Instructions to use strangerzonehf/NFT-Plus-InsideOut-Perspective with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use strangerzonehf/NFT-Plus-InsideOut-Perspective with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("strangerzonehf/NFT-Plus-InsideOut-Perspective") prompt = "NFT ++, An eye-level view of a glass milk carton with the words \"prithivml\" written on it in black letters. The carton is filled with a few dark gray clouds, and there is a white barcode on the left side of the carton. The background is a light blue color." image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
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README.md
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- template:diffusion-lora
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- flux
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- nft
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- art
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output:
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url: >-
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- text: 'NFT ++, a cartoon drawing of a cats face is seen in a clear glass container. The container is filled with a variety of brown objects, including a barcode and the word "prithiv" written in black letters. The background is a stark white, with a light brown border. The cats head is adorned with a red ribbon, adding a pop of color to the scene. The word "imo" is written on the top right corner of the container.'
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output:
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url: >-
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images/3.png
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base_model: black-forest-labs/FLUX.1-dev
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instance_prompt:
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license: creativeml-openrail-m
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---
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<Gallery />
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# Model description for
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Image Processing Parameters
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| Parameter | Value
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| LR Scheduler | constant | Noise Offset | 0.03 |
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| Optimizer | AdamW
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| Network Dim |
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| Network Alpha |
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| Epoch |
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Labeling: florence2-en(natural language & English)
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Total Images Used for Training :
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## Best Dimensions & Inference
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| **Dimensions** | **Aspect Ratio** | **Recommendation**
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|-----------------|------------------|---------------------------|
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### Inference Range
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- **Recommended Inference Steps:** 30–35
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## Setting Up
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```python
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import torch
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from
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base_model = "black-forest-labs/FLUX.1-dev"
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pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
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pipe.load_lora_weights(lora_repo)
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device = torch.device("cuda")
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pipe.to(device)
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```
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## Trigger words
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You should use `NFT ++` to trigger the image generation.
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## Data Source
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quaphadoodle
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## Download model
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Weights for this model are available in Safetensors format.
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[Download](/strangerzonehf/NFT-Plus-InsideOut-Perspective/tree/main) them in the Files & versions tab.
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- template:diffusion-lora
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- flux
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- nft
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- pfp
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- art
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- cartoon
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- text: 'YellowGang, A front-facing view of the character from image_0.png wearing a back-turned black snapback cap, a red basketball jersey with "1" and "YellowGang" logo, gold chains, and holding a small basketball, solid blue background.'
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output:
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url: >-
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https://cdn-uploads.huggingface.co/production/uploads/no-id/placeholder-yellowgang-default.png
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- text: 'YellowGang, A character from image_0.png wearing a futuristic cyborg motorcycle helmet with a glowing red LED visor, an astronaut suit with the "YellowGang" patch, and a starry cosmic background.'
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output:
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url: >-
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https://cdn-uploads.huggingface.co/production/uploads/no-id/placeholder-yellowgang-cyborg.png
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- text: 'YellowGang, The yellow character from image_0.png clean-shaven (bald), wearing a classic black and white zebra bandana, sleek black aviator sunglasses, and a pink Japanese Kimono with the "YellowGang" logo.'
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output:
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url: >-
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https://cdn-uploads.huggingface.co/production/uploads/no-id/placeholder-yellowgang-kimono.png
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- text: 'YellowGang, A close-up portrait of the character from image_0.png with large over-ear DJ headphones, angry red eyes, a spiked leather choker collar, and a blue streetwear hoodie with the "YellowGang" logo, dark city background.'
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output:
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url: >-
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https://cdn-uploads.huggingface.co/production/uploads/no-id/placeholder-yellowgang-punk.png
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- text: 'YellowGang, A character from image_0.png with neon green spike hair, "X" dead-style eyes, a tattered military uniform, and a zombie-groan mouth expression.'
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output:
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url: >-
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https://cdn-uploads.huggingface.co/production/uploads/no-id/placeholder-yellowgang-zombie.png
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base_model: black-forest-labs/FLUX.1-dev
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instance_prompt: YellowGang
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license: creativeml-openrail-m
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---
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<Gallery />
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# Model description for YellowGang-Generator-LoRA
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This LoRA model is a specialized generator for the "YellowGang" collection, based on the signature yellow-skinned character style with bold cartoon outlines, as seen in `image_0.png`. It is fine-tuned to preserve the character's facial features and proportions while allowing for high-fidelity generation of diverse traits, including various headwear (bandanas, crowns, helmets), eyewear (cyborg, classic, sleepy), outfits (hoodies, jerseys, armor, suits), and neck accessories (chains, chokers, scarfs). The model ensures the "YellowGang" logo is consistently applied to all outfits.
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Image Processing Parameters (Used for Training)
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| Parameter | Value | Parameter | Value |
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|---------------------------|----------|---------------------------|--------|
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| LR Scheduler | constant | Noise Offset | 0.03 |
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| Optimizer | AdamW | Multires Noise Discount | 0.1 |
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| Network Dim | 128 | Multires Noise Iterations | 10 |
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| Network Alpha | 64 | Repeat & Steps | 25 & 3500 |
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| Epoch | 15 | Save Every N Epochs | 2 |
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Labeling: florence2-en(natural language & English) with specific trait tagging.
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Total Images Used for Training : 20 (Base character + variation set)
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## Best Dimensions & Inference
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| **Dimensions** | **Aspect Ratio** | **Recommendation** |
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|-----------------|------------------|---------------------------|
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| 1024 x 1024 | 1:1 (Square PFP) | Best (Default for PFP) |
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| 1280 x 832 | 3:2 | Recommended |
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### Inference Range
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- **Recommended Inference Steps:** 30–35
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## Setting Up
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To use this LoRA in your Diffusers pipeline, ensure you use the `YellowGang` instance prompt.
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```python
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import torch
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from diffusers import DiffusionPipeline
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# Base model on which the LoRA was trained
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base_model = "black-forest-labs/FLUX.1-dev"
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pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
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# The location of the fine-tuned LoRA
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lora_repo = "your_username/YellowGang-Generator-LoRA"
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pipe.load_lora_weights(lora_repo)
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# Trigger word to activate the specific character identity
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trigger_word = "YellowGang"
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device = torch.device("cuda")
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pipe.to(device)
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