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import matplotlib.pyplot as plt
import gradio as gr
from diffusers import StableDiffusionPipeline
import matplotlib.pyplot as plt
import torch
model_id1 = "dreamlike-art/dreamlike-diffusion-1.0"
model_id2 = "stabilityai/stable-diffusion-xl-base-1.0"
model_id3 = "stabilityai/stable-diffusion-2"
pipe = StableDiffusionPipeline.from_pretrained(model_id1, torch_dtype=torch.float16, use_safetensors=True)
pipe = pipe.to("cuda")
def generate_image_interface(prompt, num_inference_steps, height, width):
params = {
'prompt': prompt,
'num_inference_steps': num_inference_steps,
'num_images_per_prompt': 2,
'height': height,
'width': width
}
img = pipe(**params).images # Ensure the `pipe` call correctly matches the expected API
num_images = len(img)
if num_images > 1:
fig, ax = plt.subplots(nrows=1, ncols=num_images, figsize=(15, 5))
for i in range(num_images):
ax[i].imshow(img[i])
ax[i].axis('off')
else:
fig = plt.figure()
plt.imshow(img[0])
plt.axis('off')
plt.tight_layout()
plt.show()
return fig
# Define the Gradio interface
inputs = [
gr.Textbox(label="Enter your prompt"),
gr.Slider(minimum=1, maximum=100, value=50, label="Number of Inference Steps"),
gr.Slider(minimum=512, maximum=1024, value=768, label="Height"),
gr.Slider(minimum=512, maximum=1024, value=768, label="Width")
]
outputs = gr.Plot()
demo = gr.Interface(fn=generate_image_interface, inputs=inputs, outputs=outputs)
demo.launch(share=True)
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