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import spaces
import gradio as gr
import torch
from diffusers import LCMScheduler, AutoPipelineForText2Image
from diffusers import AutoPipelineForInpainting, LCMScheduler
from diffusers import DiffusionPipeline, LCMScheduler
from PIL import Image, ImageEnhance
import io
@spaces.GPU
def generate_image(prompt, num_inference_steps, guidance_scale):
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
adapter_id = "latent-consistency/lcm-lora-sdxl"
pipe = AutoPipelineForText2Image.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.to("cuda")
# Load and fuse lcm lora
pipe.load_lora_weights(adapter_id)
pipe.fuse_lora()
# Generate the image
image = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale).images[0]
return image
def inpaint_image(prompt, init_image, mask_image, num_inference_steps, guidance_scale):
pipe = AutoPipelineForInpainting.from_pretrained(
"diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
torch_dtype=torch.float16,
variant="fp16",
).to("cuda")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")
pipe.fuse_lora()
if init_image is not None:
init_image_path = init_image.name # Get the file path
init_image = Image.open(init_image_path).resize((1024, 1024))
else:
raise ValueError("Initial image not provided or invalid")
if mask_image is not None:
mask_image_path = mask_image.name # Get the file path
mask_image = Image.open(mask_image_path).resize((1024, 1024))
else:
raise ValueError("Mask image not provided or invalid")
# Generate the inpainted image
generator = torch.manual_seed(42)
image = pipe(
prompt=prompt,
image=init_image,
mask_image=mask_image,
generator=generator,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
).images[0]
return image
def generate_image_with_adapter(prompt, num_inference_steps, guidance_scale):
pipe = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
variant="fp16",
torch_dtype=torch.float16
).to("cuda")
# set scheduler
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
# Load and fuse lcm lora
pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl", adapter_name="lcm")
pipe.load_lora_weights("TheLastBen/Papercut_SDXL", weight_name="papercut.safetensors", adapter_name="papercut")
# Combine LoRAs
pipe.set_adapters(["lcm", "papercut"], adapter_weights=[1.0, 0.8])
pipe.fuse_lora()
generator = torch.manual_seed(0)
# Generate the image
image = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, generator=generator).images[0]
pipe.unfuse_lora()
return image
def modify_image(image, brightness, contrast):
# Function to modify brightness and contrast
image = Image.open(io.BytesIO(image))
enhancer = ImageEnhance.Brightness(image)
image = enhancer.enhance(brightness)
enhancer = ImageEnhance.Contrast(image)
image = enhancer.enhance(contrast)
return image
with gr.Blocks(gr.themes.Soft()) as demo:
with gr.Row():
image_output = gr.Image(label="Generated Image")
with gr.Row():
with gr.Accordion(label="Configuration Options"):
prompt_input = gr.Textbox(label="Prompt", placeholder="Self-portrait oil painting, a beautiful cyborg with golden hair, 8k")
steps_input = gr.Slider(minimum=1, maximum=10, label="Inference Steps", value=4)
guidance_input = gr.Slider(minimum=0, maximum=2, label="Guidance Scale", value=1)
generate_button = gr.Button("Generate Image")
with gr.Row():
with gr.Accordion(label="Papercut Image Generation"):
adapter_prompt_input = gr.Textbox(label="Prompt", placeholder="papercut, a cute fox")
adapter_steps_input = gr.Slider(minimum=1, maximum=10, label="Inference Steps", value=4)
adapter_guidance_input = gr.Slider(minimum=0, maximum=2, label="Guidance Scale", value=1)
adapter_generate_button = gr.Button("Generate Image with Adapter")
with gr.Row():
with gr.Accordion(label="Inpainting"):
inpaint_prompt_input = gr.Textbox(label="Prompt for Inpainting", placeholder="a castle on top of a mountain, highly detailed, 8k")
init_image_input = gr.File(label="Initial Image")
mask_image_input = gr.File(label="Mask Image")
inpaint_steps_input = gr.Slider(minimum=1, maximum=10, label="Inference Steps", value=4)
inpaint_guidance_input = gr.Slider(minimum=0, maximum=2, label="Guidance Scale", value=1)
inpaint_button = gr.Button("Inpaint Image")
with gr.Row():
with gr.Accordion(label="Image Modification (Experimental)"):
brightness_slider = gr.Slider(minimum=0.5, maximum=1.5, step=1, label="Brightness")
contrast_slider = gr.Slider(minimum=0.5, maximum=1.5, step=1, label="Contrast")
modify_button = gr.Button("Modify Image")
generate_button.click(
generate_image,
inputs=[prompt_input, steps_input, guidance_input],
outputs=image_output
)
modify_button.click(
modify_image,
inputs=[image_output, brightness_slider, contrast_slider],
outputs=image_output
)
inpaint_button.click(
inpaint_image,
inputs=[inpaint_prompt_input, init_image_input, mask_image_input, inpaint_steps_input, inpaint_guidance_input],
outputs=image_output
)
adapter_generate_button.click(
generate_image_with_adapter,
inputs=[adapter_prompt_input, adapter_steps_input, adapter_guidance_input],
outputs=image_output
)
demo.launch()
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