Text-to-Image
Diffusers
ONNX
Safetensors
English
StableDiffusionXLPipeline
common-canvas
stable-diffusion
sdxl
Instructions to use common-canvas/CommonCanvas-XL-C with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use common-canvas/CommonCanvas-XL-C with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("common-canvas/CommonCanvas-XL-C", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
I'm having trouble generating images that follow the prompt.
#3
by njasa - opened
njasa changed discussion title from I'm having trouble generating images with prompt following. to I'm having trouble generating images that follow the prompt.
njasa changed discussion status to closed
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The bigger issue is that it doesn't do as well on abstract noun because the captioner is less likely to tag them and instead of pick a more descriptive description. You can try a more specific prompt like a "a man standing in a room" and it works significantly better. It would be less likely to use more general concepts like "person" or other words for which a more descriptive noun exists:
