[Docs] Weight prompting using compel (huggingface#2574)

* add docs

* correct

* finish

* Apply suggestions from code review

Co-authored-by: Will Berman <[email protected]>
Co-authored-by: YiYi Xu <[email protected]>

* update deps table

* Apply suggestions from code review

Co-authored-by: Pedro Cuenca <[email protected]>

---------

Co-authored-by: Will Berman <[email protected]>
Co-authored-by: YiYi Xu <[email protected]>
Co-authored-by: Pedro Cuenca <[email protected]>

Author: 4 people

docs/source/en/_toctree.yml

@@ -48,6 +48,8 @@
       title: How to contribute a Pipeline
     - local: using-diffusers/using_safetensors
       title: Using safetensors
+    - local: using-diffusers/weighted_prompts
+      title: Weighting Prompts
     title: Pipelines for Inference
   - sections:
     - local: using-diffusers/rl

docs/source/en/using-diffusers/controlling_generation.mdx

@@ -36,6 +36,7 @@ Unless otherwise mentioned, these are techniques that work with existing models
 8. [DreamBooth](#dreambooth)
 9. [Textual Inversion](#textual-inversion)
 10. [ControlNet](#controlnet)
+11. [Prompt Weighting](#prompt-weighting)
 
 ## Instruct Pix2Pix
 
@@ -158,3 +159,9 @@ depth maps, and semantic segmentations.
 
 See [here](../api/pipelines/stable_diffusion/controlnet) for more information on how to use it.
 
+## Prompt Weighting
+
+Prompt weighting is a simple technique that puts more attention weight on certain parts of the text 
+input. 
+
+For a more in-detail explanation and examples, see [here](../using-diffusers/weighted_prompts).

docs/source/en/using-diffusers/weighted_prompts.mdx

@@ -0,0 +1,98 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Weighting prompts
+
+Text-guided diffusion models generate images based on a given text prompt. The text prompt
+can include multiple concepts that the model should generate and it's often desirable to weight
+certain parts of the prompt more or less. 
+
+Diffusion models work by conditioning the cross attention layers of the diffusion model with contextualized text embeddings (see the [Stable Diffusion Guide for more information](../stable-diffusion)).
+Thus a simple way to emphasize (or de-emphasize) certain parts of the prompt is by increasing or reducing the scale of the text embedding vector that corresponds to the relevant part of the prompt.
+This is called "prompt-weighting" and has been a highly demanded feature by the community (see issue [here](https://github.com/huggingface/diffusers/issues/2431)).
+
+## How to do prompt-weighting in Diffusers
+
+We believe the role of `diffusers` is to be a toolbox that provides essential features that enable other projects, such as [InvokeAI](https://github.com/invoke-ai/InvokeAI) or [diffuzers](https://github.com/abhishekkrthakur/diffuzers), to build powerful UIs. In order to support arbitrary methods to manipulate prompts, `diffusers` exposes a [`prompt_embeds`](https://huggingface.co/docs/diffusers/v0.14.0/en/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline.__call__.prompt_embeds) function argument to many pipelines such as [`StableDiffusionPipeline`], allowing to directly pass the "prompt-weighted"/scaled text embeddings to the pipeline.
+
+The [compel library](https://github.com/damian0815/compel) provides an easy way to emphasize or de-emphasize portions of the prompt for you. We strongly recommend it instead of preparing the embeddings yourself.
+
+Let's look at a simple example. Imagine you want to generate an image of `"a red cat playing with a ball"` as 
+follows:
+
+```py
+from diffusers import StableDiffusionPipeline, UniPCMultistepScheduler
+
+pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+
+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
+```
+
+This gives you:
+
+![img](https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/compel/forest_0.png)
+
+As you can see, there is no "ball" in the image. Let's emphasize this part!
+
+For this we should install the `compel` library:
+
+```
+pip install compel
+```
+
+and then create a `Compel` object:
+
+```py
+from compel import Compel
+
+compel_proc = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder)
+```
+
+Now we emphasize the part "ball" with the `"++"` syntax:
+
+```py
+prompt = "a red cat playing with a ball++"
+```
+
+and instead of passing this to the pipeline directly, we have to process it using `compel_proc`:
+
+```py
+prompt_embeds = compel_proc(prompt)
+```
+
+Now we can pass `prompt_embeds` directly to the pipeline:
+
+```py
+generator = torch.Generator(device="cpu").manual_seed(33)
+
+images = pipe(prompt_embeds=prompt_embeds, generator=generator, num_inference_steps=20).images[0]
+image
+```
+
+We now get the following image which has a "ball"!
+
+![img](https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/compel/forest_1.png)
+
+Similarly, we de-emphasize parts of the sentence by using the `--` suffix for words, feel free to give it 
+a try!
+
+If your favorite pipeline does not have a `prompt_embeds` input, please make sure to open an issue, the 
+diffusers team tries to be as responsive as possible.
+
+Also, please check out the documentation of the [compel](https://github.com/damian0815/compel) library for 
+more information.

setup.py

@@ -80,6 +80,7 @@
 _deps = [
     "Pillow",  # keep the PIL.Image.Resampling deprecation away
     "accelerate>=0.11.0",
+    "compel==0.1.8",
     "black~=23.1",
     "datasets",
     "filelock",
@@ -182,6 +183,7 @@ def run(self):
 extras["docs"] = deps_list("hf-doc-builder")
 extras["training"] = deps_list("accelerate", "datasets", "tensorboard", "Jinja2")
 extras["test"] = deps_list(
+    "compel",
     "datasets",
     "Jinja2",
     "k-diffusion",

src/diffusers/dependency_versions_table.py

@@ -4,6 +4,7 @@
 deps = {
     "Pillow": "Pillow",
     "accelerate": "accelerate>=0.11.0",
+    "compel": "compel==0.1.8",
     "black": "black~=23.1",
     "datasets": "datasets",
     "filelock": "filelock",

src/diffusers/utils/import_utils.py

@@ -232,6 +232,14 @@
     _tensorboard_available = False
 
 
+_compel_available = importlib.util.find_spec("compel")
+try:
+    _compel_version = importlib_metadata.version("compel")
+    logger.debug(f"Successfully imported compel version {_compel_version}")
+except importlib_metadata.PackageNotFoundError:
+    _compel_available = False
+
+
 def is_torch_available():
     return _torch_available
 
@@ -296,6 +304,10 @@ def is_tensorboard_available():
     return _tensorboard_available
 
 
+def is_compel_available():
+    return _compel_available
+
+
 # docstyle-ignore
 FLAX_IMPORT_ERROR = """
 {0} requires the FLAX library but it was not found in your environment. Checkout the instructions on the
@@ -368,6 +380,12 @@ def is_tensorboard_available():
 install tensorboard`
 """
 
+
+# docstyle-ignore
+COMPEL_IMPORT_ERROR = """
+{0} requires the compel library but it was not found in your environment. You can install it with pip: `pip install compel`
+"""
+
 BACKENDS_MAPPING = OrderedDict(
     [
         ("flax", (is_flax_available, FLAX_IMPORT_ERROR)),
@@ -382,6 +400,7 @@ def is_tensorboard_available():
         ("wandb", (is_wandb_available, WANDB_IMPORT_ERROR)),
         ("omegaconf", (is_omegaconf_available, OMEGACONF_IMPORT_ERROR)),
         ("tensorboard", (_tensorboard_available, TENSORBOARD_IMPORT_ERROR)),
+        ("compel", (_compel_available, COMPEL_IMPORT_ERROR)),
     ]
 )
 

src/diffusers/utils/testing_utils.py

@@ -16,7 +16,7 @@
 import requests
 from packaging import version
 
-from .import_utils import is_flax_available, is_onnx_available, is_torch_available
+from .import_utils import is_compel_available, is_flax_available, is_onnx_available, is_torch_available
 from .logging import get_logger
 
 
@@ -175,6 +175,14 @@ def require_flax(test_case):
     return unittest.skipUnless(is_flax_available(), "test requires JAX & Flax")(test_case)
 
 
+def require_compel(test_case):
+    """
+    Decorator marking a test that requires compel: https://github.com/damian0815/compel. These tests are skipped when
+    the library is not installed.
+    """
+    return unittest.skipUnless(is_compel_available(), "test requires compel")(test_case)
+
+
 def require_onnxruntime(test_case):
     """
     Decorator marking a test that requires onnxruntime. These tests are skipped when onnxruntime isn't installed.

tests/test_pipelines.py

@@ -49,11 +49,12 @@
     StableDiffusionPipeline,
     UNet2DConditionModel,
     UNet2DModel,
+    UniPCMultistepScheduler,
     logging,
 )
 from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
 from diffusers.utils import CONFIG_NAME, WEIGHTS_NAME, floats_tensor, is_flax_available, nightly, slow, torch_device
-from diffusers.utils.testing_utils import CaptureLogger, get_tests_dir, require_torch_gpu
+from diffusers.utils.testing_utils import CaptureLogger, get_tests_dir, load_numpy, require_compel, require_torch_gpu
 
 
 torch.backends.cuda.matmul.allow_tf32 = False
@@ -1058,6 +1059,37 @@ def test_from_flax_from_pt(self):
 
         assert np.abs(image_0 - image_1).sum() < 1e-5, "Models don't give the same forward pass"
 
+    @require_compel
+    def test_weighted_prompts_compel(self):
+        from compel import Compel
+
+        pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+        pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+        pipe.enable_model_cpu_offload()
+        pipe.enable_attention_slicing()
+
+        compel = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder)
+
+        prompt = "a red cat playing with a ball{}"
+
+        prompts = [prompt.format(s) for s in ["", "++", "--"]]
+
+        prompt_embeds = compel(prompts)
+
+        generator = [torch.Generator(device="cpu").manual_seed(33) for _ in range(prompt_embeds.shape[0])]
+
+        images = pipe(
+            prompt_embeds=prompt_embeds, generator=generator, num_inference_steps=20, output_type="numpy"
+        ).images
+
+        for i, image in enumerate(images):
+            expected_image = load_numpy(
+                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
+                f"/compel/forest_{i}.npy"
+            )
+
+            assert np.abs(image - expected_image).max() < 1e-3
+
 
 @nightly
 @require_torch_gpu