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 WuerstchenDecoderPipeline, WuerstchenPriorPipeline
from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS
from previewer.modules import Previewer

from gallery_history import fetch_gallery_history, show_gallery_history

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

DESCRIPTION = "# Waves Weaves"
DESCRIPTION += "\n<p style=\"text-align: center\"></p>"
if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶</p>"

MAX_SEED = np.iinfo(np.int32).max
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
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 = True

dtype = torch.float16
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
    prior_pipeline = WuerstchenPriorPipeline.from_pretrained("warp-ai/wuerstchen-prior-model-interpolated", torch_dtype=dtype)
    decoder_pipeline = WuerstchenDecoderPipeline.from_pretrained("warp-ai/wuerstchen", torch_dtype=dtype)
    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="reduce-overhead", fullgraph=True)

    if PREVIEW_IMAGES:
        previewer = Previewer()
        previewer.load_state_dict(torch.load("previewer/text2img_wurstchen_b_v1_previewer_100k.pt")["state_dict"])
        previewer.eval().requires_grad_(False).to(device).to(dtype)

        def callback_prior(i, t, latents):
            output = previewer(latents)
            output = numpy_to_pil(output.clamp(0, 1).permute(0, 2, 3, 1).cpu().numpy())
            return output

    else:
        previewer = None
        callback_prior = None
else:
    prior_pipeline = None
    decoder_pipeline = None


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


def generate(
    prompt: str,
    negative_prompt: str = "",
    seed: int = 0,
    width: int = 1024,
    height: int = 1024,
    prior_num_inference_steps: int = 60,
    # 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,
) -> PIL.Image.Image:
    generator = torch.Generator().manual_seed(seed)

    prior_output = prior_pipeline(
        prompt=prompt,
        height=height,
        width=width,
        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=callback_prior,
    )

    if PREVIEW_IMAGES:
        for _ in range(len(DEFAULT_STAGE_C_TIMESTEPS)):
            r = next(prior_output)
            if isinstance(r, list):
                yield r
        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
    yield decoder_output

examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
]

with gr.Blocks(css="style.css") 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="Imagine... 'A puppy', 'A Delicious Fruit Cake', 'Copacabana Beach'...",
                container=False,
            )
            run_button = gr.Button("Weave", scale=0)
        result = gr.Gallery(label="Result", show_label=False)
    with gr.Accordion("Advanced options", open=False):
        negative_prompt = gr.Text(
            label="What I do NOT want",
            max_lines=1,
            placeholder="Uncheck seed to iterate and finetune.",
            value="lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"
        )

        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=2,
            )
        with gr.Row():
            prior_guidance_scale = gr.Slider(
                label="Prior Guidance Scale",
                minimum=0,
                maximum=20,
                step=0.1,
                value=17.0,
            )
            prior_num_inference_steps = gr.Slider(
                label="Prior Inference Steps",
                minimum=30,
                maximum=60,
                step=1,
                value=30,
            )

            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=12,
            )

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

    history = show_gallery_history()


    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,
    ]
    prompt.submit(
        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",
    ).then(
        fn=fetch_gallery_history, inputs=[prompt, result], outputs=history, queue=False
    )
    negative_prompt.submit(
        fn=randomize_seed_fn,
        inputs=[seed, randomize_seed],
        outputs=seed,
        queue=False,
        api_name=False,
    ).then(
        fn=generate,
        inputs=inputs,
        outputs=result,
        api_name=False,
    ).then(
        fn=fetch_gallery_history, inputs=[prompt, result], outputs=history, queue=False
    )
    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=False,
    ).then(
        fn=fetch_gallery_history, inputs=[prompt, result], outputs=history, queue=False
    )

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