Upload 4 files

app.py

@@ -0,0 +1,162 @@
+from utils.image2image import stable_diffusion_img2img
+from utils.text2image import stable_diffusion_text2img
+
+import gradio as gr
+
+stable_model_list = [
+    "runwayml/stable-diffusion-v1-5",
+    "stabilityai/stable-diffusion-2",
+    "stabilityai/stable-diffusion-2-base",
+    "stabilityai/stable-diffusion-2-1",
+    "stabilityai/stable-diffusion-2-1-base"
+]
+stable_prompt_list = [
+        "a photo of a man.",
+        "a photo of a girl."
+    ]
+
+stable_negative_prompt_list = [
+        "bad, ugly",
+        "deformed"
+    ]
+app = gr.Blocks()
+with app:
+    gr.Markdown("# **<h2 align='center'>Stable Diffusion WebUI<h2>**")
+    gr.Markdown(
+        """
+        <h5 style='text-align: center'>
+        Follow me for more! 
+        <a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a>
+        </h5>
+        """
+    )
+    with gr.Row():
+        with gr.Column():
+            with gr.Tab('Text2Image'):
+                text2image_model_id = gr.Dropdown(
+                    choices=stable_model_list, 
+                    value=stable_model_list[0], 
+                    label='Text-Image Model Id'
+                )
+
+                text2image_prompt = gr.Textbox(
+                    lines=1, 
+                    value=stable_prompt_list[0], 
+                    label='Prompt'
+                )
+
+                text2image_negative_prompt = gr.Textbox(
+                    lines=1, 
+                    value=stable_negative_prompt_list[0], 
+                    label='Negative Prompt'
+                )
+
+                with gr.Accordion("Advanced Options", open=False):
+                    text2image_guidance_scale = gr.Slider(
+                        minimum=0.1, 
+                        maximum=15, 
+                        step=0.1, 
+                        value=7.5, 
+                        label='Guidance Scale'
+                    )
+
+                    text2image_num_inference_step = gr.Slider(
+                        minimum=1, 
+                        maximum=100, 
+                        step=1, 
+                        value=50, 
+                        label='Num Inference Step'
+                    )
+
+                    text2image_height = gr.Slider(
+                        minimum=128, 
+                        maximum=1280, 
+                        step=32, 
+                        value=512, 
+                        label='Tile Height'
+                    )
+
+                    text2image_width = gr.Slider(
+                        minimum=128, 
+                        maximum=1280, 
+                        step=32, 
+                        value=768, 
+                        label='Tile Height'
+                    )
+
+                text2image_predict = gr.Button(value='Generator')
+
+
+            with gr.Tab('Image2Image'):
+                image2image2_image_file = gr.Image(label='Image')
+
+                image2image_model_id = gr.Dropdown(
+                    choices=stable_model_list, 
+                    value=stable_model_list[0], 
+                    label='Image-Image Model Id'
+                )
+
+                image2image_prompt = gr.Textbox(
+                    lines=1, 
+                    value=stable_prompt_list[0], 
+                    label='Prompt'
+                )
+
+                image2image_negative_prompt = gr.Textbox(
+                    lines=1, 
+                    value=stable_negative_prompt_list[0], 
+                    label='Negative Prompt'
+                )
+
+                with gr.Accordion("Advanced Options", open=False):
+                    image2image_guidance_scale = gr.Slider(
+                        minimum=0.1, 
+                        maximum=15, 
+                        step=0.1, 
+                        value=7.5, 
+                        label='Guidance Scale'
+                    )
+
+                    image2image_num_inference_step = gr.Slider(
+                        minimum=1, 
+                        maximum=100, 
+                        step=1, 
+                        value=50, 
+                        label='Num Inference Step'
+                    )
+
+                image2image_predict = gr.Button(value='Generator')
+
+
+    with gr.Tab('Generator'):
+        with gr.Column():
+            output_image = gr.Image(label='Image')
+            
+        text2image_predict.click(
+            fn = stable_diffusion_text2img,
+            inputs = [
+                text2image_model_id,
+                text2image_prompt, 
+                text2image_negative_prompt, 
+                text2image_guidance_scale,
+                text2image_num_inference_step, 
+                text2image_height,
+                text2image_width,
+            ],
+            outputs = [output_image],
+        )  
+
+        image2image_predict.click(
+            fn = stable_diffusion_img2img,
+            inputs = [
+                image2image2_image_file,
+                image2image_model_id, 
+                image2image_prompt, 
+                image2image_negative_prompt,
+                image2image_guidance_scale, 
+                image2image_num_inference_step,
+            ],
+            outputs = [output_image],
+        )  
+
+app.launch()          

requirements.txt

@@ -0,0 +1,5 @@
+transformers 
+bitsandbytes==0.35.0 
+xformers 
+controlnet_aux 
+diffusers

utils/image2image.py

@@ -0,0 +1,36 @@
+from diffusers import StableDiffusionImg2ImgPipeline, DDIMScheduler
+from IPython.display import display
+from PIL import Image
+import torch
+
+def stable_diffusion_img2img(
+    model_path:str,
+    image_path:str,
+    prompt:str,
+    negative_prompt:str,
+    num_samples:int,
+    guidance_scale:int,
+    num_inference_step:int,
+    ):
+
+    image = Image.open(image_path)
+
+    pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+        model_path, 
+        safety_checker=None, 
+        torch_dtype=torch.float16
+    )
+    pipe.to("cuda")
+    pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+    pipe.enable_xformers_memory_efficient_attention()
+
+    output = pipe(
+        prompt = prompt,
+        image = image,
+        negative_prompt = negative_prompt,
+        num_images_per_prompt = num_samples,
+        num_inference_steps = num_inference_step,
+        guidance_scale = guidance_scale,
+    ).images
+
+    return output

utils/text2image.py

@@ -0,0 +1,33 @@
+from diffusers import StableDiffusionPipeline, DDIMScheduler
+import torch
+
+def stable_diffusion_text2img(
+    model_path:str,
+    prompt:str,
+    negative_prompt:str,
+    guidance_scale:int,
+    num_inference_step:int,
+    height:int,
+    width:int,
+    ):
+
+    pipe = StableDiffusionPipeline.from_pretrained(
+        model_path, 
+        safety_checker=None, 
+        torch_dtype=torch.float16
+    ).to("cuda")
+
+    pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+    pipe.enable_xformers_memory_efficient_attention()
+
+    images = pipe(
+        prompt,
+        height=height,
+        width=width,
+        negative_prompt=negative_prompt,
+        num_images_per_prompt=1,
+        num_inference_steps=num_inference_step,
+        guidance_scale=guidance_scale,
+    ).images
+
+    return images