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import gradio as gr
import numpy as np
import random
import os
import torch
from diffusers import StableDiffusionPipeline
from peft import PeftModel, LoraConfig
device = "cuda" if torch.cuda.is_available() else "cpu"
model_id_default = "stable-diffusion-v1-5/stable-diffusion-v1-5"
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
# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
prompt,
negative_prompt,
width=512,
height=512,
model_id=model_id_default,
seed=42,
guidance_scale=7.0,
lora_scale=1.0,
num_inference_steps=20,
progress=gr.Progress(track_tqdm=True),
):
generator = torch.Generator(device).manual_seed(seed)
ckpt_dir='./model_output'
unet_sub_dir = os.path.join(ckpt_dir, "unet")
text_encoder_sub_dir = os.path.join(ckpt_dir, "text_encoder")
if model_id is None:
raise ValueError("Please specify the base model name or path")
pipe = StableDiffusionPipeline.from_pretrained(model_id,
torch_dtype=torch_dtype,
safety_checker=None).to(device)
pipe.unet = PeftModel.from_pretrained(pipe.unet, unet_sub_dir)
pipe.text_encoder = PeftModel.from_pretrained(pipe.text_encoder, text_encoder_sub_dir)
pipe.unet.load_state_dict({k: lora_scale*v for k, v in pipe.unet.state_dict().items()})
pipe.text_encoder.load_state_dict({k: lora_scale*v for k, v in pipe.text_encoder.state_dict().items()})
if torch_dtype in (torch.float16, torch.bfloat16):
pipe.unet.half()
pipe.text_encoder.half()
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
css = """
#col-container {
margin: 0 auto;
max-width: 640px;
}
"""
def controlnet_params(show_extra):
return gr.update(visible=show_extra)
with gr.Blocks(css=css, fill_height=True) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(" # Text-to-Image demo")
with gr.Row():
model_id = gr.Textbox(
label="Model ID",
max_lines=1,
placeholder="Enter model id",
value=model_id_default,
)
prompt = gr.Textbox(
label="Prompt",
max_lines=1,
placeholder="Enter your prompt",
)
negative_prompt = gr.Textbox(
label="Negative prompt",
max_lines=1,
placeholder="Enter your negative prompt",
)
with gr.Row():
seed = gr.Number(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=42,
)
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=30.0,
step=0.1,
value=7.0, # Replace with defaults that work for your model
)
with gr.Row():
lora_scale = gr.Slider(
label="LoRA scale",
minimum=0.0,
maximum=1.0,
step=0.01,
value=1.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=100,
step=1,
value=20, # Replace with defaults that work for your model
)
with gr.Row():
controlnet_checkbox = gr.Checkbox(
label="ControlNet",
)
# with gr.Group(visible=False) as controlnet_params:
# control_strength = gr.Slider(
# label="ControlNet conditioning scale",
# minimum=0.0,
# maximum=1.0,
# step=0.01,
# value=1.0,
# )
# control_mode = gr.Dropdown(
# label="ControlNet mode",
# choices=["edge_detection", "other"],
# value="edge_detection",
# max_choices=1
# )
# controlnet_checkbox.change(
# controlnet_params,
# inputs=controlnet_checkbox,
# outputs=controlnet_params
# )
with gr.Accordion("Optional Settings", open=False):
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
)
run_button = gr.Button("Run", scale=0, variant="primary")
result = gr.Image(label="Result", show_label=False)
gr.on(
triggers=[run_button.click],
fn=infer,
inputs=[
prompt,
negative_prompt,
width,
height,
model_id,
seed,
guidance_scale,
lora_scale,
num_inference_steps
],
outputs=[result],
)
if __name__ == "__main__":
demo.launch() |