Update app.py

app.py

@@ -1,47 +1,126 @@
 import gradio as gr
+import spaces
 import numpy as np
 import random
 from diffusers import DiffusionPipeline
 import torch
+import threading
+from PIL import Image
 
+MODEL_ID = "cagliostrolab/animagine-xl-3.1"
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 if torch.cuda.is_available():
     torch.cuda.max_memory_allocated(device=device)
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
-    pipe.enable_xformers_memory_efficient_attention()
-    pipe = pipe.to(device)
-else: 
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
-    pipe = pipe.to(device)
+    pipe = DiffusionPipeline.from_pretrained(
+        MODEL_ID, 
+        torch_dtype=torch.float16, 
+        use_safetensors=True, 
+    )
+else:
+    pipe = DiffusionPipeline.from_pretrained(MODEL_ID, use_safetensors=True)
+pipe = pipe.to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
+MAX_IMAGE_SIZE = 1024 + 512
 
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+def latents_to_rgb(latents):
+    weights = (
+        (60, -60, 25, -70),
+        (60, -5, 15, -50),
+        (60, 10, -5, -35)
+    )
 
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
+    weights_tensor = torch.tensor(weights, dtype=latents.dtype, device=latents.device).T
+    biases_tensor = torch.tensor((150, 140, 130), dtype=latents.dtype, device=latents.device)
+    rgb_tensor = torch.einsum("...lxy,lr -> ...rxy", latents, weights_tensor) + biases_tensor.view(-1, 1, 1)
+    image_array = rgb_tensor.clamp(0, 255)[0].byte().cpu().numpy()
+    image_array = image_array.transpose(1, 2, 0)  # Change the order of dimensions
+
+    pil_image = Image.fromarray(image_array)
+
+    resized_image = pil_image.resize((pil_image.size[0] * 2, pil_image.size[1] * 2), Image.LANCZOS)  # Resize 128x128 * ...
+    return resized_image
+
+class BaseGenerator:
+    def __init__(self, pipe):
+        self.pipe = pipe
+        self.image = None
+        self.new_image_event = threading.Event()
+        self.generation_finished = threading.Event()
+        self.intermediate_image_concurrency(5)
+
+    def intermediate_image_concurrency(self, concurrency):
+        self.concurrency = concurrency
     
-    image = 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] 
+    def decode_tensors(self, pipe, step, timestep, callback_kwargs):
+        latents = callback_kwargs["latents"]
+        if step % self.concurrency == 0:  # every how many steps
+            print(step)
+            self.image = latents_to_rgb(latents)
+            self.new_image_event.set()  # Signal that a new image is available
+        return callback_kwargs
+
+    def show_images(self):
+        while not self.generation_finished.is_set() or self.new_image_event.is_set():
+            self.new_image_event.wait()  # Wait for a new image
+            self.new_image_event.clear()  # Clear the event flag
+
+            if self.image:
+                yield self.image  # Yield the new image
+
+    def generate_images(self, **kwargs):
+        if kwargs.get('randomize_seed', False):
+            kwargs['seed'] = random.randint(0, MAX_SEED)
+            
+        generator = torch.Generator().manual_seed(kwargs['seed'])
     
-    return image
+        self.image = None
+        self.image = self.pipe(
+            height=kwargs['height'],
+            width=kwargs['width'],
+            prompt=kwargs['prompt'],
+            negative_prompt=kwargs['negative_prompt'],
+            guidance_scale=kwargs['guidance_scale'],
+            num_inference_steps=kwargs['num_inference_steps'],
+            generator=generator,
+            callback_on_step_end=self.decode_tensors,
+            callback_on_step_end_tensor_inputs=["latents"],
+        ).images[0]
+        print("finish")
+        self.new_image_event.set()  # Result image
+        self.generation_finished.set()  # Signal that generation is finished
+
+    def stream(self, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+        self.generation_finished.clear()
+        threading.Thread(target=self.generate_images, args=(), kwargs=dict(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            seed=seed,
+            randomize_seed=randomize_seed,
+            width=width,
+            height=height,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps
+        )).start()
+        return self.show_images()
+
+image_generator = BaseGenerator(pipe)
+
+@spaces.GPU
+def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, concurrency):
+
+    image_generator.intermediate_image_concurrency(concurrency)
+    
+    stream = image_generator.stream(
+        prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps
+    )
+
+    yield None
+    
+    for image in stream:
+        yield image
 
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
 
 css="""
 #col-container {
@@ -59,33 +138,36 @@ with gr.Blocks(css=css) as demo:
     
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""
-        # Text-to-Image Gradio Template
+        # Text-to-Image: Display each generation step
+        Gradio template for displaying preview images during generation steps
         Currently running on {power_device}.
         """)
+
+        prompt = gr.Text(
+            label="Prompt",
+            show_label=False,
+            max_lines=1,
+            placeholder="Enter your prompt",
+            container=False,
+            value="1girl, souryuu asuka langley, neon genesis evangelion, solo, upper body, v, smile, looking at viewer, outdoors, night",
+        )
+
+        negative_prompt = gr.Text(
+            label="Negative prompt",
+            max_lines=1,
+            placeholder="Enter a negative prompt",
+            visible=True,
+            value="nsfw, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]",
+        )
         
         with gr.Row():
             
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            
             run_button = gr.Button("Run", scale=0)
         
         result = gr.Image(label="Result", show_label=False)
 
         with gr.Accordion("Advanced Settings", open=False):
             
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-            
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
@@ -103,7 +185,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=512,
+                    value=832,
                 )
                 
                 height = gr.Slider(
@@ -111,7 +193,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=512,
+                    value=1216,
                 )
             
             with gr.Row():
@@ -119,28 +201,32 @@ with gr.Blocks(css=css) as demo:
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
                     minimum=0.0,
-                    maximum=10.0,
+                    maximum=30.0,
                     step=0.1,
-                    value=0.0,
+                    value=7.0,
                 )
                 
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
-                    maximum=12,
+                    maximum=100,
                     step=1,
-                    value=2,
+                    value=76,
                 )
-        
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
-        )
+
+            concurrency_gui = gr.Slider(
+                label="Number of steps to show the next preview image",
+                minimum=1,
+                maximum=20,
+                step=1,
+                value=3,
+            )
 
     run_button.click(
         fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result]
+        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, concurrency_gui],
+        outputs = [result],
+        show_progress="minimal",
     )
 
 demo.queue().launch()