Update app.py

app.py

@@ -38,94 +38,31 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
 def generate(
     prompt: str,
     negative_prompt: str = "",
-    prompt_2: str = "",
-    negative_prompt_2: str = "",
     use_negative_prompt: bool = False,
-    use_prompt_2: bool = False,
-    use_negative_prompt_2: bool = False,
     seed: int = 0,
     width: int = 1024,
     height: int = 1024,
     guidance_scale_base: float = 5.0,
     num_inference_steps_base: int = 25,
-    controlnet_conditioning_scale: float = 1,
-    control_guidance_start: float = 0,
-    control_guidance_end: float = 1,
-    strength_img2img: float = 0.7,
     use_vae: bool = False,
     use_lora: bool = False,
     use_lora2: bool = False,
     model = 'stabilityai/stable-diffusion-xl-base-1.0',
-    vaecall = 'madebyollin/sdxl-vae-fp16-fix',
+    vaemodel = 'madebyollin/sdxl-vae-fp16-fix',
     lora = '',
     lora2 = '',
-    controlnet_model = 'diffusers/controlnet-canny-sdxl-1.0',
     lora_scale: float = 0.7,
     lora_scale2: float = 0.7,
     use_img2img: bool = False,
-    use_controlnet: bool = False,
-    use_controlnetimg2img: bool = False,
-    controlnet_img = '',
-    controlnet_img2img = '',
 ):
     if torch.cuda.is_available():
 
         if not use_img2img:
-            scheduler = DPMSolverMultistepScheduler.from_pretrained(model, subfolder="scheduler")
-            pipe = DiffusionPipeline.from_pretrained(model, scheduler=scheduler, torch_dtype=torch.float16)
-
-            pipe.to(device)
+            pipe = DiffusionPipeline.from_pretrained(model, torch_dtype=torch.float16)
             
             if use_vae:  
-                vae = AutoencoderKL.from_pretrained(vaecall, torch_dtype=torch.float16)
+                vae = AutoencoderKL.from_pretrained(vaemodel, torch_dtype=torch.float16)
                 pipe = DiffusionPipeline.from_pretrained(model, vae=vae, torch_dtype=torch.float16)
-
-                pipe.to(device)
-                
-        if use_img2img:
-            pipe = AutoPipelineForImage2Image.from_pretrained(model, torch_dtype=torch.float16)
-
-            img = Image.open(url)
-            img = img.resize((width,height))
-            return img
-
-            if use_vae:  
-                vae = AutoencoderKL.from_pretrained(vaecall, torch_dtype=torch.float16)
-                pipe = AutoPipelineForImage2Image.from_pretrained(model, vae=vae, torch_dtype=torch.float16)
-
-        if use_controlnet:
-            controlnet = ControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.float16)
-            pipe = StableDiffusionXLControlNetPipeline.from_pretrained(model, controlnet=controlnet, torch_dtype=torch.float16)
-
-            image = load_image(controlnet_img)
-            
-            image = np.array(image)
-            image = cv2.Canny(image, 100, 200)
-            image = image[:, :, None]
-            image = np.concatenate([image, image, image], axis=2)
-            image = Image.fromarray(image)
-
-            if use_vae:
-                vae = AutoencoderKL.from_pretrained(vaecall, torch_dtype=torch.float16)
-                pipe = StableDiffusionXLControlNetPipeline.from_pretrained(model, controlnet=controlnet, vae=vae, torch_dtype=torch.float16)
-
-        if use_controlnetimg2img:
-            controlnet = ControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.float16)
-            pipe = StableDiffusionXLControlNetInpaintPipeline.from_pretrained(model, controlnet=controlnet, torch_dtype=torch.float16)
-
-            image_start = load_image(controlnet_img)
-            image = load_image(controlnet_img)
-            image_mask = load_image(controlnet_img2img)
-            
-            image = np.array(image)
-            image = cv2.Canny(image, 100, 200)
-            image = image[:, :, None]
-            image = np.concatenate([image, image, image], axis=2)
-            image = Image.fromarray(image)
-
-            if use_vae:
-                vae = AutoencoderKL.from_pretrained(vaecall, torch_dtype=torch.float16)
-                pipe = StableDiffusionXLControlNetInpaintPipeline.from_pretrained(model, controlnet=controlnet, vae=vae, torch_dtype=torch.float16)
                 
         if use_lora:
             pipe.load_lora_weights(lora)
@@ -140,74 +77,19 @@ def generate(
     
     if not use_negative_prompt:
         negative_prompt = None  # type: ignore
-    if not use_prompt_2:
-        prompt_2 = None  # type: ignore
-    if not use_negative_prompt_2:
-        negative_prompt_2 = None  # type: ignore
-
-    if use_controlnetimg2img:
-        image = pipe(
-            prompt=prompt,
-            strength=strength_img2img,
-            controlnet_conditioning_scale=controlnet_conditioning_scale,
-            eta=0.0,
-            mask_image=image_mask,
-            image=image_start,
-            control_image=image,
-            negative_prompt=negative_prompt,
-            width=width,
-            height=height,
-            guidance_scale=guidance_scale_base,
-            num_inference_steps=num_inference_steps_base,
-            generator=generator,
-        ).images[0]
-        return image
-    if use_controlnet:
-        image = pipe(
-            prompt=prompt,
-            controlnet_conditioning_scale=controlnet_conditioning_scale,
-            control_guidance_start=control_guidance_start,
-            control_guidance_end=control_guidance_end,
-            image=image,
-            negative_prompt=negative_prompt,
-            prompt_2=prompt_2,
-            width=width,
-            height=height,
-            negative_prompt_2=negative_prompt_2,
-            guidance_scale=guidance_scale_base,
-            num_inference_steps=num_inference_steps_base,
-            generator=generator,
-        ).images[0]
-        return image
-    elif use_img2img:
-        images = pipe(
-            prompt=prompt,
-            image=img,
-            strength=strength_img2img,
-            negative_prompt=negative_prompt,
-            prompt_2=prompt_2,
-            negative_prompt_2=negative_prompt_2,
-            width=width,
-            height=height,
-            guidance_scale=guidance_scale_base,
-            num_inference_steps=num_inference_steps_base,
-            generator=generator,
-            output_type="pil",
-        ).images[0]
-        return images
-    else:
-        return pipe(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            prompt_2=prompt_2,
-            negative_prompt_2=negative_prompt_2,
-            width=width,
-            height=height,
-            guidance_scale=guidance_scale_base,
-            num_inference_steps=num_inference_steps_base,
-            generator=generator,
-            output_type="pil",
-        ).images[0]
+        
+    return pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        prompt_2=prompt_2,
+        negative_prompt_2=negative_prompt_2,
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale_base,
+        num_inference_steps=num_inference_steps_base,
+        generator=generator,
+        output_type="pil",
+    ).images[0]
 
 theme = gr.themes.Monochrome(
     text_size=gr.themes.Size(lg="18px", md="15px", sm="13px", xl="22px", xs="12px", xxl="24px", xxs="9px"),
@@ -227,10 +109,22 @@ with gr.Blocks(theme=theme, css="style.css") as demo:
     
     with gr.Group():
         model = gr.Text(label='Model', value='stabilityai/stable-diffusion-xl-base-1.0', placeholder='e.g. stabilityai/stable-diffusion-xl-base-1.0', info='Enter the HUB path for this model! The model must be converted to diffusers to work. We recommend SG161222/RealVisXL_V4.0 for realistic generations, for example. By default, SDXL 1.0 (stabilityai/stable-diffusion-xl-base-1.0) will be used.')
-        vaecall = gr.Text(label='VAE', placeholder='e.g. madebyollin/sdxl-vae-fp16-fix', info='You can use the VAE that suits you best! Using an unfamiliar VAE or one that is not from the same base model mentioned above will result in errors or distorted images. You must enable "Use VAE" in advanced settings if you want to use a VAE.')
-        controlnet_model = gr.Text(label='Controlnet', placeholder='e.g diffusers/controlnet-canny-sdxl-1.0')
-        controlnet_img = gr.Text(label='URL (Controlnet)', placeholder='e.g https://example.com/image.png')
-        controlnet_img2img = gr.Text(label='URL (Controlnet - IMG2IMG)', placeholder='e.g https://example.com/image.png')
+        vaemodel = gr.Text(label='VAE', placeholder='e.g. madebyollin/sdxl-vae-fp16-fix', info='You can use the VAE that suits you best! Using an unfamiliar VAE or one that is not from the same base model mentioned above will result in errors or distorted images. You must enable "Use VAE" in advanced settings if you want to use a VAE.')
+        with gr.Row():
+            width = gr.Slider(
+                label="Width",
+                minimum=256,
+                maximum=MAX_IMAGE_SIZE,
+                step=32,
+                value=1024,
+            )
+            height = gr.Slider(
+                label="Height",
+                minimum=256,
+                maximum=MAX_IMAGE_SIZE,
+                step=32,
+                value=1024,
+            )
         num_inference_steps_base = gr.Slider(
                 info="Number of denoising steps",
                 label="Number of inference steps",
@@ -251,15 +145,10 @@ with gr.Blocks(theme=theme, css="style.css") as demo:
         result = gr.Image(label="Result", show_label=False)
     with gr.Accordion("Advanced options", open=False):
         with gr.Row():
-            use_controlnet = gr.Checkbox(label='Use Controlnet', value=False)
-            use_controlnetimg2img = gr.Checkbox(label='Use Controlnet Img2Img', value=False)
-            use_img2img = gr.Checkbox(label='Use Img2Img', value=False)
             use_vae = gr.Checkbox(label='Use VAE', value=False)
             use_lora = gr.Checkbox(label='Use Lora 1', value=False)
             use_lora2 = gr.Checkbox(label='Use Lora 2', value=False)
             use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False)
-            use_prompt_2 = gr.Checkbox(label="Use prompt 2", value=False)
-            use_negative_prompt_2 = gr.Checkbox(label="Use negative prompt 2", value=False)
         with gr.Group():
             gr.Markdown("""### LoRAs
                         <small>To activate the use of LoRAs, you should check only the 'Use 1 LoRA' checkbox if you want to use just one LoRA, and only the 'Use 2 LoRAs' checkbox if you want to use 2 LoRAs (i.e., blend styles)! It's important that all LoRAs are from the same base version of the selected model. For example, if the model is SDXL 1.0, the LoRA must also be.</small>"""
@@ -284,11 +173,6 @@ with gr.Blocks(theme=theme, css="style.css") as demo:
                 value=0.7,
                 visible=False,
                 )
-        with gr.Group():
-            gr.Markdown("""### Img2Img
-                        <small>To activate the use of Img2Img and submit an image as a reference for generation, you must check the 'Use Img2Img' checkbox.</small>"""
-                       )
-            url = gr.Image(label="Target Image", type="filepath")
         with gr.Group():
             negative_prompt = gr.Text(
                 placeholder="Input Negative Prompt",
@@ -296,19 +180,6 @@ with gr.Blocks(theme=theme, css="style.css") as demo:
                 max_lines=1,
                 visible=False,
             )
-            prompt_2 = gr.Text(
-                placeholder="Input Prompt 2",
-                label="Prompt 2",
-                max_lines=1,
-                visible=False,
-            )
-            negative_prompt_2 = gr.Text(
-                placeholder="Input Negative Prompt 2",
-                label="Negative prompt 2",
-                max_lines=1,
-                visible=False,
-            )
-    
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
@@ -317,23 +188,7 @@ with gr.Blocks(theme=theme, css="style.css") as demo:
                 value=0,
             )
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-        with gr.Row():
-            width = gr.Slider(
-                label="Width",
-                minimum=256,
-                maximum=MAX_IMAGE_SIZE,
-                step=32,
-                value=1024,
-            )
-            height = gr.Slider(
-                label="Height",
-                minimum=256,
-                maximum=MAX_IMAGE_SIZE,
-                step=32,
-                value=1024,
-            )
-            
-        with gr.Row():
+        with gr.Group():
             guidance_scale_base = gr.Slider(
                 info="Scale for classifier-free guidance",
                 label="Guidance scale",
@@ -342,43 +197,7 @@ with gr.Blocks(theme=theme, css="style.css") as demo:
                 step=0.1,
                 value=5.0,
             )
-        with gr.Row():
-            controlnet_conditioning_scale = gr.Slider(
-                info="controlnet_conditioning_scale",
-                label="controlnet_conditioning_scale",
-                minimum=0.01,
-                maximum=2,
-                step=0.01,
-                value=1,
-            )
-        with gr.Row():
-            control_guidance_start = gr.Slider(
-                info="control_guidance_start",
-                label="control_guidance_start",
-                minimum=0.01,
-                maximum=1,
-                step=0.01,
-                value=0,
-            )
-        with gr.Row():
-            control_guidance_end = gr.Slider(
-                info="control_guidance_end",
-                label="control_guidance_end",
-                minimum=0.01,
-                maximum=1,
-                step=0.01,
-                value=1,
-            )
-        with gr.Row():
-            strength_img2img = gr.Slider(
-                info="Strength for Img2Img",
-                label="Strength",
-                minimum=0,
-                maximum=1,
-                step=0.01,
-                value=0.7,
-            )
-
+            
     use_negative_prompt.change(
         fn=lambda x: gr.update(visible=x),
         inputs=use_negative_prompt,
@@ -386,59 +205,24 @@ with gr.Blocks(theme=theme, css="style.css") as demo:
         queue=False,
         api_name=False,
     )
-    use_prompt_2.change(
-        fn=lambda x: gr.update(visible=x),
-        inputs=use_prompt_2,
-        outputs=prompt_2,
-        queue=False,
-        api_name=False,
-    )
-    use_negative_prompt_2.change(
-        fn=lambda x: gr.update(visible=x),
-        inputs=use_negative_prompt_2,
-        outputs=negative_prompt_2,
-        queue=False,
-        api_name=False,
-    )
     use_vae.change(
         fn=lambda x: gr.update(visible=x),
         inputs=use_vae,
-        outputs=vaecall,
+        outputs=vaemodel,
         queue=False,
         api_name=False,
     )
     use_lora.change(
-    fn=lambda x: [gr.update(visible=x), gr.update(visible=x)],
-    inputs=use_lora,
-    outputs=[lora, lora_scale],
-    queue=False,
-    api_name=False,
-    )
-    use_lora2.change(
-    fn=lambda x: [gr.update(visible=x), gr.update(visible=x), gr.update(visible=x), gr.update(visible=x)],
-    inputs=use_lora2,
-    outputs=[lora, lora2, lora_scale, lora_scale2],
-    queue=False,
-    api_name=False,
-    )
-    use_img2img.change(
-        fn=lambda x: gr.update(visible=x),
-        inputs=use_img2img,
-        outputs=url,
+        fn=lambda x: [gr.update(visible=x), gr.update(visible=x)],
+        inputs=use_lora,
+        outputs=[lora, lora_scale],
         queue=False,
         api_name=False,
     )
-    use_controlnet.change(
-        fn=lambda x: gr.update(visible=x),
-        inputs=use_controlnet,
-        outputs=controlnet_img,
-        queue=False,
-        api_name=False,
-    )
-    use_controlnetimg2img.change(
-        fn=lambda x: gr.update(visible=x),
-        inputs=use_controlnetimg2img,
-        outputs=controlnet_img2img,
+    use_lora2.change(
+        fn=lambda x: [gr.update(visible=x), gr.update(visible=x), gr.update(visible=x), gr.update(visible=x)],
+        inputs=use_lora2,
+        outputs=[lora, lora2, lora_scale, lora_scale2],
         queue=False,
         api_name=False,
     )
@@ -447,8 +231,6 @@ with gr.Blocks(theme=theme, css="style.css") as demo:
         triggers=[
             prompt.submit,
             negative_prompt.submit,
-            prompt_2.submit,
-            negative_prompt_2.submit,
             run_button.click,
         ],
         fn=randomize_seed_fn,
@@ -461,36 +243,21 @@ with gr.Blocks(theme=theme, css="style.css") as demo:
         inputs=[
             prompt,
             negative_prompt,
-            prompt_2,
-            negative_prompt_2,
             use_negative_prompt,
-            use_prompt_2,
-            use_negative_prompt_2,
             seed,
             width,
             height,
             guidance_scale_base,
             num_inference_steps_base,
-            controlnet_conditioning_scale,
-            control_guidance_start,
-            control_guidance_end,
-            strength_img2img,
             use_vae,
             use_lora,
             use_lora2,
             model,
-            vaecall,
+            vaemodel,
             lora,
             lora2,
-            controlnet_model,
             lora_scale,
             lora_scale2,
-            use_img2img,
-            use_controlnet,
-            use_controlnetimg2img,
-            url,
-            controlnet_img,
-            controlnet_img2img,
         ],
         outputs=result,
         api_name="run",