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import gradio as gr
import PIL
import torch
from diffusers import StableDiffusionInstructPix2PixPipeline
cartoonization_id = "instruction-tuning-sd/cartoonizer"
image_proc_id = "instruction-tuning-sd/low-level-img-proc"
title = "Instruction-tuned Stable Diffusion"
description = "This Space demonstrates the instruction-tuning on Stable Diffusion. To know more, please check out the [corresponding blog post](https://hf.co/blog/instruction-tuning-sd). Some experimentation tips on the hyperparameters are available from [the original InstructPix2Pix Space](https://huggingface.co/spaces/timbrooks/instruct-pix2pix). You can quickly try out the samples provided at the bottom of this demo."
def load_pipeline(id: str):
pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(
id, torch_dtype=torch.float16
).to("cuda")
pipeline.enable_xformers_memory_efficient_attention()
pipeline.set_progress_bar_config(disable=True)
return pipeline
def infer_cartoonization(
prompt: str,
negative_prompt: str,
image: PIL.Image.Image,
steps: int,
img_cfg: float,
text_cfg: float,
seed: int,
):
pipeline = load_pipeline(cartoonization_id)
images = pipeline(
prompt,
image,
negative_prompt=negative_prompt,
num_inference_steps=int(steps),
image_guidance_scale=img_cfg,
guidance_scale=text_cfg,
generator=torch.manual_seed(int(seed)),
num_images_per_prompt=4
).images
return images
def infer_img_proc(
prompt: str,
negative_prompt: str,
image: PIL.Image.Image,
steps: int,
img_cfg: float,
text_cfg: float,
seed: int,
):
pipeline = load_pipeline(image_proc_id)
images = pipeline(
prompt,
image,
negative_prompt=negative_prompt,
num_inference_steps=int(steps),
image_guidance_scale=img_cfg,
guidance_scale=text_cfg,
generator=torch.manual_seed(int(seed)),
).images
return images
examples = [
["cartoonize this image", "low quality", "examples/mountain.png", 20, 1.5, 7.5, 0],
["derain this image", "low quality", "examples/duck.png", 20, 1.5, 7.5, 0],
]
with gr.Blocks(theme="gradio/soft") as demo:
gr.Markdown(f"## {title}")
gr.Markdown(description)
with gr.Tab("Cartoonization"):
prompt = gr.Textbox(label="Prompt")
neg_prompt = gr.Textbox(label="Negative Prompt")
input_image = gr.Image(label="Input Image", type="pil")
steps = gr.Slider(minimum=5, maximum=100, step=1, label="Steps")
img_cfg = gr.Number(value=1.5, label=f"Image CFG", interactive=True)
text_cfg = gr.Number(value=7.5, label=f"Text CFG", interactive=True)
seed = gr.Slider(minimum=0, maximum=100000, step=1, label="Seed")
car_output_gallery = gr.Gallery().style(grid=[2], height="auto")
submit_btn = gr.Button(value="Submit")
all_car_inputs = [prompt, neg_prompt, input_image, steps, img_cfg, text_cfg, seed]
submit_btn.click(
fn=infer_cartoonization,
inputs=all_car_inputs,
outputs=[car_output_gallery],
)
gr.Markdown("### Cartoonization example")
gr.Examples(
[examples[0]],
inputs=all_car_inputs,
outputs=car_output_gallery,
fn=infer_cartoonization,
cache_examples=True,
)
with gr.Tab("Low-level image processing"):
rompt = gr.Textbox(label="Prompt")
neg_prompt = gr.Textbox(label="Negative Prompt")
input_image = gr.Image(label="Input Image", type="pil")
steps = gr.Slider(minimum=5, maximum=100, step=1)
img_cfg = gr.Number(value=1.5, label=f"Image CFG", interactive=True)
text_cfg = gr.Number(value=7.5, label=f"Text CFG", interactive=True)
seed = gr.Slider(minimum=0, maximum=100000, step=1)
img_proc_output_gallery = gr.Gallery().style(grid=[2], height="auto")
submit_btn = gr.Button(value="Submit")
all_img_proc_inputs = [prompt, neg_prompt, input_image, steps, img_cfg, text_cfg, seed]
submit_btn.click(
fn=infer_img_proc,
inputs=all_img_proc_inputs,
outputs=[img_proc_output_gallery],
)
gr.Markdown("### Low-level image processing example")
gr.Examples(
[examples[1]],
inputs=all_img_proc_inputs,
outputs=img_proc_output_gallery,
fn=infer_img_proc,
cache_examples=True,
)
demo.launch(enable_queue=True)
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