Update app.py

app.py

@@ -1,53 +1,53 @@
-import torch
-import spaces
-from PIL import Image
-from generationPipeline import generate
-from transformers import CLIPTokenizer
-from loadModel import preload_models_from_standard_weights
-import gradio as gr
-
-
-Device = 'cuda'
-
-print(f"Using device: {Device}")
-
-
-tokenizer = CLIPTokenizer("vocab.json", merges_file="merges.txt")
-model_file = "weights2.ckpt"
-models = preload_models_from_standard_weights(model_file, Device)
-
-
-@spaces.GPU(duration = 242)
-def generate_image(prompt, strength, seed):
-    # Your generate function adapted to accept parameters
-    output_image = generate(
-        prompt=prompt,
-        uncond_prompt="",
-        input_image=None,
-        strength=strength,
-        do_cfg=True,
-        cfg_scale=8,
-        sampler_name="ddpm",
-        n_inference_steps=50,
-        seed=seed,
-        models=models,
-        device=Device,
-        idle_device="cpu",
-        tokenizer=tokenizer,
-    )
-    return Image.fromarray(output_image)
-
-
-iface = gr.Interface(
-    fn=generate_image,
-    inputs=[
-        gr.inputs.Textbox(label="Prompt"),
-        gr.inputs.Slider(0, 1, step=0.01, label="Strength (For Image-2-Image): Strength = 1 (Output further from input image), Strength = 0 (Output similar as Input image)"),
-        gr.inputs.Number(default=42, label="Seed"),
-    ],
-    outputs=gr.outputs.Image(label="Generated Image"),
-    title="Stable Diffusion Image Generator",
-    description="Generate images from text prompts using Stable Diffusion.",
-)
-
-iface.launch(debug = True)
+import torch
+import spaces
+from PIL import Image
+from generationPipeline import generate
+from transformers import CLIPTokenizer
+from loadModel import preload_models_from_standard_weights
+import gradio as gr
+
+
+Device = 'cuda'
+
+print(f"Using device: {Device}")
+
+
+tokenizer = CLIPTokenizer("vocab.json", merges_file="merges.txt")
+model_file = "weights-inkpen.ckpt"
+models = preload_models_from_standard_weights(model_file, Device)
+
+
+@spaces.GPU(duration = 242)
+def generate_image(prompt, strength, seed):
+    # Your generate function adapted to accept parameters
+    output_image = generate(
+        prompt=prompt,
+        uncond_prompt="",
+        input_image=None,
+        strength=strength,
+        do_cfg=True,
+        cfg_scale=8,
+        sampler_name="ddpm",
+        n_inference_steps=50,
+        seed=seed,
+        models=models,
+        device=Device,
+        idle_device="cpu",
+        tokenizer=tokenizer,
+    )
+    return Image.fromarray(output_image)
+
+
+iface = gr.Interface(
+    fn=generate_image,
+    inputs=[
+        gr.inputs.Textbox(label="Prompt"),
+        gr.inputs.Slider(0, 1, step=0.01, label="Strength (For Image-2-Image): Strength = 1 (Output further from input image), Strength = 0 (Output similar as Input image)"),
+        gr.inputs.Number(default=42, label="Seed"),
+    ],
+    outputs=gr.outputs.Image(label="Generated Image"),
+    title="Stable Diffusion Image Generator",
+    description="Generate images from text prompts using Stable Diffusion.",
+)
+
+iface.launch(debug = True)