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"""See https://huggingface.co/fusing/latent-diffusion-text2im-large.""" | |
from logzero import logger | |
from install import install | |
try: | |
import gradio as gr | |
except ModuleNotFoundError: | |
try: | |
install("gradio") | |
import gradio as gr | |
except Exception as exc: | |
logger.error(exc) | |
raise SystemExit(1) | |
import PIL | |
from diffusers import DiffusionPipeline | |
ldm = DiffusionPipeline.from_pretrained("fu sing/latent-diffusion-text2im-large") | |
generator = torch.manual_seed(42) | |
examples = ["A street sign that reads Huggingface", "A painting of a squirrel eating a burger"] | |
prompt_ = "A painting of a squirrel eating a burger" | |
def fn(prompt=prompt_): | |
image = ldm( | |
[prompt], | |
generator=generator, | |
eta=0.3, | |
guidance_scale=6.0, | |
num_inference_steps=50, | |
) | |
image_processed = image.cpu().permute(0, 2, 3, 1) | |
image_processed = image_processed * 255. | |
image_processed = image_processed.numpy().astype(np.uint8) | |
image_pil = PIL.Image.fromarray(image_processed[0]) | |
# save image | |
# image_pil.save("test.png") | |
return image_pil | |
iface = gr.Interface( | |
fn=fn, | |
inputs="text", | |
outputs="image", | |
examples=examples, | |
live=True, | |
) | |
iface.launch() | |
# gr.Interface.load("fusing/latent-diffusion-text2im-large", examples=examples).launch() |