Update app.py

app.py

@@ -1,4 +1,4 @@
-from diffusers import StableDiffusionXLPipeline, AutoencoderKL
+from diffusers import AutoPipelineForText2Image
 import torch
 import random
 import os
@@ -35,7 +35,7 @@ css = """
 }
 """
 
-def generate(prompt, neg_prompt, samp_steps, cfg_scale, batch_size, seed, height, width, progress=gr.Progress(track_tqdm=True)):
+def generate(prompt, samp_steps, batch_size, seed, height, width, progress=gr.Progress(track_tqdm=True)):
     prompt = prompt.lower()
     if nsfw_filter:
         if prompt[:10] == "krebzonide":
@@ -49,10 +49,9 @@ def generate(prompt, neg_prompt, samp_steps, cfg_scale, batch_size, seed, height
         seed = random.randint(1,999999)
     images = pipe(
         prompt,
-        negative_prompt=neg_prompt,
         num_inference_steps=samp_steps,
-        guidance_scale=cfg_scale,
         num_images_per_prompt=batch_size,
+        guidance_scale=0.0,
         height=height,
         width=width,
         generator=torch.manual_seed(seed),
@@ -63,16 +62,13 @@ def set_base_model(base_model_id):
     vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
     global model_url_list
     model_url = "https://huggingface.co/" + model_url_list[base_model_id]
-    pipe = StableDiffusionXLPipeline.from_single_file(
-        model_url,
+    pipe = AutoPipelineForText2Image.from_pretrained(
+        "stabilityai/sdxl-turbo",
         torch_dtype = torch.float16,
-        variant = "fp16",
-        vae = vae,
-        use_safetensors = True,
-        use_auth_token=hf_token
+        variant = "fp16"
+        #use_auth_token=hf_token
     )
     pipe.to("cuda")
-    pipe.enable_xformers_memory_efficient_attention()
     return pipe
 
 def update_pixel_ratio(num1, num2):
@@ -100,12 +96,10 @@ examples = [
 with gr.Blocks(css=css) as demo:
     with gr.Column():
         prompt = gr.Textbox(label="Prompt")
-        negative_prompt = gr.Textbox(label="Negative Prompt")
         submit_btn = gr.Button("Generate", elem_classes="btn-green")
         with gr.Row():
             samp_steps = gr.Slider(1, 30, value=20, step=1, label="Sampling steps")
-            cfg_scale = gr.Slider(1, 10, value=4, step=0.5, label="Guidance scale")
-            batch_size = gr.Slider(1, 6, value=1, step=1, label="Batch size", interactive=False)
+            batch_size = gr.Slider(1, 6, value=1, step=1, label="Batch size", interactive=True)
         with gr.Row():
             height = gr.Slider(label="Height", value=1024, minimum=8, maximum=1536, step=8)
             width = gr.Slider(label="Width", value=1024, minimum=8, maximum=1024, step=8)
@@ -116,7 +110,7 @@ with gr.Blocks(css=css) as demo:
         with gr.Row():
             lastSeed = gr.Number(label="Last Seed", value=-1, interactive=False)
         ex = gr.Examples(examples=examples, inputs=[prompt, negative_prompt])
-    submit_btn.click(generate, [prompt, negative_prompt, samp_steps, cfg_scale, batch_size, seed, height, width], [gallery, lastSeed], queue=True)
+    submit_btn.click(generate, [prompt, samp_steps, batch_size, seed, height, width], [gallery, lastSeed], queue=True)
     height.release(update_pixel_ratio, [height, width], [pixels, height], queue=False)
     width.release(update_pixel_ratio, [width, height], [pixels, width], queue=False)