Update app.py

app.py

@@ -1,146 +1,176 @@
-import gradio as gr
-import numpy as np
-import random
-from diffusers import DiffusionPipeline
+from diffusers import DiffusionPipeline, LCMScheduler, AutoencoderTiny
+from compel import Compel, ReturnedEmbeddingsType
 import torch
+import os
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-if torch.cuda.is_available():
-    torch.cuda.max_memory_allocated(device=device)
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
-    pipe.enable_xformers_memory_efficient_attention()
-    pipe = pipe.to(device)
-else: 
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
-    pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
-
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
-    
-    image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
-    
-    return image
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
-}
-"""
+try:
+    import intel_extension_for_pytorch as ipex
+except:
+    pass
+
+from PIL import Image
+import numpy as np
+import gradio as gr
+import psutil
+from sfast.compilers.stable_diffusion_pipeline_compiler import (
+    compile,
+    CompilationConfig,
+)
+
+
+SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None)
+HF_TOKEN = os.environ.get("HF_TOKEN", None)
+# check if MPS is available OSX only M1/M2/M3 chips
+mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
+xpu_available = hasattr(torch, "xpu") and torch.xpu.is_available()
+device = torch.device(
+    "cuda" if torch.cuda.is_available() else "xpu" if xpu_available else "cpu"
+)
+torch_device = device
+torch_dtype = torch.float16
+
+print(f"SAFETY_CHECKER: {SAFETY_CHECKER}")
+print(f"device: {device}")
+
+if mps_available:
+    device = torch.device("mps")
+    torch_device = "cpu"
+    torch_dtype = torch.float32
+
+model_id = "stabilityai/stable-diffusion-xl-base-1.0"
 
-if torch.cuda.is_available():
-    power_device = "GPU"
+if SAFETY_CHECKER == "True":
+    pipe = DiffusionPipeline.from_pretrained(model_id)
 else:
-    power_device = "CPU"
+    pipe = DiffusionPipeline.from_pretrained(model_id, safety_checker=None)
 
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+pipe.load_lora_weights(
+    "latent-consistency/lcm-lora-sdxl",
+    use_auth_token=HF_TOKEN,
+)
+if device.type != "mps":
+    pipe.unet.to(memory_format=torch.channels_last)
+pipe.to(device=torch_device, dtype=torch_dtype).to(device)
+
+# Load LCM LoRA
+
+config = CompilationConfig.Default()
+config.enable_xformers = True
+config.enable_triton = True
+config.enable_cuda_graph = True
+pipe = compile(pipe, config=config)
+
+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],
+)
+
+
+def predict(
+    prompt,
+    guidance,
+    steps,
+    seed=1231231,
+    randomize_bt=False,
+    progress=gr.Progress(track_tqdm=True),
+):
+    if randomize_bt:
+        seed = np.random.randint(0, 2**32 - 1)
+    generator = torch.manual_seed(seed)
+    prompt_embeds, pooled_prompt_embeds = compel_proc(prompt)
+
+    results = pipe(
+        prompt_embeds=prompt_embeds,
+        pooled_prompt_embeds=pooled_prompt_embeds,
+        generator=generator,
+        num_inference_steps=steps,
+        guidance_scale=guidance,
+        width=1024,
+        height=1024,
+        # original_inference_steps=params.lcm_steps,
+        output_type="pil",
+    )
+    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.")
+    return results.images[0], seed
+
+
+css = """
+#container{
+    margin: 0 auto;
+    max-width: 40rem;
+}
+#intro{
+    max-width: 100%;
+    text-align: center;
+    margin: 0 auto;
+}
+"""
 with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        Currently running on {power_device}.
-        """)
-        
+    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](https://huggingface.co/blog/lcm_lora) or [technical report](https://huggingface.co/papers/2311.05556).
+            """,
+            elem_id="intro",
+        )
         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.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-            
-            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():
-                
-                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,
+                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):
+            guidance = gr.Slider(
+                label="Guidance", minimum=0.0, maximum=5, value=0.3, step=0.001
+            )
+            steps = gr.Slider(label="Steps", value=4, minimum=2, maximum=10, step=1)
             with gr.Row():
-                
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,
-                )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=12,
+                seed = gr.Slider(
+                    randomize=True,
+                    minimum=0,
+                    maximum=12013012031030,
+                    label="Seed",
                     step=1,
-                    value=2,
+                    scale=5,
                 )
-        
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
-        )
+                with gr.Group():
+                    randomize_bt = gr.Checkbox(label="Randomize", value=False)
+                    random_seed = gr.Textbox(show_label=False)
+        with gr.Accordion("Run 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") 
+            pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+            pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl") #yes, it's a normal LoRA
+            results = pipe(
+                prompt="The spirit of a tamagotchi wandering in the city of Vienna",
+                num_inference_steps=4,
+                guidance_scale=0.0,
+            )
+            results.images[0]
+            ```
+            """
+            )
 
-    run_button.click(
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result]
-    )
+        inputs = [prompt, guidance, steps, seed, randomize_bt]
+        generate_bt.click(fn=predict, inputs=inputs, outputs=[image, random_seed])
 
-demo.queue().launch()
+demo.queue(api_open=False)
+demo.launch(show_api=False)