Instructions to use mitrick2/mc-scleroscope with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mitrick2/mc-scleroscope 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("mitrick2/mc-scleroscope") prompt = "A person in a bustling cafe mcsclero" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
mc-scleroscope
Model trained with AI Toolkit by Ostris

- Prompt
- A person in a bustling cafe mcsclero

- Prompt
- A man emerges from the forest mcsclero

- Prompt
- mcsclero working on old synthesizers
Trigger words
You should use mcsclero 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('mitrick2/mc-scleroscope', weight_name='mc-scleroscope')
image = pipeline('A person in a bustling cafe mcsclero').images[0]
image.save("my_image.png")
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
- Downloads last month
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Model tree for mitrick2/mc-scleroscope
Base model
black-forest-labs/FLUX.1-dev