more images, or breakage

app.py

@@ -1,13 +1,14 @@
-import spaces
+#import spaces
+import random
+
 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
+from diffusers import (EulerAncestralDiscreteScheduler,
+                       StableDiffusionXLPipeline)
+from PIL import Image
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
@@ -53,7 +54,7 @@ def process_long_prompt(prompt, negative_prompt=""):
         print(f"Long prompt processing failed: {e}, falling back to standard processing")
         return None, None
     
-@spaces.GPU
+#@spaces.GPU
 def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
     # 変更: Remove the 60-word limit warning and add long prompt check
     use_long_prompt = len(prompt.split()) > 60 or len(prompt) > 300
@@ -66,15 +67,13 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
     try:
         # 追加: Try long prompt processing first if prompt is long
         if use_long_prompt:
-            print("Using long prompt processing...")
+            print("Long prompts enabled.")
             conditioning, pooled = process_long_prompt(prompt, negative_prompt)
             
             if conditioning is not None:
                 output_image = pipe(
-                    prompt_embeds=conditioning[0:1],
-                    pooled_prompt_embeds=pooled[0:1],
-                    negative_prompt_embeds=conditioning[1:2],
-                    negative_pooled_prompt_embeds=pooled[1:2],
+                    prompt=prompt,
+                    negative_prompt=negative_prompt,
                     guidance_scale=guidance_scale,
                     num_inference_steps=num_inference_steps,
                     width=width,
@@ -82,18 +81,151 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
                     generator=generator
                 ).images[0]
                 seed = seed - 1
+
                 output_image2 = pipe(
-                    prompt_embeds=conditioning[0:1],
-                    pooled_prompt_embeds=pooled[0:1],
-                    negative_prompt_embeds=conditioning[1:2],
-                    negative_pooled_prompt_embeds=pooled[1:2],
+                    prompt=prompt,
+                    negative_prompt=negative_prompt,
                     guidance_scale=guidance_scale,
                     num_inference_steps=num_inference_steps,
                     width=width,
                     height=height,
                     generator=generator
                 ).images[0]
-                return output_image, output_image2
+                seed = seed - 1
+
+                output_image3 = pipe(
+                    prompt=prompt,
+                    negative_prompt=negative_prompt,
+                    guidance_scale=guidance_scale,
+                    num_inference_steps=num_inference_steps,
+                    width=width,
+                    height=height,
+                    generator=generator
+                ).images[0]
+                seed = seed - 1
+
+                output_image4 = pipe(
+                    prompt=prompt,
+                    negative_prompt=negative_prompt,
+                    guidance_scale=guidance_scale,
+                    num_inference_steps=num_inference_steps,
+                    width=width,
+                    height=height,
+                    generator=generator
+                ).images[0]
+                seed = seed - 1
+
+                output_image5 = pipe(
+                    prompt=prompt,
+                    negative_prompt=negative_prompt,
+                    guidance_scale=guidance_scale,
+                    num_inference_steps=num_inference_steps,
+                    width=width,
+                    height=height,
+                    generator=generator
+                ).images[0]
+                seed = seed - 1
+
+                output_image6 = pipe(
+                    prompt=prompt,
+                    negative_prompt=negative_prompt,
+                    guidance_scale=guidance_scale,
+                    num_inference_steps=num_inference_steps,
+                    width=width,
+                    height=height,
+                    generator=generator
+                ).images[0]
+                seed = seed - 1
+
+                output_image7 = pipe(
+                    prompt=prompt,
+                    negative_prompt=negative_prompt,
+                    guidance_scale=guidance_scale,
+                    num_inference_steps=num_inference_steps,
+                    width=width,
+                    height=height,
+                    generator=generator
+                ).images[0]
+                seed = seed - 1
+
+                output_image8 = pipe(
+                    prompt=prompt,
+                    negative_prompt=negative_prompt,
+                    guidance_scale=guidance_scale,
+                    num_inference_steps=num_inference_steps,
+                    width=width,
+                    height=height,
+                    generator=generator
+                ).images[0]
+                seed = seed - 1
+
+                output_image9 = pipe(
+                    prompt=prompt,
+                    negative_prompt=negative_prompt,
+                    guidance_scale=guidance_scale,
+                    num_inference_steps=num_inference_steps,
+                    width=width,
+                    height=height,
+                    generator=generator
+                ).images[0]
+                seed = seed - 1
+
+                output_image0 = pipe(
+                    prompt=prompt,
+                    negative_prompt=negative_prompt,
+                    guidance_scale=guidance_scale,
+                    num_inference_steps=num_inference_steps,
+                    width=width,
+                    height=height,
+                    generator=generator
+                ).images[0]
+
+                outputs = [
+                    output_image,
+                    output_image2,
+                    output_image3,
+                    output_image4,
+                    output_image5,
+                    output_image6,
+                    output_image7,
+                    output_image8,
+                    output_image9,
+                    output_image0,
+                ]
+
+                return outputs
+                
+                
+                
+                
+                
+                
+                
+                
+                #output_image = pipe(
+                #    prompt_embeds=conditioning[0:1],
+                #    pooled_prompt_embeds=pooled[0:1],
+                #    negative_prompt_embeds=conditioning[1:2],
+                #    negative_pooled_prompt_embeds=pooled[1:2],
+                #    guidance_scale=guidance_scale,
+                #    num_inference_steps=num_inference_steps,
+                #    width=width,
+                #    height=height,
+                #    generator=generator
+                #).images[0]
+                #seed = seed - 1
+                #output_image2 = pipe(
+                #    prompt_embeds=conditioning[0:1],
+                #    pooled_prompt_embeds=pooled[0:1],
+                #    negative_prompt_embeds=conditioning[1:2],
+                #    negative_pooled_prompt_embeds=pooled[1:2],
+                #    guidance_scale=guidance_scale,
+                #    num_inference_steps=num_inference_steps,
+                #    width=width,
+                #    height=height,
+                #    generator=generator
+                #).images[0]
+                #return output_image, output_image2
 
         # Fall back to standard processing
         output_image = pipe(
@@ -115,7 +247,96 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
             height=height,
             generator=generator
         ).images[0]
-        return output_image, output_image2
+        output_image3 = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator
+        ).images[0]
+        seed = seed - 1
+        output_image4 = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator
+        ).images[0]
+        output_image5 = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator
+        ).images[0]
+        seed = seed - 1
+        output_image6 = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator
+        ).images[0]
+        output_image7 = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator
+        ).images[0]
+        seed = seed - 1
+        output_image8 = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator
+        ).images[0]
+        output_image9 = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator
+        ).images[0]
+        seed = seed - 1
+        output_image0 = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator
+        ).images[0]
+        
+        outputs = [
+            output_image,
+            output_image2,
+            output_image3,
+            output_image4,
+            output_image5,
+            output_image6,
+            output_image7,
+            output_image8,
+            output_image9,
+            output_image0,
+        ]
+        return outputs
     
     except RuntimeError as e:
         print(f"Error during generation: {e}")
@@ -127,7 +348,7 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
 css = """
 #col-container {
     margin: 0 auto;
-    max-width: 1024px;
+    max-width: 2000px;
 }
 """
 
@@ -139,32 +360,45 @@ with gr.Blocks(css=css) as demo:
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
-                max_lines=1,
+                max_lines=3,
                 placeholder="Enter your prompt (long prompts are automatically supported)",
+                value="Show me a glorious mountain range covered in colorful crystaline trees with a laser disco show from space.",
                 container=False,
             )
 
             run_button = gr.Button("Run", scale=0)
 
-        result = gr.Image(format="png", label="Result", show_label=False)
-        result2 = gr.Image(format="png", label="Result2", show_label=False)
+            with gr.Row():
+                result = gr.Image(format="png", label="Result", show_label=False)
+                result2 = gr.Image(format="png", label="Result2", show_label=False)
+            with gr.Row():
+                result3 = gr.Image(format="png", label="Result3", show_label=False)
+                result4 = gr.Image(format="png", label="Result4", show_label=False)
+            with gr.Row():
+                result5 = gr.Image(format="png", label="Result5", show_label=False)
+                result6 = gr.Image(format="png", label="Result6", show_label=False)
+            with gr.Row():
+                result7 = gr.Image(format="png", label="Result7", show_label=False)
+                result8 = gr.Image(format="png", label="Result8", show_label=False)
+            with gr.Row():
+                result9 = gr.Image(format="png", label="Result9", show_label=False)
+                result0 = gr.Image(format="png", label="Result0", show_label=False)
         
         
         with gr.Accordion("Advanced Settings", open=False):
 
             negative_prompt = gr.Text(
                 label="Negative prompt",
-                max_lines=1,
+                max_lines=3,
                 placeholder="Enter a negative prompt",
-#                value="bad quality,worst quality,worst detail,sketch,censor,"
-                value=" ((dull, unimaginative, lifeless, tedious, lack of emotions, dry, flat, static, stiff, uninspired), bad quality, bad art, ugly, overexposed, too bright, washed out, high exposure, vague details, ambiguous shapes, undefined, poorly render, rough, low resolution, artifact, compression artifacts, low poly, blocky, banding, color bleed, texture seams, oversaturation, fused fingers, mutated, malformed eyes, missing iris sclera, poorly drawn background, poor body language)"
+                value=" ((unimaginative, dry, flat, static, stiff, uninspired), bad quality overexposed, too bright, washed out, high exposure, low resolution, artifact, compression artifacts, low poly, blocky, banding, color bleed, texture seams, oversaturation, fused fingers, malformed eyes, missing iris sclera, poorly drawn background)"
             )
 
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
                 maximum=MAX_SEED,
-                step=1,
+                step=20,
                 value=0,
             )
 
@@ -176,7 +410,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=512,
+                    value=2000,
                 )
 
                 height = gr.Slider(
@@ -184,22 +418,22 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=512,
+                    value=2000,
                 )
 
             with gr.Row():
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
                     minimum=0.0,
-                    maximum=20.0,
+                    maximum=30.0,
                     step=0.1,
-                    value=9,
+                    value=7,
                 )
 
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
-                    maximum=40,
+                    maximum=99,
                     step=1,
                     value=20,
                 )
@@ -207,7 +441,7 @@ with gr.Blocks(css=css) as demo:
     run_button.click(
         fn=infer,
         inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs=[result, result2],
+        outputs=[result, result2, result3, result4, result5, result6, result7, result8, result9, result0],
 
     )
 

requirements.txt

@@ -6,4 +6,4 @@ transformers
 xformers
 compel
 pydantic==2.10.6
-gradio==5.12.0
+gradio==5.12.0