CompVis

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ldm-text2im-large-256

like 30

Looks good!

Looks great, just have two comments:

1. I think we should link the Spaces for the demo: https://huggingface.co/spaces/CompVis/ldm-text2im-large-256-diffusers
2. The demo code is using a bit of old API code - and also the post-processing code is longer needed as by default now the pipeline returns a PIL image already - so the usage part can get even simpler:

# !pip install diffusers
from diffusers import LDMTextToImagePipeline
import PIL.Image
import numpy as np

model_id = "CompVis/ldm-text2im-large-256"

# load model and scheduler
ldm = LDMTextToImagePipeline.from_pretrained(model_id)

# run pipeline in inference (sample random noise and denoise)
prompt = "A painting of a squirrel eating a burger"
images = ldm([prompt], num_inference_steps=50, eta=0.3, guidance_scale=0.6, batch_size=1)["sample"]

# save images
for idx, image in enumerate(images):
    image.save(f"squirrel-{idx}.png")

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Cool feedback @multimodalart ! Think we need to merge this in the next couple of hours. Should we maybe just open a new PR with your proposed changes?