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##### GOT IT FROM https://github.com/huggingface/diffusers/issues/3579
import torch
import torchvision
from PIL import Image
from diffusers import EulerDiscreteScheduler, StableDiffusionPipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = StableDiffusionPipeline.from_pretrained(
"prompthero/openjourney-v4", torch_dtype=torch.float16, safety_checker=None)
pipe = pipe.to(device)
pipe.enable_attention_slicing()
pipe.scheduler = EulerDiscreteScheduler.from_config(
pipe.scheduler.config, use_karras_sigmas=True
)
prompt = "A futuristic cityscape at sunset"
negative_prompt = "low quality"
# num_images_per_prompt=4,
def progress(step, timestep, latents):
print(step, timestep, latents[0][0][0][0])
with torch.no_grad():
latents = 1 / 0.18215 * latents
image = pipe.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
# convert to PIL Images
image = pipe.numpy_to_pil(image)
# do something with the Images
for i, img in enumerate(image):
img.save(f"step_{step}_img{i}.png")
result = pipe(prompt=prompt,
num_inference_steps=20,
height=512, width=512,
guidance_scale=7,
negative_prompt=negative_prompt,
callback=progress,
callback_steps=5
)
image = result.images[0]
image.save(f"outputs/cikar goster.png")
print(result.nsfw_content_detected)
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