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Update app.py
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app.py
CHANGED
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@@ -1,4 +1,4 @@
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# app.py -
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import gradio as gr
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from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
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import torch
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# === OPTIMIERTE EINSTELLUNGEN ===
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device = "cuda" if torch.cuda.is_available() else "cpu"
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torch_dtype = torch.float16 if device == "cuda" else torch.float32
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IMG_SIZE = 512
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STEPS = 35
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print(f"Running on: {device}")
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@@ -56,13 +56,12 @@ def text_to_image(prompt):
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pipe = load_txt2img()
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# Optimierte Generierung für bessere Qualität
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image = pipe(
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prompt=prompt,
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height=IMG_SIZE,
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width=IMG_SIZE,
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num_inference_steps=STEPS,
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guidance_scale=7.5,
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).images[0]
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end_time = time.time()
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traceback.print_exc()
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return None
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def img_to_image(image, prompt="", strength=0.6): # Strength Range anpassen
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try:
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if image is None:
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return None
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print(f"Image-to-Image mit Strength: {strength}")
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pipe = load_img2img()
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img_resized = image.convert("RGB").resize((512, 512))
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@@ -94,20 +94,6 @@ def img_to_image(image, prompt="", strength=0.6): # Strength Range anpassen
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guidance_scale=7.5
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)
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print("✅ Bild 100% generiert - sende an UI")
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return result.images[0]
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except Exception as e:
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print(f"❌ Fehler: {e}")
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return None
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# SLIDER ANPASSEN:
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strength_slider = gr.Slider(
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0.5, 0.8, 0.6, # Jetzt ab 0.5 möglich!
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label="Stärke der Veränderung"
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)
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end_time = time.time()
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print(f"✅ Bild transformiert in {end_time - start_time:.2f} Sekunden")
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with gr.Row():
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img_prompt = gr.Textbox(
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placeholder="
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lines=2,
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label="Transformations-Prompt (Englisch)"
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)
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with gr.Row():
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strength_slider = gr.Slider(
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0.
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label="Stärke der Veränderung"
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)
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with gr.Row():
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gr.Markdown(
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"**Stärke-Einstellung:** "
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"• **0.
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"• **0.
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"• **0.
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)
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transform_btn = gr.Button("🔄 Bild transformieren", variant="primary")
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# app.py - KORRIGIERTE VERSION
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import gradio as gr
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from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
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import torch
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# === OPTIMIERTE EINSTELLUNGEN ===
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device = "cuda" if torch.cuda.is_available() else "cpu"
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torch_dtype = torch.float16 if device == "cuda" else torch.float32
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IMG_SIZE = 512
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STEPS = 35
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print(f"Running on: {device}")
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pipe = load_txt2img()
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image = pipe(
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prompt=prompt,
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height=IMG_SIZE,
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width=IMG_SIZE,
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num_inference_steps=STEPS,
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guidance_scale=7.5,
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).images[0]
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end_time = time.time()
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traceback.print_exc()
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return None
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def img_to_image(image, prompt="", strength=0.6):
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try:
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if image is None:
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return None
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print(f"Image-to-Image mit Strength: {strength}")
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start_time = time.time()
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pipe = load_img2img()
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img_resized = image.convert("RGB").resize((512, 512))
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guidance_scale=7.5
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)
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end_time = time.time()
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print(f"✅ Bild transformiert in {end_time - start_time:.2f} Sekunden")
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with gr.Row():
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img_prompt = gr.Textbox(
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placeholder="background only: winter forest, keep girl and snowman unchanged",
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lines=2,
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label="Transformations-Prompt (Englisch)"
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)
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with gr.Row():
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strength_slider = gr.Slider(
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0.5, 0.8, 0.6, # Jetzt ab 0.5 möglich!
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label="Stärke der Veränderung"
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)
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with gr.Row():
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gr.Markdown(
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"**Stärke-Einstellung:** "
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"• **0.5-0.6:** Behält fast alles Original bei "
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"• **0.65-0.7:** Gute Balance "
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"• **0.75-0.8:** Stärkere Veränderungen"
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)
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transform_btn = gr.Button("🔄 Bild transformieren", variant="primary")
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