Update app.py

app.py

@@ -1,16 +1,47 @@
 import os
 import random
 import uuid
-
+import json
 import gradio as gr
 import numpy as np
 from PIL import Image
 import spaces
 import torch
-from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler, StableDiffusion3Img2ImgPipeline
-from huggingface_hub import snapshot_download
+from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
+
+#Load the HTML content
+#html_file_url = "https://prithivmlmods-hamster-static.static.hf.space/index.html"
+#html_content = f'<iframe src="{html_file_url}" style="width:100%; height:180px; border:none;"></iframe>'
+#html_file_url = "https://prithivmlmods-static-loading-theme.static.hf.space/index.html"
+
+#html_file_url = "https://prithivhamster.vercel.app/"
+#html_content = f'<iframe src="{html_file_url}" style="width:100%; height:400px; border:none"></iframe>'
+
+DESCRIPTIONx = """## STABLE HAMSTER
+"""
+
+css = '''
+.gradio-container{max-width: 560px !important}
+h1{text-align:center}
+footer {
+    visibility: hidden
+}
+'''
+
+examples = [
+    "3d image, cute girl, in the style of Pixar --ar 1:2 --stylize 750, 4K resolution highlights, Sharp focus, octane render, ray tracing, Ultra-High-Definition, 8k, UHD, HDR, (Masterpiece:1.5), (best quality:1.5)",
+    "Cold coffee in a cup bokeh --ar 85:128 --v 6.0 --style raw5, 4K",
+    "A young woman smiles in front of colorful walls, in the style of canon eos 5d mark iv, sigma 35mm f/1.4 dg hsm art, ilford xp2, rainbowcore, leather/hide, street-inspired, orderly symmetry  --ar 3:2 --v 5",
+    "Curvy model posing in swimsuit, smiley face, sea at sunset  --ar 3:2 --s 750 --v 5",
+    "A classic cheeseburger with tomato and fries and soda on a yellow checkered table cloth, in the style of playing with light and shadow, tabletop photography, bold colors and patterns, pigeoncore, vibrant street scenes, organic geometries, magenta and brown  --ar 3:2 --v 5"
+
+    
+]
 
-huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
+#Set an os.Getenv variable
+#set VAR_NAME=”VALUE”
+#Fetch an environment variable
+#echo %VAR_NAME%
 
 MODEL_ID = os.getenv("MODEL_REPO")
 MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))
@@ -18,7 +49,7 @@ USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
 ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
 BATCH_SIZE = int(os.getenv("BATCH_SIZE", "1"))  # Allow generating multiple images at once
 
-# Load model outside of function
+#Load model outside of function
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 pipe = StableDiffusionXLPipeline.from_pretrained(
     MODEL_ID,
@@ -37,17 +68,6 @@ if ENABLE_CPU_OFFLOAD:
     pipe.enable_model_cpu_offload()
 
 MAX_SEED = np.iinfo(np.int32).max
-CACHE_EXAMPLES = False
-
-DESCRIPTION = """# Stable Diffusion XL"""
-if not torch.cuda.is_available():
-    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>"
-
-def load_pipeline(pipeline_type):
-    if pipeline_type == "text2img":
-        return pipe
-    elif pipeline_type == "img2img":
-        return StableDiffusion3Img2ImgPipeline.from_pretrained(MODEL_ID, torch_dtype=torch.float16)
 
 def save_image(img):
     unique_name = str(uuid.uuid4()) + ".png"
@@ -59,309 +79,157 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
         seed = random.randint(0, MAX_SEED)
     return seed
 
-@spaces.GPU
+@spaces.GPU(duration=60, enable_queue=True)
 def generate(
     prompt: str,
     negative_prompt: str = "",
     use_negative_prompt: bool = False,
-    seed: int = 0,
+    seed: int = 1,
     width: int = 1024,
     height: int = 1024,
-    guidance_scale: float = 7,
+    guidance_scale: float = 3,
+    num_inference_steps: int = 25,
     randomize_seed: bool = False,
-    num_inference_steps=30,
-    NUM_IMAGES_PER_PROMPT=1,
-    use_resolution_binning: bool = True,
+    use_resolution_binning: bool = True, 
+    num_images: int = 1,  # Number of images to generate
     progress=gr.Progress(track_tqdm=True),
 ):
-    pipe = load_pipeline("text2img")
-    pipe.to(device)
     seed = int(randomize_seed_fn(seed, randomize_seed))
-    generator = torch.Generator().manual_seed(seed)
-
-    if not use_negative_prompt:
-        negative_prompt = None  # type: ignore
-
-    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="battery",
-    ).images
-
-    return output
-
-@spaces.GPU
-def img2img_generate(
-    prompt: str,
-    init_image: gr.Image,
-    negative_prompt: str = "",
-    use_negative_prompt: bool = False,
-    seed: int = 0,
-    guidance_scale: float = 7,
-    randomize_seed: bool = False,
-    num_inference_steps=30,
-    strength: float = 0.8,
-    NUM_IMAGES_PER_PROMPT=1,
-    use_resolution_binning: bool = True,
-    progress=gr.Progress(track_tqdm=True),
-):
-    pipe = load_pipeline("img2img")
-    pipe.to(device)
-    seed = int(randomize_seed_fn(seed, randomize_seed))
-    generator = torch.Generator().manual_seed(seed)
-
-    if not use_negative_prompt:
-        negative_prompt = None  # type: ignore
-
-    init_image = init_image.resize((768, 768))
-
-    output = pipe(
-        prompt=prompt,
-        image=init_image,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        generator=generator,
-        strength=strength,
-        num_images_per_prompt=NUM_IMAGES_PER_PROMPT,
-        output_type="battery",
-    ).images
-
-    return output
-
-examples = [
-    "A cardboard with text 'New York' which is large and sits on a theater stage.",
-    "A red sofa on top of a white building.",
-    "A painting of an astronaut riding a pig wearing a tutu holding a pink umbrella.",
-    "Studio photograph closeup of a chameleon over a black background.",
-    "Closeup portrait photo of beautiful goth woman, makeup.",
-    "A living room, bright modern Scandinavian style house, large windows.",
-    "Portrait photograph of an anthropomorphic tortoise seated on a New York City subway train.",
-    "Batman, cute modern Disney style, Pixar 3d portrait, ultra detailed, gorgeous, 3d zbrush, trending on dribbble, 8k render.",
-    "Cinnamon bun on the plate, watercolor painting, detailed, brush strokes, light palette, light, cozy.",
-    "A lion, colorful, low-poly, cyan and orange eyes, poly-hd, 3d, low-poly game art, polygon mesh, jagged, blocky, wireframe edges, centered composition.",
-    "Long exposure photo of Tokyo street, blurred motion, streaks of light, surreal, dreamy, ghosting effect, highly detailed.",
-    "A glamorous digital magazine photoshoot, a fashionable model wearing avant-garde clothing, set in a futuristic cyberpunk roof-top environment, with a neon-lit city background, intricate high fashion details, backlit by vibrant city glow, Vogue fashion photography.",
-    "Masterpiece, best quality, girl, collarbone, wavy hair, looking at viewer, blurry foreground, upper body, necklace, contemporary, plain pants, intricate, print, pattern, ponytail, freckles, red hair, dappled sunlight, smile, happy."
-]
-
-css = '''
-.gradio-container{max-width: 1000px !important}
-h1{text-align:center}
-'''
-with gr.Blocks(css=css, theme="snehilsanyal/scikit-learn") as demo:
-    with gr.Row():
-        with gr.Column():
-            gr.HTML(
-                """
-                <h1 style='text-align: center'>
-                Stable Diffusion XL
-                </h1>
-                """
+    generator = torch.Generator(device=device).manual_seed(seed)
+
+    #Options 
+    options = {
+        "prompt": [prompt] * num_images,
+        "negative_prompt": [negative_prompt] * num_images if use_negative_prompt else None,
+        "width": width,
+        "height": height,
+        "guidance_scale": guidance_scale,
+        "num_inference_steps": num_inference_steps,
+        "generator": generator,
+        "output_type": "pil",
+    }
+
+    #VRAM usage Lesser
+    if use_resolution_binning:
+        options["use_resolution_binning"] = True
+
+    #Images potential batches
+    images = []
+    for i in range(0, num_images, BATCH_SIZE):
+        batch_options = options.copy()
+        batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE]
+        if "negative_prompt" in batch_options:
+            batch_options["negative_prompt"] = options["negative_prompt"][i:i+BATCH_SIZE]
+        images.extend(pipe(**batch_options).images)
+
+    image_paths = [save_image(img) for img in images]
+    return image_paths, seed
+#Main gr.Block
+with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
+    gr.Markdown(DESCRIPTIONx)
+    with gr.Group():
+        with gr.Row():
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
             )
-            gr.HTML(
-                """
-              
-                """
+            run_button = gr.Button("Run", scale=0)
+        result = gr.Gallery(label="Result", columns=1, show_label=False) 
+    with gr.Accordion("Advanced options", open=False, visible=False):
+        num_images = gr.Slider(
+            label="Number of Images",
+            minimum=1,
+            maximum=4,
+            step=1,
+            value=1,
+        )
+        with gr.Row():
+            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
+            negative_prompt = gr.Text(
+                label="Negative prompt",
+                max_lines=5,
+                lines=4,
+                placeholder="Enter a negative prompt",
+                value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",
+                visible=True,
             )
-
-    with gr.Tabs():
-        with gr.TabItem("Text to Image"):
-            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)
-                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,
-                )
-
-                steps = gr.Slider(
-                    label="Steps",
-                    minimum=0,
-                    maximum=60,
-                    step=1,
-                    value=25,
-                )
-                number_image = gr.Slider(
-                    label="Number of Images",
-                    minimum=1,
-                    maximum=4,
-                    step=1,
-                    value=2,
-                )
-                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                with gr.Row(visible=True):
-                    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=0.1,
-                        maximum=10,
-                        step=0.1,
-                        value=7.0,
-                    )
-
-            gr.Examples(
-                examples=examples,
-                inputs=prompt,
-                outputs=[result],
-                fn=generate,
-                cache_examples=CACHE_EXAMPLES,
+        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(visible=True):
+            width = gr.Slider(
+                label="Width",
+                minimum=512,
+                maximum=MAX_IMAGE_SIZE,
+                step=64,
+                value=1024,
             )
-
-            use_negative_prompt.change(
-                fn=lambda x: gr.update(visible=x),
-                inputs=use_negative_prompt,
-                outputs=negative_prompt,
-                api_name=False,
+            height = gr.Slider(
+                label="Height",
+                minimum=512,
+                maximum=MAX_IMAGE_SIZE,
+                step=64,
+                value=1024,
             )
-
-            gr.on(
-                triggers=[
-                    prompt.submit,
-                    negative_prompt.submit,
-                    run_button.click,
-                ],
-                fn=generate,
-                inputs=[
-                    prompt,
-                    negative_prompt,
-                    use_negative_prompt,
-                    seed,
-                    width,
-                    height,
-                    guidance_scale,
-                    randomize_seed,
-                    steps,
-                    number_image,
-                ],
-                outputs=[result],
-                api_name="run",
+        with gr.Row():
+            guidance_scale = gr.Slider(
+                label="Guidance Scale",
+                minimum=0.1,
+                maximum=6,
+                step=0.1,
+                value=3.0,
             )
-        with gr.TabItem("Image to Image"):
-            with gr.Group():
-                with gr.Row(equal_height=True):
-                    with gr.Column(scale=1):
-                        img2img_prompt = gr.Text(
-                            label="Prompt",
-                            show_label=False,
-                            max_lines=1,
-                            placeholder="Enter your prompt",
-                            container=False,
-                        )
-                        init_image = gr.Image(label="Input Image", type="pil")
-                        with gr.Row():
-                            img2img_run_button = gr.Button("Generate", variant="primary")
-                    with gr.Column(scale=1):
-                        img2img_output = gr.Gallery(label="Result", elem_id="gallery")
-                with gr.Accordion("Advanced options", open=False):
-                    with gr.Row():
-                        img2img_use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
-                        img2img_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,
-                        )
-                    img2img_seed = gr.Slider(
-                        label="Seed",
-                        minimum=0,
-                        maximum=MAX_SEED,
-                        step=1,
-                        value=0,
-                    )
-                    img2img_steps = gr.Slider(
-                        label="Steps",
-                        minimum=0,
-                        maximum=60,
-                        step=1,
-                        value=25,
-                    )
-                    img2img_number_image = gr.Slider(
-                        label="Number of Images",
-                        minimum=1,
-                        maximum=4,
-                        step=1,
-                        value=2,
-                    )
-                    img2img_randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                    with gr.Row():
-                        img2img_guidance_scale = gr.Slider(
-                            label="Guidance Scale",
-                            minimum=0.1,
-                            maximum=10,
-                            step=0.1,
-                            value=7.0,
-                        )
-                        strength = gr.Slider(label="Img2Img Strength", minimum=0.0, maximum=1.0, step=0.01, value=0.8)
-
-            img2img_use_negative_prompt.change(
-                fn=lambda x: gr.update(visible=x),
-                inputs=img2img_use_negative_prompt,
-                outputs=img2img_negative_prompt,
-                api_name=False,
+            num_inference_steps = gr.Slider(
+                label="Number of inference steps",
+                minimum=1,
+                maximum=25,
+                step=1,
+                value=23,
             )
 
-            gr.on(
-                triggers=[
-                    img2img_prompt.submit,
-                    img2img_negative_prompt.submit,
-                    img2img_run_button.click,
-                ],
-                fn=img2img_generate,
-                inputs=[
-                    img2img_prompt,
-                    init_image,
-                    img2img_negative_prompt,
-                    img2img_use_negative_prompt,
-                    img2img_seed,
-                    img2img_guidance_scale,
-                    img2img_randomize_seed,
-                    img2img_steps,
-                    strength,
-                    img2img_number_image,
-                ],
-                outputs=[img2img_output],
-                api_name="img2img_run",
-            )
+    gr.Examples(
+        examples=examples,
+        inputs=prompt,
+        cache_examples=False
+    )
+
+    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,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+            randomize_seed,
+            num_images
+        ],
+        outputs=[result, seed],
+        api_name="run",
+    )   
+   # gr.HTML(html_content)
 if __name__ == "__main__":
-    demo.queue().launch(show_api=False, debug=False)
+    demo.queue(max_size=40).launch()