diff --git a/examples/community/README.md b/examples/community/README.md index 2ef84291fc7d..bb3964e1a7c4 100644 --- a/examples/community/README.md +++ b/examples/community/README.md @@ -18,6 +18,7 @@ If a community doesn't work as expected, please open an issue and ping the autho | Composable Stable Diffusion| Stable Diffusion Pipeline that supports prompts that contain "|" in prompts (as an AND condition) and weights (separated by "|" as well) to positively / negatively weight prompts. | [Composable Stable Diffusion](#composable-stable-diffusion) | - | [Mark Rich](https://github.com/MarkRich) | | Seed Resizing Stable Diffusion| Stable Diffusion Pipeline that supports resizing an image and retaining the concepts of the 512 by 512 generation. | [Seed Resizing](#seed-resizing) | - | [Mark Rich](https://github.com/MarkRich) | +| Imagic Stable Diffusion | Stable Diffusion Pipeline that enables writing a text prompt to edit an existing image| [Imagic Stable Diffusion](#imagic-stable-diffusion) | - | [Mark Rich](https://github.com/MarkRich) | To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly. @@ -373,6 +374,49 @@ for i in range(4): for i, img in enumerate(images): img.save(f"./composable_diffusion/image_{i}.png") ``` + +### Imagic Stable Diffusion +Allows you to edit an image using stable diffusion. + +```python +import requests +from PIL import Image +from io import BytesIO +import torch +from diffusers import DiffusionPipeline, DDIMScheduler +has_cuda = torch.cuda.is_available() +device = torch.device('cpu' if not has_cuda else 'cuda') +pipe = DiffusionPipeline.from_pretrained( + "CompVis/stable-diffusion-v1-4", + safety_checker=None, + use_auth_token=True, + custom_pipeline="imagic_stable_diffusion", + scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False) +).to(device) +generator = th.Generator("cuda").manual_seed(0) +seed = 0 +prompt = "A photo of Barack Obama smiling with a big grin" +url = 'https://www.dropbox.com/s/6tlwzr73jd1r9yk/obama.png?dl=1' +response = requests.get(url) +init_image = Image.open(BytesIO(response.content)).convert("RGB") +init_image = init_image.resize((512, 512)) +res = pipe.train( + prompt, + init_image, + guidance_scale=7.5, + num_inference_steps=50, + generator=generator) +res = pipe(alpha=1) +image = res.images[0] +image.save('./imagic/imagic_image_alpha_1.png') +res = pipe(alpha=1.5) +image = res.images[0] +image.save('./imagic/imagic_image_alpha_1_5.png') +res = pipe(alpha=2) +image = res.images[0] +image.save('./imagic/imagic_image_alpha_2.png') +``` + ### Seed Resizing Test seed resizing. Originally generate an image in 512 by 512, then generate image with same seed at 512 by 592 using seed resizing. Finally, generate 512 by 592 using original stable diffusion pipeline. @@ -456,4 +500,4 @@ res = pipe_compare( image = res.images[0] image.save('./seed_resize/seed_resize_{w}_{h}_image_compare.png'.format(w=width, h=height)) -``` \ No newline at end of file +``` diff --git a/examples/community/imagic_stable_diffusion.py b/examples/community/imagic_stable_diffusion.py new file mode 100644 index 000000000000..92aa677b4626 --- /dev/null +++ b/examples/community/imagic_stable_diffusion.py @@ -0,0 +1,476 @@ +""" + modeled after the textual_inversion.py / train_dreambooth.py and the work + of justinpinkney here: https://github.com/justinpinkney/stable-diffusion/blob/main/notebooks/imagic.ipynb +""" +import inspect +import warnings +from typing import List, Optional, Union + +import numpy as np +import torch +import torch.nn.functional as F + +import PIL +from accelerate import Accelerator +from diffusers.models import AutoencoderKL, UNet2DConditionModel +from diffusers.pipeline_utils import DiffusionPipeline +from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput +from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker +from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler +from diffusers.utils import logging +from tqdm.auto import tqdm +from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +def preprocess(image): + w, h = image.size + w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 + image = image.resize((w, h), resample=PIL.Image.LANCZOS) + image = np.array(image).astype(np.float32) / 255.0 + image = image[None].transpose(0, 3, 1, 2) + image = torch.from_numpy(image) + return 2.0 * image - 1.0 + + +class ImagicStableDiffusionPipeline(DiffusionPipeline): + r""" + Pipeline for imagic image editing. + See paper here: https://arxiv.org/pdf/2210.09276.pdf + + This model inherits from [`DiffusionPipeline`]. 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 ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. + text_encoder ([`CLIPTextModel`]): + Frozen text-encoder. Stable Diffusion uses the text portion of + [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), 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 ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. + scheduler ([`SchedulerMixin`]): + A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of + [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. + safety_checker ([`StableDiffusionSafetyChecker`]): + Classification module that estimates whether generated images could be considered offsensive or harmful. + Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) 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: AutoencoderKL, + text_encoder: CLIPTextModel, + tokenizer: CLIPTokenizer, + unet: UNet2DConditionModel, + scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], + safety_checker: StableDiffusionSafetyChecker, + feature_extractor: CLIPFeatureExtractor, + ): + super().__init__() + self.register_modules( + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + unet=unet, + scheduler=scheduler, + safety_checker=safety_checker, + feature_extractor=feature_extractor, + ) + + def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"): + r""" + Enable sliced attention computation. + When this option is enabled, the attention module will split the input tensor in slices, to compute attention + in several steps. This is useful to save some memory in exchange for a small speed decrease. + Args: + slice_size (`str` or `int`, *optional*, defaults to `"auto"`): + When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If + a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, + `attention_head_dim` must be a multiple of `slice_size`. + """ + if slice_size == "auto": + # half the attention head size is usually a good trade-off between + # speed and memory + slice_size = self.unet.config.attention_head_dim // 2 + self.unet.set_attention_slice(slice_size) + + def disable_attention_slicing(self): + r""" + Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go + back to computing attention in one step. + """ + # set slice_size = `None` to disable `attention slicing` + self.enable_attention_slicing(None) + + def train( + self, + prompt: Union[str, List[str]], + init_image: Union[torch.FloatTensor, PIL.Image.Image], + height: Optional[int] = 512, + width: Optional[int] = 512, + generator: Optional[torch.Generator] = None, + embedding_learning_rate: float = 0.001, + diffusion_model_learning_rate: float = 2e-6, + text_embedding_optimization_steps: int = 500, + model_fine_tuning_optimization_steps: int = 1000, + **kwargs, + ): + r""" + Function invoked when calling the pipeline for generation. + Args: + prompt (`str` or `List[str]`): + The prompt or prompts to guide the image generation. + height (`int`, *optional*, defaults to 512): + The height in pixels of the generated image. + width (`int`, *optional*, defaults to 512): + The width in pixels of the generated image. + 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. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to + [`schedulers.DDIMScheduler`], will be ignored for others. + generator (`torch.Generator`, *optional*): + A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation + deterministic. + latents (`torch.FloatTensor`, *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`. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a + plain tuple. + Returns: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] 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`. + """ + accelerator = Accelerator( + gradient_accumulation_steps=1, + mixed_precision="fp16", + ) + + if "torch_device" in kwargs: + device = kwargs.pop("torch_device") + warnings.warn( + "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0." + " Consider using `pipe.to(torch_device)` instead." + ) + + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + self.to(device) + + if height % 8 != 0 or width % 8 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") + + # Freeze vae and unet + self.vae.requires_grad_(False) + self.unet.requires_grad_(False) + self.text_encoder.requires_grad_(False) + self.unet.eval() + self.vae.eval() + self.text_encoder.eval() + + if accelerator.is_main_process: + accelerator.init_trackers( + "imagic", + config={ + "embedding_learning_rate": embedding_learning_rate, + "text_embedding_optimization_steps": text_embedding_optimization_steps, + }, + ) + + # get text embeddings for prompt + text_input = self.tokenizer( + prompt, + padding="max_length", + max_length=self.tokenizer.model_max_length, + truncaton=True, + return_tensors="pt", + ) + text_embeddings = torch.nn.Parameter( + self.text_encoder(text_input.input_ids.to(self.device))[0], requires_grad=True + ) + text_embeddings = text_embeddings.detach() + text_embeddings.requires_grad_() + text_embeddings_orig = text_embeddings.clone() + + # Initialize the optimizer + optimizer = torch.optim.Adam( + [text_embeddings], # only optimize the embeddings + lr=embedding_learning_rate, + ) + + if isinstance(init_image, PIL.Image.Image): + init_image = preprocess(init_image) + + latents_dtype = text_embeddings.dtype + init_image = init_image.to(device=self.device, dtype=latents_dtype) + init_latent_image_dist = self.vae.encode(init_image).latent_dist + init_image_latents = init_latent_image_dist.sample(generator=generator) + init_image_latents = 0.18215 * init_image_latents + + progress_bar = tqdm(range(text_embedding_optimization_steps), disable=not accelerator.is_local_main_process) + progress_bar.set_description("Steps") + + global_step = 0 + + logger.info("First optimizing the text embedding to better reconstruct the init image") + for _ in range(text_embedding_optimization_steps): + with accelerator.accumulate(text_embeddings): + # Sample noise that we'll add to the latents + noise = torch.randn(init_image_latents.shape).to(init_image_latents.device) + timesteps = torch.randint(1000, (1,), device=init_image_latents.device) + + # Add noise to the latents according to the noise magnitude at each timestep + # (this is the forward diffusion process) + noisy_latents = self.scheduler.add_noise(init_image_latents, noise, timesteps) + + # Predict the noise residual + noise_pred = self.unet(noisy_latents, timesteps, text_embeddings).sample + + loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean() + accelerator.backward(loss) + + optimizer.step() + optimizer.zero_grad() + + # Checks if the accelerator has performed an optimization step behind the scenes + if accelerator.sync_gradients: + progress_bar.update(1) + global_step += 1 + + logs = {"loss": loss.detach().item()} # , "lr": lr_scheduler.get_last_lr()[0]} + progress_bar.set_postfix(**logs) + accelerator.log(logs, step=global_step) + + accelerator.wait_for_everyone() + + text_embeddings.requires_grad_(False) + + # Now we fine tune the unet to better reconstruct the image + self.unet.requires_grad_(True) + self.unet.train() + optimizer = torch.optim.Adam( + self.unet.parameters(), # only optimize unet + lr=diffusion_model_learning_rate, + ) + progress_bar = tqdm(range(model_fine_tuning_optimization_steps), disable=not accelerator.is_local_main_process) + + logger.info("Next fine tuning the entire model to better reconstruct the init image") + for _ in range(model_fine_tuning_optimization_steps): + with accelerator.accumulate(self.unet.parameters()): + # Sample noise that we'll add to the latents + noise = torch.randn(init_image_latents.shape).to(init_image_latents.device) + timesteps = torch.randint(1000, (1,), device=init_image_latents.device) + + # Add noise to the latents according to the noise magnitude at each timestep + # (this is the forward diffusion process) + noisy_latents = self.scheduler.add_noise(init_image_latents, noise, timesteps) + + # Predict the noise residual + noise_pred = self.unet(noisy_latents, timesteps, text_embeddings).sample + + loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean() + accelerator.backward(loss) + + optimizer.step() + optimizer.zero_grad() + + # Checks if the accelerator has performed an optimization step behind the scenes + if accelerator.sync_gradients: + progress_bar.update(1) + global_step += 1 + + logs = {"loss": loss.detach().item()} # , "lr": lr_scheduler.get_last_lr()[0]} + progress_bar.set_postfix(**logs) + accelerator.log(logs, step=global_step) + + accelerator.wait_for_everyone() + self.text_embeddings_orig = text_embeddings_orig + self.text_embeddings = text_embeddings + + @torch.no_grad() + def __call__( + self, + alpha: float = 1.2, + height: Optional[int] = 512, + width: Optional[int] = 512, + num_inference_steps: Optional[int] = 50, + generator: Optional[torch.Generator] = None, + output_type: Optional[str] = "pil", + return_dict: bool = True, + guidance_scale: float = 7.5, + eta: float = 0.0, + **kwargs, + ): + r""" + Function invoked when calling the pipeline for generation. + Args: + prompt (`str` or `List[str]`): + The prompt or prompts to guide the image generation. + height (`int`, *optional*, defaults to 512): + The height in pixels of the generated image. + width (`int`, *optional*, defaults to 512): + The width in pixels of the generated image. + 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. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to + [`schedulers.DDIMScheduler`], will be ignored for others. + generator (`torch.Generator`, *optional*): + A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation + deterministic. + latents (`torch.FloatTensor`, *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`. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a + plain tuple. + Returns: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] 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`. + """ + if height % 8 != 0 or width % 8 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") + if self.text_embeddings is None: + raise ValueError("Please run the pipe.train() before trying to generate an image.") + if self.text_embeddings_orig is None: + raise ValueError("Please run the pipe.train() before trying to generate an image.") + + text_embeddings = alpha * self.text_embeddings_orig + (1 - alpha) * self.text_embeddings + + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + # get unconditional embeddings for classifier free guidance + if do_classifier_free_guidance: + uncond_tokens = [""] + max_length = self.tokenizer.model_max_length + uncond_input = self.tokenizer( + uncond_tokens, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ) + uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0] + + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = uncond_embeddings.shape[1] + uncond_embeddings = uncond_embeddings.view(1, seq_len, -1) + + # 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 + text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) + + # get the initial random noise unless the user supplied it + + # Unlike in other pipelines, latents need to be generated in the target device + # for 1-to-1 results reproducibility with the CompVis implementation. + # However this currently doesn't work in `mps`. + latents_shape = (1, self.unet.in_channels, height // 8, width // 8) + latents_dtype = text_embeddings.dtype + if self.device.type == "mps": + # randn does not exist on mps + latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to( + self.device + ) + else: + latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype) + + # set timesteps + self.scheduler.set_timesteps(num_inference_steps) + + # Some schedulers like PNDM have timesteps as arrays + # It's more optimized to move all timesteps to correct device beforehand + timesteps_tensor = self.scheduler.timesteps.to(self.device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + for i, t in enumerate(self.progress_bar(timesteps_tensor)): + # expand the latents if we are doing classifier free guidance + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + # predict the noise residual + noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample + + latents = 1 / 0.18215 * latents + image = self.vae.decode(latents).sample + + image = (image / 2 + 0.5).clamp(0, 1) + + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 + image = image.cpu().permute(0, 2, 3, 1).float().numpy() + + if self.safety_checker is not None: + safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to( + self.device + ) + image, has_nsfw_concept = self.safety_checker( + images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype) + ) + else: + has_nsfw_concept = None + + if output_type == "pil": + image = self.numpy_to_pil(image) + + if not return_dict: + return (image, has_nsfw_concept) + + return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)