Skip to content

[docs] Update prompt weighting docs #10843

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Feb 24, 2025
Merged

[docs] Update prompt weighting docs #10843

Changes from 1 commit
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
78b2a5d
sd_embed
stevhliu Feb 20, 2025
2f6a960
feedback
stevhliu Feb 21, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
306 changes: 132 additions & 174 deletions docs/source/en/using-diffusers/weighted_prompts.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -215,144 +215,100 @@ image

Prompt weighting provides a way to emphasize or de-emphasize certain parts of a prompt, allowing for more control over the generated image. A prompt can include several concepts, which gets turned into contextualized text embeddings. The embeddings are used by the model to condition its cross-attention layers to generate an image (read the Stable Diffusion [blog post](https://huggingface.co/blog/stable_diffusion) to learn more about how it works).

Prompt weighting works by increasing or decreasing the scale of the text embedding vector that corresponds to its concept in the prompt because you may not necessarily want the model to focus on all concepts equally. The easiest way to prepare the prompt-weighted embeddings is to use [Compel](https://github.com/damian0815/compel), a text prompt-weighting and blending library. Once you have the prompt-weighted embeddings, you can pass them to any pipeline that has a [`prompt_embeds`](https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline.__call__.prompt_embeds) (and optionally [`negative_prompt_embeds`](https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline.__call__.negative_prompt_embeds)) parameter, such as [`StableDiffusionPipeline`], [`StableDiffusionControlNetPipeline`], and [`StableDiffusionXLPipeline`].
Prompt weighting works by increasing or decreasing the scale of the text embedding vector that corresponds to its concept in the prompt because you may not necessarily want the model to focus on all concepts equally. The easiest way to prepare the prompt embeddings is to use [Stable Diffusion Long Prompt Weighted Embedding](https://github.com/xhinker/sd_embed) (sd_embed). Once you have the prompt-weighted embeddings, you can pass them to any pipeline that has a [prompt_embeds](https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline.__call__.prompt_embeds) (and optionally [negative_prompt_embeds](https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline.__call__.negative_prompt_embeds)) parameter, such as [`StableDiffusionPipeline`], [`StableDiffusionControlNetPipeline`], and [`StableDiffusionXLPipeline`].

<Tip>

If your favorite pipeline doesn't have a `prompt_embeds` parameter, please open an [issue](https://github.com/huggingface/diffusers/issues/new/choose) so we can add it!

</Tip>

This guide will show you how to weight and blend your prompts with Compel in 🤗 Diffusers.
This guide will show you how to weight your prompts with sd_embed.

Before you begin, make sure you have the latest version of Compel installed:
Before you begin, make sure you have the latest version of sd_embed installed:

```py
# uncomment to install in Colab
#!pip install compel --upgrade
```bash
pip install git+https://github.com/xhinker/sd_embed.git@main
```

For this guide, let's generate an image with the prompt `"a red cat playing with a ball"` using the [`StableDiffusionPipeline`]:
For this example, let's use [`StableDiffusionXLPipeline`].

```py
from diffusers import StableDiffusionPipeline, UniPCMultistepScheduler
from diffusers import StableDiffusionXLPipeline, UniPCMultistepScheduler
import torch

pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_safetensors=True)
pipe = StableDiffusionXLPipeline.from_pretrained("Lykon/dreamshaper-xl-1-0", torch_dtype=torch.float16)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.to("cuda")

prompt = "a red cat playing with a ball"

generator = torch.Generator(device="cpu").manual_seed(33)

image = pipe(prompt, generator=generator, num_inference_steps=20).images[0]
image
```

<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/compel/forest_0.png"/>
</div>

### Weighting

You'll notice there is no "ball" in the image! Let's use compel to upweight the concept of "ball" in the prompt. Create a [`Compel`](https://github.com/damian0815/compel/blob/main/doc/compel.md#compel-objects) object, and pass it a tokenizer and text encoder:

```py
from compel import Compel

compel_proc = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder)
```

compel uses `+` or `-` to increase or decrease the weight of a word in the prompt. To increase the weight of "ball":
To upweight or downweight a concept, surround the text with parentheses. More parentheses applies a heavier weight on the text. You can also append a numerical multiplier to the text to indicate how much you want to increase or decrease its weights by.

<Tip>

`+` corresponds to the value `1.1`, `++` corresponds to `1.1^2`, and so on. Similarly, `-` corresponds to `0.9` and `--` corresponds to `0.9^2`. Feel free to experiment with adding more `+` or `-` in your prompt!
| format | multiplier |
|---|---|
| `(hippo)` | increase by 1.1x |
| `((hippo))` | increase by 1.21x |
| `(hippo:1.5)` | increase by 1.5x |
| `(hippo:0.5)` | decrease by 4x |

</Tip>
Create a prompt and use a combination of parentheses and numerical multipliers to upweight various text.

```py
prompt = "a red cat playing with a ball++"
```

Pass the prompt to `compel_proc` to create the new prompt embeddings which are passed to the pipeline:

```py
prompt_embeds = compel_proc(prompt)
generator = torch.manual_seed(33)

image = pipe(prompt_embeds=prompt_embeds, generator=generator, num_inference_steps=20).images[0]
image
```

<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/compel/forest_1.png"/>
</div>

To downweight parts of the prompt, use the `-` suffix:

```py
prompt = "a red------- cat playing with a ball"
prompt_embeds = compel_proc(prompt)

generator = torch.manual_seed(33)

image = pipe(prompt_embeds=prompt_embeds, generator=generator, num_inference_steps=20).images[0]
image
```

<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/compel-neg.png"/>
</div>

You can even up or downweight multiple concepts in the same prompt:

```py
prompt = "a red cat++ playing with a ball----"
prompt_embeds = compel_proc(prompt)

generator = torch.manual_seed(33)

image = pipe(prompt_embeds=prompt_embeds, generator=generator, num_inference_steps=20).images[0]
image
from sd_embed.embedding_funcs import get_weighted_text_embeddings_sdxl

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))
"""
```

<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/compel-pos-neg.png"/>
</div>

### Blending

You can also create a weighted *blend* of prompts by adding `.blend()` to a list of prompts and passing it some weights. Your blend may not always produce the result you expect because it breaks some assumptions about how the text encoder functions, so just have fun and experiment with it!
Use the `get_weighted_text_embeddings_sdxl` function to generate the prompt embeddings and the negative prompt embeddings. It'll also generated the pooled and negative pooled prompt embeddings since you're using the SDXL model.

```py
prompt_embeds = compel_proc('("a red cat playing with a ball", "jungle").blend(0.7, 0.8)')
generator = torch.Generator(device="cuda").manual_seed(33)
(
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, generator=generator, num_inference_steps=20).images[0]
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]
image
```

<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/compel-blend.png"/>
<div class="flex jsutify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sd_embed_sdxl.png"/>
</div>

### Conjunction

A conjunction diffuses each prompt independently and concatenates their results by their weighted sum. Add `.and()` to the end of a list of prompts to create a conjunction:

```py
prompt_embeds = compel_proc('["a red cat", "playing with a", "ball"].and()')
generator = torch.Generator(device="cuda").manual_seed(55)

image = pipe(prompt_embeds=prompt_embeds, generator=generator, num_inference_steps=20).images[0]
image
```

<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/compel-conj.png"/>
</div>
> [!TIP]
> Refer to the [sd_embed](https://github.com/xhinker/sd_embed) repository for additional details about long prompt weighting for FLUX.1, Stable Cascade, and Stable Diffusion 1.5.

### Textual inversion

Expand All @@ -363,35 +319,63 @@ Create a pipeline and use the [`~loaders.TextualInversionLoaderMixin.load_textua
```py
import torch
from diffusers import StableDiffusionPipeline
from compel import Compel, DiffusersTextualInversionManager

pipe = StableDiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16,
use_safetensors=True, variant="fp16").to("cuda")
"stable-diffusion-v1-5/stable-diffusion-v1-5",
torch_dtype=torch.float16,
).to("cuda")
pipe.load_textual_inversion("sd-concepts-library/midjourney-style")
```

Compel provides a `DiffusersTextualInversionManager` class to simplify prompt weighting with textual inversion. Instantiate `DiffusersTextualInversionManager` and pass it to the `Compel` class:
Add the `<midjourney-style>` text to the prompt to trigger the textual inversion.

```py
textual_inversion_manager = DiffusersTextualInversionManager(pipe)
compel_proc = Compel(
tokenizer=pipe.tokenizer,
text_encoder=pipe.text_encoder,
textual_inversion_manager=textual_inversion_manager)
from sd_embed.embedding_funcs import get_weighted_text_embeddings_sd15

prompt = """<midjourney-style> 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))
"""
```

Incorporate the concept to condition a prompt with using the `<concept>` syntax:
Use the `get_weighted_text_embeddings_sd15` function to generate the prompt embeddings and the negative prompt embeddings.

```py
prompt_embeds = compel_proc('("A red cat++ playing with a ball <midjourney-style>")')
(
prompt_embeds,
prompt_neg_embeds,
) = get_weighted_text_embeddings_sd15(
pipe,
prompt=prompt,
neg_prompt=neg_prompt
)

image = pipe(prompt_embeds=prompt_embeds).images[0]
image = pipe(
prompt_embeds=prompt_embeds,
negative_prompt_embeds=prompt_neg_embeds,
height=768,
width=896,
guidance_scale=4.0,
generator=torch.Generator("cuda").manual_seed(2)
).images[0]
image
```

<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/compel-text-inversion.png"/>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sd_embed_textual_inversion.png"/>
</div>

### DreamBooth
Expand All @@ -401,70 +385,44 @@ image
```py
import torch
from diffusers import DiffusionPipeline, UniPCMultistepScheduler
from compel import Compel

pipe = DiffusionPipeline.from_pretrained("sd-dreambooth-library/dndcoverart-v1", torch_dtype=torch.float16).to("cuda")
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
```

Create a `Compel` class with a tokenizer and text encoder, and pass your prompt to it. Depending on the model you use, you'll need to incorporate the model's unique identifier into your prompt. For example, the `dndcoverart-v1` model uses the identifier `dndcoverart`:
Depending on the model you use, you'll need to incorporate the model's unique identifier into your prompt. For example, the `dndcoverart-v1` model uses the identifier `dndcoverart`:

```py
compel_proc = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder)
prompt_embeds = compel_proc('("magazine cover of a dndcoverart dragon, high quality, intricate details, larry elmore art style").and()')
image = pipe(prompt_embeds=prompt_embeds).images[0]
image
```

<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/compel-dreambooth.png"/>
</div>

### Stable Diffusion XL

Stable Diffusion XL (SDXL) has two tokenizers and text encoders so it's usage is a bit different. To address this, you should pass both tokenizers and encoders to the `Compel` class:

```py
from compel import Compel, ReturnedEmbeddingsType
from diffusers import DiffusionPipeline
from diffusers.utils import make_image_grid
import torch

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]
from sd_embed.embedding_funcs import get_weighted_text_embeddings_sd15

prompt = """dndcoverart of 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
)
```

This time, let's upweight "ball" by a factor of 1.5 for the first prompt, and downweight "ball" by 0.6 for the second prompt. The [`StableDiffusionXLPipeline`] also requires [`pooled_prompt_embeds`](https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLInpaintPipeline.__call__.pooled_prompt_embeds) (and optionally [`negative_pooled_prompt_embeds`](https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLInpaintPipeline.__call__.negative_pooled_prompt_embeds)) so you should pass those to the pipeline along with the conditioning tensors:

```py
# apply weights
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
make_image_grid(images, rows=1, cols=2)
```

<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/compel/sdxl_ball1.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">"a red cat playing with a (ball)1.5"</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/compel/sdxl_ball2.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">"a red cat playing with a (ball)0.6"</figcaption>
</div>
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sd_embed_dreambooth.png"/>
</div>