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import gradio as gr
import torch
from diffusers import StableDiffusionImg2ImgPipeline
from .utils.schedulers import SCHEDULER_LIST, get_scheduler_list
from .utils.prompt2prompt import generate
from .utils.device import get_device
from PIL import Image
from .download import get_share_js, get_community_loading_icon, CSS
IMG2IMG_MODEL_LIST = {
"OpenJourney v4" : "prompthero/openjourney-v4",
"StableDiffusion 1.5" : "runwayml/stable-diffusion-v1-5",
"StableDiffusion 2.1" : "stabilityai/stable-diffusion-2-1",
"DreamLike 1.0" : "dreamlike-art/dreamlike-diffusion-1.0",
"DreamLike 2.0" : "dreamlike-art/dreamlike-photoreal-2.0",
"DreamShaper" : "Lykon/DreamShaper",
"NeverEnding-Dream" : "Lykon/NeverEnding-Dream"
}
class StableDiffusionImage2ImageGenerator:
def __init__(self):
self.pipe = None
def load_model(self, model_path, scheduler):
model_path = IMG2IMG_MODEL_LIST[model_path]
if self.pipe is None:
self.pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
model_path, safety_checker=None, torch_dtype=torch.float32
)
device = get_device()
self.pipe = get_scheduler_list(pipe=self.pipe, scheduler=scheduler)
self.pipe.to(device)
self.pipe.enable_attention_slicing()
return self.pipe
def generate_image(
self,
image_path: str,
model_path: str,
prompt: str,
negative_prompt: str,
scheduler: str,
guidance_scale: int,
num_inference_step: int,
seed_generator=0,
):
pipe = self.load_model(
model_path=model_path,
scheduler=scheduler,
)
if seed_generator == 0:
random_seed = torch.randint(0, 1000000, (1,))
generator = torch.manual_seed(random_seed)
else:
generator = torch.manual_seed(seed_generator)
image = Image.open(image_path)
images = pipe(
prompt,
image=image,
negative_prompt=negative_prompt,
num_images_per_prompt=1,
num_inference_steps=num_inference_step,
guidance_scale=guidance_scale,
generator=generator,
).images
return images
def app():
demo = gr.Blocks(css=CSS)
with demo:
with gr.Row():
with gr.Column():
image2image_image_file = gr.Image(
type="filepath", label="Upload",elem_id="image-upload-img2img"
).style(height=260)
image2image_prompt = gr.Textbox(
lines=1,
placeholder="Prompt, keywords that describe the changes you want to apply to your image",
show_label=False,
elem_id="prompt-text-input-img2img",
value=''
)
image2image_negative_prompt = gr.Textbox(
lines=1,
placeholder="Negative Prompt, keywords that describe what you don't want in your image",
show_label=False,
elem_id = "negative-prompt-text-input-img2img",
value=''
)
# add button for generating a prompt from the prompt
image2image_generate_prompt_button = gr.Button(
label="Generate Prompt",
type="primary",
align="center",
value = "Generate Prompt"
)
# show a text box with the generated prompt
image2image_generated_prompt = gr.Textbox(
lines=1,
placeholder="Generated Prompt",
label = "Generated Prompt",
show_label=True,
info="Auto generated prompts for inspiration.",
)
image2image_model_path = gr.Dropdown(
choices=list(IMG2IMG_MODEL_LIST.keys()),
value=list(IMG2IMG_MODEL_LIST.keys())[0],
label="Imaget2Image Model Selection",
elem_id="model-dropdown-img2img",
info="Select the model you want to use for image2image generation."
)
image2image_scheduler = gr.Dropdown(
choices=SCHEDULER_LIST,
value=SCHEDULER_LIST[0],
label="Scheduler",
elem_id="scheduler-dropdown-img2img",
info="Scheduler list for models. Different schdulers result in different outputs."
)
image2image_seed_generator = gr.Slider(
label="Seed(0 for random)",
minimum=0,
maximum=1000000,
value=0,
elem_id="seed-slider-img2img",
info="Set the seed to a specific value to reproduce the results."
)
image2image_guidance_scale = gr.Slider(
minimum=0.1,
maximum=15,
step=0.1,
value=7.5,
label="Guidance Scale",
elem_id = "guidance-scale-slider-img2img",
info = "Guidance scale determines how much the prompt will affect the image. Higher the value, more the effect."
)
image2image_num_inference_step = gr.Slider(
minimum=1,
maximum=100,
step=1,
value=50,
label="Num Inference Step",
elem_id = "num-inference-step-slider-img2img",
info = "Number of inference step determines the quality of the image. Higher the number, better the quality."
)
image2image_predict_button = gr.Button(value="Generate image")
with gr.Column():
output_image = gr.Gallery(
label="Generated images",
show_label=False,
elem_id="gallery-img2img",
).style(grid=(1, 2))
with gr.Group(elem_id="container-advanced-btns"):
with gr.Group(elem_id="share-btn-container"):
community_icon_html, loading_icon_html = get_community_loading_icon("img2img")
community_icon = gr.HTML(community_icon_html)
loading_icon = gr.HTML(loading_icon_html)
share_button = gr.Button("Save artwork", elem_id="share-btn-img2img")
# Create an html for describing the models
gr.HTML(
"""
<div id="model-description-img2img">
<h3>Image2Image Models</h3>
<p>Image2Image models are trained to generate images from a given prompt. The prompt should specify what to change in general for the provided image.</p>
<p>For example with a butterfly image, the prompt can be "blue butterfly".</p>
<p>Negative prompt can be used to specify what not to change in the image. For example, with the butterfly image, the negative prompt can be "dark blue, blurry image".</p>
<hr>
<p>Stable Diffusion 1.5 & 2.1: Default model for many tasks. </p>
<p>OpenJourney v4: Generates fantasy themed images similar to the Midjourney model. </p>
<p>Dreamlike Photoreal 1.0 & 2.0 is SD 1.5 that generates realistic images. </p>
</div>
"""
)
image2image_predict_button.click(
fn=StableDiffusionImage2ImageGenerator().generate_image,
inputs=[
image2image_image_file,
image2image_model_path,
image2image_prompt,
image2image_negative_prompt,
image2image_scheduler,
image2image_guidance_scale,
image2image_num_inference_step,
image2image_seed_generator,
],
outputs=[output_image],
)
image2image_generate_prompt_button.click(
fn=generate,
inputs=[image2image_prompt],
outputs=[image2image_generated_prompt],
)
return demo
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