Initial commit

ddpo_pytorch/diffusers_patch/ddim_with_logprob.py

@@ -0,0 +1,143 @@
+# Copied from https://github.com/huggingface/diffusers/blob/fc6acb6b97e93d58cb22b5fee52d884d77ce84d8/src/diffusers/schedulers/scheduling_ddim.py
+# with the following modifications:
+# -
+
+from typing import Optional, Tuple, Union
+
+import math
+import torch
+
+from diffusers.utils import randn_tensor
+from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput, DDIMScheduler
+
+
+def ddim_step_with_logprob(
+    self: DDIMScheduler,
+    model_output: torch.FloatTensor,
+    timestep: int,
+    sample: torch.FloatTensor,
+    eta: float = 0.0,
+    use_clipped_model_output: bool = False,
+    generator=None,
+    prev_sample: Optional[torch.FloatTensor] = None,
+) -> Union[DDIMSchedulerOutput, Tuple]:
+    """
+    Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
+    process from the learned model outputs (most often the predicted noise).
+
+    Args:
+        model_output (`torch.FloatTensor`): direct output from learned diffusion model.
+        timestep (`int`): current discrete timestep in the diffusion chain.
+        sample (`torch.FloatTensor`):
+            current instance of sample being created by diffusion process.
+        eta (`float`): weight of noise for added noise in diffusion step.
+        use_clipped_model_output (`bool`): if `True`, compute "corrected" `model_output` from the clipped
+            predicted original sample. Necessary because predicted original sample is clipped to [-1, 1] when
+            `self.config.clip_sample` is `True`. If no clipping has happened, "corrected" `model_output` would
+            coincide with the one provided as input and `use_clipped_model_output` will have not effect.
+        generator: random number generator.
+        variance_noise (`torch.FloatTensor`): instead of generating noise for the variance using `generator`, we
+            can directly provide the noise for the variance itself. This is useful for methods such as
+            CycleDiffusion. (https://arxiv.org/abs/2210.05559)
+        return_dict (`bool`): option for returning tuple rather than DDIMSchedulerOutput class
+
+    Returns:
+        [`~schedulers.scheduling_utils.DDIMSchedulerOutput`] or `tuple`:
+        [`~schedulers.scheduling_utils.DDIMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
+        returning a tuple, the first element is the sample tensor.
+
+    """
+    assert isinstance(self, DDIMScheduler)
+    if self.num_inference_steps is None:
+        raise ValueError(
+            "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
+        )
+
+    # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
+    # Ideally, read DDIM paper in-detail understanding
+
+    # Notation (<variable name> -> <name in paper>
+    # - pred_noise_t -> e_theta(x_t, t)
+    # - pred_original_sample -> f_theta(x_t, t) or x_0
+    # - std_dev_t -> sigma_t
+    # - eta -> η
+    # - pred_sample_direction -> "direction pointing to x_t"
+    # - pred_prev_sample -> "x_t-1"
+
+    # 1. get previous step value (=t-1)
+    prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
+
+    # 2. compute alphas, betas
+    self.alphas_cumprod = self.alphas_cumprod.to(timestep.device)
+    self.final_alpha_cumprod = self.final_alpha_cumprod.to(timestep.device)
+    alpha_prod_t = self.alphas_cumprod.gather(0, timestep)
+    alpha_prod_t_prev = torch.where(prev_timestep >= 0, self.alphas_cumprod.gather(0, prev_timestep), self.final_alpha_cumprod)
+    print(timestep)
+    print(alpha_prod_t)
+    print(alpha_prod_t_prev)
+    print(prev_timestep)
+
+    beta_prod_t = 1 - alpha_prod_t
+
+    # 3. compute predicted original sample from predicted noise also called
+    # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+    if self.config.prediction_type == "epsilon":
+        pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
+        pred_epsilon = model_output
+    elif self.config.prediction_type == "sample":
+        pred_original_sample = model_output
+        pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
+    elif self.config.prediction_type == "v_prediction":
+        pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
+        pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
+    else:
+        raise ValueError(
+            f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
+            " `v_prediction`"
+        )
+
+    # 4. Clip or threshold "predicted x_0"
+    if self.config.thresholding:
+        pred_original_sample = self._threshold_sample(pred_original_sample)
+    elif self.config.clip_sample:
+        pred_original_sample = pred_original_sample.clamp(
+            -self.config.clip_sample_range, self.config.clip_sample_range
+        )
+
+    # 5. compute variance: "sigma_t(η)" -> see formula (16)
+    # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
+    variance = self._get_variance(timestep, prev_timestep)
+    std_dev_t = eta * variance ** (0.5)
+
+    if use_clipped_model_output:
+        # the pred_epsilon is always re-derived from the clipped x_0 in Glide
+        pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
+
+    # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+    pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * pred_epsilon
+
+    # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+    prev_sample_mean = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
+
+    if prev_sample is not None and generator is not None:
+        raise ValueError(
+            "Cannot pass both generator and prev_sample. Please make sure that either `generator` or"
+            " `prev_sample` stays `None`."
+        )
+
+    if prev_sample is None:
+        variance_noise = randn_tensor(
+            model_output.shape, generator=generator, device=model_output.device, dtype=model_output.dtype
+        )
+        prev_sample = prev_sample_mean + std_dev_t * variance_noise
+
+    # log prob of prev_sample given prev_sample_mean and std_dev_t
+    log_prob = (
+        -((prev_sample.detach() - prev_sample_mean) ** 2) / (2 * (std_dev_t**2))
+        - torch.log(std_dev_t)
+        - torch.log(torch.sqrt(2 * torch.as_tensor(math.pi)))
+    )
+    # mean along all but batch dimension
+    log_prob = log_prob.mean(dim=tuple(range(1, log_prob.ndim)))
+
+    return prev_sample, log_prob

ddpo_pytorch/diffusers_patch/pipeline_with_logprob.py

@@ -0,0 +1,225 @@
+# Copied from https://github.com/huggingface/diffusers/blob/fc6acb6b97e93d58cb22b5fee52d884d77ce84d8/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
+# with the following modifications:
+# -
+
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import torch
+
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import (
+    StableDiffusionPipeline,
+    rescale_noise_cfg,
+)
+from diffusers.schedulers.scheduling_ddim import DDIMScheduler
+from .ddim_with_logprob import ddim_step_with_logprob
+
+
+@torch.no_grad()
+def pipeline_with_logprob(
+    self: StableDiffusionPipeline,
+    prompt: Union[str, List[str]] = None,
+    height: Optional[int] = None,
+    width: Optional[int] = None,
+    num_inference_steps: int = 50,
+    guidance_scale: float = 7.5,
+    negative_prompt: Optional[Union[str, List[str]]] = None,
+    num_images_per_prompt: Optional[int] = 1,
+    eta: float = 0.0,
+    generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+    latents: Optional[torch.FloatTensor] = None,
+    prompt_embeds: Optional[torch.FloatTensor] = None,
+    negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+    output_type: Optional[str] = "pil",
+    return_dict: bool = True,
+    callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+    callback_steps: int = 1,
+    cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+    guidance_rescale: float = 0.0,
+):
+    r"""
+    Function invoked when calling the pipeline for generation.
+
+    Args:
+        prompt (`str` or `List[str]`, *optional*):
+            The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+            instead.
+        height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+            The height in pixels of the generated image.
+        width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+            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.
+        negative_prompt (`str` or `List[str]`, *optional*):
+            The prompt or prompts not to guide the image generation. If not defined, one has to pass
+            `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+            less than `1`).
+        num_images_per_prompt (`int`, *optional*, defaults to 1):
+            The number of images to generate per prompt.
+        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` or `List[torch.Generator]`, *optional*):
+            One or a list of [torch generator(s)](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`.
+        prompt_embeds (`torch.FloatTensor`, *optional*):
+            Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+            provided, text embeddings will be generated from `prompt` input argument.
+        negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+            Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+            weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+            argument.
+        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 `np.array`.
+        return_dict (`bool`, *optional*, defaults to `True`):
+            Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+            plain tuple.
+        callback (`Callable`, *optional*):
+            A function that will be called every `callback_steps` steps during inference. The function will be
+            called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+        callback_steps (`int`, *optional*, defaults to 1):
+            The frequency at which the `callback` function will be called. If not specified, the callback will be
+            called at every step.
+        cross_attention_kwargs (`dict`, *optional*):
+            A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+            `self.processor` in
+            [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
+        guidance_rescale (`float`, *optional*, defaults to 0.7):
+            Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
+            Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
+            [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
+            Guidance rescale factor should fix overexposure when using zero terminal SNR.
+
+    Examples:
+
+    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`.
+    """
+    # 0. Default height and width to unet
+    height = height or self.unet.config.sample_size * self.vae_scale_factor
+    width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+    # 1. Check inputs. Raise error if not correct
+    self.check_inputs(prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)
+
+    # 2. Define call parameters
+    if prompt is not None and isinstance(prompt, str):
+        batch_size = 1
+    elif prompt is not None and isinstance(prompt, list):
+        batch_size = len(prompt)
+    else:
+        batch_size = prompt_embeds.shape[0]
+
+    device = self._execution_device
+    # 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
+
+    # 3. Encode input prompt
+    text_encoder_lora_scale = cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
+    prompt_embeds = self._encode_prompt(
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt,
+        prompt_embeds=prompt_embeds,
+        negative_prompt_embeds=negative_prompt_embeds,
+        lora_scale=text_encoder_lora_scale,
+    )
+
+    # 4. Prepare timesteps
+    self.scheduler.set_timesteps(num_inference_steps, device=device)
+    timesteps = self.scheduler.timesteps
+
+    # 5. Prepare latent variables
+    num_channels_latents = self.unet.config.in_channels
+    latents = self.prepare_latents(
+        batch_size * num_images_per_prompt,
+        num_channels_latents,
+        height,
+        width,
+        prompt_embeds.dtype,
+        device,
+        generator,
+        latents,
+    )
+
+    # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+    extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+    # 7. Denoising loop
+    num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+    all_latents = [latents]
+    all_log_probs = []
+    with self.progress_bar(total=num_inference_steps) as progress_bar:
+        for i, t in enumerate(timesteps):
+            # 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=prompt_embeds,
+                cross_attention_kwargs=cross_attention_kwargs,
+                return_dict=False,
+            )[0]
+
+            # 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)
+
+            if do_classifier_free_guidance and guidance_rescale > 0.0:
+                # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents, log_prob = ddim_step_with_logprob(self.scheduler, noise_pred, t, latents, **extra_step_kwargs)
+
+            all_latents.append(latents)
+            all_log_probs.append(log_prob)
+
+            # call the callback, if provided
+            if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                progress_bar.update()
+                if callback is not None and i % callback_steps == 0:
+                    callback(i, t, latents)
+
+    if not output_type == "latent":
+        image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+        image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+    else:
+        image = latents
+        has_nsfw_concept = None
+
+    if has_nsfw_concept is None:
+        do_denormalize = [True] * image.shape[0]
+    else:
+        do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
+
+    image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
+
+    # Offload last model to CPU
+    if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+        self.final_offload_hook.offload()
+
+    return image, has_nsfw_concept, all_latents, all_log_probs

ddpo_pytorch/prompts.py

@@ -0,0 +1,54 @@
+from importlib import resources
+import functools
+import random
+import inflect
+
+IE = inflect.engine()
+ASSETS_PATH = resources.files("ddpo_pytorch.assets")
+
+
+@functools.cache
+def load_lines(name):
+    with ASSETS_PATH.joinpath(name).open() as f:
+        return [line.strip() for line in f.readlines()]
+
+
+def imagenet(low, high):
+    return random.choice(load_lines("imagenet_classes.txt")[low:high]), {}
+
+
+def imagenet_all():
+    return imagenet(0, 1000)
+
+
+def imagenet_animals():
+    return imagenet(0, 398)
+
+
+def imagenet_dogs():
+    return imagenet(151, 269)
+
+
+def nouns_activities(nouns_file, activities_file):
+    nouns = load_lines(nouns_file)
+    activities = load_lines(activities_file)
+    return f"{IE.a(random.choice(nouns))} {random.choice(activities)}", {}
+
+
+def counting(nouns_file, low, high):
+    nouns = load_lines(nouns_file)
+    number = IE.number_to_words(random.randint(low, high))
+    noun = random.choice(nouns)
+    plural_noun = IE.plural(noun)
+    prompt = f"{number} {plural_noun}"
+    metadata = {
+        "questions": [
+            f"How many {plural_noun} are there in this image?",
+            f"What animal is in this image?",
+        ],
+        "answers": [
+            number,
+            noun,
+        ],
+    }
+    return prompt, metadata

ddpo_pytorch/rewards.py

@@ -0,0 +1,29 @@
+from PIL import Image
+import io
+import numpy as np
+import torch
+
+
+def jpeg_incompressibility():
+    def _fn(images, prompts, metadata):
+        if isinstance(images, torch.Tensor):
+            images = (images * 255).round().clamp(0, 255).to(torch.uint8).cpu().numpy()
+            images = images.transpose(0, 2, 3, 1) # NCHW -> NHWC
+        images = [Image.fromarray(image) for image in images]
+        buffers = [io.BytesIO() for _ in images]
+        for image, buffer in zip(images, buffers):
+            image.save(buffer, format="JPEG", quality=95)
+        sizes = [buffer.tell() / 1000 for buffer in buffers]
+        return np.array(sizes), {}
+
+    return _fn
+
+
+def jpeg_compressibility():
+    jpeg_fn = jpeg_incompressibility()
+
+    def _fn(images, prompts, metadata):
+        rew, meta = jpeg_fn(images, prompts, metadata)
+        return -rew, meta
+
+    return _fn

ddpo_pytorch/stat_tracking.py

@@ -0,0 +1,34 @@
+import numpy as np
+from collections import deque
+
+
+class PerPromptStatTracker:
+    def __init__(self, buffer_size, min_count):
+        self.buffer_size = buffer_size
+        self.min_count = min_count
+        self.stats = {}
+
+    def update(self, prompts, rewards):
+        unique = np.unique(prompts)
+        advantages = np.empty_like(rewards)
+        for prompt in unique:
+            prompt_rewards = rewards[prompts == prompt]
+            if prompt not in self.stats:
+                self.stats[prompt] = deque(maxlen=self.buffer_size)
+            self.stats[prompt].extend(prompt_rewards)
+
+            if len(self.stats[prompt]) < self.min_count:
+                mean = np.mean(rewards)
+                std = np.std(rewards) + 1e-6
+            else:
+                mean = np.mean(self.stats[prompt])
+                std = np.std(self.stats[prompt]) + 1e-6
+            advantages[prompts == prompt] = (prompt_rewards - mean) / std
+
+        return advantages
+
+    def get_stats(self):
+        return {
+            k: {"mean": np.mean(v), "std": np.std(v), "count": len(v)}
+            for k, v in self.stats.items()
+        }