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pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("Polycruz9/anime-v2")
prompt = "animex, a vibrant cartoon drawing of a mans face is depicted. The mans eyes are a piercing blue, and he has a serious expression on his face. His hair is spiky, and his bangs are a darker shade of purple. His eyebrows are a lighter shade of brown, while his mouth is slightly open. He is wearing a red hoodie, and a black jacket with a white stripe down the center of his chest. The hoodie is draped over his shoulders, adding a pop of color to the otherwise monochromatic image. The background is a deep, vibrant purple, and there are a few wispy red lines running across the image, adding depth to the composition."
image = pipe(prompt).images[0]
______ ______ __ __ __ ______ ______ __ __ ______ __ ______
/\ == \/\ __ \ /\ \ /\ \_\ \ /\ ___\ /\ == \ /\ \/\ \ /\___ \ /\ \ /\ __ \
\ \ _-/\ \ \/\ \\ \ \____\ \____ \\ \ \____\ \ __< \ \ \_\ \\/_/ /__ \ \ \\ \ \/\ \
\ \_\ \ \_____\\ \_____\\/\_____\\ \_____\\ \_\ \_\\ \_____\ /\_____\ \ \_\\ \_____\
\/_/ \/_____/ \/_____/ \/_____/ \/_____/ \/_/ /_/ \/_____/ \/_____/ \/_/ \/_____/



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 | 35 & 4800 |
| Epoch | 20 | Save Every N Epochs | 1 |
Labeling: florence2-en(natural language & English)
Total Images Used for Training : 57 [ 11@5 ] [ Hi -Res ]
import torch
from pipelines import DiffusionPipeline
base_model = "black-forest-labs/FLUX.1-dev"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
lora_repo = "Polycruz9/anime-v2"
trigger_word = "Animex"
pipe.load_lora_weights(lora_repo)
device = torch.device("cuda")
pipe.to(device)
You should use Animex to trigger the image generation.
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
# Gated model: Login with a HF token with gated access permission hf auth login