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import torch
import gradio as gr
from diffusers import StableDiffusionPipeline
from diffusers import ControlNetModel, DDIMScheduler,EulerDiscreteScheduler,EulerAncestralDiscreteScheduler,UniPCMultistepScheduler
from diffusers import KDPM2DiscreteScheduler,KDPM2AncestralDiscreteScheduler,PNDMScheduler,StableDiffusionPipeline
from diffusers import DPMSolverMultistepScheduler
import random
def set_pipeline(model_id_repo,scheduler):
model_ids_dict = {
"runwayml": "runwayml/stable-diffusion-v1-5",
"Realistic_Vision_V5_1_noVAE":"SG161222/Realistic_Vision_V5.1_noVAE"
}
model_id = model_id_repo
model_repo = model_ids_dict.get(model_id)
print("model_repo :",model_repo)
pipe = StableDiffusionPipeline.from_pretrained(
model_repo,
# torch_dtype=torch.float16, # to run on cpu
use_safetensors=True,
).to("cpu")
scheduler_classes = {
"DDIM": DDIMScheduler,
"Euler": EulerDiscreteScheduler,
"Euler a": EulerAncestralDiscreteScheduler,
"UniPC": UniPCMultistepScheduler,
"DPM2 Karras": KDPM2DiscreteScheduler,
"DPM2 a Karras": KDPM2AncestralDiscreteScheduler,
"PNDM": PNDMScheduler,
"DPM++ 2M Karras": DPMSolverMultistepScheduler,
"DPM++ 2M SDE Karras": DPMSolverMultistepScheduler,
}
sampler_name = scheduler # Example sampler name, replace with the actual value
scheduler_class = scheduler_classes.get(sampler_name)
if scheduler_class is not None:
print("sampler_name:",sampler_name)
pipe.scheduler = scheduler_class.from_config(pipe.scheduler.config)
else:
pass
return pipe
def img_args(
prompt,
negative_prompt,
model_id_repo = "Realistic_Vision_V5_1_noVAE",
scheduler= "Euler a",
height=896,
width=896,
num_inference_steps = 30,
guidance_scale = 7.5,
num_images_per_prompt = 1,
seed = 0
):
pipe = set_pipeline(model_id_repo,scheduler)
if seed == 0:
seed = random.randint(0,25647981548564)
print(f"random seed :{seed}")
generator = torch.manual_seed(seed)
else:
generator = torch.manual_seed(seed)
print(f"manual seed :{seed}")
print("Prompt:",prompt)
image = pipe(prompt=prompt,
negative_prompt = negative_prompt,
height = height,
width = width,
num_inference_steps = num_inference_steps,
guidance_scale = guidance_scale,
num_images_per_prompt = num_images_per_prompt, # default 1
generator = generator,
).images
return image
block = gr.Blocks().queue()
block.title = "Inpaint Anything"
with block as image_gen:
with gr.Column():
with gr.Row():
gr.Markdown("## Image Generation")
with gr.Row():
with gr.Column():
# with gr.Row():
prompt = gr.Textbox(placeholder="what you want to generate",label="Positive Prompt")
negative_prompt = gr.Textbox(placeholder="what you don't want to generate",label="Negative prompt")
run_btn = gr.Button("image generation", elem_id="select_btn", variant="primary")
with gr.Accordion(label="Advance Options",open=False):
model_selection = gr.Dropdown(choices=["runwayml","Realistic_Vision_V5_1_noVAE"],value="Realistic_Vision_V5_1_noVAE",label="Models")
schduler_selection = gr.Dropdown(choices=["DDIM","Euler","Euler a","UniPC","DPM2 Karras","DPM2 a Karras","PNDM","DPM++ 2M Karras","DPM++ 2M SDE Karras"],value="Euler a",label="Scheduler")
guidance_scale_slider = gr.Slider(label="guidance_scale", minimum=0, maximum=15, value=7.5, step=0.5)
num_images_per_prompt_slider = gr.Slider(label="num_images_per_prompt", minimum=0, maximum=5, value=1, step=1)
height_slider = gr.Slider(label="height", minimum=0, maximum=2048, value=896, step=1)
width_slider = gr.Slider(label="width", minimum=0, maximum=2048, value=896, step=1)
num_inference_steps_slider = gr.Slider(label="num_inference_steps", minimum=0, maximum=150, value=30, step=1)
seed_slider = gr.Slider(label="Seed Slider", minimum=0, maximum=256479815, value=0, step=1)
with gr.Column():
# out_img = gr.Image(type="pil",label="Output",height=480)
out_img = gr.Gallery(label='Output', show_label=False, elem_id="gallery", preview=True)
run_btn.click(fn=img_args,inputs=[prompt,negative_prompt,model_selection,schduler_selection,height_slider,width_slider,num_inference_steps_slider,guidance_scale_slider,num_images_per_prompt_slider,seed_slider],outputs=[out_img])
image_gen.launch()
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