Add image2image example script. (#231)
* boom boom * reorganise examples * add image2image in example inference * add readme * fix example * update colab url * Apply suggestions from code review Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * fix init_timestep * update colab url * update main readme * rename readme Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
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Suraj Patil
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@ -23,7 +23,8 @@ More precisely, 🤗 Diffusers offers:
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- State-of-the-art diffusion pipelines that can be run in inference with just a couple of lines of code (see [src/diffusers/pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines)).
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- Various noise schedulers that can be used interchangeably for the prefered speed vs. quality trade-off in inference (see [src/diffusers/schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers)).
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- Multiple types of models, such as UNet, can be used as building blocks in an end-to-end diffusion system (see [src/diffusers/models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models)).
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- Training examples to show how to train the most popular diffusion models (see [examples](https://github.com/huggingface/diffusers/tree/main/examples)).
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- Training examples to show how to train the most popular diffusion models (see [examples/training](https://github.com/huggingface/diffusers/tree/main/examples/training)).
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- Inference examples to show how to create custom pipelines for advanced tasks such as image2image, in-painting (see [examples/inference](https://github.com/huggingface/diffusers/tree/main/examples/inference))
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## Quickstart
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@ -0,0 +1,161 @@
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import inspect
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from typing import List, Optional, Union
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import numpy as np
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import torch
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import PIL
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from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, PNDMScheduler, UNet2DConditionModel
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from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
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from tqdm.auto import tqdm
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from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
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def preprocess(image):
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w, h = image.size
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w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
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image = image.resize((w, h), resample=PIL.Image.LANCZOS)
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image = np.array(image).astype(np.float32) / 255.0
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image = image[None].transpose(0, 3, 1, 2)
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image = torch.from_numpy(image)
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return 2.0 * image - 1.0
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class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
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def __init__(
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self,
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vae: AutoencoderKL,
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text_encoder: CLIPTextModel,
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tokenizer: CLIPTokenizer,
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unet: UNet2DConditionModel,
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scheduler: Union[DDIMScheduler, PNDMScheduler],
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safety_checker: StableDiffusionSafetyChecker,
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feature_extractor: CLIPFeatureExtractor,
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):
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super().__init__()
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scheduler = scheduler.set_format("pt")
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self.register_modules(
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vae=vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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unet=unet,
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scheduler=scheduler,
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safety_checker=safety_checker,
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feature_extractor=feature_extractor,
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)
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@torch.no_grad()
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def __call__(
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self,
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prompt: Union[str, List[str]],
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init_image: torch.FloatTensor,
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strength: float = 0.8,
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num_inference_steps: Optional[int] = 50,
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guidance_scale: Optional[float] = 7.5,
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eta: Optional[float] = 0.0,
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generator: Optional[torch.Generator] = None,
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output_type: Optional[str] = "pil",
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):
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if isinstance(prompt, str):
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batch_size = 1
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elif isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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# set timesteps
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accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
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extra_set_kwargs = {}
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offset = 0
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if accepts_offset:
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offset = 1
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extra_set_kwargs["offset"] = 1
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self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
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# encode the init image into latents and scale the latents
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init_latents = self.vae.encode(init_image.to(self.device)).sample()
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init_latents = 0.18215 * init_latents
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# prepare init_latents noise to latents
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init_latents = torch.cat([init_latents] * batch_size)
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# get the original timestep using init_timestep
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init_timestep = int(num_inference_steps * strength) + offset
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init_timestep = min(init_timestep, num_inference_steps)
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timesteps = self.scheduler.timesteps[-init_timestep]
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timesteps = torch.tensor([timesteps] * batch_size, dtype=torch.long, device=self.device)
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# add noise to latents using the timesteps
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noise = torch.randn(init_latents.shape, generator=generator, device=self.device)
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init_latents = self.scheduler.add_noise(init_latents, noise, timesteps)
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# get prompt text embeddings
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text_input = self.tokenizer(
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prompt,
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padding="max_length",
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max_length=self.tokenizer.model_max_length,
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truncation=True,
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return_tensors="pt",
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)
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text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
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# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
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# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
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# corresponds to doing no classifier free guidance.
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do_classifier_free_guidance = guidance_scale > 1.0
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# get unconditional embeddings for classifier free guidance
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if do_classifier_free_guidance:
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max_length = text_input.input_ids.shape[-1]
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uncond_input = self.tokenizer(
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[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
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)
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uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
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# For classifier free guidance, we need to do two forward passes.
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# Here we concatenate the unconditional and text embeddings into a single batch
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# to avoid doing two forward passes
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text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
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# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
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# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
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# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
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# and should be between [0, 1]
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accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
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extra_step_kwargs = {}
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if accepts_eta:
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extra_step_kwargs["eta"] = eta
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latents = init_latents
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t_start = max(num_inference_steps - init_timestep + offset, 0)
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for i, t in tqdm(enumerate(self.scheduler.timesteps[t_start:])):
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# expand the latents if we are doing classifier free guidance
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latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
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# predict the noise residual
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noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings)["sample"]
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# perform guidance
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if do_classifier_free_guidance:
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noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
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noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
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# compute the previous noisy sample x_t -> x_t-1
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latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)["prev_sample"]
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# scale and decode the image latents with vae
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latents = 1 / 0.18215 * latents
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image = self.vae.decode(latents)
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image = (image / 2 + 0.5).clamp(0, 1)
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image = image.cpu().permute(0, 2, 3, 1).numpy()
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# run safety checker
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safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
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image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values)
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if output_type == "pil":
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image = self.numpy_to_pil(image)
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return {"sample": image, "nsfw_content_detected": has_nsfw_concept}
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@ -0,0 +1,50 @@
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# Inference Examples
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## Installing the dependencies
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Before running the scipts, make sure to install the library's dependencies:
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```bash
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pip install diffusers transformers ftfy
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```
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## Image-to-Image text-guided generation with Stable Diffusion
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The `image_to_image.py` script implements `StableDiffusionImg2ImgPipeline`. It lets you pass a text prompt and an initial image to condition the generation of new images. This example also showcases how you can write custom diffusion pipelines using `diffusers`!
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### How to use it
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```python
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from torch import autocast
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import requests
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from PIL import Image
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from io import BytesIO
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from image_to_image import StableDiffusionImg2ImgPipeline, preprocess
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# load the pipeline
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device = "cuda"
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pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
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"CompVis/stable-diffusion-v1-4",
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revision="fp16",
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torch_dtype=torch.float16,
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use_auth_token=True
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).to(device)
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# let's download an initial image
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url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
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response = requests.get(url)
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init_image = Image.open(BytesIO(response.content)).convert("RGB")
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init_image = init_image.resize((768, 512))
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init_image = preprocess(init_image)
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prompt = "A fantasy landscape, trending on artstation"
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with autocast("cuda"):
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images = pipe(prompt=prompt, init_image=init_image, strength=0.75, guidance_scale=7.5)["sample"]
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images[0].save("fantasy_landscape.png")
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```
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You can also run this example on colab [](https://colab.research.google.com/github/patil-suraj/Notebooks/blob/master/image_2_image_using_diffusers.ipynb)
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