BigScience Biomedical Datasets
non-profit
AI & ML interests
We aim to unify the schema across many different biomedical NLP resources.
Recent Activity
bigbio's activity
prithivMLmodsΒ
posted
an
update
3 days ago
prithivMLmodsΒ
posted
an
update
6 days ago
Post
2058
Qwen2VL Models: Vision and Language Processing π
πFT; [ Latex OCR, Math Parsing, Text Analogy OCRTest ]
βοΈDemo : prithivMLmods/Qwen2-VL-2B . The demo includes the Qwen2VL 2B Base Model.
π―The space handles documenting content from the input image along with standardized plain text. It includes adjustment tools with over 30 font styles, file formatting support for PDF and DOCX, textual alignments, font size adjustments, and line spacing modifications.
πPDFs are rendered using the ReportLab software library toolkit.
π§΅Models :
+ prithivMLmods/Qwen2-VL-OCR-2B-Instruct
+ prithivMLmods/Qwen2-VL-Ocrtest-2B-Instruct
+ prithivMLmods/Qwen2-VL-Math-Prase-2B-Instruct
πSample Document :
+ https://drive.google.com/file/d/1Hfqqzq4Xc-3eTjbz-jcQY84V5E1YM71E/view?usp=sharing
π¦Collection :
+ prithivMLmods/vision-language-models-67639f790e806e1f9799979f
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@prithivMLmods π€
πFT; [ Latex OCR, Math Parsing, Text Analogy OCRTest ]
βοΈDemo : prithivMLmods/Qwen2-VL-2B . The demo includes the Qwen2VL 2B Base Model.
π―The space handles documenting content from the input image along with standardized plain text. It includes adjustment tools with over 30 font styles, file formatting support for PDF and DOCX, textual alignments, font size adjustments, and line spacing modifications.
πPDFs are rendered using the ReportLab software library toolkit.
π§΅Models :
+ prithivMLmods/Qwen2-VL-OCR-2B-Instruct
+ prithivMLmods/Qwen2-VL-Ocrtest-2B-Instruct
+ prithivMLmods/Qwen2-VL-Math-Prase-2B-Instruct
πSample Document :
+ https://drive.google.com/file/d/1Hfqqzq4Xc-3eTjbz-jcQY84V5E1YM71E/view?usp=sharing
π¦Collection :
+ prithivMLmods/vision-language-models-67639f790e806e1f9799979f
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@prithivMLmods π€
- 1 reply
prithivMLmodsΒ
posted
an
update
7 days ago
Post
3168
π Here Before - Xmasπ
β¨
π§π»βπModels
+ [ Xmas 2D Illustration ] : strangerzonehf/Flux-Xmas-Illustration-LoRA
+ [ Xmas 3D Art ] : strangerzonehf/Flux-Xmas-3D-LoRA
+ [ Xmas Chocolate ] : strangerzonehf/Flux-Xmas-Chocolate-LoRA
+ [ Xmas Isometric Kit ] : strangerzonehf/Flux-Xmas-Isometric-Kit-LoRA
+ [ Xmas Realpix ] : strangerzonehf/Flux-Xmas-Realpix-LoRA
+ [ Xmas Anime ] : strangerzonehf/Flux-Anime-Xmas-LoRA
βοΈCollections
+ [ Xmas Art ] : strangerzonehf/christmas-pack-6758b199487adafaddb68f82
+ [ Stranger Zone Collection ] : prithivMLmods/stranger-zone-collections-org-6737118adcf2cb40d66d0c7e
π₯ΆPage
+ [ Stranger Zone ] : https://huggingface.co./strangerzonehf
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@prithivMLmods π€
π§π»βπModels
+ [ Xmas 2D Illustration ] : strangerzonehf/Flux-Xmas-Illustration-LoRA
+ [ Xmas 3D Art ] : strangerzonehf/Flux-Xmas-3D-LoRA
+ [ Xmas Chocolate ] : strangerzonehf/Flux-Xmas-Chocolate-LoRA
+ [ Xmas Isometric Kit ] : strangerzonehf/Flux-Xmas-Isometric-Kit-LoRA
+ [ Xmas Realpix ] : strangerzonehf/Flux-Xmas-Realpix-LoRA
+ [ Xmas Anime ] : strangerzonehf/Flux-Anime-Xmas-LoRA
βοΈCollections
+ [ Xmas Art ] : strangerzonehf/christmas-pack-6758b199487adafaddb68f82
+ [ Stranger Zone Collection ] : prithivMLmods/stranger-zone-collections-org-6737118adcf2cb40d66d0c7e
π₯ΆPage
+ [ Stranger Zone ] : https://huggingface.co./strangerzonehf
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@prithivMLmods π€
prithivMLmodsΒ
posted
an
update
12 days ago
phloboΒ
updated
2
datasets
16 days ago
prithivMLmodsΒ
posted
an
update
19 days ago
Post
3817
Near 3:2 { 1280*832 } Adapters π₯
π§ͺThe datasets were prepared for a 3:2 aspect ratio by processing images of any dimension (width Γ height) in alignment with the adapter's concept. This involved using techniques such as magic expand, magic fill, or outpainting to adjust the remaining parts of the image to achieve the 3:2 ratio & posts training. This approach enhanced the desired image quality to up to 2 MB for detailed prompts and reduced artifacts in images sized at 1280 Γ 832.
πThis approach was used instead of cropping down the 2x or 3x zoomed positions in the actual image. It generative filling to adjust the image's aspect ratio proportionally within the dataset.
π§I used Canva's Magic Expand, Firefly's Generative Fill, and Flux's Outpaint for aspect ratio adjustments.
β¬οΈModel DLC :
+ [ Microworld Nft ] : strangerzonehf/Flux-Microworld-NFT-LoRA
+ [ Creative Stocks ] : strangerzonehf/Flux-Creative-Stocks-LoRA
+ [ Icon-Kit ] : strangerzonehf/Flux-Icon-Kit-LoRA
+ [ Claymation ] : strangerzonehf/Flux-Claymation-XC-LoRA
+ [ Super Portrait ] : strangerzonehf/Flux-Super-Portrait-LoRA
+ [ Ghibli Art ] : strangerzonehf/Flux-Ghibli-Art-LoRA
+ [ Isometric Site ] : strangerzonehf/Flux-Isometric-Site-LoRA
π§¨Page :
1] Stranger Zone: https://huggingface.co./strangerzonehf
π£Space :
1] Flux LoRA DLC: prithivMLmods/FLUX-LoRA-DLC
π¦Collections :
1] strangerzonehf/flux-3dxl-engine-674833c14a001d5b1fdb5139
2] prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be
3] strangerzonehf/animaker-engine-673714956dec98c400c30cf6
4] strangerzonehf/mixer-engine-673582c9c5939d8aa5bf9533
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@prithivMLmods
π§ͺThe datasets were prepared for a 3:2 aspect ratio by processing images of any dimension (width Γ height) in alignment with the adapter's concept. This involved using techniques such as magic expand, magic fill, or outpainting to adjust the remaining parts of the image to achieve the 3:2 ratio & posts training. This approach enhanced the desired image quality to up to 2 MB for detailed prompts and reduced artifacts in images sized at 1280 Γ 832.
πThis approach was used instead of cropping down the 2x or 3x zoomed positions in the actual image. It generative filling to adjust the image's aspect ratio proportionally within the dataset.
π§I used Canva's Magic Expand, Firefly's Generative Fill, and Flux's Outpaint for aspect ratio adjustments.
β¬οΈModel DLC :
+ [ Microworld Nft ] : strangerzonehf/Flux-Microworld-NFT-LoRA
+ [ Creative Stocks ] : strangerzonehf/Flux-Creative-Stocks-LoRA
+ [ Icon-Kit ] : strangerzonehf/Flux-Icon-Kit-LoRA
+ [ Claymation ] : strangerzonehf/Flux-Claymation-XC-LoRA
+ [ Super Portrait ] : strangerzonehf/Flux-Super-Portrait-LoRA
+ [ Ghibli Art ] : strangerzonehf/Flux-Ghibli-Art-LoRA
+ [ Isometric Site ] : strangerzonehf/Flux-Isometric-Site-LoRA
π§¨Page :
1] Stranger Zone: https://huggingface.co./strangerzonehf
π£Space :
1] Flux LoRA DLC: prithivMLmods/FLUX-LoRA-DLC
π¦Collections :
1] strangerzonehf/flux-3dxl-engine-674833c14a001d5b1fdb5139
2] prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be
3] strangerzonehf/animaker-engine-673714956dec98c400c30cf6
4] strangerzonehf/mixer-engine-673582c9c5939d8aa5bf9533
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@prithivMLmods
- 1 reply
phloboΒ
updated
a
dataset
19 days ago
ImranzamanMLΒ
posted
an
update
20 days ago
Post
438
Deep understanding of (C-index) evaluation measure for better model
Lets start with three patients groups:
Group A
Group B
Group C
For each patient, we will predict risk score (higher score means higher risk of early event).
Step 1: Understanding Concordance Index
The Concordance Index (C-index) evaluate that how well the model ranks survival times.
Understand with sample data:
Group A has 3 patients with actual survival times and predicted risk scores:
Patient Actual Survival Time Predicted Risk Score
P1 5 months 0.8
P2 3 months 0.9
P3 10 months 0.2
Comparable pairs:
(P1, P2): P2 has a shorter survival time and a higher risk score β Concordant β
(P1, P3): P3 has a longer survival time and a lower risk score β Concordant β
(P2, P3): P3 has a longer survival time and a lower risk score β Concordant β
Total pairs = 3
Total concordant pairs = 3
C-index for Group A = Concordant pairs/Total pairs= 3/3 = 1.0
Step 2: Calculate C-index for All Groups
Repeat the process for all groups. For now we can assume:
Group A: C-index = 1.0
Group B: C-index = 0.8
Group C: C-index = 0.6
Step 3: Stratified Concordance Index
The Stratified Concordance Index combines the C-index scores of all groups and focusing on the following:
Average performance across groups (mean of C-indices).
Consistency across groups (low standard deviation of C-indices).
Formula:
Stratified C-index = Mean(C-index scores) - Standard Deviation(C-index scores)
Calculate the mean:
Mean=1.0 + 0.8 + 0.6/3 = 0.8
Calculate the standard deviation:
Standard Deviation= sqrt((1.0-0.8)^2 + (0.8-0.8)^2 + (0.6-0.8)^/3) = 0.16
Stratified C-index:
Stratified C-index = 0.8 - 0.16 = 0.64
Step 4: Interpret the Results
A high Stratified C-index means:
The model predicts well overall (high mean C-index).
Lets start with three patients groups:
Group A
Group B
Group C
For each patient, we will predict risk score (higher score means higher risk of early event).
Step 1: Understanding Concordance Index
The Concordance Index (C-index) evaluate that how well the model ranks survival times.
Understand with sample data:
Group A has 3 patients with actual survival times and predicted risk scores:
Patient Actual Survival Time Predicted Risk Score
P1 5 months 0.8
P2 3 months 0.9
P3 10 months 0.2
Comparable pairs:
(P1, P2): P2 has a shorter survival time and a higher risk score β Concordant β
(P1, P3): P3 has a longer survival time and a lower risk score β Concordant β
(P2, P3): P3 has a longer survival time and a lower risk score β Concordant β
Total pairs = 3
Total concordant pairs = 3
C-index for Group A = Concordant pairs/Total pairs= 3/3 = 1.0
Step 2: Calculate C-index for All Groups
Repeat the process for all groups. For now we can assume:
Group A: C-index = 1.0
Group B: C-index = 0.8
Group C: C-index = 0.6
Step 3: Stratified Concordance Index
The Stratified Concordance Index combines the C-index scores of all groups and focusing on the following:
Average performance across groups (mean of C-indices).
Consistency across groups (low standard deviation of C-indices).
Formula:
Stratified C-index = Mean(C-index scores) - Standard Deviation(C-index scores)
Calculate the mean:
Mean=1.0 + 0.8 + 0.6/3 = 0.8
Calculate the standard deviation:
Standard Deviation= sqrt((1.0-0.8)^2 + (0.8-0.8)^2 + (0.6-0.8)^/3) = 0.16
Stratified C-index:
Stratified C-index = 0.8 - 0.16 = 0.64
Step 4: Interpret the Results
A high Stratified C-index means:
The model predicts well overall (high mean C-index).
- 1 reply
Post
285
How Do I Contribute (HDIC)
Exciting times to come? We are working on a layer self-esteem technique to score their contribution to the final prediction. For now, it unlocks a lot of knowledge already stored in weights we couldn't force the model to extract by further fine-tuning!
Exciting times to come? We are working on a layer self-esteem technique to score their contribution to the final prediction. For now, it unlocks a lot of knowledge already stored in weights we couldn't force the model to extract by further fine-tuning!
Post
426
What AI-enhanced research tools would you recommend for searching and analyzing scientific papers?
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- 5 replies
prithivMLmodsΒ
posted
an
update
22 days ago
Post
2627
Milestone for Flux.1 Dev π₯
π’The Flux.1 Dev model has crossed 1οΈβ£0οΈβ£,0οΈβ£0οΈβ£0οΈβ£ creative public adapters! π
π https://huggingface.co./models?other=base_model:adapter:black-forest-labs/FLUX.1-dev
π’This includes:
- 266 Finetunes
- 19 Quants
- 4 Merges
π’ Hereβs the 10,000th public adapter : π
+ strangerzonehf/Flux-3DXL-Partfile-0006
π’ Page :
+ https://huggingface.co./strangerzonehf
π’ Collection :
+ prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be
π’The Flux.1 Dev model has crossed 1οΈβ£0οΈβ£,0οΈβ£0οΈβ£0οΈβ£ creative public adapters! π
π https://huggingface.co./models?other=base_model:adapter:black-forest-labs/FLUX.1-dev
π’This includes:
- 266 Finetunes
- 19 Quants
- 4 Merges
π’ Hereβs the 10,000th public adapter : π
+ strangerzonehf/Flux-3DXL-Partfile-0006
π’ Page :
+ https://huggingface.co./strangerzonehf
π’ Collection :
+ prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be
Post
433
π The Stanford Institute for Human-Centered AI (https://aiindex.stanford.edu/vibrancy/) has released its 2024 Global AI Vibrancy Tool, a way to explore and compare AI progress across 36 countries.
π It measures progress across the 8 broad pillars of R&D, Responsible AI, Economy, Education, Diversity, Policy and Governance, Public Opinion and Infrastructure. (Each of these pillars have a number of Sub Indices)
π As a whole it is not surprising that the USA was at the top in terms of overall score as of 2023 (AI investment activity is a large part of the economic pillar for example and that is a large part of the overall USA ranking) but drilling in to more STRATEGIC Macro pillars like Education, Infrastructure or R&D reveal interesting growth patterns in Asia (particularly China) and Western Europe that I suspect the 2024 metrics will bear out.
π€ Hopefully the 2024 Global Vibrancy ranking will break out AI and ML verticals like Computer Vision or NLP and or the AI Agent space as that may also from a global macro level give indications of what is to come globally for AI in 2025.
π It measures progress across the 8 broad pillars of R&D, Responsible AI, Economy, Education, Diversity, Policy and Governance, Public Opinion and Infrastructure. (Each of these pillars have a number of Sub Indices)
π As a whole it is not surprising that the USA was at the top in terms of overall score as of 2023 (AI investment activity is a large part of the economic pillar for example and that is a large part of the overall USA ranking) but drilling in to more STRATEGIC Macro pillars like Education, Infrastructure or R&D reveal interesting growth patterns in Asia (particularly China) and Western Europe that I suspect the 2024 metrics will bear out.
π€ Hopefully the 2024 Global Vibrancy ranking will break out AI and ML verticals like Computer Vision or NLP and or the AI Agent space as that may also from a global macro level give indications of what is to come globally for AI in 2025.
Post
1176
We built a new small language model SmolLM2-MedIT-Upscale-2B, based on SmolLM2-1.7B-Instruct from Hugging Face. The premise was simple - increasing the vector in attention layers would positively impact the model's capabilities.
What did we prove?
In total, not much really, since we don't have the original trained under the same conditions as our upscale. However...
1. We scaled up the model without losing its quality
2. We confirmed that the method we devised works
3. After extremely short fine-tuning, the model achieved much better results in IFEval compared to the original (53.68 vs 64.29) and a higher overall average score in Open LLM Leaderboard (14.75 vs 15.17)
I consider this a big success π, since surpassing the original in metrics is often very time-consuming, generates high costs, and doesn't always work out.
Meanwhile, we're moving forward, training SmolLM2 400M Instruct as an upscale of 136M.
We're curious about how increasing the base and intermediate vectors will affect the model's quality. We'll compare it to the original and the 360M Instruct version released by Hugging Face.
License: Apache 2.0ββββββββββββββββ
meditsolutions/SmolLM2-MedIT-Upscale-2B
What did we prove?
In total, not much really, since we don't have the original trained under the same conditions as our upscale. However...
1. We scaled up the model without losing its quality
2. We confirmed that the method we devised works
3. After extremely short fine-tuning, the model achieved much better results in IFEval compared to the original (53.68 vs 64.29) and a higher overall average score in Open LLM Leaderboard (14.75 vs 15.17)
I consider this a big success π, since surpassing the original in metrics is often very time-consuming, generates high costs, and doesn't always work out.
Meanwhile, we're moving forward, training SmolLM2 400M Instruct as an upscale of 136M.
We're curious about how increasing the base and intermediate vectors will affect the model's quality. We'll compare it to the original and the 360M Instruct version released by Hugging Face.
License: Apache 2.0ββββββββββββββββ
meditsolutions/SmolLM2-MedIT-Upscale-2B
prithivMLmodsΒ
posted
an
update
27 days ago
Post
2726
Fine-Textured [Polygon] Character 3D Design Renders π
Adapters capable of providing better lighting control (Bn+, Bn-) and richer textures compared to previous sets require more contextual prompts for optimal performance.
The ideal settings are achieved at inference steps around 30β35, with the best dimensions being 1280 x 832 [ 3:2 ]. However, it also performs well with the default settings of 1024 x 1024 [ 1:1 ].
π’Models DLC :
+ strangerzonehf/Flux-3DXL-Partfile-0001
+ strangerzonehf/Flux-3DXL-Partfile-0002
+ strangerzonehf/Flux-3DXL-Partfile-0003
+ strangerzonehf/Flux-3DXL-Partfile-0004
+ strangerzonehf/Flux-3DXL-Partfile-C0001
π’Collections :
1] strangerzonehf/flux-3dxl-engine-674833c14a001d5b1fdb5139
2] prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be
π’Space :
1] prithivMLmods/FLUX-LoRA-DLC
π’Page :
1] Stranger Zone: https://huggingface.co./strangerzonehf
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@prithivMLmods π€
Adapters capable of providing better lighting control (Bn+, Bn-) and richer textures compared to previous sets require more contextual prompts for optimal performance.
The ideal settings are achieved at inference steps around 30β35, with the best dimensions being 1280 x 832 [ 3:2 ]. However, it also performs well with the default settings of 1024 x 1024 [ 1:1 ].
π’Models DLC :
+ strangerzonehf/Flux-3DXL-Partfile-0001
+ strangerzonehf/Flux-3DXL-Partfile-0002
+ strangerzonehf/Flux-3DXL-Partfile-0003
+ strangerzonehf/Flux-3DXL-Partfile-0004
+ strangerzonehf/Flux-3DXL-Partfile-C0001
π’Collections :
1] strangerzonehf/flux-3dxl-engine-674833c14a001d5b1fdb5139
2] prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be
π’Space :
1] prithivMLmods/FLUX-LoRA-DLC
π’Page :
1] Stranger Zone: https://huggingface.co./strangerzonehf
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@prithivMLmods π€
prithivMLmodsΒ
posted
an
update
about 1 month ago
Post
3272
HF Posts Receipts ππ
[ HF POSTS RECEIPT ] : prithivMLmods/HF-POSTS-RECEIPT
π₯ The one thing that needs to be remembered is the 'username'.
π₯ And yeah, thank you, @maxiw , for creating the awesome dataset and sharing them here! π
π₯ [ Dataset ] : maxiw/hf-posts
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@prithivMLmods
[ HF POSTS RECEIPT ] : prithivMLmods/HF-POSTS-RECEIPT
π₯ The one thing that needs to be remembered is the 'username'.
π₯ And yeah, thank you, @maxiw , for creating the awesome dataset and sharing them here! π
π₯ [ Dataset ] : maxiw/hf-posts
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@prithivMLmods
Post
693
π€π» Function Calling is a key component of Agent workflows. To call functions, an LLM needs a way to interact with other systems and run code. This usually means connecting it to a runtime environment that can handle function calls, data, and security.
Per the Berkeley Function-Calling Leaderboard there are only 2 fully open source models (The other 2 in the top 20 that are not closed source have cc-by-nc-4.0 licenses) out of the top 20 models that currently have function calling built in as of 17 Nov 2024.
https://gorilla.cs.berkeley.edu/leaderboard.html
The 2 Open Source Models out of the top 20 that currently support function calling are:
meetkai/functionary-medium-v3.1
Team-ACE/ToolACE-8B
This is a both a huge disadvantage AND an opportunity for the Open Source community as Enterprises, Small Business, Government Agencies etc. quickly adopt Agents and Agent workflows over the next few months. Open Source will have a lot of catching up to do as Enterprises will be hesitant to switch from the closed source models that they may initially build their Agent workflows on in the next few months to an open source alternative later.
Hopefully more open source models will support function calling in the near future.
Per the Berkeley Function-Calling Leaderboard there are only 2 fully open source models (The other 2 in the top 20 that are not closed source have cc-by-nc-4.0 licenses) out of the top 20 models that currently have function calling built in as of 17 Nov 2024.
https://gorilla.cs.berkeley.edu/leaderboard.html
The 2 Open Source Models out of the top 20 that currently support function calling are:
meetkai/functionary-medium-v3.1
Team-ACE/ToolACE-8B
This is a both a huge disadvantage AND an opportunity for the Open Source community as Enterprises, Small Business, Government Agencies etc. quickly adopt Agents and Agent workflows over the next few months. Open Source will have a lot of catching up to do as Enterprises will be hesitant to switch from the closed source models that they may initially build their Agent workflows on in the next few months to an open source alternative later.
Hopefully more open source models will support function calling in the near future.
prithivMLmodsΒ
posted
an
update
about 1 month ago
Post
4101
CRISP π₯ [ Isometric-3D-Cinematography / Isometric-3D-Obj / 3D-Kawaii / Long Toons ]
[ Flux DLC ] : prithivMLmods/FLUX-LoRA-DLC
[ Stranger Zone ] : https://huggingface.co./strangerzonehf
π[ Isometric 3D Cinematography ] : strangerzonehf/Flux-Isometric-3D-Cinematography
π[ Isometric 3D ] : strangerzonehf/Flux-Isometric-3D-LoRA
π[ Cute 3D Kawaii ] : strangerzonehf/Flux-Cute-3D-Kawaii-LoRA
π[ Long Toon 3D ] : prithivMLmods/Flux-Long-Toon-LoRA
[ Stranger Zone Collection ] : https://huggingface.co./collections/prithivMLmods/stranger-zone-collections-6737118adcf2cb40d66d0c7e
[ Flux Collection ] : prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be
[ Flux Mix ] : prithivMLmods/Midjourney-Flux
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@prithivMLmods
[ Flux DLC ] : prithivMLmods/FLUX-LoRA-DLC
[ Stranger Zone ] : https://huggingface.co./strangerzonehf
π[ Isometric 3D Cinematography ] : strangerzonehf/Flux-Isometric-3D-Cinematography
π[ Isometric 3D ] : strangerzonehf/Flux-Isometric-3D-LoRA
π[ Cute 3D Kawaii ] : strangerzonehf/Flux-Cute-3D-Kawaii-LoRA
π[ Long Toon 3D ] : prithivMLmods/Flux-Long-Toon-LoRA
[ Stranger Zone Collection ] : https://huggingface.co./collections/prithivMLmods/stranger-zone-collections-6737118adcf2cb40d66d0c7e
[ Flux Collection ] : prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be
[ Flux Mix ] : prithivMLmods/Midjourney-Flux
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@prithivMLmods
prithivMLmodsΒ
posted
an
update
about 1 month ago
Post
2904
Weekend Dribble π¦πΊ
Adapters for Product Ad Backdrops, Smooth Polaroids, Minimalist Sketch cards, Super Blends!!
π€Demo on: prithivMLmods/FLUX-LoRA-DLC
Stranger Zones :
ππΌ{ Super Blend } : strangerzonehf/Flux-Super-Blend-LoRA
ππΌ{ Product Concept Ad } : prithivMLmods/Flux-Product-Ad-Backdrop
ππΌ{ Frosted Mock-ups } : prithivMLmods/Flux.1-Dev-Frosted-Container-LoRA
ππΌ{ Polaroid Plus } : prithivMLmods/Flux-Polaroid-Plus
ππΌ{Sketch Cards} : prithivMLmods/Flux.1-Dev-Sketch-Card-LoRA
πStranger Zone: https://huggingface.co./strangerzonehf
πFlux LoRA Collections: prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be
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@prithivMLmods π€
Adapters for Product Ad Backdrops, Smooth Polaroids, Minimalist Sketch cards, Super Blends!!
π€Demo on: prithivMLmods/FLUX-LoRA-DLC
Stranger Zones :
ππΌ{ Super Blend } : strangerzonehf/Flux-Super-Blend-LoRA
ππΌ{ Product Concept Ad } : prithivMLmods/Flux-Product-Ad-Backdrop
ππΌ{ Frosted Mock-ups } : prithivMLmods/Flux.1-Dev-Frosted-Container-LoRA
ππΌ{ Polaroid Plus } : prithivMLmods/Flux-Polaroid-Plus
ππΌ{Sketch Cards} : prithivMLmods/Flux.1-Dev-Sketch-Card-LoRA
πStranger Zone: https://huggingface.co./strangerzonehf
πFlux LoRA Collections: prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be
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@prithivMLmods π€