from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, UNet2DConditionModel
from diffusers import LMSDiscreteScheduler
import torch
from tqdm.auto import tqdm
from PIL import Image
import gradio as gr

tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")

vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-ema")
unet = UNet2DConditionModel.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="unet")

beta_start, beta_end = 0.00085, 0.012
height = 512
width = 512
num_inference_steps = 70
guidance_scale = 7.5
batch_size = 1
scheduler = LMSDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear", num_train_timesteps=1000)

def text_enc(prompts, maxlen=None):
    if maxlen is None:
        maxlen = tokenizer.model_max_length
    inp = tokenizer(prompts, padding="max_length", max_length=maxlen, truncation=True, return_tensors="pt")
    input_ids = inp.input_ids
    input_ids = input_ids.to(torch.int)
    return text_encoder(input_ids)[0]

def do_both(prompts):
    def mk_img(t):
        image = (t/2+0.5).clamp(0,1).detach().cpu().permute(1, 2, 0).numpy()
        return Image.fromarray((image*255).round().astype("uint8"))

    def mk_samples(prompts, g=7.5, seed=100, steps=70):
        bs = len(prompts)
        text = text_enc(prompts)
        uncond = text_enc([""] * bs, text.shape[1])
        emb = torch.cat([uncond, text])
        if seed:
            torch.manual_seed(seed)

        latents = torch.randn((bs, unet.config.in_channels, height//8, width//8))
        scheduler.set_timesteps(steps)
        latents = latents.float() * scheduler.init_noise_sigma

        for i,ts in enumerate(tqdm(scheduler.timesteps)):
            inp = scheduler.scale_model_input(torch.cat([latents] * 2), ts)
            with torch.no_grad(): u,t = unet(inp, ts, encoder_hidden_states=emb).sample.chunk(2)
            pred = u + g*(t-u)
            latents = scheduler.step(pred, ts, latents).prev_sample

        with torch.no_grad(): return vae.decode(1 / 0.18215 * latents).sample

    images = mk_samples([prompts])
    for img in images: return(mk_img(img))

gr.Interface(do_both, gr.Text(), gr.Image(), title='Stable Diffusion model from scratch').launch(share=True, debug=True)