app.py

import os
import random
import gradio as gr
import numpy as np
import PIL.Image
import torch
from typing import List
from diffusers.utils import numpy_to_pil
from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline
from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS
import spaces 
from previewer.modules import Previewer
import user_history

os.environ['TOKENIZERS_PARALLELISM'] = 'false'

DESCRIPTION = "# Stable Cascade"
DESCRIPTION += "\n<p style=\"text-align: center\">Unofficial demo for <a href='https://huggingface.co/stabilityai/stable-cascade' target='_blank'>Stable Casacade</a>, a new high resolution text-to-image model by Stability AI, built on the Würstchen architecture - <a href='https://huggingface.co/stabilityai/stable-cascade/blob/main/LICENSE' target='_blank'>non-commercial research license</a></p>"
if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶</p>"

MAX_SEED = np.iinfo(np.int32).max
CACHE_EXAMPLES = False #torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") != "0"
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1536"))
USE_TORCH_COMPILE = False
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
PREVIEW_IMAGES = False

dtype = torch.bfloat16
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
    prior_pipeline = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", torch_dtype=dtype)#.to(device)
    decoder_pipeline = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade",  torch_dtype=dtype)#.to(device) 

    if ENABLE_CPU_OFFLOAD:
        prior_pipeline.enable_model_cpu_offload()
        decoder_pipeline.enable_model_cpu_offload()
    else:
        prior_pipeline.to(device)
        decoder_pipeline.to(device)

    if USE_TORCH_COMPILE:
        prior_pipeline.prior = torch.compile(prior_pipeline.prior, mode="reduce-overhead", fullgraph=True)
        decoder_pipeline.decoder = torch.compile(decoder_pipeline.decoder, mode="max-autotune", fullgraph=True)
    
    if PREVIEW_IMAGES:
        previewer = Previewer()
        previewer_state_dict = torch.load("previewer/previewer_v1_100k.pt", map_location=torch.device('cpu'))["state_dict"]
        previewer.load_state_dict(previewer_state_dict)
        def callback_prior(pipeline, step_index, t, callback_kwargs):
            latents = callback_kwargs["latents"]
            output = previewer(latents)
            output = numpy_to_pil(output.clamp(0, 1).permute(0, 2, 3, 1).float().cpu().numpy())
            callback_kwargs["preview_output"] = output
            return callback_kwargs
        callback_steps = 1
    else:
        previewer = None
        callback_prior = None
        callback_steps = None
else:
    prior_pipeline = None
    decoder_pipeline = None


def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    print("randomizing seed")
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed

@spaces.GPU
def generate(
    prompt: str,
    negative_prompt: str = "",
    seed: int = 0,
    width: int = 1024,
    height: int = 1024,
    prior_num_inference_steps: int = 30,
    # prior_timesteps: List[float] = None,
    prior_guidance_scale: float = 4.0,
    decoder_num_inference_steps: int = 12,
    # decoder_timesteps: List[float] = None,
    decoder_guidance_scale: float = 0.0,
    num_images_per_prompt: int = 2,
    profile: gr.OAuthProfile | None = None,
) -> PIL.Image.Image:
    
    #previewer.eval().requires_grad_(False).to(device).to(dtype)
    prior_pipeline.to(device)
    decoder_pipeline.to(device)
    
    generator = torch.Generator().manual_seed(seed)
    print("prior_num_inference_steps: ", prior_num_inference_steps)
    prior_output = prior_pipeline(
        prompt=prompt,
        height=height,
        width=width,
        num_inference_steps=prior_num_inference_steps,
        timesteps=DEFAULT_STAGE_C_TIMESTEPS,
        negative_prompt=negative_prompt,
        guidance_scale=prior_guidance_scale,
        num_images_per_prompt=num_images_per_prompt,
        generator=generator,
        #callback_on_step_end=callback_prior,
        #callback_on_step_end_tensor_inputs=['latents']
    )
    if PREVIEW_IMAGES:
        for _ in range(len(DEFAULT_STAGE_C_TIMESTEPS)):
            r = next(prior_output)
            if isinstance(r, list):
                yield r[0]
        prior_output = r

    decoder_output = decoder_pipeline(
        image_embeddings=prior_output.image_embeddings,
        prompt=prompt,
        num_inference_steps=decoder_num_inference_steps,
        # timesteps=decoder_timesteps,
        guidance_scale=decoder_guidance_scale,
        negative_prompt=negative_prompt,
        generator=generator,
        output_type="pil",
    ).images
    print(decoder_output)
    #Save images
    for image in decoder_output:
        user_history.save_image(
            profile=profile,
            image=image,
            label=prompt,
            metadata={
                "negative_prompt": negative_prompt,
                "seed": seed,
                "width": width,
                "height": height,
                "prior_guidance_scale": prior_guidance_scale,
                "decoder_num_inference_steps": decoder_num_inference_steps,
                "decoder_guidance_scale": decoder_guidance_scale,
                "num_images_per_prompt": num_images_per_prompt,
            },
        )
    
    yield decoder_output[0]


examples = [
    "A futuristic cityscape at sunset",
    "pair of shoes made of dried fruit skins, 3d render, bright colours, clean composition, beautiful artwork, logo",
    "post-apocalyptic wasteland, the most delicate beautiful flower with green leaves growing from dust and rubble, vibrant colours, cinematic",
    "Mixed media artwork, Emotional cyborg girl, Elegant dress, Skin lesions as a storytelling element, In the style of surrealist expressionism, muted color scheme, dreamlike atmosphere, abstract and distorted forms",
    "rendering, side shot, falf-strange body with complex system equipment with hyper detail robot, gaze, sci-fi, gloomy environment, foggy with light shader, cyan and yellow illuminations, dramatic lighting, RTX shader, hyper detail texture with reflection, HDRI, cyborg, grunge, bolt, UHD",
    "vintage Japanese postcard, in the style of Kentaro Miura, featuring a black cat holding a vinyl record in its paws, with vintage colors including light beige and red tones on a white background, very detailed artwork."
]

with gr.Blocks() as demo:
    gr.Markdown(DESCRIPTION)
    gr.DuplicateButton(
        value="Duplicate Space for private use",
        elem_id="duplicate-button",
        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
    )
    with gr.Group():
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )
            run_button = gr.Button("Run", scale=0)
        result = gr.Image(label="Result", show_label=False)
    with gr.Accordion("Advanced options", open=False):
        negative_prompt = gr.Text(
            label="Negative prompt",
            max_lines=1,
            placeholder="Enter a Negative Prompt",
        )

        seed = gr.Slider(
            label="Seed",
            minimum=0,
            maximum=MAX_SEED,
            step=1,
            value=0,
        )
        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
        with gr.Row():
            width = gr.Slider(
                label="Width",
                minimum=1024,
                maximum=MAX_IMAGE_SIZE,
                step=512,
                value=1024,
            )
            height = gr.Slider(
                label="Height",
                minimum=1024,
                maximum=MAX_IMAGE_SIZE,
                step=512,
                value=1024,
            )
            num_images_per_prompt = gr.Slider(
                label="Number of Images",
                minimum=1,
                maximum=2,
                step=1,
                value=1,
            )
        with gr.Row():
            prior_guidance_scale = gr.Slider(
                label="Prior Guidance Scale",
                minimum=0,
                maximum=20,
                step=0.1,
                value=4.0,
            )
            prior_num_inference_steps = gr.Slider(
                label="Prior Inference Steps",
                minimum=10,
                maximum=30,
                step=1,
                value=20,
            )

            decoder_guidance_scale = gr.Slider(
                label="Decoder Guidance Scale",
                minimum=0,
                maximum=0,
                step=0.1,
                value=0.0,
            )
            decoder_num_inference_steps = gr.Slider(
                label="Decoder Inference Steps",
                minimum=4,
                maximum=12,
                step=1,
                value=10,
            )

    gr.Examples(
        examples=examples,
        inputs=prompt,
        outputs=result,
        fn=generate,
        cache_examples=CACHE_EXAMPLES,
    )

    inputs = [
            prompt,
            negative_prompt,
            seed,
            width,
            height,
            prior_num_inference_steps,
            # prior_timesteps,
            prior_guidance_scale,
            decoder_num_inference_steps,
            # decoder_timesteps,
            decoder_guidance_scale,
            num_images_per_prompt,
    ]
    gr.on(
        triggers=[prompt.submit, negative_prompt.submit, run_button.click],
        fn=randomize_seed_fn,
        inputs=[seed, randomize_seed],
        outputs=seed,
        queue=False,
        api_name=False,
    ).then(
        fn=generate,
        inputs=inputs,
        outputs=result,
        api_name="run",
    )
    
with gr.Blocks(css="style.css") as demo_with_history:
    with gr.Tab("App"):
        demo.render()
    with gr.Tab("Past generations"):
        user_history.render()

if __name__ == "__main__":
    demo_with_history.queue(max_size=20).launch()