Update app.py

app.py

@@ -1,85 +1,106 @@
 import gradio as gr
 import numpy as np
 import random
-from diffusers import AuraFlowPipeline
 import torch
-import spaces
+from PIL import Image
+import os
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-#torch.set_float32_matmul_precision("high")
-
-#torch._inductor.config.conv_1x1_as_mm = True
-#torch._inductor.config.coordinate_descent_tuning = True
-#torch._inductor.config.epilogue_fusion = False
-#torch._inductor.config.coordinate_descent_check_all_directions = True
-
-pipe = AuraFlowPipeline.from_pretrained(
-	"fal/AuraFlow",
-    torch_dtype=torch.float16
-).to("cuda")
+from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor
+from kolors.pipelines.pipeline_stable_diffusion_xl_chatglm_256_ipadapter import StableDiffusionXLPipeline
+from kolors.models.modeling_chatglm import ChatGLMModel
+from kolors.models.tokenization_chatglm import ChatGLMTokenizer
+from kolors.models.unet_2d_condition import UNet2DConditionModel
+from diffusers import AutoencoderKL, EulerDiscreteScheduler
 
-#pipe.transformer.to(memory_format=torch.channels_last)
-#pipe.transformer = torch.compile(pipe.transformer, mode="reduce-overhead", fullgraph=True)
-#pipe.transformer.to(memory_format=torch.channels_last)
-#pipe.vae.to(memory_format=torch.channels_last)
+from huggingface_hub import snapshot_download
 
-#pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune", fullgraph=True)
-#pipe.vae.decode = torch.compile(pipe.vae.decode, mode="max-autotune", fullgraph=True)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+ckpt_dir = f'{root_dir}/weights/Kolors'
+
+snapshot_download(repo_id="Kwai-Kolors/Kolors", local_dir=ckpt_dir)
+snapshot_download(repo_id="Kwai-Kolors/Kolors-IP-Adapter-Plus", local_dir=f"{root_dir}/weights/Kolors-IP-Adapter-Plus")
+
+# Load models
+text_encoder = ChatGLMModel.from_pretrained(f'{ckpt_dir}/text_encoder', torch_dtype=torch.float16).half().to(device)
+tokenizer = ChatGLMTokenizer.from_pretrained(f'{ckpt_dir}/text_encoder')
+vae = AutoencoderKL.from_pretrained(f"{ckpt_dir}/vae", revision=None).half().to(device)
+scheduler = EulerDiscreteScheduler.from_pretrained(f"{ckpt_dir}/scheduler")
+unet = UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half().to(device)
+
+image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+    f'{root_dir}/weights/Kolors-IP-Adapter-Plus/image_encoder',
+    ignore_mismatched_sizes=True
+).to(dtype=torch.float16, device=device)
+
+ip_img_size = 336
+clip_image_processor = CLIPImageProcessor(size=ip_img_size, crop_size=ip_img_size)
+
+pipe = StableDiffusionXLPipeline(
+    vae=vae,
+    text_encoder=text_encoder,
+    tokenizer=tokenizer,
+    unet=unet,
+    scheduler=scheduler,
+    image_encoder=image_encoder,
+    feature_extractor=clip_image_processor,
+    force_zeros_for_empty_prompt=False
+)
+
+pipe = pipe.to(device)
+#pipe.enable_model_cpu_offload()
+
+if hasattr(pipe.unet, 'encoder_hid_proj'):
+    pipe.unet.text_encoder_hid_proj = pipe.unet.encoder_hid_proj
+
+pipe.load_ip_adapter(f'{root_dir}/weights/Kolors-IP-Adapter-Plus', subfolder="", weight_name=["ip_adapter_plus_general.bin"])
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-@spaces.GPU
-def infer(prompt, negative_prompt="", seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
-
+def infer(prompt, ip_adapter_image, negative_prompt="", seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=50, ip_adapter_scale=0.5, progress=gr.Progress(track_tqdm=True)):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
+    
+    generator = torch.Generator(device="cpu").manual_seed(seed)
+    
+    pipe.set_ip_adapter_scale([ip_adapter_scale])
     
     image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        width=width,
+        prompt=prompt,
+        ip_adapter_image=[ip_adapter_image],
+        negative_prompt=negative_prompt,
         height=height,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        generator = generator
-    ).images[0] 
+        width=width,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        num_images_per_prompt=1,
+        generator=generator,
+    ).images[0]
     
     return image, seed
 
 examples = [
-    "A photo of a lavender cat",
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+    ["A photo of a lavender cat", "https://upload.wikimedia.org/wikipedia/commons/thumb/4/4d/Cat_November_2010-1a.jpg/640px-Cat_November_2010-1a.jpg"],
+    ["Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", "https://upload.wikimedia.org/wikipedia/commons/thumb/b/b5/Astronaut_EVA.jpg/640px-Astronaut_EVA.jpg"],
+    ["An astronaut riding a green horse", "https://upload.wikimedia.org/wikipedia/commons/thumb/f/f7/Haflinger_in-motion.jpg/640px-Haflinger_in-motion.jpg"],
+    ["A delicious ceviche cheesecake slice", "https://upload.wikimedia.org/wikipedia/commons/thumb/9/9c/Ceviche_mixto.jpg/640px-Ceviche_mixto.jpg"],
 ]
 
 css="""
 #col-container {
     margin: 0 auto;
-    max-width: 520px;
+    max-width: 720px;
 }
 """
-
-if torch.cuda.is_available():
-    power_device = "GPU"
-else:
-    power_device = "CPU"
-
 with gr.Blocks(css=css) as demo:
-    
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""
-        # AuraFlow 0.1
-        Demo of the [AuraFlow 0.1](https://huggingface.co/fal/AuraFlow) 6.8B parameters open source diffusion transformer model
-        [[blog](https://blog.fal.ai/auraflow/)] [[model](https://huggingface.co/fal/AuraFlow)] [[fal](https://fal.ai/models/fal-ai/aura-flow)]
+        # Kolors Demo
+        Demo of the Kolors model with IP-Adapter integration
         """)
         
         with gr.Row():
-            
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
@@ -87,19 +108,18 @@ with gr.Blocks(css=css) as demo:
                 placeholder="Enter your prompt",
                 container=False,
             )
-            
             run_button = gr.Button("Run", scale=0)
         
-        result = gr.Image(label="Result", show_label=False)
+        with gr.Row():
+            ip_adapter_image = gr.Image(label="IP-Adapter Image", type="pil")
+            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",
             )
-            
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
@@ -107,11 +127,8 @@ with gr.Blocks(css=css) as demo:
                 step=1,
                 value=0,
             )
-            
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
             with gr.Row():
-                
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
@@ -119,7 +136,6 @@ with gr.Blocks(css=css) as demo:
                     step=32,
                     value=1024,
                 )
-                
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
@@ -127,9 +143,7 @@ with gr.Blocks(css=css) as demo:
                     step=32,
                     value=1024,
                 )
-            
             with gr.Row():
-                
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
                     minimum=0.0,
@@ -137,28 +151,34 @@ with gr.Blocks(css=css) as demo:
                     step=0.1,
                     value=5.0,
                 )
-                
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
-                    maximum=50,
+                    maximum=100,
                     step=1,
-                    value=28,
+                    value=50,
                 )
+            ip_adapter_scale = gr.Slider(
+                label="IP-Adapter Scale",
+                minimum=0.0,
+                maximum=1.0,
+                step=0.01,
+                value=0.5,
+            )
         
         gr.Examples(
-            examples = examples,
-            fn = infer,
-            inputs = [prompt],
-            outputs = [result, seed],
+            examples=examples,
+            fn=infer,
+            inputs=[prompt, ip_adapter_image],
+            outputs=[result, seed],
             cache_examples="lazy"
         )
 
     gr.on(
-        triggers=[run_button.click, prompt.submit, negative_prompt.submit],
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result, seed]
+        triggers=[run_button.click, prompt.submit],
+        fn=infer,
+        inputs=[prompt, ip_adapter_image, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, ip_adapter_scale],
+        outputs=[result, seed]
     )
 
 demo.queue().launch()