Edit model card

lala

Model trained with AI Toolkit by Ostris

Prompt
a small, fluffy poodle with a soft, curly coat in a light cream color,sitting on a soft, oversized blanket, surrounded by an array of colorful storybooks and plush toys, with a cozy reading nook in the background. The poodle gazes up with a joyful expression, as if inviting you to join in on the adventure. LALA_HK
Prompt
a small, fluffy poodle with a soft, curly coat in a light cream color, A serene image of the fluffy poodle enjoying a pampering spa day, sitting on a plush towel surrounded by soothing candles and fragrant flowers. The dog has a blissful expression, showcasing its relaxed demeanor while wearing a cute spa headband, embodying ultimate relaxation. LALA_HK
Prompt
A delightful photograph of the fluffy poodle prancing through a vibrant garden filled with blooming flowers and fluttering butterflies. The dog’s curls bounce as it explores, with a playful expression that captures the essence of a curious adventurer in a magical world.colorful ambiance, lively energy, enchanting details, joyful exploration, nature's beauty, whimsical spirit, LALA_HK

Trigger words

You should use LALA_HK to trigger the image generation.

Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.

Weights for this model are available in Safetensors format.

Download them in the Files & versions tab.

Use it with the 🧨 diffusers library

from diffusers import AutoPipelineForText2Image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('chau9ho/lala', weight_name='lala.safetensors')
image = pipeline(' a small, fluffy poodle with a soft, curly coat in a light cream color,sitting on a soft, oversized blanket, surrounded by an array of colorful storybooks and plush toys, with a cozy reading nook in the background. The poodle gazes up with a joyful expression, as if inviting you to join in on the adventure. LALA_HK').images[0]
image.save("my_image.png")

For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers

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