prompt_weight.py

#!/usr/bin/env python3
import torch
import os
from compel import Compel, ReturnedEmbeddingsType
from diffusers import DiffusionPipeline
from huggingface_hub import HfApi
from pathlib import Path

api = HfApi()

pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", variant="fp16", use_safetensors=True, torch_dtype=torch.float16).to("cuda")

compel = Compel(tokenizer=[pipeline.tokenizer, pipeline.tokenizer_2] , text_encoder=[pipeline.text_encoder, pipeline.text_encoder_2], returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED, requires_pooled=[False, True])

# upweight "ball"
prompt = ["a red cat playing with a (ball)1.5", "a red cat playing with a (ball)0.6"]
conditioning, pooled = compel(prompt)


# generate image
generator = [torch.Generator().manual_seed(33) for _ in range(len(prompt))]
images = pipeline(prompt_embeds=conditioning, pooled_prompt_embeds=pooled, generator=generator, num_inference_steps=30).images

for i, image in enumerate(images):
    file_name = f"bb_1_{i}"
    path = os.path.join(Path.home(), "images", f"{file_name}.png")
    image.save(path)

    api.upload_file(
        path_or_fileobj=path,
        path_in_repo=path.split("/")[-1],
        repo_id="patrickvonplaten/images",
        repo_type="dataset",
    )
    print(f"https://huggingface.co/datasets/patrickvonplaten/images/blob/main/{file_name}.png")