Instructions to use aa-studio/aa_studio_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use aa-studio/aa_studio_data with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("aa-studio/aa_studio_data", 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
Download ComfyUI/node_helpers.py from aa-studio/aa_studio_data: direct link, hf CLI and curl.
- Browser
- Download file 699 Bytes
-
https://huggingface.co/aa-studio/aa_studio_data/resolve/f39f64c8b5576d3ca4afc1e63c4f78ca706bf4e2/ComfyUI/node_helpers.py
- Command line
-
hf download hf://aa-studio/aa_studio_data@f39f64c8b5576d3ca4afc1e63c4f78ca706bf4e2/ComfyUI/node_helpers.py
-
curl -L -o node_helpers.py https://huggingface.co/aa-studio/aa_studio_data/resolve/f39f64c8b5576d3ca4afc1e63c4f78ca706bf4e2/ComfyUI/node_helpers.py
699 Bytes
| from PIL import ImageFile, UnidentifiedImageError | |
| def conditioning_set_values(conditioning, values={}): | |
| c = [] | |
| for t in conditioning: | |
| n = [t[0], t[1].copy()] | |
| for k in values: | |
| n[1][k] = values[k] | |
| c.append(n) | |
| return c | |
| def pillow(fn, arg): | |
| prev_value = None | |
| try: | |
| x = fn(arg) | |
| except (OSError, UnidentifiedImageError, ValueError): #PIL issues #4472 and #2445, also fixes ComfyUI issue #3416 | |
| prev_value = ImageFile.LOAD_TRUNCATED_IMAGES | |
| ImageFile.LOAD_TRUNCATED_IMAGES = True | |
| x = fn(arg) | |
| finally: | |
| if prev_value is not None: | |
| ImageFile.LOAD_TRUNCATED_IMAGES = prev_value | |
| return x | |