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#1
by
kadirnar
- opened
- app.py +65 -2
- requirements.txt +2 -1
- utils/inpaint.py +53 -0
app.py
CHANGED
@@ -1,6 +1,6 @@
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from utils.image2image import stable_diffusion_img2img
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from utils.text2image import stable_diffusion_text2img
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-
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import gradio as gr
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stable_model_list = [
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@@ -10,6 +10,12 @@ stable_model_list = [
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"stabilityai/stable-diffusion-2-1",
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"stabilityai/stable-diffusion-2-1-base"
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]
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stable_prompt_list = [
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"a photo of a man.",
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"a photo of a girl."
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@@ -21,7 +27,7 @@ stable_negative_prompt_list = [
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]
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app = gr.Blocks()
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with app:
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gr.Markdown("# **<h2 align='center'>Stable Diffusion WebUI<h2>**")
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gr.Markdown(
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"""
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<h5 style='text-align: center'>
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@@ -127,6 +133,50 @@ with app:
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image2image_predict = gr.Button(value='Generator')
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with gr.Tab('Generator'):
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with gr.Column():
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@@ -159,4 +209,17 @@ with app:
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outputs = [output_image],
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)
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app.launch()
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from utils.image2image import stable_diffusion_img2img
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from utils.text2image import stable_diffusion_text2img
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+
from utils.inpaint import stable_diffusion_inpaint
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import gradio as gr
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stable_model_list = [
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"stabilityai/stable-diffusion-2-1",
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"stabilityai/stable-diffusion-2-1-base"
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]
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+
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stable_inpiant_model_list = [
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"stabilityai/stable-diffusion-2-inpainting",
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"runwayml/stable-diffusion-inpainting"
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]
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stable_prompt_list = [
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"a photo of a man.",
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"a photo of a girl."
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]
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app = gr.Blocks()
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with app:
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gr.Markdown("# **<h2 align='center'>Stable Diffusion + ControlNet WebUI<h2>**")
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gr.Markdown(
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"""
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<h5 style='text-align: center'>
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image2image_predict = gr.Button(value='Generator')
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with gr.Tab('Inpaint'):
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inpaint_image_file = gr.Image(
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source="upload",
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type="numpy",
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tool="sketch",
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elem_id="source_container"
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)
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inpaint_model_id = gr.Dropdown(
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choices=stable_inpiant_model_list,
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value=stable_inpiant_model_list[0],
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label='Inpaint Model Id'
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)
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inpaint_prompt = gr.Textbox(
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lines=1,
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value=stable_prompt_list[0],
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label='Prompt'
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)
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inpaint_negative_prompt = gr.Textbox(
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lines=1,
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value=stable_negative_prompt_list[0],
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label='Negative Prompt'
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)
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with gr.Accordion("Advanced Options", open=False):
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inpaint_guidance_scale = gr.Slider(
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minimum=0.1,
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maximum=15,
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step=0.1,
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value=7.5,
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label='Guidance Scale'
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)
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inpaint_num_inference_step = gr.Slider(
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minimum=1,
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maximum=100,
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step=1,
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value=50,
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label='Num Inference Step'
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)
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inpaint_predict = gr.Button(value='Generator')
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with gr.Tab('Generator'):
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with gr.Column():
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outputs = [output_image],
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)
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inpaint_predict.click(
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fn = stable_diffusion_inpaint,
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inputs = [
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inpaint_image_file,
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inpaint_model_id,
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inpaint_prompt,
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inpaint_negative_prompt,
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inpaint_guidance_scale,
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inpaint_num_inference_step,
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],
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outputs = [output_image],
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)
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app.launch()
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requirements.txt
CHANGED
@@ -2,4 +2,5 @@ transformers
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bitsandbytes==0.35.0
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xformers
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controlnet_aux
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diffusers
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bitsandbytes==0.35.0
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xformers
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controlnet_aux
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diffusers
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imageio
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utils/inpaint.py
ADDED
@@ -0,0 +1,53 @@
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from diffusers import DiffusionPipeline, DDIMScheduler
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from PIL import Image
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import imageio
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import torch
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# https://huggingface.co/spaces/Manjushri/SD-2.0-Inpainting-CPU/blob/main/app.py
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def resize(height,img):
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baseheight = height
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img = Image.open(img)
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hpercent = (baseheight/float(img.size[1]))
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wsize = int((float(img.size[0])*float(hpercent)))
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img = img.resize((wsize,baseheight), Image.Resampling.LANCZOS)
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return img
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def img_preprocces(source_img, prompt, negative_prompt):
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imageio.imwrite("data.png", source_img["image"])
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imageio.imwrite("data_mask.png", source_img["mask"])
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src = resize(512, "data.png")
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src.save("src.png")
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mask = resize(512, "data_mask.png")
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mask.save("mask.png")
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return src, mask
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def stable_diffusion_inpaint(
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image_path:str,
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model_path:str,
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prompt:str,
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negative_prompt:str,
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guidance_scale:int,
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num_inference_step:int,
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):
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image, mask_image = img_preprocces(image_path, prompt, negative_prompt)
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pipe = DiffusionPipeline.from_pretrained(
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model_path,
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revision="fp16",
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torch_dtype=torch.float16,
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)
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pipe.to('cuda')
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pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
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pipe.enable_xformers_memory_efficient_attention()
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output = pipe(
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prompt = prompt,
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image = image,
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mask_image=mask_image,
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negative_prompt = negative_prompt,
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num_inference_steps = num_inference_step,
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guidance_scale = guidance_scale,
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).images
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return output
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