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app.py

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+import spaces
+import gradio as gr
+import torch
+from diffusers import LCMScheduler, AutoPipelineForText2Image
+from diffusers import AutoPipelineForInpainting, LCMScheduler
+from diffusers import DiffusionPipeline, LCMScheduler
+from PIL import Image, ImageEnhance
+import io
+
+@spaces.GPU
+def generate_image(prompt, num_inference_steps, guidance_scale):
+    model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+    adapter_id = "latent-consistency/lcm-lora-sdxl"
+
+    pipe = AutoPipelineForText2Image.from_pretrained(model_id, torch_dtype=torch.float32, variant="fp16")
+    pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+    pipe.to("cuda")
+
+    # Load and fuse lcm lora
+    pipe.load_lora_weights(adapter_id)
+    pipe.fuse_lora()
+
+    # Generate the image
+    image = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale).images[0]
+  
+    return image
+
+def inpaint_image(prompt, init_image, mask_image, num_inference_steps, guidance_scale):
+    pipe = AutoPipelineForInpainting.from_pretrained(
+        "diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
+        torch_dtype=torch.float32,
+        variant="fp16",
+    ).to("cuda")
+    pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+    pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")
+    pipe.fuse_lora()
+
+    if init_image is not None:
+        init_image_path = init_image.name  # Get the file path
+        init_image = Image.open(init_image_path).resize((1024, 1024))
+    else:
+        raise ValueError("Initial image not provided or invalid")
+
+    if mask_image is not None:
+        mask_image_path = mask_image.name  # Get the file path
+        mask_image = Image.open(mask_image_path).resize((1024, 1024))
+    else:
+        raise ValueError("Mask image not provided or invalid")
+
+    # Generate the inpainted image
+    generator = torch.manual_seed(42)
+    image = pipe(
+        prompt=prompt,
+        image=init_image,
+        mask_image=mask_image,
+        generator=generator,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+    ).images[0]
+
+    return image
+
+def generate_image_with_adapter(prompt, num_inference_steps, guidance_scale):
+    pipe = DiffusionPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    variant="fp16",
+    torch_dtype=torch.float32
+    ).to("cuda")
+
+    # set scheduler
+    pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+
+    # Load and fuse lcm lora
+    pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl", adapter_name="lcm")
+    pipe.load_lora_weights("Pclanglais/Mickey-1928", weight_name="pytorch_lora_weights.safetensors", adapter_name="mickey")
+
+    # Combine LoRAs
+    pipe.set_adapters(["lcm", "mickey"], adapter_weights=[1.0, 0.8])
+    pipe.fuse_lora()
+    generator = torch.manual_seed(0)
+    # Generate the image
+    image = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, generator=generator).images[0]
+    pipe.unfuse_lora()
+    return image
+
+
+def modify_image(image, brightness, contrast):
+    # Function to modify brightness and contrast
+    image = Image.open(io.BytesIO(image))
+    enhancer = ImageEnhance.Brightness(image)
+    image = enhancer.enhance(brightness)
+    enhancer = ImageEnhance.Contrast(image)
+    image = enhancer.enhance(contrast)
+    return image
+
+with gr.Blocks(gr.themes.Soft()) as demo:
+    with gr.Row():
+        image_output = gr.Image(label="Generated Image")
+
+    with gr.Row():
+        with gr.Accordion(label="Configuration Options"):
+            prompt_input = gr.Textbox(label="Prompt", placeholder="Self-portrait oil painting, a beautiful cyborg with golden hair, 8k")
+            steps_input = gr.Slider(minimum=1, maximum=10, label="Inference Steps", value=4)
+            guidance_input = gr.Slider(minimum=0, maximum=2, label="Guidance Scale", value=1)
+            generate_button = gr.Button("Generate Image")
+    with gr.Row():
+        with gr.Accordion(label="Wiki-Mouse Image Generation"):
+            adapter_prompt_input = gr.Textbox(label="Prompt", placeholder="papercut, a cute fox")
+            adapter_steps_input = gr.Slider(minimum=1, maximum=10, label="Inference Steps", value=4)
+            adapter_guidance_input = gr.Slider(minimum=0, maximum=2, label="Guidance Scale", value=1)
+            adapter_generate_button = gr.Button("Generate Image with Adapter")
+
+    
+
+    generate_button.click(
+        generate_image,
+        inputs=[prompt_input, steps_input, guidance_input],
+        outputs=image_output
+    )
+
+    modify_button.click(
+        modify_image,
+        inputs=[image_output, brightness_slider, contrast_slider],
+        outputs=image_output
+    )
+    inpaint_button.click(
+        inpaint_image,
+        inputs=[inpaint_prompt_input, init_image_input, mask_image_input, inpaint_steps_input, inpaint_guidance_input],
+        outputs=image_output
+    )
+    adapter_generate_button.click(
+        generate_image_with_adapter,
+        inputs=[adapter_prompt_input, adapter_steps_input, adapter_guidance_input],
+        outputs=image_output
+    )
+
+demo.launch()

requirements.txt

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+git+https://github.com/huggingface/diffusers.git
+git+https://github.com/huggingface/transformers.git
+git+https://github.com/huggingface/peft.git
+--extra-index-url https://download.pytorch.org/whl/cu113
+torch
+pydantic
+Pillow
+accelerate
+spaces
+invisible_watermark
+