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import numpy as np
import random
from diffusers import DiffusionPipeline
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
# ✅ WAI Illustrious 1.6 model
model_repo_id = "WAI-Illustrious/WAI-Illustrious-1.6"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
pipe = pipe.to(device)
# ✅ Performance optimizations
if torch.cuda.is_available():
pipe.enable_xformers_memory_efficient_attention()
pipe.enable_model_cpu_offload()
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
def infer(
prompt,
negative_prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
progress=gr.Progress(track_tqdm=True),
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
# ✅ Generate 4 images
images = pipe(
prompt=[prompt] * 4,
negative_prompt=[negative_prompt] * 4 if negative_prompt else None,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
).images
return images, seed
examples = [
"masterpiece, best quality, anime girl, detailed eyes",
"1girl, silver hair, fantasy armor, glowing sword",
"anime landscape, sunset, cinematic lighting",
]
css = """
#col-container {
margin: 0 auto;
max-width: 720px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown("# WAI Illustrious 1.6 - Text to Image")
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, variant="primary")
# ✅ Gallery instead of single image
result = gr.Gallery(label="Results", show_label=False, columns=2)
with gr.Accordion("Advanced Settings", open=False):
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="low quality, bad anatomy",
)
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=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=5.0, # ✅ better default
)
num_inference_steps = gr.Slider(
label="Steps",
minimum=1,
maximum=50,
step=1,
value=25, # ✅ better default
)
gr.Examples(examples=examples, inputs=[prompt])
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[
prompt,
negative_prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
],
outputs=[result, seed],
)
if __name__ == "__main__":
demo.launch() |