stable_cascade_easy
Added 2024-02-14 15:36:23 +0000 UTCFrom my github: https://github.com/another-ai/stable_cascade_easy
Text to Img with Stable Cascade, required less vram than original example on official Hugginface(https://huggingface.co/stabilityai/stable-cascade):
- 44 seconds for a 1280x1536 image with a nvidia RTX3060 with 12 GB VRAM
Installation:
- Install Python 3.10.6, checking "Add Python to PATH".
- Install git.
- On terminal:
git clone https://github.com/shiroppo/stable_cascade_easy
cd stable_cascade_easy
py -m venv venv
.\venv\Scripts\activate
pip install -r requirements.txt- and then:
pip install git+https://github.com/kashif/diffusers.git@wuerstchen-v3Run:
On terminal:
.\venv\Scripts\activate
py app.pyCode:
import torch
from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline
import gc
device = "cuda"
num_images_per_prompt = 1
prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", torch_dtype=torch.bfloat16).to(device)
prior.safety_checker = None
prior.requires_safety_checker = False
prompt = "a cat"
negative_prompt = ""
prior_output = prior(
prompt=prompt,
width=1280,
height=1536,
negative_prompt=negative_prompt,
guidance_scale=4.0,
num_images_per_prompt=num_images_per_prompt,
num_inference_steps=20
)
del prior
gc.collect()
torch.cuda.empty_cache()
decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", torch_dtype=torch.float16).to(device)
decoder.safety_checker = None
decoder.requires_safety_checker = False
decoder_output = decoder(
image_embeddings=prior_output.image_embeddings.half(),
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=0.0,
output_type="pil",
num_inference_steps=12
).images[0].save("image.png")
# del decoder
# gc.collect()
# torch.cuda.empty_cache()
