Update app.py

app.py

@@ -3,38 +3,49 @@ import numpy as np
 import random
 from diffusers import DiffusionPipeline
 import torch
+from src.euler_scheduler import MyEulerAncestralDiscreteScheduler
+from diffusers.pipelines.auto_pipeline import AutoPipelineForImage2Image
+from src.sdxl_inversion_pipeline import SDXLDDIMPipeline
+from src.config import RunConfig
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-if torch.cuda.is_available():
-    torch.cuda.max_memory_allocated(device=device)
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
-    pipe.enable_xformers_memory_efficient_attention()
-    pipe = pipe.to(device)
-else: 
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
-    pipe = pipe.to(device)
+
+scheduler_class = MyEulerAncestralDiscreteScheduler
+
+
+pipe_inversion = SDXLDDIMPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True).to(device)
+pipe_inference = AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True).to(device)
+pipe_inference.scheduler            = scheduler_class.from_config(pipe_inference.scheduler.config)
+pipe_inversion.scheduler            = scheduler_class.from_config(pipe_inversion.scheduler.config)
+pipe_inversion.scheduler_inference  = scheduler_class.from_config(pipe_inference.scheduler.config)
+
+
+# if torch.cuda.is_available():
+#     torch.cuda.max_memory_allocated(device=device)
+#     pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
+#     pipe.enable_xformers_memory_efficient_attention()
+#     pipe = pipe.to(device)
+# else: 
+#     pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
+#     pipe = pipe.to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
 
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
-    
-    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] 
+
+
+
+def infer(input_image, description_prompt, target_prompt, guidance_scale, num_inference_steps=4, num_inversion_steps=4, inversion_max_step=0.6):
+    config = RunConfig(num_inference_steps=num_inference_steps,
+                       num_inversion_steps=num_inversion_steps,
+                       guidance_scale=guidance_scale,
+                       inversion_max_step=inversion_max_step)
     
+    editor = ImageEditorDemo(pipe_inversion, pipe_inference, input_image, description_prompt, config)
+
+    editor.edit(target_prompt)
     return image
 
 examples = [
@@ -56,63 +67,38 @@ else:
     power_device = "CPU"
 
 with gr.Blocks(css=css) as demo:
-    
+
+    gr.Markdown(f"""
+    # RNRI briel and links on device: {power_device}.
+    """)
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        Currently running on {power_device}.
-        """)
+
+        with gr.Row():
+            input_image = gr.Image(label="Input image", sources=['upload', 'webcam', 'clipboard'], type="pil")
         
         with gr.Row():
             
-            prompt = gr.Text(
-                label="Prompt",
+            description_prompt = gr.Text(
+                label="Image description",
                 show_label=False,
                 max_lines=1,
-                placeholder="Enter your prompt",
+                placeholder="Enter your image description",
                 container=False,
             )
-            
-            run_button = gr.Button("Run", scale=0)
+          
         
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            negative_prompt = gr.Text(
-                label="Negative prompt",
+        with gr.Row():
+  
+            target_prompt = gr.Text(
+                label="Edit prompt",
+                show_label=False,
                 max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
+                placeholder="Enter your edit prompt",
+                container=False,
             )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
+
+        
+        with gr.Accordion("Advanced Settings", open=False):
             
             with gr.Row():
                 
@@ -125,21 +111,29 @@ with gr.Blocks(css=css) as demo:
                 )
                 
                 num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
+                    label="Number of RNRI iterations",
                     minimum=1,
                     maximum=12,
                     step=1,
                     value=2,
                 )
+
         
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
-        )
+        with gr.Row():
+            run_button = gr.Button("Edit", scale=0)
+
+    with gr.Column(elem_id="col-container"):
+    
+        result = gr.Image(label="Result", show_label=False)
+    
+        # gr.Examples(
+        #     examples = examples,
+        #     inputs = [prompt]
+        # )
 
     run_button.click(
         fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        inputs = [input_image, description_prompt, target_prompt, guidance_scale, num_inference_steps, num_inference_steps],
         outputs = [result]
     )