Create app.py

app.py

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+import spaces
+import os
+from peft import PeftModel
+import gradio as gr
+from diffusers import StableDiffusionImg2ImgPipeline
+from diffusers import AutoPipelineForImage2Image
+from diffusers import DiffusionPipeline
+import torch
+from PIL import Image
+from diffusers import StableDiffusionPipeline
+# Load the model
+# model_id = "nitrosocke/Ghibli-Diffusion"
+# pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
+tk=os.getenv('ghtoken')
+print("ttttt",tk)
+model_id = "black-forest-labs/FLUX.1-dev"
+# pipe =DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16,token=tk)
+pipe =AutoPipelineForImage2Image.from_pretrained(model_id, torch_dtype=torch.bfloat16,token=tk)
+
+
+# # 1. 选择一个基础模型，例如 SD 1.5
+# base_model_id = "runwayml/stable-diffusion-v1-5"
+
+# # 2. 加载基础模型
+# pipe = StableDiffusionPipeline.from_pretrained(
+#     base_model_id,
+#     torch_dtype=torch.float32
+# )
+
+# # 3. 加载 LoRA 权重
+# lora_model_id = "openfree/flux-chatgpt-ghibli-lora"
+# pipe.load_lora_weights(lora_model_id)
+
+# pipe = AutoPipelineForImage2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16,token=True)
+# pipe.load_lora_weights('openfree/flux-chatgpt-ghibli-lora', weight_name='flux-chatgpt-ghibli-lora.safetensors')
+pipe.load_lora_weights("alvarobartt/ghibli-characters-flux-lora")
+
+
+# Move pipeline to GPU if available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = pipe.to(device)
+
+# Define the inference function
+@spaces.GPU
+def ghibli_transform(input_image, prompt="GHBLI anime style photo", guidance_scale=3.5, num_steps=30):
+    print('canshu_guidance_scale',guidance_scale)
+    print('canshu_num_steps',num_steps)
+    if input_image is None:
+        raise gr.Error("No image uploaded! Please upload an image before clicking Transform.")
+    
+    # Process the input image (keep it as PIL Image)
+    try:
+        init_image = input_image.convert("RGB").resize((1024, 768))
+    except Exception as e:
+        raise gr.Error(f"Failed to process image: {str(e)}")
+    
+    # Generate the Ghibli-style image
+    try:
+        output = pipe(
+            prompt=prompt,
+            image=init_image,
+            # strength=strength,
+            # guidance_scale=guidance_scale,
+            # num_inference_steps=num_steps  # Use the UI-provided value
+            ######
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_steps
+            
+            ######
+        ).images[0]
+    except Exception as e:
+        raise gr.Error(f"Pipeline error: {str(e)}")
+    
+    return output
+
+# Create the Gradio interface
+with gr.Blocks(title="Transformer") as demo:
+    gr.Markdown("# Transformer")
+    gr.Markdown("Upload an image and transform it! [Website:](http://imagetoghibli.online/)")
+    
+    with gr.Row():
+        with gr.Column():
+            input_img = gr.Image(label="Upload Image", type="pil")
+            prompt = gr.Textbox(label="Prompt", value="GHBLI anime style photo")
+           
+            guidance = gr.Slider(1, 20, value=3.5, step=0.5, label="Guidance Scale")
+            num_steps = gr.Slider(10, 100, value=30, step=5, label="Inference Steps (Higher = Better Quality, Slower)")
+            submit_btn = gr.Button("Transform")
+        with gr.Column():
+            output_img = gr.Image(label="Ghibli-Style Output")
+    
+    # Connect the button to the function
+    submit_btn.click(
+        fn=ghibli_transform,
+        inputs=[input_img, prompt, guidance, num_steps],
+        outputs=output_img
+    )
+
+# Launch the Space with share=True for public link
+demo.launch(share=True)