In [35]:
images = pipe(prompts, num_inference_steps=num_inference_steps, generator=generator).images
grid = image_grid(images, rows=n_rows, cols=n_cols)
grid
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Out[35]:
I'm using this https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb#scrollTo=-YAFLvWWrSdM tutorial as a reference
!nvidia-smi
Sun May 21 03:31:21 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.85.12 Driver Version: 525.85.12 CUDA Version: 12.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |
| N/A 49C P8 10W / 70W | 0MiB / 15360MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
!pip install diffusers==0.11.1
!pip install transformers scipy ftfy accelerate
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import torch
from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
pipe = pipe.to("cuda")
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`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config["id2label"]` will be overriden.
The config attributes {'scaling_factor': 0.18215} were passed to AutoencoderKL, but are not expected and will be ignored. Please verify your config.json configuration file.
from PIL import Image
def image_grid(imgs, rows, cols):
assert len(imgs) == rows*cols
w, h = imgs[0].size
grid = Image.new('RGB', size=(cols*w, rows*h))
grid_w, grid_h = grid.size
for i, img in enumerate(imgs):
grid.paste(img, box=(i%cols*w, i//cols*h))
return grid
n_cols = n_rows = 2
seed = 120
num_inference_steps = 30
generator = torch.Generator("cuda").manual_seed(seed)
prompt = "infinity symbol icon in blue and bold font"
prompts = [prompt] * (n_cols * n_rows)
images = pipe(prompts, num_inference_steps=num_inference_steps, generator=generator).images
grid = image_grid(images, rows=n_rows, cols=n_cols)
grid
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images[2]
from google.colab import drive
drive.mount('/content/drive')
images[2].save(f"/content/drive/My Drive/Colab Notebooks/images/logo.png")