Overcoming the 77-token prompt limitation, generating long-weighted prompt embeddings for Stable Diffusion, this module supports generating embedding and pooled embeddings for long prompt weighted. The generated embedding is compatible with Huggingface Diffusers.
The prompt format is compatible with AUTOMATIC1111 stable-diffusion-webui
- Support unlimited prompt length for SD1.5 and SDXL
- Support weighting like
a (white:1.2) cat - Support parentheses like
a ((white)) cat - For SD3, support max 512 tokens (T5 model support max 512 tokens)
Support Stable Diffusion v1.5, SDXL and Stable Diffusion 3.
The detailed implementation is covered in chapter 10 of book Using Stable Diffusion with Python
- [06/30/2024] Add support Stable Diffusion 3 pipeline without T5 encoder.
model_path = "stabilityai/stable-diffusion-3-medium-diffusers"
pipe = StableDiffusion3Pipeline.from_pretrained(
model_path
, torch_dtype = torch.float16
, text_encoder_3 = None # <- load SD3 without T5 encoder
)pip install git+https://github.com/xhinker/sd_embed.git@mainGenerate long prompt weighted embeddings for Stable Diffusion 3. A
Load up SD3 model:
import gc
import torch
from diffusers import StableDiffusion3Pipeline
from sd_embed.embedding_funcs import get_weighted_text_embeddings_sd3
model_path = "stabilityai/stable-diffusion-3-medium-diffusers"
pipe = StableDiffusion3Pipeline.from_pretrained(
model_path,
torch_dtype=torch.float16
)Generate the embedding and use it to generate images:
pipe.to('cuda')
prompt = """A whimsical and creative image depicting a hybrid creature that is a mix of a waffle and a hippopotamus.
This imaginative creature features the distinctive, bulky body of a hippo,
but with a texture and appearance resembling a golden-brown, crispy waffle.
The creature might have elements like waffle squares across its skin and a syrup-like sheen.
It's set in a surreal environment that playfully combines a natural water habitat of a hippo with elements of a breakfast table setting,
possibly including oversized utensils or plates in the background.
The image should evoke a sense of playful absurdity and culinary fantasy.
"""
neg_prompt = """\
skin spots,acnes,skin blemishes,age spot,(ugly:1.2),(duplicate:1.2),(morbid:1.21),(mutilated:1.2),\
(tranny:1.2),mutated hands,(poorly drawn hands:1.5),blurry,(bad anatomy:1.2),(bad proportions:1.3),\
extra limbs,(disfigured:1.2),(missing arms:1.2),(extra legs:1.2),(fused fingers:1.5),\
(too many fingers:1.5),(unclear eyes:1.2),lowers,bad hands,missing fingers,extra digit,\
bad hands,missing fingers,(extra arms and legs),(worst quality:2),(low quality:2),\
(normal quality:2),lowres,((monochrome)),((grayscale))
"""
(
prompt_embeds
, prompt_neg_embeds
, pooled_prompt_embeds
, negative_pooled_prompt_embeds
) = get_weighted_text_embeddings_sd3(
pipe
, prompt = prompt
, neg_prompt = neg_prompt
)
image = pipe(
prompt_embeds = prompt_embeds
, negative_prompt_embeds = prompt_neg_embeds
, pooled_prompt_embeds = pooled_prompt_embeds
, negative_pooled_prompt_embeds = negative_pooled_prompt_embeds
, num_inference_steps = 30
, height = 1024
, width = 1024 + 512
, guidance_scale = 4.0
, generator = torch.Generator("cuda").manual_seed(2)
).images[0]
display(image)
del prompt_embeds, prompt_neg_embeds,pooled_prompt_embeds, negative_pooled_prompt_embeds
pipe.to('cpu')
gc.collect()
torch.cuda.empty_cache()Using long weighted embedding result:
Without long prompt weighted embedding result:
To use the long prompt weighted embedding for SDXL, simply import the embedding function - from sd_embed.embedding_funcs import get_weighted_text_embeddings_sdxl for sdxl.
import gc
import torch
from diffusers import StableDiffusionXLPipeline
from sd_embed.embedding_funcs import get_weighted_text_embeddings_sdxl
model_path = "Lykon/dreamshaper-xl-1-0"
pipe = StableDiffusionXLPipeline.from_pretrained(
model_path,
torch_dtype=torch.float16
)
pipe.to('cuda')
prompt = """A whimsical and creative image depicting a hybrid creature that is a mix of a waffle and a hippopotamus.
This imaginative creature features the distinctive, bulky body of a hippo,
but with a texture and appearance resembling a golden-brown, crispy waffle.
The creature might have elements like waffle squares across its skin and a syrup-like sheen.
It's set in a surreal environment that playfully combines a natural water habitat of a hippo with elements of a breakfast table setting,
possibly including oversized utensils or plates in the background.
The image should evoke a sense of playful absurdity and culinary fantasy.
"""
neg_prompt = """\
skin spots,acnes,skin blemishes,age spot,(ugly:1.2),(duplicate:1.2),(morbid:1.21),(mutilated:1.2),\
(tranny:1.2),mutated hands,(poorly drawn hands:1.5),blurry,(bad anatomy:1.2),(bad proportions:1.3),\
extra limbs,(disfigured:1.2),(missing arms:1.2),(extra legs:1.2),(fused fingers:1.5),\
(too many fingers:1.5),(unclear eyes:1.2),lowers,bad hands,missing fingers,extra digit,\
bad hands,missing fingers,(extra arms and legs),(worst quality:2),(low quality:2),\
(normal quality:2),lowres,((monochrome)),((grayscale))
"""
(
prompt_embeds
, prompt_neg_embeds
, pooled_prompt_embeds
, negative_pooled_prompt_embeds
) = get_weighted_text_embeddings_sdxl(
pipe
, prompt = prompt
, neg_prompt = neg_prompt
)
image = pipe(
prompt_embeds = prompt_embeds
, negative_prompt_embeds = prompt_neg_embeds
, pooled_prompt_embeds = pooled_prompt_embeds
, negative_pooled_prompt_embeds = negative_pooled_prompt_embeds
, num_inference_steps = 30
, height = 1024
, width = 1024 + 512
, guidance_scale = 4.0
, generator = torch.Generator("cuda").manual_seed(2)
).images[0]
display(image)
del prompt_embeds, prompt_neg_embeds,pooled_prompt_embeds, negative_pooled_prompt_embeds
pipe.to('cpu')
gc.collect()
torch.cuda.empty_cache()Using long prompt weighted embedding:
Without using long prompt weighted embedding:
To use the long prompt weighted embedding for SDXL, use the embedding function - get_weighted_text_embeddings_sd15.
import gc
import torch
from diffusers import StableDiffusionPipeline
from sd_embed.embedding_funcs import get_weighted_text_embeddings_sd15
model_path = "stablediffusionapi/deliberate-v2"
pipe = StableDiffusionPipeline.from_pretrained(
model_path,
torch_dtype=torch.float16
)
pipe.to('cuda')
prompt = """A whimsical and creative image depicting a hybrid creature that is a mix of a waffle and a hippopotamus.
This imaginative creature features the distinctive, bulky body of a hippo,
but with a texture and appearance resembling a golden-brown, crispy waffle.
The creature might have elements like waffle squares across its skin and a syrup-like sheen.
It's set in a surreal environment that playfully combines a natural water habitat of a hippo with elements of a breakfast table setting,
possibly including oversized utensils or plates in the background.
The image should evoke a sense of playful absurdity and culinary fantasy.
"""
neg_prompt = """\
skin spots,acnes,skin blemishes,age spot,(ugly:1.2),(duplicate:1.2),(morbid:1.21),(mutilated:1.2),\
(tranny:1.2),mutated hands,(poorly drawn hands:1.5),blurry,(bad anatomy:1.2),(bad proportions:1.3),\
extra limbs,(disfigured:1.2),(missing arms:1.2),(extra legs:1.2),(fused fingers:1.5),\
(too many fingers:1.5),(unclear eyes:1.2),lowers,bad hands,missing fingers,extra digit,\
bad hands,missing fingers,(extra arms and legs),(worst quality:2),(low quality:2),\
(normal quality:2),lowres,((monochrome)),((grayscale))
"""
(
prompt_embeds
, prompt_neg_embeds
) = get_weighted_text_embeddings_sd15(
pipe
, prompt = prompt
, neg_prompt = neg_prompt
)
image = pipe(
prompt_embeds = prompt_embeds
, negative_prompt_embeds = prompt_neg_embeds
, num_inference_steps = 30
, height = 768
, width = 896
, guidance_scale = 8.0
, generator = torch.Generator("cuda").manual_seed(2)
).images[0]
display(image)
del prompt_embeds, prompt_neg_embeds
pipe.to('cpu')
gc.collect()
torch.cuda.empty_cache()Using long prompt weighted embedding:
Without using long prompt weighted embedding:
If you use sd_embed in your research, please cite the following work:
@misc{sd_embed_2024,
author = {Shudong Zhu(Andrew Zhu)},
title = {Long Prompt Weighted Stable Diffusion Embedding},
howpublished = {\url{https://github.com/xhinker/sd_embed}},
year = {2024},
}