Create app.py

app.py

@@ -0,0 +1,88 @@
+import os
+import sys
+
+import gradio as gr
+import numpy as np
+import torch
+import random
+
+from diffusers import AutoPipelineForText2Image
+from diffusers.pipelines.wuerstchen.pipeline_wuerstchen_prior import DEFAULT_STAGE_C_TIMESTEPS
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+pipe = AutoPipelineForText2Image.from_pretrained("warp-ai/wuerstchen",
+    torch_dtype=torch.float32)
+pipe.to(device)
+
+pipe.safety_checker = None
+
+'''
+#### 9min a sample (2 cores)
+caption = "Anthropomorphic cat dressed as a fire fighter"
+images = pipe(
+    caption,
+    width=512,
+    height=512,
+    prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS, #### length of 30
+    prior_guidance_scale=4.0,
+    num_images_per_prompt=1,
+    num_inference_steps = 6, #### default num of 12, 6 favour
+).images
+'''
+
+def process(prompt, num_samples, image_resolution, sample_steps, seed,):
+    from PIL import Image
+    with torch.no_grad():
+        if seed == -1:
+            seed = random.randint(0, 65535)
+        #control_image = Image.fromarray(detected_map)
+
+        # run inference
+        #generator = torch.Generator(device=device).manual_seed(seed)
+        H = image_resolution
+        W = image_resolution
+        images = []
+        for i in range(num_samples):
+            image = pipe(
+                prompt,
+                prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS,
+                prior_guidance_scale=4.0,
+                num_inference_steps = sample_steps,
+                num_images_per_prompt=1,
+                height=H, width=W).images[0]
+            images.append(np.asarray(image))
+
+        results = images
+    return results
+    #return [255 - detected_map] + results
+
+block = gr.Blocks().queue()
+with block:
+    with gr.Row():
+        gr.Markdown("## Rapid Diffusion model from warp-ai/wuerstchen")
+        #gr.Markdown("This _example_ was **drive** from <br/><b><h4>[https://github.com/svjack/ControlLoRA-Chinese](https://github.com/svjack/ControlLoRA-Chinese)</h4></b>\n")
+    with gr.Row():
+        with gr.Column():
+            #input_image = gr.Image(source='upload', type="numpy", value = "hate_dog.png")
+            prompt = gr.Textbox(label="Prompt", value = "Anthropomorphic cat dressed as a fire fighter")
+            run_button = gr.Button(label="Run")
+            with gr.Accordion("Advanced options", open=False):
+                num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
+                image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=256)
+                #low_threshold = gr.Slider(label="Canny low threshold", minimum=1, maximum=255, value=100, step=1)
+                #high_threshold = gr.Slider(label="Canny high threshold", minimum=1, maximum=255, value=200, step=1)
+                sample_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=6, step=1)
+                #scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
+                seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
+                #eta = gr.Number(label="eta", value=0.0)
+                #a_prompt = gr.Textbox(label="Added Prompt", value='')
+                #n_prompt = gr.Textbox(label="Negative Prompt",
+                #                      value='低质量，模糊，混乱')
+        with gr.Column():
+            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
+    #ips = [None, prompt, None, None, num_samples, image_resolution, sample_steps, None, seed, None, None, None]
+    ips = [prompt, num_samples, image_resolution, sample_steps, seed]
+    run_button.click(fn=process, inputs=ips, outputs=[result_gallery], show_progress = True)
+
+
+block.launch(server_name='0.0.0.0')