Gradio Diffusers example

environment.yaml

@@ -34,5 +34,6 @@ dependencies:
     - -e git+https://github.com/openai/CLIP.git@main#egg=clip
     - openai
     - gradio
+    - streamlit
     - seaborn
     - git+https://github.com/crowsonkb/k-diffusion.git

gradio-diffusers-demo.py

@@ -0,0 +1,91 @@
+import gradio as gr
+import numpy as np
+from PIL import Image
+
+import torch
+from diffusers import StableDiffusionInstructPix2PixPipeline
+
+
+def image_processing_app(prompt, num_inference_steps, guidance_scale, image_guidance_scale, input_image):
+    if input_image is not None:
+        output_image = run_function(input_image, num_inference_steps, guidance_scale, image_guidance_scale)
+        output_image = np.array(output_image).astype('uint8')
+
+        return input_image, output_image
+    else:
+        return None
+
+def main():
+    model_id = "timbrooks/instruct-pix2pix"
+    pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
+        model_id, torch_dtype=torch.float16, safety_checker=None
+    ).to("cuda")
+    def generate(input_image, instruction, steps, randomize_seed, seed, randomize_cfg, text_cfg_scale, image_cfg_scale, **kwargs):
+        run = True
+        img = input_image.resize((512, 512), resample=Image.Resampling.LANCZOS).convert("RGB")
+        while run == True:
+        # Add your custom processing here
+            image = pipe(prompt=instruction, image=img, num_inference_steps=steps, guidance_scale=text_cfg_scale, image_guidance_scale=image_cfg_scale).images
+            return [seed, text_cfg_scale, image_cfg_scale, image[0]]
+            #yield target.right_column.image(image[0], use_column_width=True)
+
+    with gr.Blocks(css="footer {visibility: hidden}") as demo:
+            with gr.Row():
+                with gr.Column(scale=1, min_width=100):
+                    generate_button = gr.Button("Generate")
+                with gr.Column(scale=1, min_width=100):
+                    load_button = gr.Button("Load Example")
+                with gr.Column(scale=1, min_width=100):
+                    reset_button = gr.Button("Reset")
+                with gr.Column(scale=3):
+                    instruction = gr.Textbox(lines=1, label="Edit Instruction", interactive=True)
+
+            with gr.Row():
+                input_image = gr.Image(label="Input Image", type="pil", interactive=True)
+                edited_image = gr.Image(label=f"Edited Image", type="pil", interactive=False)
+                input_image.style(height=512, width=512)
+                edited_image.style(height=512, width=512)
+
+            with gr.Row():
+                steps = gr.Number(value=100, precision=0, label="Steps", interactive=True)
+                randomize_seed = gr.Radio(
+                    ["Fix Seed", "Randomize Seed"],
+                    value="Randomize Seed",
+                    type="index",
+                    show_label=False,
+                    interactive=True,
+                )
+                seed = gr.Number(value=1371, precision=0, label="Seed", interactive=True)
+                randomize_cfg = gr.Radio(
+                    ["Fix CFG", "Randomize CFG"],
+                    value="Fix CFG",
+                    type="index",
+                    show_label=False,
+                    interactive=True,
+                )
+                text_cfg_scale = gr.Number(value=7.5, label=f"Text CFG", interactive=True)
+                image_cfg_scale = gr.Number(value=1.5, label=f"Image CFG", interactive=True)
+
+            gr.Markdown("help")
+
+            generate_button.click(
+                fn=generate,
+                inputs=[
+                    input_image,
+                    instruction,
+                    steps,
+                    randomize_seed,
+                    seed,
+                    randomize_cfg,
+                    text_cfg_scale,
+                    image_cfg_scale,
+                ],
+                outputs=[seed, text_cfg_scale, image_cfg_scale, edited_image],
+            )
+
+    demo.queue(concurrency_count=1)
+    demo.launch(share=False)
+
+
+if __name__ == "__main__":
+    main()