Spaces:
Running
Running
File size: 2,430 Bytes
27d203c e855a91 27d203c 2188326 e855a91 3b3bbf5 e855a91 7e9bd57 e855a91 09759e3 3b3bbf5 33484d2 3b3bbf5 7e9bd57 3b3bbf5 7e9bd57 3b3bbf5 e855a91 33484d2 2188326 09759e3 2188326 09759e3 2188326 e855a91 33484d2 e855a91 09759e3 e855a91 27d203c e855a91 09759e3 e855a91 7e9bd57 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
import gradio as gr
from diffusers import StableDiffusionImg2ImgPipeline
import torch
from PIL import Image
# Load the model
model_id = "nitrosocke/Ghibli-Diffusion"
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
# Move pipeline to GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = pipe.to(device)
# Define the inference function
def ghibli_transform(input_image, prompt="ghibli style", strength=0.75, guidance_scale=7.5, num_steps=50):
if input_image is None:
raise gr.Error("No image uploaded! Please upload an image before clicking Transform.")
# Process the input image (keep it as PIL Image)
try:
init_image = input_image.convert("RGB").resize((768, 768))
except Exception as e:
raise gr.Error(f"Failed to process image: {str(e)}")
# Generate the Ghibli-style image
try:
output = pipe(
prompt=prompt,
image=init_image,
strength=strength,
guidance_scale=guidance_scale,
num_inference_steps=num_steps # Use the UI-provided value
).images[0]
except Exception as e:
raise gr.Error(f"Pipeline error: {str(e)}")
return output
# Create the Gradio interface
with gr.Blocks(title="Ghibli Diffusion Image Transformer") as demo:
gr.Markdown("# Ghibli Diffusion Image Transformer")
gr.Markdown("Upload an image and transform it into Studio Ghibli style using nitrosocke/Ghibli-Diffusion!")
with gr.Row():
with gr.Column():
input_img = gr.Image(label="Upload Image", type="pil")
prompt = gr.Textbox(label="Prompt", value="ghibli style")
strength = gr.Slider(0, 1, value=0.75, step=0.05, label="Strength (How much to transform)")
guidance = gr.Slider(1, 20, value=7.5, step=0.5, label="Guidance Scale")
num_steps = gr.Slider(10, 100, value=50, step=5, label="Inference Steps (Higher = Better Quality, Slower)")
submit_btn = gr.Button("Transform")
with gr.Column():
output_img = gr.Image(label="Ghibli-Style Output")
# Connect the button to the function
submit_btn.click(
fn=ghibli_transform,
inputs=[input_img, prompt, strength, guidance, num_steps],
outputs=output_img
)
# Launch the Space with share=True for public link
demo.launch(share=True) |