[docs] Update prompt weighting docs (#10843)

stevhliu · web-flow · commit 3fdf17308435 · 2025-02-24T08:46:26.000-08:00
* sd_embed

* feedback
diff --git a/docs/source/en/using-diffusers/weighted_prompts.md b/docs/source/en/using-diffusers/weighted_prompts.md
@@ -215,144 +215,107 @@ 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++"
+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))
+"""
 ```
 
-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
-```
+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.
 
-<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
-```
-
-<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!
+> [!TIP]
+> You can safely ignore the error message below about the token index length exceeding the models maximum sequence length. All your tokens will be used in the embedding process.
+>
+> ```
+> Token indices sequence length is longer than the specified maximum sequence length for this 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"/>
+  <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
 
@@ -363,35 +326,63 @@ 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
@@ -401,70 +392,44 @@ 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`:
-
-```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:
+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
-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>
