Update app.py

app.py

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+# pip install diffusers, transformers, accelerate, safetensors, huggingface_hub
+
+
+import os
+os.system("pip install -U peft")
+import random
+
+import gradio as gr
+import numpy as np
+import PIL.Image
+# import spaces
+import torch
+from diffusers import AutoPipelineForText2Image, DPMSolverMultistepScheduler
+from huggingface_hub import hf_hub_download
+
+DESCRIPTION = """
+# Res-Adapter
+**Demo by [ameer azam] - [Twitter](https://twitter.com/Ameerazam18) - [GitHub](https://github.com/AMEERAZAM08)) - [Hugging Face](https://huggingface.co/ameerazam08)**
+This is a demo of  https://huggingface.co/jiaxiangc/res-adapter LORAs by ByteDance
+"""
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<h1>Running on CPU 🥶 This demo does not work on CPU.</a> instead</h1>"
+
+MAX_SEED = np.iinfo(np.int32).max
+CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "0") == "1"
+MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1024"))
+USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
+ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+if torch.cuda.is_available():
+    pipe = AutoPipelineForText2Image.from_pretrained('Lykon/dreamshaper-xl-1-0', torch_dtype=torch.float16, variant="fp16")
+    pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++")
+    pipe = pipe.to("cuda")
+
+
+pipe.load_lora_weights(
+    hf_hub_download(
+        repo_id="jiaxiangc/res-adapter", 
+        subfolder="sdxl-i", 
+        filename="resolution_lora.safetensors",
+    ),
+    adapter_name="res_adapter",
+)
+pipe.set_adapters(["res_adapter"], adapter_weights=[1.0])
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+
+# @spaces.GPU(enable_queue=True)
+def generate(
+    prompt: str,
+    negative_prompt: str = "",
+    prompt_2: str = "",
+    negative_prompt_2: str = "",
+    use_negative_prompt: bool = False,
+    use_prompt_2: bool = False,
+    use_negative_prompt_2: bool = False,
+    seed: int = 0,
+    width: int = 1024,
+    height: int = 1024,
+    guidance_scale_base: float = 5.0,
+    num_inference_steps_base: int = 20,
+    progress=gr.Progress(track_tqdm=True),
+) -> PIL.Image.Image:
+    print(f"** Generating image for: \"{prompt}\" **")
+    generator = torch.Generator().manual_seed(seed)
+
+    if not use_negative_prompt:
+        negative_prompt = None  # type: ignore
+    if not use_prompt_2:
+        prompt_2 = None  # type: ignore
+    if not use_negative_prompt_2:
+        negative_prompt_2 = None  # type: ignore
+
+    base_image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        prompt_2=prompt_2,
+        negative_prompt_2=negative_prompt_2,
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale_base,
+        num_inference_steps=num_inference_steps_base,
+        generator=generator,
+        output_type="pil").images[0]
+    
+    
+    res_adapt=pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        prompt_2=prompt_2,
+        negative_prompt_2=negative_prompt_2,
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale_base,
+        num_inference_steps=num_inference_steps_base,
+        generator=generator,
+        output_type="pil",
+    ).images[0]
+    return [base_image,res_adapt]
+
+
+examples = [
+    "A realistic photograph of an astronaut in a jungle, cold color palette, detailed, 8k",
+    "An astronaut riding a green horse",
+]
+
+theme = gr.themes.Base(
+    font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'],
+)
+with gr.Blocks(css="footer{display:none !important}", theme=theme) as demo:
+    gr.Markdown(DESCRIPTION)
+    gr.DuplicateButton(
+        value="Duplicate Space for private use",
+        elem_id="duplicate-button",
+        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
+    )
+    with gr.Group():
+        prompt = gr.Text(
+            label="Prompt",
+            show_label=False,
+            max_lines=1,
+            container=False,
+            placeholder="Enter your prompt",
+        )
+        run_button = gr.Button("Generate")
+    # result = gr.Gallery(label="Left is Base and Right is Lora"),
+    with gr.Accordion("Advanced options", open=False):
+        with gr.Row():
+            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False)
+            use_prompt_2 = gr.Checkbox(label="Use prompt 2", value=False)
+            use_negative_prompt_2 = gr.Checkbox(label="Use negative prompt 2", value=False)
+        negative_prompt = gr.Text(
+            label="Negative prompt",
+            max_lines=1,
+            placeholder="Enter a negative prompt",
+            visible=False,
+        )
+        prompt_2 = gr.Text(
+            label="Prompt 2",
+            max_lines=1,
+            placeholder="Enter your prompt",
+            visible=False,
+        )
+        negative_prompt_2 = gr.Text(
+            label="Negative prompt 2",
+            max_lines=1,
+            placeholder="Enter a negative prompt",
+            visible=False,
+        )
+
+        seed = gr.Slider(
+            label="Seed",
+            minimum=0,
+            maximum=MAX_SEED,
+            step=1,
+            value=0,
+        )
+        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=1024,
+            )
+            height = gr.Slider(
+                label="Height",
+                minimum=256,
+                maximum=MAX_IMAGE_SIZE,
+                step=32,
+                value=1024,
+            )
+        with gr.Row():
+            guidance_scale_base = gr.Slider(
+                label="Guidance scale for base",
+                minimum=1,
+                maximum=20,
+                step=0.1,
+                value=7.5,
+            )
+            num_inference_steps_base = gr.Slider(
+                label="Number of inference steps for base",
+                minimum=10,
+                maximum=100,
+                step=1,
+                value=20,
+            )
+    gr.Examples(
+        examples=examples,
+        inputs=prompt,
+        outputs=None,
+        fn=generate,
+        cache_examples=CACHE_EXAMPLES,
+    )
+
+    use_negative_prompt.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_negative_prompt,
+        outputs=negative_prompt,
+        queue=False,
+        api_name=False,
+    )
+    use_prompt_2.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_prompt_2,
+        outputs=prompt_2,
+        queue=False,
+        api_name=False,
+    )
+    use_negative_prompt_2.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_negative_prompt_2,
+        outputs=negative_prompt_2,
+        queue=False,
+        api_name=False,
+    )
+    gr.on(
+        triggers=[
+            prompt.submit,
+            negative_prompt.submit,
+            prompt_2.submit,
+            negative_prompt_2.submit,
+            run_button.click,
+        ],
+        fn=randomize_seed_fn,
+        inputs=[seed, randomize_seed],
+        outputs=seed,
+        queue=False,
+        api_name=False,
+    ).then(
+        fn=generate,
+        inputs=[
+            prompt,
+            negative_prompt,
+            prompt_2,
+            negative_prompt_2,
+            use_negative_prompt,
+            use_prompt_2,
+            use_negative_prompt_2,
+            seed,
+            width,
+            height,
+            guidance_scale_base,
+            num_inference_steps_base,
+        ],
+        outputs=gr.Gallery(label="Left is Base and Right is Lora"),
+        api_name="run",
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=20, api_open=False).launch(show_api=False)