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import gradio as gr
import numpy as np
import random
from peft import PeftModel, LoraConfig
# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "stable-diffusion-v1-5/stable-diffusion-v1-5" # Replace to the model you would like to use
if torch.cuda.is_available():
torch_dtype = torch.float16
else:
torch_dtype = torch.float32
pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
#pipe.unet.load_adapter("um235/cartoon_cat_stickers")
pipe.unet = PeftModel.from_pretrained(pipe.unet,"um235/cartoon_cat_stickers")
pipe = pipe.to(device)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
model_id,
prompt,
negative_prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
lscale,
controlnet_enabled,
control_strength,
control_mode,
control_image,
ip_adapter_enabled,
ip_adapter_scale,
ip_adapter_image,
progress=gr.Progress(track_tqdm=True),
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
if (model_repo_id=="stable-diffusion-v1-5/stable-diffusion-v1-5"):
pipe.unet = PeftModel.from_pretrained(pipe.unet,"um235/cartoon_cat_stickers")
pipe.scale_lora(lscale)
pipe = pipe.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 = [
"Sticker, cartoon-style cat character with soft yellow fur. A gentle cat with expressive eyes that shine with a sad, emotional look. The cat, with a small pink nose and a flower on its head, appears to be crying, with blue teardrops around its eyes, giving the sticker a simple yet poignant design.",
"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")
model_id_input = gr.Text(
label="Enter Model ID",
value="stable-diffusion-v1-5/stable-diffusion-v1-5",
show_label=True,
placeholder="Enter model",
)
with gr.Row():
lscale = gr.Slider(
label="Lora scale",
minimum=0,
maximum=2,
step=0.05,
value=1, # Replace with defaults that work for your model
)
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
with gr.Accordion("ControlNet Settings", open=False):
controlnet_enabled = gr.Checkbox(label="Enable ControlNet", value=False)
with gr.Row():
control_strength = gr.Slider(
label="ControlNet Strength",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.75,
visible=False,
)
control_mode = gr.Dropdown(
label="ControlNet Mode",
choices=["edge_detection", "pose_estimation", "depth_estimation"],
value="edge_detection",
visible=False,
)
control_image = gr.Image(label="ControlNet Image", type="pil", visible=False)
with gr.Accordion("IP-Adapter Settings", open=False):
ip_adapter_enabled = gr.Checkbox(label="Enable IP-Adapter", value=False)
with gr.Row():
ip_adapter_scale = gr.Slider(
label="IP-Adapter Scale",
minimum=0.0,
maximum=2.0,
step=0.05,
value=1.0,
visible=False,
)
ip_adapter_image = gr.Image(label="IP-Adapter Image", type="pil", visible=False)
with gr.Row():
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=True,
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=42,
)
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=512, # Replace with defaults that work for your model
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=512, # 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_input,
prompt,
negative_prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
lscale,
controlnet_enabled,
control_strength,
control_mode,
control_image,
ip_adapter_enabled,
ip_adapter_scale,
ip_adapter_image,
],
outputs=[result, seed],
)
if __name__ == "__main__":
demo.launch()
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