Python library!Exciting updates include:
ποΈ Dataclasses everywhere for improved developer experience!
πΎ HfFileSystem optimizations!
π§©
PyTorchHubMixin now supports configs and safetensors!β¨
audio-to-audio supported in the InferenceClient!π Translated docs in Simplified Chinese and French!
π Breaking changes: simplified API for listing models and datasets!
Check out the full release notes for more details: Wauplin/huggingface_hub#4 π€π»
-
- 4 replies
posted
an
update
about 1 year ago
Post
MANIFESTO
After working in fashion e-commerce for years I've come to the conclusion that in e-commerce we do not sell clothes... we sell images of clothes. Compressed, digital versions of physical products. As Roland Barthes pointed out in The Fashion System, a product image is a symbol or metaphor of a product. Media--in this case images--mediates the space between customer and product; viewer and object. Images can be altered, changed, corrupted, photoshopped, edited, deleted, or imagined. E-commerce products (or e-commerce photos) can thought of as a possibility space of digital pixels. AI/ML can analyze, manipulate, and create within this "possibility space of pixels"--thus it can be observed that there are opportunities to intervene in the physical fashion world through the imagination of artificial intelligence. Not to replace human creativity--but to augment it. To make it ART-ificial. Art is an artificial representation of reality. AI images are an artificial representation of reality. The sewing machine greatly increased the efficiency of clothing production. Similarly, AI has greatly increased efficiency image production, in our case product photo production. The fashion design paradigm of the past century (design->produce->photograph) has been flipped on this head. Instead of going from physical clothing to digital image via photography--we can go from digital image to physical clothing via stable diffusion. We are writing the chapter of Understanding Media that Marshall McLuhan never imagined. Virtual production hasn't replaced the physical production; it has simply made it out of style.
After working in fashion e-commerce for years I've come to the conclusion that in e-commerce we do not sell clothes... we sell images of clothes. Compressed, digital versions of physical products. As Roland Barthes pointed out in The Fashion System, a product image is a symbol or metaphor of a product. Media--in this case images--mediates the space between customer and product; viewer and object. Images can be altered, changed, corrupted, photoshopped, edited, deleted, or imagined. E-commerce products (or e-commerce photos) can thought of as a possibility space of digital pixels. AI/ML can analyze, manipulate, and create within this "possibility space of pixels"--thus it can be observed that there are opportunities to intervene in the physical fashion world through the imagination of artificial intelligence. Not to replace human creativity--but to augment it. To make it ART-ificial. Art is an artificial representation of reality. AI images are an artificial representation of reality. The sewing machine greatly increased the efficiency of clothing production. Similarly, AI has greatly increased efficiency image production, in our case product photo production. The fashion design paradigm of the past century (design->produce->photograph) has been flipped on this head. Instead of going from physical clothing to digital image via photography--we can go from digital image to physical clothing via stable diffusion. We are writing the chapter of Understanding Media that Marshall McLuhan never imagined. Virtual production hasn't replaced the physical production; it has simply made it out of style.
- 4 replies
reacted to
fffiloni's
post with β€οΈ
about 1 year ago
Post
Just published a quick community blog post mainly aimed at Art and Design students, but which is also an attempt to nudge AI researchers who would like to better consider benefits from collaboration with designers and artists π
Feel free to share your thoughts !
"Breaking Barriers: The Critical Role of Art and Design in Advancing AI Capabilities" π https://huggingface.co/blog/fffiloni/the-critical-role-of-art-and-design-in-advancing-a
β
This short publication follows the results of two AI Workshops that took place at Γcole des Arts DΓ©coratifs - Paris, lead by Etienne Mineur, Vadim Bernard, Martin de Bie, Antoine Pintout & Sylvain Filoni.
Feel free to share your thoughts !
"Breaking Barriers: The Critical Role of Art and Design in Advancing AI Capabilities" π https://huggingface.co/blog/fffiloni/the-critical-role-of-art-and-design-in-advancing-a
β
This short publication follows the results of two AI Workshops that took place at Γcole des Arts DΓ©coratifs - Paris, lead by Etienne Mineur, Vadim Bernard, Martin de Bie, Antoine Pintout & Sylvain Filoni.
- 3 replies