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import gradio as gr
import numpy as np
import random
import spaces
from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
import torch
from src.linfusion import LinFusion
device = "cuda" if torch.cuda.is_available() else "cpu"
all_model_id = {
"DreamShaper-8": "Lykon/dreamshaper-8",
"RealisticVision-v4.0": "SG161222/Realistic_Vision_V4.0_noVAE",
"SD-v1.4": "CompVis/stable-diffusion-v1-4"
}
if torch.cuda.is_available():
torch_dtype = torch.float16
else:
torch_dtype = torch.float32
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 16384
pipes = {}
for model_id, repo_id in all_model_id.items():
pipes[model_id + '_t2i'] = StableDiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch_dtype)
LinFusion.construct_for(pipes[model_id + '_t2i'])
pipes[model_id + '_ip_adapter'] = StableDiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch_dtype)
pipes[model_id + '_ip_adapter'].load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter-plus_sd15.bin")
LinFusion.construct_for(pipes[model_id + '_ip_adapter'])
pipes[model_id + '_i2i'] = StableDiffusionImg2ImgPipeline.from_pretrained(repo_id, torch_dtype=torch_dtype)
LinFusion.construct_for(pipes[model_id + '_i2i'])
@spaces.GPU
def infer_t2i(model, 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)
pipe = pipes[model + '_t2i'].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
@spaces.GPU
def infer_i2i(model, prompt, image, strength, 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)
pipe = pipes[model + '_i2i'].to(device)
image = pipe(
prompt = prompt,
image = image.resize((width, height)),
strength = strength,
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
@spaces.GPU
def infer_ip_adapter(model, prompt, image, scale, 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)
pipe = pipes[model + '_ip_adapter'].to(device)
pipe.set_ip_adapter_scale(scale)
image = pipe(
prompt = prompt,
image = image.resize((width, height)),
negative_prompt = negative_prompt,
guidance_scale = guidance_scale,
num_inference_steps = num_inference_steps,
ip_adapter_image = image,
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.Tab("Text-to-Image"):
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""
# LinFusion Text-to-Image Gradio Demo
""")
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)
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,
)
model_choice = gr.Dropdown(label="Choose Model", choices=list(all_model_id.keys()), value=list(all_model_id.keys())[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=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=7.5, #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=25, #Replace with defaults that work for your model
)
gr.Examples(
examples = examples,
inputs = [prompt]
)
run_button.click(
fn=infer_t2i,
inputs = [model_choice, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs = [result, seed]
)
with gr.Tab("Image-to-Image"):
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""
# LinFusion Image-to-Image Gradio Demo
""")
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)
image_upload_input = gr.Image(label="Upload an Image", type="pil")
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)
model_choice = gr.Dropdown(label="Choose Model", choices=list(all_model_id.keys()), value=list(all_model_id.keys())[0])
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=7.5, #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=25, #Replace with defaults that work for your model
)
editing_strength = gr.Slider(
label="Strength of editing",
minimum=0,
maximum=1,
step=0.01,
value=0.5, #Replace with defaults that work for your model
)
gr.Examples(
examples = examples,
inputs = [prompt]
)
run_button.click(
fn=infer_i2i,
inputs = [model_choice, prompt, image_upload_input, editing_strength, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs = [result, seed]
)
with gr.Tab("IP-Adapter"):
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""
# LinFusion IP-Adapter Gradio Demo
""")
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)
image_upload_input = gr.Image(label="Upload an Image", type="pil")
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)
model_choice = gr.Dropdown(label="Choose Model", choices=list(all_model_id.keys()), value=list(all_model_id.keys())[0])
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=7.5, #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=25, #Replace with defaults that work for your model
)
ip_adapter_scale = gr.Slider(
label="Strength of image condition",
minimum=0,
maximum=1,
step=0.01,
value=0.4, #Replace with defaults that work for your model
)
gr.Examples(
examples = examples,
inputs = [prompt]
)
run_button.click(
fn=infer_ip_adapter,
inputs = [model_choice, prompt, image_upload_input, ip_adapter_scale, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs = [result, seed]
)
demo.queue().launch() |