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import gradio as gr
import torch
from diffusers import UniDiffuserPipeline
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model_id = "thu-ml/unidiffuser-v1"
# model_id = "thu-ml/unidiffuser-v0"
pipeline = UniDiffuserPipeline.from_pretrained(
model_id,
)
pipeline.to(device)
def convert_to_none(s):
if s:
return s
else:
return None
def set_mode(mode):
if mode == "joint":
pipeline.set_joint_mode()
elif mode == "text2img":
pipeline.set_text_to_image_mode()
elif mode == "img2text":
pipeline.set_image_text_mode()
elif mode == "text":
pipeline.set_text_mode()
elif mode == "img":
pipeline.set_image_mode()
def sample(mode, prompt, image, num_inference_steps, guidance_scale, seed):
set_mode(mode)
prompt = convert_to_none(prompt)
image = convert_to_none(image)
generator = torch.Generator(device=device).manual_seed(seed)
output_sample = pipeline(
prompt=prompt,
image=image,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
generator=generator,
)
sample_image = None
sample_text = ""
if output_sample.images is not None:
sample_image = output_sample.images[0]
if output_sample.text is not None:
sample_text = output_sample.text[0]
return sample_image, sample_text
iface = gr.Interface(
fn=sample,
inputs=[
gr.Textbox(value="", label="Generation Task"),
gr.Textbox(value="", label="Conditioning prompt"),
gr.Image(value=None, label="Conditioning image", type="pil"),
gr.Number(value=20, label="Num Inference Steps", precision=0),
gr.Number(value=8.0, label="Guidance Scale"),
gr.Number(value=0, label="Seed", precision=0),
],
outputs=[
gr.Image(label="Sample image"),
gr.Textbox(label="Sample text"),
],
)
iface.launch()
# from unidiffuser.sample_v0 import sample
# from unidiffuser.sample_v0_test import sample
# from unidiffuser.sample_v1 import sample
# from unidiffuser.sample_v1_test import sample
# def predict(mode, prompt, image, sample_steps, guidance_scale, seed):
# output_images, output_text = sample(
# mode, prompt, image, sample_steps=sample_steps, scale=guidance_scale, seed=seed,
# )
# sample_image = None
# sample_text = ""
# if output_images is not None:
# sample_image = output_images[0]
# if output_text is not None:
# sample_text = output_text[0]
# return sample_image, sample_text
# iface = gr.Interface(
# fn=predict,
# inputs=[
# gr.Textbox(value="", label="Generation Task"),
# gr.Textbox(value="", label="Conditioning prompt"),
# gr.Image(value=None, label="Conditioning image", type="filepath"),
# gr.Number(value=50, label="Num Inference Steps", precision=0),
# gr.Number(value=7.0, label="Guidance Scale"),
# gr.Number(value=1234, label="Seed", precision=0),
# ],
# outputs=[
# gr.Image(label="Sample image"),
# gr.Textbox(label="Sample text"),
# ],
# )
# iface.launch() |