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import gradio as gr
import numpy as np
import random
# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "stabilityai/sdxl-turbo" # Replace to the model you would like to use
# Force model to use float32 to avoid dtype mismatch
torch_dtype = torch.float32
pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
# Explicitly convert submodules to float32 to prevent dtype mismatch
pipe.to(device)
pipe.text_encoder.to(device, dtype=torch.float32)
pipe.vae.to(device, dtype=torch.float32)
pipe.unet.to(device, dtype=torch.float32)
def force_float32(model):
for param in model.parameters():
param.data = param.data.to(torch.float32)
for buffer in model.buffers():
buffer.data = buffer.data.to(torch.float32)
force_float32(pipe.text_encoder)
force_float32(pipe.vae)
force_float32(pipe.unet)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
# @spaces.GPU #[uncomment to use ZeroGPU]
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(device).manual_seed(seed)
# Ensure text inputs are moved to the correct device and dtype
prompt = str(prompt) if prompt else ""
negative_prompt = str(negative_prompt) if negative_prompt else ""
# Convert text inputs to Long tensors if necessary
text_input_ids = torch.tensor([ord(c) for c in prompt], dtype=torch.long, device=device)
neg_text_input_ids = torch.tensor([ord(c) for c in negative_prompt], dtype=torch.long, device=device)
image = pipe(
prompt=text_input_ids,
negative_prompt=neg_text_input_ids,
guidance_scale=torch.tensor(guidance_scale, dtype=torch.float32, device=device),
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
).images[0]
return image, seed
examples = [
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
"An astronaut riding a green horse",
"A delicious ceviche cheesecake slice",
]
css = """
#col-container {
margin: 0 auto;
max-width: 640px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(" # Text-to-Image Gradio Template")
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")
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
visible=False,
)
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=0.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=2,
)
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()
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