#
Prompt techniques

Prompts are important because they describe what you want a diffusion model to generate. The best prompts are detailed, specific, and well-structured to help the model realize your vision. But crafting a great prompt takes time and effort and sometimes it may not be enough because language and words can be imprecise. This is where you need to boost your prompt with other techniques, such as prompt enhancing and prompt weighting, to get the results you want.

This guide will show you how you can use these prompt techniques to generate high-quality images with lower effort and adjust the weight of certain keywords in a prompt.

##
Prompt engineering

This is not an exhaustive guide on prompt engineering, but it will help you understand the necessary parts of a good prompt. We encourage you to continue experimenting with different prompts and combine them in new ways to see what works best. As you write more prompts, you’ll develop an intuition for what works and what doesn’t!

New diffusion models do a pretty good job of generating high-quality images from a basic prompt, but it is still important to create a well-written prompt to get the best results. Here are a few tips for writing a good prompt:

1. What is the image medium? Is it a photo, a painting, a 3D illustration, or something else?
2. What is the image subject? Is it a person, animal, object, or scene?
3. What details would you like to see in the image? This is where you can get really creative and have a lot of fun experimenting with different words to bring your image to life. For example, what is the lighting like? What is the vibe and aesthetic? What kind of art or illustration style are you looking for? The more specific and precise words you use, the better the model will understand what you want to generate.

"A photo of a banana-shaped couch in a living room"
"A vibrant yellow banana-shaped couch sits in a cozy living room, its curve cradling a pile of colorful cushions. on the wooden floor, a patterned rug adds a touch of eclectic charm, and a potted plant sits in the corner, reaching towards the sunlight filtering through the windows"

##
Prompt enhancing with GPT2

Prompt enhancing is a technique for quickly improving prompt quality without spending too much effort constructing one. It uses a model like GPT2 pretrained on Stable Diffusion text prompts to automatically enrich a prompt with additional important keywords to generate high-quality images.

The technique works by curating a list of specific keywords and forcing the model to generate those words to enhance the original prompt. This way, your prompt can be “a cat” and GPT2 can enhance the prompt to “cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain quality sharp focus beautiful detailed intricate stunning amazing epic”.

You should also use a offset noise LoRA to improve the contrast in bright and dark images and create better lighting overall. This LoRA is available from stabilityai/stable-diffusion-xl-base-1.0.

Start by defining certain styles and a list of words (you can check out a more comprehensive list of words and styles used by Fooocus) to enhance a prompt with.

    import torch
    from transformers import GenerationConfig, GPT2LMHeadModel, GPT2Tokenizer, LogitsProcessor, LogitsProcessorList
    from diffusers import StableDiffusionXLPipeline

    styles = {
        "cinematic": "cinematic film still of {prompt}, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain",
        "anime": "anime artwork of {prompt}, anime style, key visual, vibrant, studio anime, highly detailed",
        "photographic": "cinematic photo of {prompt}, 35mm photograph, film, professional, 4k, highly detailed",
        "comic": "comic of {prompt}, graphic illustration, comic art, graphic novel art, vibrant, highly detailed",
        "lineart": "line art drawing {prompt}, professional, sleek, modern, minimalist, graphic, line art, vector graphics",
        "pixelart": " pixel-art {prompt}, low-res, blocky, pixel art style, 8-bit graphics",
    }

    words = [
        "aesthetic", "astonishing", "beautiful", "breathtaking", "composition", "contrasted", "epic", "moody", "enhanced",
        "exceptional", "fascinating", "flawless", "glamorous", "glorious", "illumination", "impressive", "improved",
        "inspirational", "magnificent", "majestic", "hyperrealistic", "smooth", "sharp", "focus", "stunning", "detailed",
        "intricate", "dramatic", "high", "quality", "perfect", "light", "ultra", "highly", "radiant", "satisfying",
        "soothing", "sophisticated", "stylish", "sublime", "terrific", "touching", "timeless", "wonderful", "unbelievable",
        "elegant", "awesome", "amazing", "dynamic", "trendy",
    ]

You may have noticed in the `words` list, there are certain words that can be paired together to create something more meaningful. For example, the words “high” and “quality” can be combined to create “high quality”. Let’s pair these words together and remove the words that can’t be paired.

    word_pairs = ["highly detailed", "high quality", "enhanced quality", "perfect composition", "dynamic light"]

    def find_and_order_pairs(s, pairs):
        words = s.split()
        found_pairs = []
        for pair in pairs:
            pair_words = pair.split()
            if pair_words[0] in words and pair_words[1] in words:
                found_pairs.append(pair)
                words.remove(pair_words[0])
                words.remove(pair_words[1])

        for word in words[:]:
            for pair in pairs:
                if word in pair.split():
                    words.remove(word)
                    break
        ordered_pairs = ", ".join(found_pairs)
        remaining_s = ", ".join(words)
        return ordered_pairs, remaining_s

Next, implement a custom LogitsProcessor class that assigns tokens in the `words` list a value of 0 and assigns tokens not in the `words` list a negative value so they aren’t picked during generation. This way, generation is biased towards words in the `words` list. After a word from the list is used, it is also assigned a negative value so it isn’t picked again.

    class CustomLogitsProcessor(LogitsProcessor):
        def __init__(self, bias):
            super().__init__()
            self.bias = bias

        def __call__(self, input_ids, scores):
            if len(input_ids.shape) == 2:
                last_token_id = input_ids[0, -1]
                self.bias[last_token_id] = -1e10
            return scores + self.bias

    word_ids = [tokenizer.encode(word, add_prefix_space=True)[0] for word in words]
    bias = torch.full((tokenizer.vocab_size,), -float("Inf")).to("cuda")
    bias[word_ids] = 0
    processor = CustomLogitsProcessor(bias)
    processor_list = LogitsProcessorList([processor])

Combine the prompt and the `cinematic` style prompt defined in the `styles` dictionary earlier.

    prompt = "a cat basking in the sun on a roof in Turkey"
    style = "cinematic"

    prompt = styles[style].format(prompt=prompt)
    prompt
    "cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain"

Load a GPT2 tokenizer and model from the Gustavosta/MagicPrompt-Stable-Diffusion checkpoint (this specific checkpoint is trained to generate prompts) to enhance the prompt.

    tokenizer = GPT2Tokenizer.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion")
    model = GPT2LMHeadModel.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion", torch_dtype=torch.float16).to(
        "cuda"
    )
    model.eval()

    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    token_count = inputs["input_ids"].shape[1]
    max_new_tokens = 50 - token_count

    generation_config = GenerationConfig(
        penalty_alpha=0.7,
        top_k=50,
        eos_token_id=model.config.eos_token_id,
        pad_token_id=model.config.eos_token_id,
        pad_token=model.config.pad_token_id,
        do_sample=True,
    )

    with torch.no_grad():
        generated_ids = model.generate(
            input_ids=inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
            max_new_tokens=max_new_tokens,
            generation_config=generation_config,
            logits_processor=proccesor_list,
        )

Then you can combine the input prompt and the generated prompt. Feel free to take a look at what the generated prompt (`generated_part`) is, the word pairs that were found (`pairs`), and the remaining words (`words`). This is all packed together in the `enhanced_prompt`.

    output_tokens = [tokenizer.decode(generated_id, skip_special_tokens=True) for generated_id in generated_ids]
    input_part, generated_part = output_tokens[0][: len(prompt)], output_tokens[0][len(prompt) :]
    pairs, words = find_and_order_pairs(generated_part, word_pairs)
    formatted_generated_part = pairs + ", " + words
    enhanced_prompt = input_part + ", " + formatted_generated_part
    enhanced_prompt
    ["cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain quality sharp focus beautiful detailed intricate stunning amazing epic"]

Finally, load a pipeline and the offset noise LoRA with a low weight to generate an image with the enhanced prompt.

    pipeline = StableDiffusionXLPipeline.from_pretrained(
        "RunDiffusion/Juggernaut-XL-v9", torch_dtype=torch.float16, variant="fp16"
    ).to("cuda")

    pipeline.load_lora_weights(
        "stabilityai/stable-diffusion-xl-base-1.0",
        weight_name="sd_xl_offset_example-lora_1.0.safetensors",
        adapter_name="offset",
    )
    pipeline.set_adapters(["offset"], adapter_weights=[0.2])

    image = pipeline(
        enhanced_prompt,
        width=1152,
        height=896,
        guidance_scale=7.5,
        num_inference_steps=25,
    ).images[0]
    image

"a cat basking in the sun on a roof in Turkey"
"cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain"

##
Prompt weighting

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 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, 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` (and optionally `negative_prompt_embeds`) parameter, such as StableDiffusionPipeline, StableDiffusionControlNetPipeline, and StableDiffusionXLPipeline.

If your favorite pipeline doesn’t have a `prompt_embeds` parameter, please open an issue so we can add it!

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

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

    # uncomment to install in Colab
    #!pip install compel --upgrade

For this guide, let’s generate an image with the prompt `"a red cat playing with a ball"` using the StableDiffusionPipeline:

    from diffusers import StableDiffusionPipeline, UniPCMultistepScheduler
    import torch

    pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_safetensors=True)
    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

###
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` object, and pass it a tokenizer and text encoder:

    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”:

`+` 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!

    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:

    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

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

    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

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

    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

###
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!

    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)

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

###
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:

    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

###
Textual inversion

Textual inversion is a technique for learning a specific concept from some images which you can use to generate new images conditioned on that concept.

Create a pipeline and use the load_textual_inversion() function to load the textual inversion embeddings (feel free to browse the Stable Diffusion Conceptualizer for 100+ trained concepts):

    import torch
    from diffusers import StableDiffusionPipeline
    from compel import Compel, DiffusersTextualInversionManager

    pipe = StableDiffusionPipeline.from_pretrained(
      "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16,
      use_safetensors=True, variant="fp16").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:

    textual_inversion_manager = DiffusersTextualInversionManager(pipe)
    compel_proc = Compel(
        tokenizer=pipe.tokenizer,
        text_encoder=pipe.text_encoder,
        textual_inversion_manager=textual_inversion_manager)

Incorporate the concept to condition a prompt with using the `<concept>` syntax:

    prompt_embeds = compel_proc('("A red cat++ playing with a ball <midjourney-style>")')

    image = pipe(prompt_embeds=prompt_embeds).images[0]
    image

###
DreamBooth

DreamBooth is a technique for generating contextualized images of a subject given just a few images of the subject to train on. It is similar to textual inversion, but DreamBooth trains the full model whereas textual inversion only fine-tunes the text embeddings. This means you should use from_pretrained() to load the DreamBooth model (feel free to browse the Stable Diffusion Dreambooth Concepts Library for 100+ trained models):

    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`:

    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

###
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:

    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]
    )

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` (and optionally `negative_pooled_prompt_embeds`) so you should pass those to the pipeline along with the conditioning tensors:

    # 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)

"a red cat playing with a (ball)1.5"
"a red cat playing with a (ball)0.6"