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---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
- flux-dev
- ultra
- realism
- photorealism
- hi-res
- face
- diffusion
widget:
- text: >-
Woman in a red jacket, snowy, in the style of hyper-realistic portraiture,
caninecore, mountainous vistas, timeless beauty, palewave, iconic,
distinctive noses --ar 72:101 --stylize 750 --v 6
output:
url: images/3.png
- text: >-
Photograph, candid shot, famous randomly couch and randomly finished with randomly cats, center point for cat, Use camera is Canon EOS 5D Mark IV with a Canon EF 24mm f/1.4L II USM lens, set at aperture f/2.8 for a depth of field that highlights the furniture clean lines with rich and many detail, randomly color and finished, soft ambient light, studio light setting, ultra realistic, UHD, many details --chaos 1 --ar 9:16 --style raw --stylize 750
output:
url: images/5.png
- text: >-
High-resolution photograph, woman, UHD, photorealistic, shot on a Sony A7III --chaos 20 --ar 1:2 --style raw --stylize 250
output:
url: images/4.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Ultra realistic
license: creativeml-openrail-m
---
# Canopus-LoRA-Flux-UltraRealism-2.0
<Gallery />
**The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.**
## Model description
**prithivMLmods/Canopus-LoRA-Flux-FaceRealism**
Image Processing Parameters
| Parameter | Value | Parameter | Value |
|---------------------------|--------|---------------------------|--------|
| LR Scheduler | constant | Noise Offset | 0.03 |
| Optimizer | AdamW | Multires Noise Discount | 0.1 |
| Network Dim | 64 | Multires Noise Iterations | 10 |
| Network Alpha | 32 | Repeat & Steps | 30 & 3.8K+ |
| Epoch | 20 | Save Every N Epochs | 1 |
Labeling: florence2-en(natural language & English)
Total Images Used for Training : 70 [ Hi-RES ] & More ...............
## Trigger words
You should use `Ultra realistic` to trigger the image generation.
## Other Versions
Here’s a table format for the Hugging Face model **"prithivMLmods/Canopus-LoRA-Flux-FaceRealism"**:
| **Attribute** | **Details** |
|---------------------------|--------------------------------------------------------------------------------------------------------------|
| **Model Name** | Canopus-LoRA-Flux-FaceRealism |
| **Model ID** | `prithivMLmods/Canopus-LoRA-Flux-FaceRealism` |
| **Hugging Face URL** | [Canopus-LoRA-Flux-FaceRealism](https://huggingface.co./prithivMLmods/Canopus-LoRA-Flux-FaceRealism) |
| **Model Type** | LoRA (Low-Rank Adaptation) |
| **Primary Use Case** | Face Realism image generation |
| **Supported Framework** | Hugging Face Diffusers |
| **Data Type** | `bfloat16`, `fp16`, `float32` |
| **Compatible Models** | Stable Diffusion, Flux models |
| **Model Author** | `prithivMLmods` |
| **LoRA Technique** | LoRA for image style transfer with a focus on generating realistic faces |
| **Model Version** | Latest |
| **License** | Open-Access |
| **Tags** | LoRA, Face Realism, Flux, Image Generation |
## Setting Up
```
import torch
from pipelines import DiffusionPipeline
base_model = "prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
lora_repo = "prithivMLmods/Canopus-LoRA-Flux-FaceRealism"
trigger_word = "Ultra realistic" # Leave trigger_word blank if not used.
pipe.load_lora_weights(lora_repo)
device = torch.device("cuda")
pipe.to(device)
```
## Download model
Weights for this model are available in Safetensors format.
[Download](/prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0/tree/main) them in the Files & versions tab. |