Upload folder using huggingface_hub

README.md

@@ -1,12 +1,6 @@
 ---
-title: Local Gradio
-emoji: 📚
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 4.36.1
+title: local_gradio
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 3.48.0
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,241 @@
+from __future__ import annotations
+
+import os
+import random
+import time
+
+import gradio as gr
+import numpy as np
+import PIL.Image
+import torch
+try:
+    import intel_extension_for_pytorch as ipex
+except:
+    pass
+
+
+from diffusers import DiffusionPipeline
+import torch
+
+import os
+import torch
+from tqdm import tqdm
+
+from concurrent.futures import ThreadPoolExecutor
+import uuid
+
+DESCRIPTION = '''# Latent Consistency Model
+Distilled from [Dreamshaper v7](https://huggingface.co/Lykon/dreamshaper-7) fine-tune of [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) with only 4,000 training iterations (~32 A100 GPU Hours). [Project page](https://latent-consistency-models.github.io)
+'''
+if torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CUDA 😀</p>"
+elif hasattr(torch, 'xpu') and torch.xpu.is_available():
+    DESCRIPTION += "\n<p>Running on XPU 🤓</p>"
+else:
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
+
+MAX_SEED = np.iinfo(np.int32).max
+CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
+MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "768"))
+USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
+
+
+
+"""
+   Operation System Options:
+      If you are using MacOS, please set the following (device="mps") ;
+      If you are using Linux & Windows with Nvidia GPU, please set the device="cuda";
+      If you are using Linux & Windows with Intel Arc GPU, please set the device="xpu";
+"""
+# device = "mps"    # MacOS
+#device = "xpu"    # Intel Arc GPU
+device = "cuda"   # Linux & Windows
+
+
+"""
+   DTYPE Options:
+      To reduce GPU memory you can set "DTYPE=torch.float16",
+      but image quality might be compromised
+"""
+DTYPE = torch.float16  # torch.float16 works as well, but pictures seem to be a bit worse
+
+
+#pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7")
+pipe = DiffusionPipeline.from_pretrained("D:/git-work/LCM_Dreamshaper_v7")
+
+
+#pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", custom_pipeline="latent_consistency_txt2img", custom_revision="main")
+pipe.to(torch_device=device, torch_dtype=DTYPE)
+
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+def save_image(img, profile: gr.OAuthProfile | None, metadata: dict, root_path='./'):
+    unique_name = str(uuid.uuid4()) + '.png'
+    unique_name = os.path.join(root_path, unique_name)
+    img.save(unique_name)
+    # gr_user_history.save_image(label=metadata["prompt"], image=img, profile=profile, metadata=metadata)
+    return unique_name
+
+def save_images(image_array, profile: gr.OAuthProfile | None, metadata: dict):
+    paths = []
+    root_path = './images/'
+    os.makedirs(root_path, exist_ok=True)
+    with ThreadPoolExecutor() as executor:
+        paths = list(executor.map(save_image, image_array, [profile]*len(image_array), [metadata]*len(image_array), [root_path]*len(image_array)))
+    return paths
+
+def generate(
+    prompt: str,
+    seed: int = 0,
+    width: int = 512,
+    height: int = 512,
+    guidance_scale: float = 8.0,
+    num_inference_steps: int = 4,
+    num_images: int = 4,
+    randomize_seed: bool = False,
+    param_dtype='torch.float16',
+    progress = gr.Progress(track_tqdm=True),
+    profile: gr.OAuthProfile | None = None,
+) -> PIL.Image.Image:
+    seed = randomize_seed_fn(seed, randomize_seed)
+    torch.manual_seed(seed)
+    pipe.to(torch_device=device, torch_dtype=torch.float16 if param_dtype == 'torch.float16' else torch.float32)
+    start_time = time.time()
+    result = pipe(
+        prompt=prompt,
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        num_images_per_prompt=num_images,
+        lcm_origin_steps=50,
+        output_type="pil",
+    ).images
+    paths = save_images(result, profile, metadata={"prompt": prompt, "seed": seed, "width": width, "height": height, "guidance_scale": guidance_scale, "num_inference_steps": num_inference_steps})
+    print(time.time() - start_time)
+    return paths, seed
+
+examples = [
+    "portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography",
+    "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
+    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    "A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece",
+]
+
+with gr.Blocks(css="style.css") as demo:
+    gr.Markdown(DESCRIPTION)
+    gr.DuplicateButton(
+        value="Duplicate Space for private use",
+        elem_id="duplicate-button",
+        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
+    )
+    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="Generated images", show_label=False, elem_id="gallery", 
+        )
+    with gr.Accordion("Advanced options", open=False):
+        seed = gr.Slider(
+            label="Seed",
+            minimum=0,
+            maximum=MAX_SEED,
+            step=1,
+            value=0,
+            randomize=True
+        )
+        randomize_seed = gr.Checkbox(label="Randomize seed across runs", value=True)
+        with gr.Row():
+            width = gr.Slider(
+                label="Width",
+                #minimum=256,
+                minimum=128,
+                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.Row():
+            guidance_scale = gr.Slider(
+                label="Guidance scale for base",
+                minimum=2,
+                maximum=14,
+                step=0.1,
+                value=8.0,
+            )
+            num_inference_steps = gr.Slider(
+                label="Number of inference steps for base",
+                minimum=1,
+                maximum=8,
+                step=1,
+                value=4,
+            )
+        with gr.Row():
+            num_images = gr.Slider(
+                label="Number of images",
+                minimum=1,
+                maximum=8,
+                step=1,
+                value=1,#生成图片的数量
+                visible=True,
+            )
+            dtype_choices = ['torch.float16','torch.float32']
+            param_dtype = gr.Radio(dtype_choices,label='torch.dtype',  
+                                      value=dtype_choices[0],
+                                      interactive=True,
+                                      info='To save GPU memory, use torch.float16. For better quality, use torch.float32.')
+
+    # with gr.Accordion("Past generations", open=False):
+    #     gr_user_history.render()
+    
+    gr.Examples(
+        examples=examples,
+        inputs=prompt,
+        outputs=result,
+        fn=generate,
+        cache_examples=CACHE_EXAMPLES,
+    )
+
+    gr.on(
+        triggers=[
+            prompt.submit,
+            run_button.click,
+        ],
+        fn=generate,
+        inputs=[
+            prompt,
+            seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+            num_images,
+            randomize_seed,
+            param_dtype
+        ],
+        outputs=[result, seed],
+        api_name="run",
+    )
+
+if __name__ == "__main__":
+    demo.queue(api_open=False)
+    # demo.queue(max_size=20).launch()
+    demo.launch(share=True)
+    #demo.launch()