Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,569 Bytes
0353592 a57b575 0353592 84b9349 2dd3270 06bf081 84b9349 06bf081 84b9349 0353592 a57b575 0353592 a57b575 0353592 |
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 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
import gradio as gr
import spaces
import torch
from diffusers import DiffusionPipeline
from PIL import Image
# Text-to-Multi-View Diffusion pipeline
text_pipeline = DiffusionPipeline.from_pretrained(
"dylanebert/mvdream",
custom_pipeline="dylanebert/multi-view-diffusion",
torch_dtype=torch.float16,
trust_remote_code=True,
).to("cuda")
# Image-to-Multi-View Diffusion pipeline
image_pipeline = DiffusionPipeline.from_pretrained(
"dylanebert/multi-view-diffusion",
custom_pipeline="dylanebert/multi-view-diffusion",
torch_dtype=torch.float16,
trust_remote_code=True,
).to("cuda")
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()
|