Test diffusers

app.py

@@ -9,9 +9,23 @@ from http import HTTPStatus
 from urllib.parse import urlparse, unquote
 from pathlib import PurePosixPath
 import requests
-from dashscope import ImageSynthesis
 import os
 
+from diffusers import DiffusionPipeline
+import torch
+
+model_name = "Qwen/Qwen-Image"
+
+# Load the pipeline
+if torch.cuda.is_available():
+    torch_dtype = torch.bfloat16
+    device = "cuda"
+else:
+    torch_dtype = torch.float32
+    device = "cpu"
+
+pipe = DiffusionPipeline.from_pretrained(model_name, torch_dtype=torch_dtype)
+
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1440
 
@@ -31,6 +45,37 @@ def get_image_size(aspect_ratio):
     else:
         return 1328, 1328
 
+@spaces.GPU(duration=60)
+def infer_diffusers(
+    prompt,
+    negative_prompt=" ",
+    seed=42,
+    randomize_seed=False,
+    aspect_ratio="16:9",
+    guidance_scale=4,
+    num_inference_steps=50,
+    progress=gr.Progress(track_tqdm=True),
+):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    width, height = get_image_size(aspect_ratio)
+    
+    print("Generating for prompt:", prompt)
+    pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        width=width,
+        height=height,
+        num_inference_steps=50,
+        true_cfg_scale=4.0,
+        generator=torch.Generator(device="cuda").manual_seed(42)
+    ).images[0]
+
+    #image.save("example.png")
+
+    return image, seed
+
+
 @spaces.GPU(duration=65)
 def infer(
     prompt,
@@ -159,7 +204,7 @@ with gr.Blocks(css=css) as demo:
         gr.Examples(examples=examples, inputs=[prompt], outputs=[result, seed], fn=infer, cache_examples=False, cache_mode="lazy")
     gr.on(
         triggers=[run_button.click, prompt.submit],
-        fn=infer,
+        fn=infer_diffusers,
         inputs=[
             prompt,
             negative_prompt,