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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 WuerstchenPrior, default_stage_c_timesteps
from previewer.modules import Previewer
os.environ['TOKENIZERS_PARALLELISM'] = 'false'
DESCRIPTION = "# Würstchen"
if not torch.cuda.is_available():
DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work 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", 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,
num_images_per_prompt=num_images_per_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="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0)
result = gr.Gallery(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=768,
maximum=MAX_IMAGE_SIZE,
step=128,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=768,
maximum=MAX_IMAGE_SIZE,
step=128,
value=1024,
)
num_images_per_prompt = gr.Slider(
label="Number of Images",
minimum=1,
maximum=6,
step=1,
value=2,
)
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=100,
step=1,
value=60,
)
decoder_guidance_scale = gr.Slider(
label="Decoder Guidance Scale",
minimum=0,
maximum=20,
step=0.1,
value=0.0,
)
decoder_num_inference_steps = gr.Slider(
label="Decoder Inference Steps",
minimum=10,
maximum=100,
step=1,
value=12,
)
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,
]
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",
)
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,
)
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,
)
if __name__ == "__main__":
demo.queue(max_size=20).launch() |