# 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.
import warnings
from functools import partial
from typing import Dict, List, Optional, Union
import jax
import jax.numpy as jnp
import numpy as np
from flax.core.frozen_dict import FrozenDict
from flax.jax_utils import unreplicate
from flax.training.common_utils import shard
from PIL import Image
from transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel
from ...models import FlaxAutoencoderKL, FlaxControlNetModel, FlaxUNet2DConditionModel
from ...schedulers import (
FlaxDDIMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, logging, replace_example_docstring
from ..pipeline_flax_utils import FlaxDiffusionPipeline
from ..stable_diffusion import FlaxStableDiffusionPipelineOutput
from ..stable_diffusion.safety_checker_flax import FlaxStableDiffusionSafetyChecker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# Set to True to use python for loop instead of jax.fori_loop for easier debugging
DEBUG = False
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import jax
>>> import numpy as np
>>> import jax.numpy as jnp
>>> from flax.jax_utils import replicate
>>> from flax.training.common_utils import shard
>>> from diffusers.utils import load_image
>>> from PIL import Image
>>> from diffusers import FlaxStableDiffusionControlNetPipeline, FlaxControlNetModel
>>> def image_grid(imgs, rows, cols):
... w, h = imgs[0].size
... grid = Image.new("RGB", size=(cols * w, rows * h))
... for i, img in enumerate(imgs):
... grid.paste(img, box=(i % cols * w, i // cols * h))
... return grid
>>> def create_key(seed=0):
... return jax.random.PRNGKey(seed)
>>> rng = create_key(0)
>>> # get canny image
>>> canny_image = load_image(
... "https://huggingface.co/datasets/YiYiXu/test-doc-assets/resolve/main/blog_post_cell_10_output_0.jpeg"
... )
>>> prompts = "best quality, extremely detailed"
>>> negative_prompts = "monochrome, lowres, bad anatomy, worst quality, low quality"
>>> # load control net and stable diffusion v1-5
>>> controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
... "lllyasviel/sd-controlnet-canny", from_pt=True, dtype=jnp.float32
... )
>>> pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
... "runwayml/stable-diffusion-v1-5", controlnet=controlnet, revision="flax", dtype=jnp.float32
... )
>>> params["controlnet"] = controlnet_params
>>> num_samples = jax.device_count()
>>> rng = jax.random.split(rng, jax.device_count())
>>> prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples)
>>> negative_prompt_ids = pipe.prepare_text_inputs([negative_prompts] * num_samples)
>>> processed_image = pipe.prepare_image_inputs([canny_image] * num_samples)
>>> p_params = replicate(params)
>>> prompt_ids = shard(prompt_ids)
>>> negative_prompt_ids = shard(negative_prompt_ids)
>>> processed_image = shard(processed_image)
>>> output = pipe(
... prompt_ids=prompt_ids,
... image=processed_image,
... params=p_params,
... prng_seed=rng,
... num_inference_steps=50,
... neg_prompt_ids=negative_prompt_ids,
... jit=True,
... ).images
>>> output_images = pipe.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:])))
>>> output_images = image_grid(output_images, num_samples // 4, 4)
>>> output_images.save("generated_image.png")
```
"""
class FlaxStableDiffusionControlNetPipeline(FlaxDiffusionPipeline):
r"""
Pipeline for text-to-image generation using Stable Diffusion with ControlNet Guidance.
This model inherits from [`FlaxDiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`FlaxAutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`FlaxCLIPTextModel`]):
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.FlaxCLIPTextModel),
specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`FlaxUNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
controlnet ([`FlaxControlNetModel`]:
Provides additional conditioning to the unet during the denoising process.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`FlaxDDIMScheduler`], [`FlaxLMSDiscreteScheduler`], [`FlaxPNDMScheduler`], or
[`FlaxDPMSolverMultistepScheduler`].
safety_checker ([`FlaxStableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
def __init__(
self,
vae: FlaxAutoencoderKL,
text_encoder: FlaxCLIPTextModel,
tokenizer: CLIPTokenizer,
unet: FlaxUNet2DConditionModel,
controlnet: FlaxControlNetModel,
scheduler: Union[
FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler
],
safety_checker: FlaxStableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
dtype: jnp.dtype = jnp.float32,
):
super().__init__()
self.dtype = dtype
if safety_checker is None:
logger.warn(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
controlnet=controlnet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
def prepare_text_inputs(self, prompt: Union[str, List[str]]):
if not isinstance(prompt, (str, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
return text_input.input_ids
def prepare_image_inputs(self, image: Union[Image.Image, List[Image.Image]]):
if not isinstance(image, (Image.Image, list)):
raise ValueError(f"image has to be of type `PIL.Image.Image` or list but is {type(image)}")
if isinstance(image, Image.Image):
image = [image]
processed_images = jnp.concatenate([preprocess(img, jnp.float32) for img in image])
return processed_images
def _get_has_nsfw_concepts(self, features, params):
has_nsfw_concepts = self.safety_checker(features, params)
return has_nsfw_concepts
def _run_safety_checker(self, images, safety_model_params, jit=False):
# safety_model_params should already be replicated when jit is True
pil_images = [Image.fromarray(image) for image in images]
features = self.feature_extractor(pil_images, return_tensors="np").pixel_values
if jit:
features = shard(features)
has_nsfw_concepts = _p_get_has_nsfw_concepts(self, features, safety_model_params)
has_nsfw_concepts = unshard(has_nsfw_concepts)
safety_model_params = unreplicate(safety_model_params)
else:
has_nsfw_concepts = self._get_has_nsfw_concepts(features, safety_model_params)
images_was_copied = False
for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):
if has_nsfw_concept:
if not images_was_copied:
images_was_copied = True
images = images.copy()
images[idx] = np.zeros(images[idx].shape, dtype=np.uint8) # black image
if any(has_nsfw_concepts):
warnings.warn(
"Potential NSFW content was detected in one or more images. A black image will be returned"
" instead. Try again with a different prompt and/or seed."
)
return images, has_nsfw_concepts
def _generate(
self,
prompt_ids: jnp.array,
image: jnp.array,
params: Union[Dict, FrozenDict],
prng_seed: jax.random.KeyArray,
num_inference_steps: int,
guidance_scale: float,
latents: Optional[jnp.array] = None,
neg_prompt_ids: Optional[jnp.array] = None,
controlnet_conditioning_scale: float = 1.0,
):
height, width = image.shape[-2:]
if height % 64 != 0 or width % 64 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 64 but are {height} and {width}.")
# get prompt text embeddings
prompt_embeds = self.text_encoder(prompt_ids, params=params["text_encoder"])[0]
# TODO: currently it is assumed `do_classifier_free_guidance = guidance_scale > 1.0`
# implement this conditional `do_classifier_free_guidance = guidance_scale > 1.0`
batch_size = prompt_ids.shape[0]
max_length = prompt_ids.shape[-1]
if neg_prompt_ids is None:
uncond_input = self.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="np"
).input_ids
else:
uncond_input = neg_prompt_ids
negative_prompt_embeds = self.text_encoder(uncond_input, params=params["text_encoder"])[0]
context = jnp.concatenate([negative_prompt_embeds, prompt_embeds])
image = jnp.concatenate([image] * 2)
latents_shape = (
batch_size,
self.unet.config.in_channels,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
if latents is None:
latents = jax.random.normal(prng_seed, shape=latents_shape, dtype=jnp.float32)
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
def loop_body(step, args):
latents, scheduler_state = args
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
latents_input = jnp.concatenate([latents] * 2)
t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step]
timestep = jnp.broadcast_to(t, latents_input.shape[0])
latents_input = self.scheduler.scale_model_input(scheduler_state, latents_input, t)
down_block_res_samples, mid_block_res_sample = self.controlnet.apply(
{"params": params["controlnet"]},
jnp.array(latents_input),
jnp.array(timestep, dtype=jnp.int32),
encoder_hidden_states=context,
controlnet_cond=image,
conditioning_scale=controlnet_conditioning_scale,
return_dict=False,
)
# predict the noise residual
noise_pred = self.unet.apply(
{"params": params["unet"]},
jnp.array(latents_input),
jnp.array(timestep, dtype=jnp.int32),
encoder_hidden_states=context,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
).sample
# perform guidance
noise_pred_uncond, noise_prediction_text = jnp.split(noise_pred, 2, axis=0)
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents, scheduler_state = self.scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple()
return latents, scheduler_state
scheduler_state = self.scheduler.set_timesteps(
params["scheduler"], num_inference_steps=num_inference_steps, shape=latents_shape
)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * params["scheduler"].init_noise_sigma
if DEBUG:
# run with python for loop
for i in range(num_inference_steps):
latents, scheduler_state = loop_body(i, (latents, scheduler_state))
else:
latents, _ = jax.lax.fori_loop(0, num_inference_steps, loop_body, (latents, scheduler_state))
# scale and decode the image latents with vae
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.apply({"params": params["vae"]}, latents, method=self.vae.decode).sample
image = (image / 2 + 0.5).clip(0, 1).transpose(0, 2, 3, 1)
return image
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt_ids: jnp.array,
image: jnp.array,
params: Union[Dict, FrozenDict],
prng_seed: jax.random.KeyArray,
num_inference_steps: int = 50,
guidance_scale: Union[float, jnp.array] = 7.5,
latents: jnp.array = None,
neg_prompt_ids: jnp.array = None,
controlnet_conditioning_scale: Union[float, jnp.array] = 1.0,
return_dict: bool = True,
jit: bool = False,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt_ids (`jnp.array`):
The prompt or prompts to guide the image generation.
image (`jnp.array`):
Array representing the ControlNet input condition. ControlNet use this input condition to generate
guidance to Unet.
params (`Dict` or `FrozenDict`): Dictionary containing the model parameters/weights
prng_seed (`jax.random.KeyArray` or `jax.Array`): Array containing random number generator key
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
latents (`jnp.array`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
controlnet_conditioning_scale (`float` or `jnp.array`, *optional*, defaults to 1.0):
The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
to the residual in the original unet.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of
a plain tuple.
jit (`bool`, defaults to `False`):
Whether to run `pmap` versions of the generation and safety scoring functions. NOTE: This argument
exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a future release.
Examples:
Returns:
[`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a
`tuple. When returning a tuple, the first element is a list with the generated images, and the second
element is a list of `bool`s denoting whether the corresponding generated image likely represents
"not-safe-for-work" (nsfw) content, according to the `safety_checker`.
"""
height, width = image.shape[-2:]
if isinstance(guidance_scale, float):
# Convert to a tensor so each device gets a copy. Follow the prompt_ids for
# shape information, as they may be sharded (when `jit` is `True`), or not.
guidance_scale = jnp.array([guidance_scale] * prompt_ids.shape[0])
if len(prompt_ids.shape) > 2:
# Assume sharded
guidance_scale = guidance_scale[:, None]
if isinstance(controlnet_conditioning_scale, float):
# Convert to a tensor so each device gets a copy. Follow the prompt_ids for
# shape information, as they may be sharded (when `jit` is `True`), or not.
controlnet_conditioning_scale = jnp.array([controlnet_conditioning_scale] * prompt_ids.shape[0])
if len(prompt_ids.shape) > 2:
# Assume sharded
controlnet_conditioning_scale = controlnet_conditioning_scale[:, None]
if jit:
images = _p_generate(
self,
prompt_ids,
image,
params,
prng_seed,
num_inference_steps,
guidance_scale,
latents,
neg_prompt_ids,
controlnet_conditioning_scale,
)
else:
images = self._generate(
prompt_ids,
image,
params,
prng_seed,
num_inference_steps,
guidance_scale,
latents,
neg_prompt_ids,
controlnet_conditioning_scale,
)
if self.safety_checker is not None:
safety_params = params["safety_checker"]
images_uint8_casted = (images * 255).round().astype("uint8")
num_devices, batch_size = images.shape[:2]
images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3)
images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit)
images = np.array(images)
# block images
if any(has_nsfw_concept):
for i, is_nsfw in enumerate(has_nsfw_concept):
if is_nsfw:
images[i] = np.asarray(images_uint8_casted[i])
images = images.reshape(num_devices, batch_size, height, width, 3)
else:
images = np.asarray(images)
has_nsfw_concept = False
if not return_dict:
return (images, has_nsfw_concept)
return FlaxStableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)
# Static argnums are pipe, num_inference_steps. A change would trigger recompilation.
# Non-static args are (sharded) input tensors mapped over their first dimension (hence, `0`).
@partial(
jax.pmap,
in_axes=(None, 0, 0, 0, 0, None, 0, 0, 0, 0),
static_broadcasted_argnums=(0, 5),
)
def _p_generate(
pipe,
prompt_ids,
image,
params,
prng_seed,
num_inference_steps,
guidance_scale,
latents,
neg_prompt_ids,
controlnet_conditioning_scale,
):
return pipe._generate(
prompt_ids,
image,
params,
prng_seed,
num_inference_steps,
guidance_scale,
latents,
neg_prompt_ids,
controlnet_conditioning_scale,
)
@partial(jax.pmap, static_broadcasted_argnums=(0,))
def _p_get_has_nsfw_concepts(pipe, features, params):
return pipe._get_has_nsfw_concepts(features, params)
def unshard(x: jnp.ndarray):
# einops.rearrange(x, 'd b ... -> (d b) ...')
num_devices, batch_size = x.shape[:2]
rest = x.shape[2:]
return x.reshape(num_devices * batch_size, *rest)
def preprocess(image, dtype):
image = image.convert("RGB")
w, h = image.size
w, h = (x - x % 64 for x in (w, h)) # resize to integer multiple of 64
image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
image = jnp.array(image).astype(dtype) / 255.0
image = image[None].transpose(0, 3, 1, 2)
return image