long_prompt_weighting_stable_diffusion.ipynb

Kernel: Unknown Kernel

Long Prompt Weighting Stable Diffusion

When inputting a prompt without the 77 token length limit, you can include text-to-image (tx2img), image-to-image (img2img), and inpainting pipelines. You can emphasize or weigh part of your prompt by using parentheses, such as "a baby deer with (big eyes)". If you want to de-emphasize part of your prompt, you can use square brackets, like "a [baby] deer with big eyes".

For precise weighting, you can adjust the emphasis with a specific value, for example, "a baby deer with (big eyes:1.3)". Prompt weighting equivalents include "a baby deer with" being equivalent to "(a baby deer with:1.0)", "(big eyes)" being equivalent to "(big eyes:1.1)", "((big eyes))" being equivalent to "(big eyes:1.21)", and "[big eyes]" being equivalent to "(big eyes:0.91)". This allows for flexible control over the importance of different elements in your prompts.

This script was contributed by SkyTNT and the notebook by ParagEkbote.

pip install diffusers torch

Requirement already satisfied: diffusers in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (0.31.0)
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Note: you may need to restart the kernel to use updated packages.

from diffusers import DiffusionPipeline
import torch

pipe = DiffusionPipeline.from_pretrained(
    'hakurei/waifu-diffusion',
    custom_pipeline="lpw_stable_diffusion",
    torch_dtype=torch.float16
)
pipe = pipe.to("cuda")

prompt = "best_quality (1girl:1.3) bow bride brown_hair closed_mouth frilled_bow frilled_hair_tubes frills (full_body:1.3) fox_ear hair_bow hair_tubes happy hood japanese_clothes kimono long_sleeves red_bow smile solo tabi uchikake white_kimono wide_sleeves cherry_blossoms"
neg_prompt = "lowres, bad_anatomy, error_body, error_hair, error_arm, error_hands, bad_hands, error_fingers, bad_fingers, missing_fingers, error_legs, bad_legs, multiple_legs, missing_legs, error_lighting, error_shadow, error_reflection, text, error, extra_digit, fewer_digits, cropped, worst_quality, low_quality, normal_quality, jpeg_artifacts, signature, watermark, username, blurry"

pipe.text2img(prompt, negative_prompt=neg_prompt, width=512, height=512, max_embeddings_multiples=3).images[0]

Token indices sequence length is longer than the specified maximum sequence length for this model (108 > 77). Running this sequence through the model will result in indexing errors

Onnxruntime

If you see Token indices sequence length is longer than the specified maximum sequence length for this model ( *** > 77 ) . Running this sequence through the model will result in indexing errors.

from diffusers import DiffusionPipeline
import torch

pipe = DiffusionPipeline.from_pretrained(
    'CompVis/stable-diffusion-v1-4',
    custom_pipeline="lpw_stable_diffusion_onnx",
    revision="onnx",
    provider="CUDAExecutionProvider"
)

prompt = "a photo of an astronaut riding a horse on mars, best quality"
neg_prompt = "lowres, bad anatomy, error body, error hair, error arm, error hands, bad hands, error fingers, bad fingers, missing fingers, error legs, bad legs, multiple legs, missing legs, error lighting, error shadow, error reflection, text, error, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry"

pipe.text2img(prompt, negative_prompt=neg_prompt, width=512, height=512, max_embeddings_multiples=3).images[0]