Diffusers
English
stable-diffusion
stable-diffusion-diffusers
inpainting
art
artistic
anime
absolute-realism
Instructions to use diffusers/tools with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use diffusers/tools with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("diffusers/tools", 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
File size: 948 Bytes
e58dd86 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | #!/usr/bin/env python3
#!/usr/bin/env python3
from diffusers import DiffusionPipeline
import torch
import time
import os
from pathlib import Path
from huggingface_hub import HfApi
api = HfApi()
start_time = time.time()
pipe = DiffusionPipeline.from_pretrained("/home/patrick/if", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
pipe.enable_model_cpu_offload()
generator = torch.Generator("cuda").manual_seed(0)
prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"'
image = pipe(prompt, generator=generator).images[0]
path = os.path.join(Path.home(), "images", "if.png")
image.save(path)
api.upload_file(
path_or_fileobj=path,
path_in_repo=path.split("/")[-1],
repo_id="patrickvonplaten/images",
repo_type="dataset",
)
print(f"https://huggingface.co/datasets/patrickvonplaten/images/blob/main/if.png")
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