Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 import numpy as np
 import random
 #import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
 import torch
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -13,22 +13,47 @@ if torch.cuda.is_available():
 else:
     torch_dtype = torch.float32
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
 #@spaces.GPU #[uncomment to use ZeroGPU]
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
+def infer_t2i(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
+
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+        
+    generator = torch.Generator().manual_seed(seed)
+
+    pipe = StableDiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+    pipe = pipe.to(device)
+    
+    image = pipe(
+        prompt = prompt, 
+        negative_prompt = negative_prompt,
+        guidance_scale = guidance_scale, 
+        num_inference_steps = num_inference_steps, 
+        width = width, 
+        height = height,
+        generator = generator
+    ).images[0] 
+    
+    return image, seed
+
+#@spaces.GPU #[uncomment to use ZeroGPU]
+def infer_i2i(prompt, image, strength, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
 
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
         
     generator = torch.Generator().manual_seed(seed)
+
+    pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+    pipe = pipe.to(device)
     
     image = pipe(
         prompt = prompt, 
+        image = image.resize((width, height)),
+        strength = strength,
         negative_prompt = negative_prompt,
         guidance_scale = guidance_scale, 
         num_inference_steps = num_inference_steps, 
@@ -136,7 +161,7 @@ with gr.Blocks(css=css) as demo:
             )
     
             run_button.click(
-                fn=infer,
+                fn=infer_t2i,
                 inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
                 outputs = [result, seed]
             )
@@ -159,6 +184,8 @@ with gr.Blocks(css=css) as demo:
                 )
                 
                 run_button = gr.Button("Run", scale=0)
+
+            image_upload_input = gr.Image(label="Upload an Image", type="pil")
             
             result = gr.Image(label="Result", show_label=False)
     
@@ -216,6 +243,14 @@ with gr.Blocks(css=css) as demo:
                         step=1,
                         value=25, #Replace with defaults that work for your model
                     )
+
+                    editing_strength = gr.Slider(
+                        label="Strength of editing",
+                        minimum=0,
+                        maximum=1,
+                        step=0.01,
+                        value=0.5, #Replace with defaults that work for your model
+                    )
             
             gr.Examples(
                 examples = examples,
@@ -223,15 +258,9 @@ with gr.Blocks(css=css) as demo:
             )
     
             run_button.click(
-                fn=infer,
-                inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+                fn=infer_i2i,
+                inputs = [prompt, image_upload_input, editing_strength, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
                 outputs = [result, seed]
             )
-    # gr.on(
-    #     triggers=[run_button.click, prompt.submit],
-    #     fn = infer,
-    #     inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-    #     outputs = [result, seed]
-    # )
 
 demo.queue().launch()