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L4
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 = "# Würstchen" | |
DESCRIPTION += "\n<p style=\"text-align: center\"><a href='https://huggingface.co/warp-ai/wuerstchen' target='_blank'>Würstchen</a> is a new fast and efficient high resolution text-to-image architecture and model</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", 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="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=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=4.0, | |
) | |
prior_num_inference_steps = gr.Slider( | |
label="Prior Inference Steps", | |
minimum=30, | |
maximum=30, | |
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() | |