first

app.py

@@ -1,5 +1,4 @@
 from diffusers import DiffusionPipeline, LCMScheduler, AutoencoderTiny
-from compel import Compel, ReturnedEmbeddingsType
 import torch
 import os
 
@@ -12,7 +11,7 @@ from PIL import Image
 import numpy as np
 import gradio as gr
 import psutil
-
+import time
 
 SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None)
 TORCH_COMPILE = os.environ.get("TORCH_COMPILE", None)
@@ -35,16 +34,15 @@ if mps_available:
     torch_device = "cpu"
     torch_dtype = torch.float32
 
-model_id = "stabilityai/stable-diffusion-xl-base-1.0"
-
 if SAFETY_CHECKER == "True":
-    pipe = DiffusionPipeline.from_pretrained(model_id)
+    pipe = DiffusionPipeline.from_pretrained("Lykon/dreamshaper-7")
 else:
-    pipe = DiffusionPipeline.from_pretrained(model_id, safety_checker=None)
+    pipe = DiffusionPipeline.from_pretrained("Lykon/dreamshaper-7", safety_checker=None)
 
 pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
 pipe.to(device=torch_device, dtype=torch_dtype).to(device)
 pipe.unet.to(memory_format=torch.channels_last)
+pipe.set_progress_bar_config(disable=True)
 
 # check if computer has less than 64GB of RAM using sys or os
 if psutil.virtual_memory().total < 64 * 1024**3:
@@ -53,47 +51,35 @@ if psutil.virtual_memory().total < 64 * 1024**3:
 if TORCH_COMPILE:
     pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
     pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=True)
-
     pipe(prompt="warmup", num_inference_steps=1, guidance_scale=8.0)
 
 # Load LCM LoRA
-pipe.load_lora_weights(
-    "latent-consistency/lcm-lora-sdxl",
-    use_auth_token=HF_TOKEN,
-)
-
-compel_proc = Compel(
-    tokenizer=[pipe.tokenizer, pipe.tokenizer_2],
-    text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
-    returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
-    requires_pooled=[False, True],
-)
+pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5")
+pipe.fuse_lora()
 
 
-def predict(
-    prompt, guidance, steps, seed=1231231, progress=gr.Progress(track_tqdm=True)
-):
+def predict(prompt, guidance, steps, seed=1231231):
     generator = torch.manual_seed(seed)
-    prompt_embeds, pooled_prompt_embeds = compel_proc(prompt)
-
+    last_time = time.time()
     results = pipe(
-        prompt_embeds=prompt_embeds,
-        pooled_prompt_embeds=pooled_prompt_embeds,
+        prompt=prompt,
         generator=generator,
         num_inference_steps=steps,
         guidance_scale=guidance,
-        width=1024,
-        height=1024,
+        width=512,
+        height=512,
         # original_inference_steps=params.lcm_steps,
         output_type="pil",
     )
+    print(f"Pipe took {time.time() - last_time} seconds")
     nsfw_content_detected = (
         results.nsfw_content_detected[0]
         if "nsfw_content_detected" in results
         else False
     )
     if nsfw_content_detected:
-        raise gr.Error("NSFW content detected.")
+        gr.Warning("NSFW content detected.")
+        return Image.new("RGB", (512, 512))
     return results.images[0]
 
 
@@ -111,8 +97,8 @@ css = """
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="container"):
         gr.Markdown(
-            """# SDXL in 4 steps with Latent Consistency LoRAs
-            SDXL is loaded with a LCM-LoRA, giving it the super power of doing inference in as little as 4 steps. [Learn more on our blog](#) or [technical report](#).
+            """# SD1.5 Latent Consistency LoRAs
+            SD1.5  is loaded with a LCM-LoRA, giving it the super power of doing inference in as little as 4 steps. [Learn more on our blog](#) or [technical report](#).
             """,
             elem_id="intro",
         )
@@ -122,7 +108,7 @@ with gr.Blocks(css=css) as demo:
                     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):
             guidance = gr.Slider(
@@ -133,16 +119,18 @@ with gr.Blocks(css=css) as demo:
                 randomize=True, minimum=0, maximum=12013012031030, label="Seed", step=1
             )
         with gr.Accordion("Run with diffusers"):
-            gr.Markdown('''## Running LCM-LoRAs it with `diffusers`
+            gr.Markdown(
+                """## Running LCM-LoRAs it with `diffusers`
             ```bash
             pip install diffusers==0.23.0
             ```
             
             ```py
-            from diffusers import DiffusionPipeline, LCMScheduler
-            pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0").to("cuda") 
+            from diffusers import DiffusionPipeline, LCMScheduler, AutoencoderTiny
+
+            pipe = DiffusionPipeline.from_pretrained("Lykon/dreamshaper-7").to("cuda") 
             pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
-            pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl") #yes, it's a normal LoRA
+            pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5") #yes, it's a normal LoRA
 
             results = pipe(
                 prompt="The spirit of a tamagotchi wandering in the city of Vienna",
@@ -151,10 +139,15 @@ with gr.Blocks(css=css) as demo:
             )
             results.images[0]
             ```
-            ''')
-            
+            """
+            )
+
         inputs = [prompt, guidance, steps, seed]
-        generate_bt.click(fn=predict, inputs=inputs, outputs=image)
+        generate_bt.click(fn=predict, inputs=inputs, outputs=image, show_progress=False)
+        prompt.input(fn=predict, inputs=inputs, outputs=image, show_progress=False)
+        guidance.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
+        steps.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
+        seed.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
 
 demo.queue()
 demo.launch()

requirements.txt

@@ -1,5 +1,4 @@
-# diffusers==0.22.2
-git+https://github.com/huggingface/diffusers.git@6110d7c95f630479cf01340cc8a8141c1e359f09
+diffusers==0.23.0
 transformers==4.34.1
 gradio==4.1.2
 --extra-index-url https://download.pytorch.org/whl/cu121