Quick demo / hack

.gitattributes

@@ -32,4 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.xz filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,2 @@
+.venv
+__pycache__

README.md

@@ -1,10 +1,10 @@
 ---
-title: StableDiffusion3
+title: ⚡ Stable Diffusion 3 ⚡
 emoji: 🖼
 colorFrom: purple
 colorTo: red
 sdk: gradio
-sdk_version: 4.26.0
+sdk_version: 4.36.1
 app_file: app.py
 pinned: false
 ---

app.py

@@ -1,146 +1,247 @@
+import os
+import random
+import uuid
+
 import gradio as gr
 import numpy as np
-import random
-from diffusers import DiffusionPipeline
+from PIL import Image
+import spaces
 import torch
+from diffusers import StableDiffusion3Pipeline, DPMSolverMultistepScheduler, AutoencoderKL, StableDiffusion3Img2ImgPipeline
+from transformers import T5EncoderModel, BitsAndBytesConfig
+from huggingface_hub import snapshot_download
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
+huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
 
-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)
+DESCRIPTION = """# Stable Diffusion 3"""
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>"
 
 MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
+CACHE_EXAMPLES = False
+MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1536"))
+USE_TORCH_COMPILE = False
+ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+def load_pipeline():
+    model_id = "stabilityai/stable-diffusion-3-medium-diffusers"
+    
+    pipe = StableDiffusion3Pipeline.from_pretrained(
+        model_id,
+        #device_map="balanced",
+        torch_dtype=torch.float16
+    )
+    return pipe
+
+
+aspect_ratios = {
+    "21:9": (21, 9),
+    "2:1": (2, 1),
+    "16:9": (16, 9),
+    "5:4": (5, 4),
+    "4:3": (4, 3),
+    "3:2": (3, 2),
+    "1:1": (1, 1),
+}
+# Function to calculate resolution
+def calculate_resolution(aspect_ratio, mode='landscape', total_pixels=1024*1024, divisibility=64):
+    if aspect_ratio not in aspect_ratios:
+        raise ValueError(f"Invalid aspect ratio: {aspect_ratio}")
+
+    width_multiplier, height_multiplier = aspect_ratios[aspect_ratio]
+    ratio = width_multiplier / height_multiplier
+    if mode == 'portrait':
+        # Swap the ratio for portrait mode
+        ratio = 1 / ratio
+
+    height = int((total_pixels / ratio) ** 0.5)
+    height -= height % divisibility
+
+    width = int(height * ratio)
+    width -= width % divisibility
 
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+    while width * height > total_pixels:
+        height -= divisibility
+        width = int(height * ratio)
+        width -= width % divisibility
 
+    return width, height
+
+
+def save_image(img):
+    unique_name = str(uuid.uuid4()) + ".png"
+    img.save(unique_name)
+    return unique_name
+
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-        
+    return seed
+
+
+@spaces.GPU
+def generate(
+    prompt:str,
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    seed: int = 0,
+    aspect: str = "1:1",
+    mode: str = "landscape",
+    guidance_scale: float = 7.5,
+    randomize_seed: bool = False,
+    num_inference_steps=30,
+    NUM_IMAGES_PER_PROMPT=1,
+    use_resolution_binning: bool = True,
+    progress=gr.Progress(track_tqdm=True),
+):
+    pipe = load_pipeline()
+    pipe.to(device)
+    seed = int(randomize_seed_fn(seed, randomize_seed))
     generator = torch.Generator().manual_seed(seed)
     
-    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] 
+    if not use_negative_prompt:
+        negative_prompt = None # type: ignore
     
-    return image
+    width, height = calculate_resolution(aspect, mode) 
+    
+    output = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+        num_images_per_prompt=NUM_IMAGES_PER_PROMPT,
+        output_type="pil",
+    ).images
 
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
+    return output
 
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
-}
-"""
 
-if torch.cuda.is_available():
-    power_device = "GPU"
-else:
-    power_device = "CPU"
 
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        Currently running on {power_device}.
-        """)
-        
-        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)
+examples = [
+    "Beautiful pixel art of a wizard with hovering text \"Achievement unlocked: Diffusion models can spell now\"",
+    "Frog sitting in a 1950s diner wearing a leather jacket and a top hat. on the table a giant burger and a small sign that says \"froggy fridays\"",
+    "This dreamlike digital art capture a vibrant kaleidoscopic bird in a rainforest",
+    "pair of shoes made of dried fruit skins, 3d render, bright colours, clean composition, beautiful artwork, logo saying \"SD3 rocks!\"",
+    "post-apocalyptic city wasteland, the most delicate beautiful flower with green leaves growing from dust and rubble, vibrant colours, cinematic",
+    "a dark-armored warrior with ornate golden details, cloaked in a flowing black cape, wielding a radiant, fiery sword, standing amidst an ominous cloudy backdrop with dramatic lighting, exuding a menacing, powerful presence.",
+    "A wise old wizard with a long white beard, flowing robes, and a gnarled staff, casting a spell, photorealistic style",
+    "Design a film poster for a noir thriller set in 1940s Los Angeles, featuring a shadowy figure under a streetlamp and a foggy, mysterious ambiance.",
+]
 
-        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,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
+css = '''
+.gradio-container{max-width: 1000px !important}
+h1{text-align:center}
+'''
+with gr.Blocks(css=css) as demo:
+    with gr.Row():
+        with gr.Column():
+            gr.HTML(
+            """
+            <h1 style='text-align: center'>
+            Stable Diffusion 3 
+            </h1>
+            """
             )
-            
-            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=512,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
+    
+            with gr.Group():
+                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)
+                with gr.Row():
+                    aspect = gr.Dropdown(label='Aspect Ratio', choices=list(aspect_ratios.keys()), value='1:1', interactive=True)
+                    mode = gr.Dropdown(label='Mode', choices=['landscape', 'portrait'], value='landscape') 
+                        
+                result = gr.Gallery(label="Result", elem_id="gallery", show_label=False)
+            with gr.Accordion("Advanced options", open=False):
+                with gr.Row():
+                    use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
+                    negative_prompt = gr.Text(
+                        label="Negative prompt",
+                        max_lines=1,
+                        value = "deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, NSFW",
+                        visible=True,
+                    )
+                seed = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=MAX_SEED,
+                    step=1,
+                    value=0,
                 )
-            
-            with gr.Row():
-                
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,
+
+                steps = gr.Slider(
+                    label="Steps",
+                    minimum=0,
+                    maximum=60,
+                    step=1,
+                    value=30,
                 )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
+                number_image = gr.Slider(
+                    label="Number of Images",
                     minimum=1,
-                    maximum=12,
+                    maximum=2,
                     step=1,
-                    value=2,
+                    value=1,
                 )
-        
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
-        )
-
-    run_button.click(
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result]
-    )
+                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+                with gr.Row():
+                    guidance_scale = gr.Slider(
+                        label="Guidance Scale",
+                        minimum=0.1,
+                        maximum=10,
+                        step=0.1,
+                        value=7.0,
+                    )
+            
+            gr.Examples(
+                examples=examples,
+                inputs=prompt,
+                outputs=[result],
+                fn=generate,
+                cache_examples=CACHE_EXAMPLES,
+            )
+
+            use_negative_prompt.change(
+                fn=lambda x: gr.update(visible=x),
+                inputs=use_negative_prompt,
+                outputs=negative_prompt,
+                api_name=False,
+            )
+
+            gr.on(
+                triggers=[
+                    prompt.submit,
+                    negative_prompt.submit,
+                    run_button.click,
+                ],
+                fn=generate,
+                inputs=[
+                    prompt,
+                    negative_prompt,
+                    use_negative_prompt,
+                    seed,
+                    aspect,
+                    mode,
+                    guidance_scale,
+                    randomize_seed,
+                    steps,
+                    number_image,
+                ],
+                outputs=[result],
+                api_name="run",
+            )
 
-demo.queue().launch()
+if __name__ == "__main__":
+    demo.queue().launch()

requirements.txt

@@ -1,6 +1,6 @@
-accelerate
-diffusers
-invisible_watermark
-torch
-transformers
-xformers
+git+https://github.com/huggingface/diffusers
+git+https://github.com/huggingface/transformers
+sentencepiece
+peft
+protobuf
+bitsandbytes