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import gradio as gr
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
from diffusers import UniPCMultistepScheduler
import torch
import torchvision.transforms as T
import torchvision.transforms.v2 as T2
import cv2
from PIL import Image
import numpy as np
output_res = (768,768)
conditioning_image_transforms = T.Compose(
[
T2.ScaleJitter(target_size=output_res, scale_range=(0.5, 3.0)),
T2.RandomCrop(size=output_res, pad_if_needed=True, padding_mode="symmetric"),
T.ToTensor(),
T.Normalize([0.5], [0.5]),
]
)
cnet = ControlNetModel.from_pretrained("./models/catcon-controlnet-wd", torch_dtype=torch.float32, from_flax=True)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"./models/wd-1-5-b2",
controlnet=cnet,
torch_dtype=torch.float32,
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
generator = torch.manual_seed(0)
# inference function takes prompt, negative prompt and image
def infer(prompt, negative_prompt, image):
# implement your inference function here
#cond_input = conditioning_image_transforms(np.array(image)))
output = pipe(
prompt,
image,
generator=generator,
num_images_per_prompt=1,
num_inference_steps=20
)
return output.images
gr.Interface(
infer,
inputs=[
gr.Textbox(
label="Enter prompt",
max_lines=1,
placeholder="1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, watercolor, night, turtleneck",
),
gr.Textbox(
label="Enter negative prompt",
max_lines=1,
placeholder="low quality",
),
gr.Image(type="pil"),
],
outputs=gr.Gallery().style(grid=[2], height="auto"),
title="Generate controlled outputs with Categorical Conditioning on Waifu Diffusion 1.5 beta 2.",
description="This Space uses image examples as style conditioning.",
examples=[["1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, watercolor, night, turtleneck", "low quality", "wikipe_cond_1.png"]],
allow_flagging=False,
).launch(enable_queue=True)
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