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app.txt

@@ -0,0 +1,216 @@
+import spaces
+import gradio as gr
+import numpy as np
+import PIL.Image
+from PIL import Image
+import random
+from diffusers import StableDiffusionXLPipeline
+from diffusers import EulerAncestralDiscreteScheduler
+import torch
+from compel import Compel, ReturnedEmbeddingsType
+import time
+import io
+import base64
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    "dhead/wai-nsfw-illustrious-sdxl-v140-sdxl",
+    torch_dtype=torch.float16,
+    variant="fp16",
+    use_safetensors=True
+)
+pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+pipe.to(device)
+
+pipe.text_encoder.to(torch.float16)
+pipe.text_encoder_2.to(torch.float16)
+pipe.vae.to(torch.float16)
+pipe.unet.to(torch.float16)
+
+compel = Compel(
+    tokenizer=[pipe.tokenizer, pipe.tokenizer_2],
+    text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
+    returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
+    requires_pooled=[False, True],
+    truncate_long_prompts=False
+)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1216
+
+# --- 変更点: PIL画像をJPEG形式のBase64データURIに変換する関数 ---
+def pil_to_base64(pil_image):
+    with io.BytesIO() as stream:
+        # PILはRGBモードでないとJPEGで保存できないため、変換を試みる
+        if pil_image.mode != "RGB":
+            pil_image = pil_image.convert("RGB")
+        pil_image.save(stream, "JPEG")
+        base64_str = base64.b64encode(stream.getvalue()).decode("utf-8")
+        return "data:image/jpeg;base64," + base64_str
+
+@spaces.GPU(duration=15)
+def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+    if not prompt.strip():
+        raise gr.Error("Prompt cannot be empty.")
+        
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator(device=device).manual_seed(seed)
+    
+    try:
+        conditioning, pooled = compel([prompt, negative_prompt])
+        prompt_embeds = conditioning[0:1]
+        pooled_prompt_embeds = pooled[0:1]
+        negative_prompt_embeds = conditioning[1:2]
+        negative_pooled_prompt_embeds = pooled[1:2]
+
+        image = pipe(
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator
+        ).images[0]
+        image_b64 = pil_to_base64(image)
+        return image, seed, image_b64
+        
+    except RuntimeError as e:
+        print(f"Error during generation: {e}")
+        blank_image = Image.new('RGB', (width, height), color=(0, 0, 0))
+        blank_b64 = pil_to_base64(blank_image)
+        return blank_image, seed, blank_b64
+
+
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 1024px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    image_b64_state = gr.State()
+
+    with gr.Column(elem_id="col-container"):
+        with gr.Row():
+            # --- 変更点: formatを"jpeg"に変更 ---
+            result = gr.Image(format="jpeg", label="Result", show_label=False, interactive=False)
+            copy_button = gr.Button("Copy", scale=0, variant="secondary")
+            
+        gr.Markdown("<br>" * 5)
+
+        with gr.Row():
+            prompt = gr.Text(
+                label="Prompt", show_label=False, max_lines=1,
+                placeholder="Enter your prompt",
+                value="", container=False,
+            )
+            run_button = gr.Button("Generate", scale=0, interactive=False)
+            consecutive_button = gr.Button("Consecutive", scale=0, interactive=False)
+            stop_button = gr.Button("Stop", scale=0, visible=True, interactive=True)
+            clear_button = gr.Button("Trash", scale=0, variant="secondary")
+            
+        with gr.Accordion("Advanced Settings", open=False):
+            negative_prompt = gr.Text(
+                label="Negative prompt", max_lines=1, placeholder="Enter a negative prompt",
+                value="bad anatomy, bad quality, low quality, worst quality, worst detail"
+            )
+            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 = gr.Slider(label="Guidance scale", minimum=0.0, maximum=20.0, step=0.1, value=7)
+                num_inference_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=28, step=1, value=25)
+            
+            interval_seconds = gr.Slider(label="Interval (seconds)", minimum=1, maximum=60, step=1, value=1)
+        gr.Markdown("<br>" * 20)
+        gr.Examples(
+            examples=[
+                ["masterpiece, solo, A little girl with blonde short side tails, red eyes, "],
+            ],
+            inputs=[prompt],
+            label="Examples (Click to copy to prompt)"
+        )
+    
+    def generation_loop(prompt, negative_prompt, current_seed, randomize_seed, width, height, guidance_scale, num_inference_steps, interval_sec):
+        if not prompt.strip():
+            raise gr.Error("Prompt cannot be empty to start consecutive generation.")
+        while True:
+            try:
+                image, new_seed, image_b64 = infer(prompt, negative_prompt, current_seed, True, width, height, guidance_scale, num_inference_steps)
+                yield {result: image, seed: new_seed, image_b64_state: image_b64}
+                time.sleep(interval_sec)
+            except gr.exceptions.CancelledError:
+                print("Generation loop cancelled by user.")
+                break
+
+    prompt.input(
+        fn=None,
+        inputs=[prompt],
+        outputs=[run_button, consecutive_button],
+        js="(p) => { const interactive = p.trim().length > 0; return [{ interactive: interactive, '__type__': 'update' }, { interactive: interactive, '__type__': 'update' }]; }"
+    )
+
+    clear_button.click(
+        fn=None,
+        inputs=None,
+        outputs=[prompt, run_button, consecutive_button],
+        js="""
+        function() {
+            return ["", { "interactive": false, "__type__": "update" }, { "interactive": false, "__type__": "update" }];
+        }
+        """
+    )
+
+    run_button.click(
+        fn=infer,
+        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs=[result, seed, image_b64_state]
+    )
+
+    gen_inputs = [
+        prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
+        interval_seconds
+    ]
+    
+    consecutive_event = consecutive_button.click(
+        fn=generation_loop,
+        inputs=gen_inputs,
+        outputs=[result, seed, image_b64_state]
+    )
+
+    stop_button.click(
+        fn=None,
+        inputs=None,
+        outputs=None,
+        cancels=[consecutive_event]
+    )
+
+    copy_button.click(
+        fn=None,
+        inputs=[image_b64_state],
+        outputs=None,
+        js="""
+        async (b64) => {
+          if (!b64) { return; }
+          try {
+            const blob = await fetch(b64).then(res => res.blob());
+            await navigator.clipboard.write([
+              new ClipboardItem({ [blob.type]: blob })
+            ]);
+            console.log('Image copied to clipboard');
+          } catch (err) {
+            console.error('Failed to copy image:', err);
+          }
+        }
+        """
+    )
+
+demo.queue().launch()

requirements.txt

@@ -6,4 +6,4 @@ transformers==4.56.2
 xformers==0.0.32.post2
 compel==2.1.1
 pydantic==2.10.6
-gradio==5.12.0
+gradio