Update app_demo.py

app_demo.py

@@ -12,8 +12,16 @@ import PIL.Image
 import torch
 from diffusers import StableDiffusionPipeline
 import uuid
+from diffusers import DiffusionPipeline
+from tqdm import tqdm
+from safetensors.torch import load_file
+import gradio_user_history as gr_user_history
+import cv2
+
+
+
+
 
-model_id = "Lykon/dreamshaper-xl-v2-turbo"
 #DESCRIPTION = '''# Fast Stable Diffusion CPU with Latent Consistency Model
 #Distilled from [Dreamshaper v7](https://huggingface.co/Lykon/dreamshaper-7) fine‑tune of SD v1-5.
 #'''
@@ -22,33 +30,23 @@ model_id = "Lykon/dreamshaper-xl-v2-turbo"
 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"))
-DTYPE = torch.float32
+USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
+DTYPE = torch.float32  # torch.float16 works as well, but pictures seem to be a bit worse
 api = HfApi()
 executor = ThreadPoolExecutor()
 model_cache = {}
 
-
-
-
-
-
-
-
-
-
-
-
-
-
-
+#custom
+model_id = "Lykon/dreamshaper-xl-v2-turbo"
+custom_pipe = DiffusionPipeline.from_pretrained(mode_id, custom_pipeline="latent_consistency_txt2img", custom_revision="main")
+#1st
+pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", custom_pipeline="latent_consistency_txt2img", custom_revision="main")
+pipe.to(torch_device="cpu", torch_dtype=DTYPE)
+pipe.safety_checker = None
 
 # Load pipeline once, disabling NSFW filter at construction time
 pipe = StableDiffusionPipeline.from_pretrained(
-    model_id,
-    safety_checker=None,
-    torch_dtype=DTYPE,
-    use_safetensors=True
-).to("cpu")
+    model_id,    safety_checker=None,    torch_dtype=DTYPE,    use_safetensors=True).to("cpu")
 
 def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     if randomize_seed:
@@ -58,14 +56,21 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
 def save_image(img, profile: gr.OAuthProfile | None, metadata: dict):
     unique_name = str(uuid.uuid4()) + '.png'
     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):
+#    with ThreadPoolExecutor() as executor:
+#        return list(executor.map(
+#            lambda args: save_image(*args),
+#            zip(image_array, [profile]*len(image_array), [metadata]*len(image_array))
+#        ))
 def save_images(image_array, profile: gr.OAuthProfile | None, metadata: dict):
+    paths = []
     with ThreadPoolExecutor() as executor:
-        return list(executor.map(
-            lambda args: save_image(*args),
-            zip(image_array, [profile]*len(image_array), [metadata]*len(image_array))
-        ))
+        paths = list(executor.map(save_image, image_array, [profile]*len(image_array), [metadata]*len(image_array)))
+    return paths
 
 def generate(
     prompt: str,
@@ -82,7 +87,6 @@ def generate(
     # prepare seed
     seed = randomize_seed_fn(seed, randomize_seed)
     torch.manual_seed(seed)
-
     start_time = time.time()
     # **Call the pipeline with only supported kwargs:**
     outputs = pipe(
@@ -94,6 +98,7 @@ def generate(
         num_inference_steps=num_inference_steps,
         num_images_per_prompt=num_images,
         output_type="pil",
+        lcm_origin_steps=50,
     ).images
 
     latency = time.time() - start_time
@@ -114,20 +119,6 @@ def generate(
 
     return paths, seed
 
-examples = [
-    "A futuristic cityscape at sunset",
-    "Steampunk airship over mountains",
-    "Portrait of a cyborg queen, hyper‑detailed",
-]
-
-
-
-
-
-
-
-
-
 
 
 
@@ -233,9 +224,7 @@ with gr.Blocks() as demo:
     with gr.Group():
         with gr.Row():
             prompt = gr.Text(
-                placeholder="Enter your prompt",
-                show_label=False,
-                container=False,
+                placeholder="Enter your prompt", show_label=False, container=False,
             )
             run_button = gr.Button("Run", scale=0)
         gallery = gr.Gallery(
@@ -256,23 +245,34 @@ with gr.Blocks() as demo:
             num_inference_steps = gr.Slider(1, 8, value=4, step=1, label="Inference Steps")
         num_images = gr.Slider(1, 8, value=1, step=1, label="Number of Images")
 
-    gr.Examples(
-        examples=examples,
-        inputs=prompt,
-        outputs=gallery,
-        fn=generate,
-        cache_examples=CACHE_EXAMPLES,
-    )
-
-
-
-demo.launch()
-
-
-'''#!/usr/bin/env python
 
+    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", grid=[2]        )
+    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, 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, visible=True,)
+    with gr.Accordion("Past generations", open=False):
+        gr_user_history.render()
 
+    gr.on(        triggers=[            prompt.submit,            run_button.click,        ],
+        fn=generate,
+        inputs=[prompt,seed,width,height,guidance_scale,num_inference_steps,num_images,randomize_seed        ],
+        outputs=[result, seed],
+        api_name="run",
+    )
 
-    demo.queue()
+if __name__ == "__main__":
+    demo.queue(api_open=False)
     demo.launch()
-'''
+