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import gradio as gr
import numpy as np
import random
import os
# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
from peft import PeftModel, LoraConfig
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
if torch.cuda.is_available():
torch_dtype = torch.float16
else:
torch_dtype = torch.float32
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
LoRA_path = 'new_model'
# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
model_id,
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)
if model_id == 'Maria_Lashina_LoRA':
adapter_name = 'a cartoonish mouse'
unet_sub_dir = os.path.join(LoRA_path, "unet")
text_encoder_sub_dir = os.path.join(LoRA_path, "text_encoder")
pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype).to(device)
pipe.unet = PeftModel.from_pretrained(pipe.unet, unet_sub_dir, adapter_name=adapter_name)
pipe.text_encoder = PeftModel.from_pretrained(pipe.text_encoder, text_encoder_sub_dir, adapter_name=adapter_name)
if torch_dtype == torch.float16:
pipe.unet.half()
pipe.text_encoder.half()
pipe.to(device)
else:
pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype).to(device)
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
).images[0]
return image, seed
examples = [
"The image of a cartoonish mouse eating from a red bowl of yellow triangle chips, her cheeks are full. The mouse is gray with big pink ears, small white eyes and a black pointed nose. It has a simple design, the background color is white. The style of the image is reminiscent of a sticker or a digital illustration.",
"The image of a cartoonish mouse with red hearts instead of eyes meaning that the mouse is in love with something. The mouse is gray with big pink ears and a black pointed nose. It has a simple design, the background color is white. The style of the image is reminiscent of a sticker or a digital illustration.",
"The image of a cartoonish mouse with sunglasses and smiling. The mouse is gray with big pink ears and a black pointed nose. It has a simple design, the background color is white. The style of the image is reminiscent of a sticker or a digital illustration.",
]
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")
MODEL_LIST = [
"CompVis/stable-diffusion-v1-4",
"stable-diffusion-v1-5/stable-diffusion-v1-5",
"Maria_Lashina_LoRA"
]
with gr.Row():
model_id = gr.Dropdown(
label="Model",
choices=MODEL_LIST
)
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=42,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
with gr.Row():
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024, # Replace with defaults that work for your model
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024, # Replace with defaults that work for your model
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=7.0, # Replace with defaults that work for your model
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=20, # Replace with defaults that work for your model
)
gr.Examples(examples=examples, inputs=[prompt])
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[
model_id,
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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