imaginAIry/imaginairy/api.py

import logging
import os
import re

import numpy as np
import torch
import torch.nn
from einops import rearrange, repeat
from PIL import Image, ImageDraw, ImageOps
from pytorch_lightning import seed_everything
from torch.cuda import OutOfMemoryError

from imaginairy.animations import make_bounce_animation
from imaginairy.enhancers.clip_masking import get_img_mask
from imaginairy.enhancers.describe_image_blip import generate_caption
from imaginairy.enhancers.face_restoration_codeformer import enhance_faces
from imaginairy.enhancers.upscale_realesrgan import upscale_image
from imaginairy.img_utils import pillow_fit_image_within, pillow_img_to_torch_image
from imaginairy.log_utils import (
    ImageLoggingContext,
    log_conditioning,
    log_img,
    log_latent,
)
from imaginairy.model_manager import get_diffusion_model
from imaginairy.modules.midas.utils import AddMiDaS
from imaginairy.outpaint import outpaint_arg_str_parse, prepare_image_for_outpaint
from imaginairy.safety import SafetyMode, create_safety_score
from imaginairy.samplers import SAMPLER_LOOKUP
from imaginairy.samplers.base import NoiseSchedule, noise_an_image
from imaginairy.samplers.editing import CFGEditingDenoiser
from imaginairy.schema import ImaginePrompt, ImagineResult
from imaginairy.utils import (
    fix_torch_group_norm,
    fix_torch_nn_layer_norm,
    get_device,
    platform_appropriate_autocast,
    randn_seeded,
)

logger = logging.getLogger(__name__)

# leave undocumented. I'd ask that no one publicize this flag. Just want a
# slight barrier to entry. Please don't use this is any way that's gonna cause
# the media or politicians to freak out about AI...
IMAGINAIRY_SAFETY_MODE = os.getenv("IMAGINAIRY_SAFETY_MODE", SafetyMode.STRICT)
if IMAGINAIRY_SAFETY_MODE in {"disabled", "classify"}:
    IMAGINAIRY_SAFETY_MODE = SafetyMode.RELAXED
elif IMAGINAIRY_SAFETY_MODE == "filter":
    IMAGINAIRY_SAFETY_MODE = SafetyMode.STRICT

# we put this in the global scope so it can be used in the interactive shell
_most_recent_result = None


def imagine_image_files(
    prompts,
    outdir,
    precision="autocast",
    record_step_images=False,
    output_file_extension="jpg",
    print_caption=False,
    make_gif=False,
    make_compare_gif=False,
    return_filename_type="generated",
):
    generated_imgs_path = os.path.join(outdir, "generated")
    os.makedirs(generated_imgs_path, exist_ok=True)

    base_count = len(os.listdir(generated_imgs_path))
    output_file_extension = output_file_extension.lower()
    if output_file_extension not in {"jpg", "png"}:
        raise ValueError("Must output a png or jpg")

    def _record_step(img, description, image_count, step_count, prompt):
        steps_path = os.path.join(outdir, "steps", f"{base_count:08}_S{prompt.seed}")
        os.makedirs(steps_path, exist_ok=True)
        filename = f"{base_count:08}_S{prompt.seed}_{image_count:04}_step{step_count:03}_{prompt_normalized(description)[:40]}.jpg"

        destination = os.path.join(steps_path, filename)
        draw = ImageDraw.Draw(img)
        draw.text((10, 10), str(description))
        img.save(destination)

    if make_gif:
        for p in prompts:
            p.collect_progress_latents = True
    result_filenames = []
    for result in imagine(
        prompts,
        precision=precision,
        debug_img_callback=_record_step if record_step_images else None,
        add_caption=print_caption,
    ):
        prompt = result.prompt
        if prompt.is_intermediate:
            # we don't save intermediate images
            continue
        img_str = ""
        if prompt.init_image:
            img_str = f"_img2img-{prompt.init_image_strength}"
        basefilename = (
            f"{base_count:06}_{prompt.seed}_{prompt.sampler_type.replace('_', '')}{prompt.steps}_"
            f"PS{prompt.prompt_strength}{img_str}_{prompt_normalized(prompt.prompt_text)}"
        )

        for image_type in result.images:
            subpath = os.path.join(outdir, image_type)
            os.makedirs(subpath, exist_ok=True)
            filepath = os.path.join(
                subpath, f"{basefilename}_[{image_type}].{output_file_extension}"
            )
            result.save(filepath, image_type=image_type)
            logger.info(f"    [{image_type}] saved to: {filepath}")
            if image_type == return_filename_type:
                result_filenames.append(filepath)

        if make_gif and result.progress_latents:
            subpath = os.path.join(outdir, "gif")
            os.makedirs(subpath, exist_ok=True)
            filepath = os.path.join(subpath, f"{basefilename}.gif")

            frames = result.progress_latents + [result.images["generated"]]

            if prompt.init_image:
                resized_init_image = pillow_fit_image_within(
                    prompt.init_image, prompt.width, prompt.height
                )
                frames = [resized_init_image] + frames
            frames.reverse()
            make_bounce_animation(
                imgs=frames,
                outpath=filepath,
                start_pause_duration_ms=1500,
                end_pause_duration_ms=1000,
            )
            logger.info(f"    [gif] {len(frames)} frames saved to: {filepath}")
        if make_compare_gif and prompt.init_image:
            subpath = os.path.join(outdir, "gif")
            os.makedirs(subpath, exist_ok=True)
            filepath = os.path.join(subpath, f"{basefilename}_[compare].gif")
            resized_init_image = pillow_fit_image_within(
                prompt.init_image, prompt.width, prompt.height
            )
            frames = [result.images["generated"], resized_init_image]

            make_bounce_animation(
                imgs=frames,
                outpath=filepath,
            )
            logger.info(f"    [gif-comparison] saved to: {filepath}")

        base_count += 1
        del result

    return result_filenames


def imagine(
    prompts,
    precision="autocast",
    debug_img_callback=None,
    progress_img_callback=None,
    progress_img_interval_steps=3,
    progress_img_interval_min_s=0.1,
    half_mode=None,
    add_caption=False,
    unsafe_retry_count=1,
):
    prompts = [ImaginePrompt(prompts)] if isinstance(prompts, str) else prompts
    prompts = [prompts] if isinstance(prompts, ImaginePrompt) else prompts

    try:
        num_prompts = str(len(prompts))
    except TypeError:
        num_prompts = "?"

    if get_device() == "cpu":
        logger.info("Running in CPU mode. it's gonna be slooooooow.")

    with torch.no_grad(), platform_appropriate_autocast(
        precision
    ), fix_torch_nn_layer_norm(), fix_torch_group_norm():
        for i, prompt in enumerate(prompts):
            logger.info(
                f"Generating 🖼  {i + 1}/{num_prompts}: {prompt.prompt_description()}"
            )
            for attempt in range(0, unsafe_retry_count + 1):
                if attempt > 0:
                    prompt.seed += 100_000_000 + attempt
                result = _generate_single_image(
                    prompt,
                    debug_img_callback=debug_img_callback,
                    progress_img_callback=progress_img_callback,
                    progress_img_interval_steps=progress_img_interval_steps,
                    progress_img_interval_min_s=progress_img_interval_min_s,
                    half_mode=half_mode,
                    add_caption=add_caption,
                )
                if not result.is_nsfw:
                    break
                if attempt < unsafe_retry_count:
                    logger.info("   Image was unsafe, retrying with new seed...")

            yield result


def _generate_single_image(
    prompt,
    debug_img_callback=None,
    progress_img_callback=None,
    progress_img_interval_steps=3,
    progress_img_interval_min_s=0.1,
    half_mode=None,
    add_caption=False,
):
    latent_channels = 4
    downsampling_factor = 8
    batch_size = 1
    global _most_recent_result  # noqa
    # handle prompt pulling in previous values
    if isinstance(prompt.init_image, str) and prompt.init_image.startswith("*prev"):
        _, img_type = prompt.init_image.strip("*").split(".")
        prompt.init_image = _most_recent_result.images[img_type]
    if isinstance(prompt.mask_image, str) and prompt.mask_image.startswith("*prev"):
        _, img_type = prompt.mask_image.strip("*").split(".")
        prompt.mask_image = _most_recent_result.images[img_type]

    model = get_diffusion_model(
        weights_location=prompt.model,
        config_path=prompt.model_config_path,
        half_mode=half_mode,
        for_inpainting=prompt.mask_image or prompt.mask_prompt or prompt.outpaint,
    )
    has_depth_channel = hasattr(model, "depth_stage_key")
    progress_latents = []

    def latent_logger(latents):
        progress_latents.append(latents)

    with ImageLoggingContext(
        prompt=prompt,
        model=model,
        debug_img_callback=debug_img_callback,
        progress_img_callback=progress_img_callback,
        progress_img_interval_steps=progress_img_interval_steps,
        progress_img_interval_min_s=progress_img_interval_min_s,
        progress_latent_callback=latent_logger
        if prompt.collect_progress_latents
        else None,
    ) as lc:
        seed_everything(prompt.seed)

        model.tile_mode(prompt.tile_mode)
        with lc.timing("conditioning"):
            # need to expand if doing batches
            neutral_conditioning = _prompts_to_embeddings(prompt.negative_prompt, model)
            log_conditioning(neutral_conditioning, "neutral conditioning")
            if prompt.conditioning is not None:
                positive_conditioning = prompt.conditioning
            else:
                positive_conditioning = _prompts_to_embeddings(prompt.prompts, model)
            log_conditioning(positive_conditioning, "positive conditioning")

        shape = [
            batch_size,
            latent_channels,
            prompt.height // downsampling_factor,
            prompt.width // downsampling_factor,
        ]
        SamplerCls = SAMPLER_LOOKUP[prompt.sampler_type.lower()]
        sampler = SamplerCls(model)
        mask = mask_image = mask_image_orig = mask_grayscale = None
        t_enc = init_latent = init_latent_noised = None
        starting_image = None
        if prompt.init_image:
            starting_image = prompt.init_image
            generation_strength = 1 - prompt.init_image_strength
            if model.cond_stage_key == "edit":
                t_enc = prompt.steps
            else:
                t_enc = int(prompt.steps * generation_strength)

            if prompt.mask_prompt:
                mask_image, mask_grayscale = get_img_mask(
                    starting_image, prompt.mask_prompt, threshold=0.1
                )
            elif prompt.mask_image:
                mask_image = prompt.mask_image.convert("L")
            if prompt.outpaint:
                outpaint_kwargs = outpaint_arg_str_parse(prompt.outpaint)
                starting_image, mask_image = prepare_image_for_outpaint(
                    starting_image, mask_image, **outpaint_kwargs
                )

            init_image = pillow_fit_image_within(
                starting_image,
                max_height=prompt.height,
                max_width=prompt.width,
            )

            if mask_image is not None:
                mask_image = pillow_fit_image_within(
                    mask_image,
                    max_height=prompt.height,
                    max_width=prompt.width,
                    convert="L",
                )

                log_img(mask_image, "init mask")

                if prompt.mask_mode == ImaginePrompt.MaskMode.REPLACE:
                    mask_image = ImageOps.invert(mask_image)

                log_img(
                    Image.composite(init_image, mask_image, mask_image),
                    "mask overlay",
                )
                mask_image_orig = mask_image
                mask_image = mask_image.resize(
                    (
                        mask_image.width // downsampling_factor,
                        mask_image.height // downsampling_factor,
                    ),
                    resample=Image.Resampling.LANCZOS,
                )
                log_img(mask_image, "latent_mask")

                mask = np.array(mask_image)
                mask = mask.astype(np.float32) / 255.0
                mask = mask[None, None]
                mask = torch.from_numpy(mask)
                mask = mask.to(get_device())
            init_image_t = pillow_img_to_torch_image(init_image)
            init_image_t = init_image_t.to(get_device())
            init_latent = model.get_first_stage_encoding(
                model.encode_first_stage(init_image_t)
            )
            shape = init_latent.shape

            log_latent(init_latent, "init_latent")
            # encode (scaled latent)
            seed_everything(prompt.seed)
            noise = randn_seeded(seed=prompt.seed, size=init_latent.size())
            noise = noise.to(get_device())

            schedule = NoiseSchedule(
                model_num_timesteps=model.num_timesteps,
                ddim_num_steps=prompt.steps,
                model_alphas_cumprod=model.alphas_cumprod,
                ddim_discretize="uniform",
            )
            if generation_strength >= 1:
                # prompt strength gets converted to time encodings,
                # which means you can't get to true 0 without this hack
                # (or setting steps=1000)
                init_latent_noised = noise
            else:
                init_latent_noised = noise_an_image(
                    init_latent,
                    torch.tensor([t_enc - 1]).to(get_device()),
                    schedule=schedule,
                    noise=noise,
                )
        batch_size = 1
        log_latent(init_latent_noised, "init_latent_noised")
        batch = {
            "txt": batch_size * [prompt.prompt_text],
        }
        c_cat = []
        c_cat_neutral = None
        depth_image_display = None
        if has_depth_channel and starting_image:
            midas_model = AddMiDaS()
            _init_image_d = np.array(starting_image.convert("RGB"))
            _init_image_d = (
                torch.from_numpy(_init_image_d).to(dtype=torch.float32) / 127.5 - 1.0
            )
            depth_image = midas_model(_init_image_d)
            depth_image = torch.from_numpy(depth_image[None, ...])
            batch[model.depth_stage_key] = depth_image.to(device=get_device())
            _init_image_d = rearrange(_init_image_d, "h w c -> 1 c h w")
            batch["jpg"] = _init_image_d
            for ck in model.concat_keys:
                cc = batch[ck]
                cc = model.depth_model(cc)
                depth_min, depth_max = torch.amin(
                    cc, dim=[1, 2, 3], keepdim=True
                ), torch.amax(cc, dim=[1, 2, 3], keepdim=True)
                display_depth = (cc - depth_min) / (depth_max - depth_min)
                depth_image_display = Image.fromarray(
                    (display_depth[0, 0, ...].cpu().numpy() * 255.0).astype(np.uint8)
                )
                cc = torch.nn.functional.interpolate(
                    cc,
                    size=shape[2:],
                    mode="bicubic",
                    align_corners=False,
                )
                depth_min, depth_max = torch.amin(
                    cc, dim=[1, 2, 3], keepdim=True
                ), torch.amax(cc, dim=[1, 2, 3], keepdim=True)
                cc = 2.0 * (cc - depth_min) / (depth_max - depth_min) - 1.0
                c_cat.append(cc)
            c_cat = [torch.cat(c_cat, dim=1)]

        if mask_image_orig and not has_depth_channel:
            mask_t = pillow_img_to_torch_image(ImageOps.invert(mask_image_orig)).to(
                get_device()
            )
            inverted_mask = 1 - mask
            masked_image_t = init_image_t * (mask_t < 0.5)
            batch.update(
                {
                    "image": repeat(
                        init_image_t.to(device=get_device()),
                        "1 ... -> n ...",
                        n=batch_size,
                    ),
                    "txt": batch_size * [prompt.prompt_text],
                    "mask": repeat(
                        inverted_mask.to(device=get_device()),
                        "1 ... -> n ...",
                        n=batch_size,
                    ),
                    "masked_image": repeat(
                        masked_image_t.to(device=get_device()),
                        "1 ... -> n ...",
                        n=batch_size,
                    ),
                }
            )

            for concat_key in getattr(model, "concat_keys", []):
                cc = batch[concat_key].float()
                if concat_key != model.masked_image_key:
                    bchw = [batch_size, 4, shape[2], shape[3]]
                    cc = torch.nn.functional.interpolate(cc, size=bchw[-2:])
                else:
                    cc = model.get_first_stage_encoding(model.encode_first_stage(cc))
                c_cat.append(cc)
            if c_cat:
                c_cat = [torch.cat(c_cat, dim=1)]
        denoiser_cls = None
        if model.cond_stage_key == "edit":
            c_cat = [model.encode_first_stage(init_image_t).mode()]
            c_cat_neutral = [torch.zeros_like(init_latent)]
            denoiser_cls = CFGEditingDenoiser

        if c_cat_neutral is None:
            c_cat_neutral = c_cat

        positive_conditioning = {
            "c_concat": c_cat,
            "c_crossattn": [positive_conditioning],
        }
        neutral_conditioning = {
            "c_concat": c_cat_neutral,
            "c_crossattn": [neutral_conditioning],
        }
        with lc.timing("sampling"):
            samples = sampler.sample(
                num_steps=prompt.steps,
                initial_latent=init_latent_noised,
                positive_conditioning=positive_conditioning,
                neutral_conditioning=neutral_conditioning,
                guidance_scale=prompt.prompt_strength,
                t_start=t_enc,
                mask=mask,
                orig_latent=init_latent,
                shape=shape,
                batch_size=1,
                denoiser_cls=denoiser_cls,
            )
        # from torch.nn.functional import interpolate
        # samples = interpolate(samples, scale_factor=2, mode='nearest')
        with lc.timing("decoding"):
            try:
                x_samples = model.decode_first_stage(samples)
            except OutOfMemoryError:
                model.cond_stage_model.to("cpu")
                model.model.to("cpu")
                x_samples = model.decode_first_stage(samples)
                model.cond_stage_model.to(get_device())
                model.model.to(get_device())

            x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)

        for x_sample in x_samples:
            x_sample = x_sample.to(torch.float32)
            x_sample = 255.0 * rearrange(x_sample.cpu().numpy(), "c h w -> h w c")
            x_sample_8_orig = x_sample.astype(np.uint8)
            img = Image.fromarray(x_sample_8_orig)
            if mask_image_orig and init_image:
                # mask_final = mask_image_orig.filter(
                #     ImageFilter.GaussianBlur(radius=3)
                # )
                mask_final = mask_image_orig.copy()
                log_img(mask_final, "reconstituting mask")
                mask_final = ImageOps.invert(mask_final)
                img = Image.composite(img, init_image, mask_final)
                log_img(img, "reconstituted image")

            upscaled_img = None
            rebuilt_orig_img = None

            if add_caption:
                caption = generate_caption(img)
                logger.info(f"Generated caption: {caption}")

            with lc.timing("safety-filter"):
                safety_score = create_safety_score(
                    img,
                    safety_mode=IMAGINAIRY_SAFETY_MODE,
                )
            if safety_score.is_filtered:
                progress_latents.clear()
            if not safety_score.is_filtered:
                if prompt.fix_faces:
                    logger.info("Fixing 😊 's in 🖼  using CodeFormer...")
                    with lc.timing("face enhancement"):
                        img = enhance_faces(img, fidelity=prompt.fix_faces_fidelity)
                if prompt.upscale:
                    logger.info("Upscaling 🖼  using real-ESRGAN...")
                    with lc.timing("upscaling"):
                        upscaled_img = upscale_image(img)

                # put the newly generated patch back into the original, full size image
                if prompt.mask_modify_original and mask_image_orig and starting_image:
                    img_to_add_back_to_original = upscaled_img if upscaled_img else img
                    img_to_add_back_to_original = img_to_add_back_to_original.resize(
                        starting_image.size,
                        resample=Image.Resampling.LANCZOS,
                    )

                    mask_for_orig_size = mask_image_orig.resize(
                        starting_image.size,
                        resample=Image.Resampling.LANCZOS,
                    )
                    # mask_for_orig_size = mask_for_orig_size.filter(
                    #     ImageFilter.GaussianBlur(radius=5)
                    # )
                    log_img(mask_for_orig_size, "mask for original image size")

                    rebuilt_orig_img = Image.composite(
                        starting_image,
                        img_to_add_back_to_original,
                        mask_for_orig_size,
                    )
                    log_img(rebuilt_orig_img, "reconstituted original")

            result = ImagineResult(
                img=img,
                prompt=prompt,
                upscaled_img=upscaled_img,
                is_nsfw=safety_score.is_nsfw,
                safety_score=safety_score,
                modified_original=rebuilt_orig_img,
                mask_binary=mask_image_orig,
                mask_grayscale=mask_grayscale,
                depth_image=depth_image_display,
                timings=lc.get_timings(),
                progress_latents=progress_latents.copy(),
            )
            _most_recent_result = result
            logger.info(f"Image Generated. Timings: {result.timings_str()}")
            return result


def _prompts_to_embeddings(prompts, model):
    total_weight = sum(wp.weight for wp in prompts)
    conditioning = sum(
        model.get_learned_conditioning(wp.text) * (wp.weight / total_weight)
        for wp in prompts
    )
    return conditioning


def prompt_normalized(prompt):
    return re.sub(r"[^a-zA-Z0-9.,\[\]-]+", "_", prompt)[:130]