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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
# pipe = StableDiffusionPipeline.from_pretrained(
# "SG161222/Realistic_Vision_V5.1_noVAE",
# torch_dtype=torch.float16,
# use_safetensors=True,
# ).to("cpu")
def set_pipeline(model_id_repo,scheduler):
# pipe = StableDiffusionPipeline.from_single_file(
# "/home/ubuntu/stable-diffusion-webui/models/Stable-diffusion/realisticVisionV51_v51VAE.safetensors",
# # torch_dtype=torch.float16,
# use_safetensors=True,
# ).to("cpu")
model_ids_dict = {
"dreamshaper": "Lykon/DreamShaper",
"deliberate": "soren127/Deliberate",
"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")
pipe = StableDiffusionPipeline.from_pretrained(
model_repo,
torch_dtype=torch.float16, # to run on cpu
use_safetensors=True,
).to("cuda")
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
# # prompt = "a photo of an astronaut riding a horse on mars"
# # pipe.enable_attention_slicing()
# image = pipe(prompt).images[0]
# image.save("1.png")
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
):
print(model_id_repo)
print(scheduler)
print(prompt,"&&&&&&&&&&&&&&&&")
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}")
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
print(image,"#############")
# image.save("1.png")
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=["dreamshaper","deliberate","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()
# block = gr.Blocks().queue()
# block.title = "Inpaint Anything"
# with block as inpaint_anything_interface:
# with gr.Column():
# with gr.Row():
# gr.Markdown("## Inpainting with Segment Anything (Multi Controlnet)")
# with gr.Row():
# with gr.Column():
# # with gr.Row():
# model_selection = gr.Dropdown(choices=["dreamshaper","deliberate","realisticVisionV51_v51VAE","revAnimated_v121Inp","runwayml","Realistic_Vision_V5_1_noVAE"],value = "Realistic_Vision_V5_1_noVAE",label="Models")
# # scheduler = 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="Sampler")
# input_image = gr.Image(type="numpy",label="input",height=400)
# run_btn = gr.Button("Run Segment", elem_id="select_btn", variant="primary")
# prompt = gr.Textbox(placeholder="what you want to generate")
# guidance_scale_slider = gr.Slider(label="Guidance Scale", minimum=0, maximum=20.0, value=7.5, step=0.5)
# inference_slider = gr.Slider(label="Guidance Scale", minimum=0, maximum=150, value=50, step=1)
# with gr.Row():
# canny_slider = gr.Slider(label="Canny Slider", minimum=0, maximum=1.0, value=0.5, step=0.1)
# depth_slider = gr.Slider(label="Depth Slider", minimum=0, maximum=1.0, value=0.5, step=0.1)
# seg_slider = gr.Slider(label="Segment Slider", minimum=0, maximum=1.0, value=0.5, step=0.1)
# out_img = gr.Image(type="pil",label="output")
# seed_slider = gr.Slider(label="Seed Slider",elem_id="expand_mask_iteration_count", minimum=0, maximum=25647981548564, value=0, step=1)
# grn_btn = gr.Button("image generation", elem_id="select_btn", variant="primary")
# # bru_btn = gr.Button("Brush generation", elem_id="select_btn", variant="primary")
# with gr.Column():
# scheduler = 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="Sampler")
# # lora_chk = gr.Checkbox(label="Use Lora", elem_id="invert_chk", show_label=True, value=False, interactive=True)
# # image_out = gr.Image(type="pil",label="Output")
# sam_image = gr.Image(label="Segment Anything image", elem_id="ia_sam_image", type="numpy", tool="sketch", brush_radius=8,
# show_label=False, interactive=True,height=400)
# mask_btn = gr.Button("Create Mask", elem_id="select_btn", variant="primary")
# with gr.Column():
# with gr.Row():
# invert_chk = gr.Checkbox(label="Invert mask", elem_id="invert_chk", show_label=True, value=True, interactive=True)
# ignore_black_chk = gr.Checkbox(label="Ignore black area", elem_id="ignore_black_chk", value=True, show_label=True, interactive=True)
# lora_chk = gr.Checkbox(label="Use Lora", elem_id="invert_chk", show_label=True, value=False, interactive=True)
# with gr.Column():
# sel_mask = gr.Image(label="Selected mask image", elem_id="ia_sel_mask", type="numpy", tool="sketch", brush_radius=12,
# show_label=False, interactive=True, height=480)
# with gr.Column():
# with gr.Row():
# expand_mask_btn = gr.Button("Expand mask region", elem_id="expand_mask_btn")
# # with gr.Column():
# expand_mask_iteration_count = gr.Slider(label="Expand Mask Iterations",
# elem_id="expand_mask_iteration_count", minimum=1, maximum=100, value=1, step=1)
# with gr.Row():
# add_mask_btn = gr.Button("Add mask by sketch", elem_id="add_mask_btn")
# apply_mask_btn = gr.Button("Trim mask by sketch", elem_id="apply_mask_btn")
# run_btn.click(fn=run_seg,inputs=[input_image],outputs=[sam_image])
# mask_btn.click(fn=select_mask,inputs=[input_image, sam_image, invert_chk, ignore_black_chk,sel_mask], outputs=[sel_mask])
# expand_mask_btn.click(expand_mask, inputs=[input_image, sel_mask, expand_mask_iteration_count], outputs=[sel_mask])
# apply_mask_btn.click(apply_mask, inputs=[input_image, sel_mask], outputs=[sel_mask])
# add_mask_btn.click(add_mask, inputs=[input_image, sel_mask], outputs=[sel_mask])
# grn_btn.click(fn=generate_image,inputs=[input_image,sam_image,prompt,seed_slider,canny_slider,depth_slider,seg_slider,model_selection,scheduler,guidance_scale_slider,inference_slider,lora_chk],outputs=[out_img])
# bru_btn.click(fn=brush_geeration,inputs=[input_image,prompt],outputs=[out_img])
# inpaint_anything_interface.launch() |