asda

README.md

@@ -8,6 +8,4 @@ sdk_version: 4.19.2
 app_file: app.py
 pinned: false
 license: mit
-disable_embedding: true
-header: mini
 ---

app.py

@@ -1,79 +1,36 @@
-from diffusers import (
-    StableDiffusionXLPipeline,
-    EulerDiscreteScheduler,
-    UNet2DConditionModel,
-    AutoencoderTiny,
-)
+import gradio as gr
 import torch
-import os
+from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
 from huggingface_hub import hf_hub_download
-
-
-from PIL import Image
-import gradio as gr
-import time
 from safetensors.torch import load_file
-import time
-import tempfile
-from pathlib import Path
-
-# Constants
-BASE = "stabilityai/stable-diffusion-xl-base-1.0"
-REPO = "ByteDance/SDXL-Lightning"
-# 1-step
-CHECKPOINT = "sdxl_lightning_2step_unet.safetensors"
-taesd_model = "madebyollin/taesdxl"
-
-# {
-#     "1-Step": ["sdxl_lightning_1step_unet_x0.safetensors", 1],
-#     "2-Step": ["sdxl_lightning_2step_unet.safetensors", 2],
-#     "4-Step": ["sdxl_lightning_4step_unet.safetensors", 4],
-#     "8-Step": ["sdxl_lightning_8step_unet.safetensors", 8],
-# }
-
+import spaces
+import os
+from PIL import Image
 
-SFAST_COMPILE = os.environ.get("SFAST_COMPILE", "0") == "1"
 SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", "0") == "1"
-USE_TAESD = os.environ.get("USE_TAESD", "0") == "1"
-
-# check if MPS is available OSX only M1/M2/M3 chips
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-torch_device = device
-torch_dtype = torch.float16
 
-print(f"SAFETY_CHECKER: {SAFETY_CHECKER}")
-print(f"SFAST_COMPILE: {SFAST_COMPILE}")
-print(f"USE_TAESD: {USE_TAESD}")
-print(f"device: {device}")
-
-
-unet = UNet2DConditionModel.from_config(BASE, subfolder="unet").to(
-    "cuda", torch.float16
-)
-unet.load_state_dict(load_file(hf_hub_download(REPO, CHECKPOINT), device="cuda"))
-pipe = StableDiffusionXLPipeline.from_pretrained(
-    BASE, unet=unet, torch_dtype=torch.float16, variant="fp16", safety_checker=False
-).to("cuda")
+# Constants
+base = "stabilityai/stable-diffusion-xl-base-1.0"
+repo = "ByteDance/SDXL-Lightning"
+checkpoints = {
+    "1-Step" : ["sdxl_lightning_1step_unet_x0.safetensors", 1],
+    "2-Step" : ["sdxl_lightning_2step_unet.safetensors", 2],
+    "4-Step" : ["sdxl_lightning_4step_unet.safetensors", 4],
+    "8-Step" : ["sdxl_lightning_8step_unet.safetensors", 8],
+}
 
-if USE_TAESD:
-    pipe.vae = AutoencoderTiny.from_pretrained(
-        taesd_model, torch_dtype=torch_dtype, use_safetensors=True
-    ).to(device)
 
+# Ensure model and scheduler are initialized in GPU-enabled function
+if torch.cuda.is_available():
+    pipe = StableDiffusionXLPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to("cuda")
 
-# Ensure sampler uses "trailing" timesteps.
-pipe.scheduler = EulerDiscreteScheduler.from_config(
-    pipe.scheduler.config, timestep_spacing="trailing"
-)
-pipe.set_progress_bar_config(disable=True)
 if SAFETY_CHECKER:
     from safety_checker import StableDiffusionSafetyChecker
     from transformers import CLIPFeatureExtractor
 
     safety_checker = StableDiffusionSafetyChecker.from_pretrained(
         "CompVis/stable-diffusion-safety-checker"
-    ).to(device)
+    ).to("cuda")
     feature_extractor = CLIPFeatureExtractor.from_pretrained(
         "openai/clip-vit-base-patch32"
     )
@@ -81,125 +38,64 @@ if SAFETY_CHECKER:
     def check_nsfw_images(
         images: list[Image.Image],
     ) -> tuple[list[Image.Image], list[bool]]:
-        safety_checker_input = feature_extractor(images, return_tensors="pt").to(device)
+        safety_checker_input = feature_extractor(images, return_tensors="pt").to("cuda")
         has_nsfw_concepts = safety_checker(
             images=[images],
-            clip_input=safety_checker_input.pixel_values.to(torch_device),
+            clip_input=safety_checker_input.pixel_values.to("cuda")
         )
 
         return images, has_nsfw_concepts
 
+# Function
+@spaces.GPU(enable_queue=True)
+def generate_image(prompt, ckpt):
+
+    checkpoint = checkpoints[ckpt][0]
+    num_inference_steps = checkpoints[ckpt][1]
+
+    if num_inference_steps==1:
+        # Ensure sampler uses "trailing" timesteps and "sample" prediction type for 1-step inference.
+        pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample")
+    else:
+        # Ensure sampler uses "trailing" timesteps.
+        pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
+
+    pipe.unet.load_state_dict(load_file(hf_hub_download(repo, checkpoint), device="cuda"))
+    results = pipe(prompt, num_inference_steps=num_inference_steps, guidance_scale=0)
 
-if SFAST_COMPILE:
-    from sfast.compilers.diffusion_pipeline_compiler import compile, CompilationConfig
-
-    # sfast compilation
-    config = CompilationConfig.Default()
-    try:
-        import xformers
-
-        config.enable_xformers = True
-    except ImportError:
-        print("xformers not installed, skip")
-    try:
-        import triton
-
-        config.enable_triton = True
-    except ImportError:
-        print("Triton not installed, skip")
-    # CUDA Graph is suggested for small batch sizes and small resolutions to reduce CPU overhead.
-    # But it can increase the amount of GPU memory used.
-    # For StableVideoDiffusionPipeline it is not needed.
-    config.enable_cuda_graph = True
-
-    pipe = compile(pipe, config)
-
-
-def predict(prompt, seed=1231231):
-    generator = torch.manual_seed(seed)
-    last_time = time.time()
-    results = pipe(
-        prompt=prompt,
-        generator=generator,
-        num_inference_steps=2,
-        guidance_scale=0.0,
-        # width=768,
-        # height=768,
-        output_type="pil",
-    )
-    print(f"Pipe took {time.time() - last_time} seconds")
     if SAFETY_CHECKER:
         images, has_nsfw_concepts = check_nsfw_images(results.images)
         if any(has_nsfw_concepts):
             gr.Warning("NSFW content detected.")
             return Image.new("RGB", (512, 512))
-    image = results.images[0]
-    with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmpfile:
-        image.save(tmpfile, "JPEG", quality=80, optimize=True, progressive=True)
-        return Path(tmpfile.name)
+        return images[0]
+    return results.images[0]
 
 
-css = """
-#container{
-    margin: 0 auto;
-    max-width: 40rem;
-}
-#intro{
-    max-width: 100%;
-    margin: 0 auto;
-}
+
+# Gradio Interface
+description = """
+This demo utilizes the SDXL-Lightning model by ByteDance, which is a lightning-fast text-to-image generative model capable of producing high-quality images in 4 steps.
+As a community effort, this demo was put together by AngryPenguin. Link to model: https://huggingface.co/ByteDance/SDXL-Lightning
 """
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="container"):
-        gr.Markdown(
-            """
-# SDXL-Lightning- Text To Image 2-Steps
-**Model**: https://huggingface.co/ByteDance/SDXL-Lightning
-            """,
-            elem_id="intro",
-        )
-        with gr.Row():
-            with gr.Row():
-                prompt = gr.Textbox(
-                    placeholder="Insert your prompt here:", scale=5, container=False
-                )
-                generate_bt = gr.Button("Generate", scale=1)
-
-        image = gr.Image(type="filepath")
-        with gr.Accordion("Advanced options", open=False):
-            seed = gr.Slider(
-                randomize=True, minimum=0, maximum=12013012031030, label="Seed", step=1
-            )
-        with gr.Accordion("Run with diffusers"):
-            gr.Markdown(
-                """## Running SDXL-Lightning with `diffusers`
-```py
-import torch
-from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler
-from huggingface_hub import hf_hub_download
-from safetensors.torch import load_file
-base = "stabilityai/stable-diffusion-xl-base-1.0"
-repo = "ByteDance/SDXL-Lightning"
-ckpt = "sdxl_lightning_2step_unet.safetensors" # Use the correct ckpt for your step setting!
-# Load model.
-unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16)
-unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cuda"))
-pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
-# Ensure sampler uses "trailing" timesteps.
-pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
-# Ensure using the same inference steps as the loaded model and CFG set to 0.
-pipe("A girl smiling", num_inference_steps=2, guidance_scale=0).images[0].save("output.png")
-```
-            """
-            )
-
-        inputs = [prompt, seed]
-        outputs = [image]
-        generate_bt.click(
-            fn=predict, inputs=inputs, outputs=outputs, show_progress=False
-        )
-        prompt.input(fn=predict, inputs=inputs, outputs=outputs, show_progress=False)
-        seed.change(fn=predict, inputs=inputs, outputs=outputs, show_progress=False)
 
-demo.queue()
-demo.launch()
+with gr.Blocks(css="style.css") as demo:
+    gr.HTML("<h1><center>Text-to-Image with SDXL-Lightning ⚡</center></h1>")
+    gr.Markdown(description)
+    with gr.Group():
+        with gr.Row():
+            prompt = gr.Textbox(label='Enter you image prompt:', scale=8)
+            ckpt = gr.Dropdown(label='Select inference steps',choices=['1-Step', '2-Step', '4-Step', '8-Step'], value='4-Step', interactive=True)
+            submit = gr.Button(scale=1, variant='primary')
+    img = gr.Image(label='SDXL-Lightning Generated Image')
+
+    prompt.submit(fn=generate_image,
+                 inputs=[prompt, ckpt],
+                 outputs=img,
+                 )
+    submit.click(fn=generate_image,
+                 inputs=[prompt, ckpt],
+                 outputs=img,
+                 )
+
+demo.queue().launch()

requirements.txt

@@ -1,16 +1,5 @@
-diffusers==0.26.3
 transformers
-gradio==4.19.2
-torch==2.1.0
-fastapi==0.104.0
-uvicorn==0.23.2
-Pillow==10.1.0
-accelerate==0.24.0
-compel==2.0.2
-controlnet-aux==0.0.7
-peft==0.6.0
-xformers
-hf_transfer
-huggingface_hub
-safetensors
-stable_fast @ https://github.com/chengzeyi/stable-fast/releases/download/v1.0.2/stable_fast-1.0.2+torch211cu121-cp310-cp310-manylinux2014_x86_64.whl; sys_platform != 'darwin' or platform_machine != 'arm64'
+diffusers
+torch
+accelerate
+gradio