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import gradio as gr
import spaces
import torch
from diffusers import DiffusionPipeline
from PIL import Image
@spaces.GPU
def load_text_pipeline():
# Text-to-Multi-View Diffusion pipeline
return DiffusionPipeline.from_pretrained(
"ashawkey/mvdream-sd2.1-diffusers",
custom_pipeline="dylanebert/multi_view_diffusion",
torch_dtype=torch.float16,
trust_remote_code=True,
).to("cuda")
@spaces.GPU
def load_image_pipeline():
# Image-to-Multi-View Diffusion pipeline
return DiffusionPipeline.from_pretrained(
"ashawkey/imagedream-ipmv-diffusers",
custom_pipeline="dylanebert/multi_view_diffusion",
torch_dtype=torch.float16,
trust_remote_code=True,
).to("cuda")
text_pipeline = load_text_pipeline()
image_pipeline = load_image_pipeline()
def create_image_grid(images):
images = [Image.fromarray((img * 255).astype("uint8")) for img in images]
width, height = images[0].size
grid_img = Image.new("RGB", (2 * width, 2 * height))
grid_img.paste(images[0], (0, 0))
grid_img.paste(images[1], (width, 0))
grid_img.paste(images[2], (0, height))
grid_img.paste(images[3], (width, height))
return grid_img
@spaces.GPU
def text_to_mv(prompt):
images = text_pipeline(
prompt, guidance_scale=5, num_inference_steps=30, elevation=0
)
return create_image_grid(images)
@spaces.GPU
def image_to_mv(image, prompt):
image = image.astype("float32") / 255.0
images = image_pipeline(
prompt, image, guidance_scale=5, num_inference_steps=30, elevation=0
)
return create_image_grid(images)
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
with gr.Tab("Text Input"):
text_input = gr.Textbox(
lines=2,
show_label=False,
placeholder="Enter a prompt here (e.g. 'a cat statue')",
)
text_btn = gr.Button("Generate Multi-View Images")
with gr.Tab("Image Input"):
image_input = gr.Image(
label="Image Input",
type="numpy",
)
optional_text_input = gr.Textbox(
lines=2,
show_label=False,
placeholder="Enter an optional prompt here",
)
image_btn = gr.Button("Generate Multi-View Images")
with gr.Column():
output = gr.Image(label="Generated Images")
text_btn.click(fn=text_to_mv, inputs=text_input, outputs=output)
image_btn.click(
fn=image_to_mv, inputs=[image_input, optional_text_input], outputs=output
)
if __name__ == "__main__":
demo.queue().launch()