Update app.py

app.py

@@ -2,69 +2,86 @@ import spaces
 import gradio as gr
 import re
 from PIL import Image
+
 import os
 import numpy as np
 import torch
-from diffusers import StableDiffusionImg2ImgPipeline
+from diffusers import FluxImg2ImgPipeline
 
-# Use float16 for lower VRAM usage
-dtype = torch.float16
+dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-# Load the lighter model on the GPU
-pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=dtype)
-pipe.to(device)
+pipe = FluxImg2ImgPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(device)
+
+
 
 def sanitize_prompt(prompt):
-    allowed_chars = re.compile(r"[^a-zA-Z0-9\s.,!?-]")
-    return allowed_chars.sub("", prompt)
+  # Allow only alphanumeric characters, spaces, and basic punctuation
+  allowed_chars = re.compile(r"[^a-zA-Z0-9\s.,!?-]")
+  sanitized_prompt = allowed_chars.sub("", prompt)
+  return sanitized_prompt
 
-def convert_to_fit_size(original_width_and_height, maximum_size=2048):
-    width, height = original_width_and_height
+def convert_to_fit_size(original_width_and_height, maximum_size = 2048):
+    width, height =original_width_and_height
     if width <= maximum_size and height <= maximum_size:
-        return width, height
-    scaling_factor = maximum_size / max(width, height)
-    return int(width * scaling_factor), int(height * scaling_factor)
+        return width,height
+    
+    if width > height:
+        scaling_factor = maximum_size / width
+    else:
+        scaling_factor = maximum_size / height
+
+    new_width = int(width * scaling_factor)
+    new_height = int(height * scaling_factor)
+    return new_width, new_height
 
 def adjust_to_multiple_of_32(width: int, height: int):
-    return width - (width % 32), height - (height % 32)
+    width = width - (width % 32)
+    height = height - (height % 32)
+    return width, height
+
+
+
 
 @spaces.GPU(duration=120)
-def process_images(image, prompt="a woman", strength=0.75, seed=0, inference_step=50, progress=gr.Progress(track_tqdm=True)):
-    progress(0, desc="Starting processing")
-    
-    def process_img2img(image, prompt="a person", strength=0.75, seed=0, num_inference_steps=50):
+def process_images(image,prompt="a girl",strength=0.75,seed=0,inference_step=4,progress=gr.Progress(track_tqdm=True)):
+    #print("start process_images")
+    progress(0, desc="Starting")
+
+
+    def process_img2img(image, prompt="a person", strength=0.75, seed=0, num_inference_steps=4):
         if image is None:
-            print("Empty input image returned")
+            print("empty input image returned")
             return None
         generator = torch.Generator(device).manual_seed(seed)
         fit_width, fit_height = convert_to_fit_size(image.size)
         width, height = adjust_to_multiple_of_32(fit_width, fit_height)
         image = image.resize((width, height), Image.LANCZOS)
     
-        output = pipe(
-            prompt=prompt,
-            init_image=image,
-            generator=generator,
-            strength=strength,
-            guidance_scale=7.5,
-            num_inference_steps=num_inference_steps,
-        )
+        output = pipe(prompt=prompt, image=image, generator=generator, strength=strength, width=width, height=height,
+                    guidance_scale=0, num_inference_steps=num_inference_steps, max_sequence_length=256)
     
         pil_image = output.images[0]
-        # Optionally, resize back to original fitted dimensions if desired
-        if pil_image.size != (fit_width, fit_height):
-            pil_image = pil_image.resize((fit_width, fit_height), Image.LANCZOS)
+        new_width, new_height = pil_image.size
+    
+        if (new_width != fit_width) or (new_height != fit_height):
+            resized_image = pil_image.resize((fit_width, fit_height), Image.LANCZOS)
+            return resized_image
         return pil_image
     
     output = process_img2img(image, prompt, strength, seed, inference_step)
     return output
 
+    
+
 def read_file(path: str) -> str:
     with open(path, 'r', encoding='utf-8') as f:
-        return f.read()
+        content = f.read()
+
+    return content
+
 
-css = """
+css="""
 #col-left {
     margin: 0 auto;
     max-width: 640px;
@@ -77,16 +94,19 @@ css = """
   display: flex;
   align-items: center;
   justify-content: center;
-  gap: 10px;
+  gap:10px
 }
+
 .image {
   width: 128px; 
   height: 128px; 
   object-fit: cover; 
 }
+
 .text {
   font-size: 16px;
 }
+
 """
 
 with gr.Blocks(css=css, elem_id="demo-container") as demo:
@@ -94,37 +114,49 @@ with gr.Blocks(css=css, elem_id="demo-container") as demo:
         gr.HTML(read_file("demo_header.html"))
         gr.HTML(read_file("demo_tools.html"))
     with gr.Row():
-        with gr.Column():
-            image = gr.Image(height=800, sources=['upload','clipboard'], image_mode='RGB', elem_id="image_upload", type="pil", label="Upload")
-            with gr.Row(elem_id="prompt-container", equal_height=False):
-                with gr.Row():
-                    prompt = gr.Textbox(label="Prompt", value="a woman", placeholder="Your prompt", elem_id="prompt")
-            btn = gr.Button("Img2Img", elem_id="run_button", variant="primary")
-            with gr.Accordion(label="Advanced Settings", open=False):
-                with gr.Row(equal_height=True):
-                    strength = gr.Number(value=0.75, minimum=0, maximum=1.0, step=0.01, label="Strength")
-                    seed = gr.Number(value=100, minimum=0, step=1, label="Seed")
-                    inference_step = gr.Number(value=50, minimum=1, step=1, label="Inference Steps")
-                id_input = gr.Text(label="Name", visible=False)
-        with gr.Column():
-            image_out = gr.Image(height=800, sources=[], label="Output", elem_id="output-img", format="jpg")
-    
+                with gr.Column():
+                    image = gr.Image(height=800,sources=['upload','clipboard'],image_mode='RGB', elem_id="image_upload", type="pil", label="Upload")
+                    with gr.Row(elem_id="prompt-container",  equal_height=False):
+                        with gr.Row():
+                            prompt = gr.Textbox(label="Prompt",value="a women",placeholder="Your prompt (what you want in place of what is erased)", elem_id="prompt")
+                            
+                    btn = gr.Button("Img2Img", elem_id="run_button",variant="primary")
+                    
+                    with gr.Accordion(label="Advanced Settings", open=False):
+                        with gr.Row( equal_height=True):
+                            strength = gr.Number(value=0.75, minimum=0, maximum=0.75, step=0.01, label="strength")
+                            seed = gr.Number(value=100, minimum=0, step=1, label="seed")
+                            inference_step = gr.Number(value=4, minimum=1, step=4, label="inference_step")
+                        id_input=gr.Text(label="Name", visible=False)
+                            
+                with gr.Column():
+                    image_out = gr.Image(height=800,sources=[],label="Output", elem_id="output-img",format="jpg")
+                   
+
+                    
+            
+
     gr.Examples(
-        examples=[
-            ["examples/draw_input.jpg", "examples/draw_output.jpg", "a woman with blue eyes"],
-            ["examples/draw-gimp_input.jpg", "examples/draw-gimp_output.jpg", "a woman with a serene expression"],
-            ["examples/gimp_input.jpg", "examples/gimp_output.jpg", "a woman in a garden"],
-            ["examples/inpaint_input.jpg", "examples/inpaint_output.jpg", "a woman in a futuristic city"]
-        ],
-        inputs=[image, image_out, prompt],
+               examples=[
+                    ["examples/draw_input.jpg", "examples/draw_output.jpg","a women ,eyes closed,mouth opened"],
+                    ["examples/draw-gimp_input.jpg", "examples/draw-gimp_output.jpg","a women ,eyes closed,mouth opened"],
+                    ["examples/gimp_input.jpg", "examples/gimp_output.jpg","a women ,hand on neck"],
+                    ["examples/inpaint_input.jpg", "examples/inpaint_output.jpg","a women ,hand on neck"]
+                         ]
+,
+                inputs=[image,image_out,prompt],
+    )
+    gr.HTML(
+       gr.HTML(read_file("demo_footer.html"))
     )
-    gr.HTML(read_file("demo_footer.html"))
     gr.on(
         triggers=[btn.click, prompt.submit],
-        fn=process_images,
-        inputs=[image, prompt, strength, seed, inference_step],
-        outputs=[image_out]
+        fn = process_images,
+        inputs = [image,prompt,strength,seed,inference_step],
+        outputs = [image_out]
     )
 
 if __name__ == "__main__":
     demo.launch(share=True, show_error=True)
+
+