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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 | |
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() | |