Create app.py

app.py

@@ -0,0 +1,191 @@
+import gradio as gr
+import numpy as np
+import random
+import spaces
+import torch
+from diffusers import SanaSprintPipeline
+import peft
+from peft.tuners.lora.layer import Linear as LoraLinear
+import types
+
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+adapter_name = "hypernoise_adapter"
+
+# Load the pipeline and adapter
+pipe = SanaSprintPipeline.from_pretrained(
+    "Efficient-Large-Model/Sana_Sprint_0.6B_1024px_diffusers",
+    torch_dtype=dtype,
+).to(device, dtype)
+
+pipe.transformer = peft.PeftModel.from_pretrained(
+    pipe.transformer,
+    "lucaeyring/HyperNoise_Sana_Sprint_0.6B",
+    adapter_name=adapter_name,
+    dtype=dtype,
+).to(device, dtype)
+
+# Define the custom forward function for LoRA
+def scaled_base_lora_forward(self, x, *args, **kwargs):
+    if self.disable_adapters: 
+        return self.base_layer(x, *args, **kwargs)
+    return self.lora_B[adapter_name](self.lora_A[adapter_name](x)) * self.scaling[adapter_name]
+
+# Apply the custom forward to proj_out module
+for name, module in pipe.transformer.base_model.model.named_modules():
+    if name == "proj_out" and isinstance(module, LoraLinear):
+        module.forward = types.MethodType(scaled_base_lora_forward, module)
+        break
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024  # Sana Sprint is optimized for 1024px
+
+@spaces.GPU()
+def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, 
+          num_inference_steps=4, guidance_scale=4.5, progress=gr.Progress(track_tqdm=True)):
+    
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    
+    # Set random seed for reproducibility
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    
+    with torch.inference_mode():
+        # Encode the prompt
+        prompt_embeds, prompt_attention_mask = pipe.encode_prompt(
+            [prompt], 
+            device=device
+        )
+        
+        # Generate initial random latents
+        init_latents = torch.randn(
+            [1, 32, 32, 32], 
+            device=device, 
+            dtype=dtype
+        )
+        
+        # Apply HyperNoise modulation with adapter enabled
+        pipe.transformer.enable_adapter_layers()
+        modulated_latents = pipe.transformer(
+            hidden_states=init_latents,
+            encoder_hidden_states=prompt_embeds,
+            encoder_attention_mask=prompt_attention_mask,
+            guidance=torch.tensor([guidance_scale], device=device, dtype=dtype) * 0.1,
+            timestep=torch.tensor([1.0], device=device, dtype=dtype),
+        ).sample + init_latents
+        
+        # Generate final image with adapter disabled
+        pipe.transformer.disable_adapter_layers()
+        image = pipe(
+            latents=modulated_latents,
+            prompt_embeds=prompt_embeds,
+            prompt_attention_mask=prompt_attention_mask,
+            num_inference_steps=num_inference_steps,
+            height=height,
+            width=width,
+        ).images[0]
+    
+    return image, seed
+
+examples = [
+    "A smiling slice of pizza doing yoga on a mountain top",
+    "A fluffy cat wearing a wizard hat casting spells",
+    "A robot painting a self-portrait in Van Gogh style",
+    "A tiny dragon sleeping in a teacup",
+    "An astronaut riding a unicorn through a rainbow",
+]
+
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown("""# HyperNoise Sana Sprint 0.6B
+        
+Fast text-to-image generation with HyperNoise adapter for Sana Sprint model.
+        
+[[Sana Sprint Model](https://huggingface.co/Efficient-Large-Model/Sana_Sprint_0.6B_1024px_diffusers)] 
+[[HyperNoise Adapter](https://huggingface.co/lucaeyring/HyperNoise_Sana_Sprint_0.6B)]
+        """)
+        
+        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):
+            
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=42,
+            )
+            
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            
+            with gr.Row():
+                width = gr.Slider(
+                    label="Width",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=64,
+                    value=1024,
+                )
+                
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=64,
+                    value=1024,
+                )
+            
+            with gr.Row():
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=20,
+                    step=1,
+                    value=4,
+                )
+                
+                guidance_scale = gr.Slider(
+                    label="Guidance Scale",
+                    minimum=1.0,
+                    maximum=10.0,
+                    step=0.5,
+                    value=4.5,
+                )
+        
+        gr.Examples(
+            examples=examples,
+            fn=infer,
+            inputs=[prompt],
+            outputs=[result, seed],
+            cache_examples="lazy"
+        )
+
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn=infer,
+        inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps, guidance_scale],
+        outputs=[result, seed]
+    )
+
+demo.launch()