Spaces:
Running
on
Zero
Running
on
Zero
Commit
·
dc528d7
1
Parent(s):
2099bb6
support multimodel
Browse files
app.py
CHANGED
@@ -9,9 +9,12 @@ from pipeline.util import (
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create_hdr_effect,
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progressive_upscale,
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select_scheduler,
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)
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device = "cuda"
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# Initialize the models and pipeline
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controlnet = ControlNetUnionModel.from_pretrained(
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@@ -19,19 +22,28 @@ controlnet = ControlNetUnionModel.from_pretrained(
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).to(device=device)
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vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16).to(device=device)
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-
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model_id
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-
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pipe.enable_vae_tiling() # << Enable this if you have limited VRAM
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pipe.enable_vae_slicing() # << Enable this if you have limited VRAM
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# region functions
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@spaces.GPU(duration=120)
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def predict(
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image,
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prompt,
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negative_prompt,
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@@ -49,6 +61,9 @@ def predict(
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):
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global pipe
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# Set selected scheduler
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print(f"Using scheduler: {scheduler}...")
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pipe.scheduler = select_scheduler(pipe, scheduler)
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@@ -97,7 +112,6 @@ def predict(
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return image
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-
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def clear_result():
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return gr.update(value=None)
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@@ -111,6 +125,38 @@ def set_maximum_resolution(max_tile_size, current_value):
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def select_tile_weighting_method(tile_weighting_method):
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return gr.update(visible=True if tile_weighting_method=="Gaussian" else False)
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# endregion
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css = """
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@@ -207,6 +253,8 @@ with gr.Blocks(css=css, theme=gr.themes.Ocean(), title="MoD ControlNet Tile Upsc
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result = gr.Image(
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label="Generated Image", show_label=True, format="png", interactive=False, scale=1, height=500, min_width=670
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)
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with gr.Row():
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with gr.Accordion("Input Prompt", open=False):
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with gr.Column():
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@@ -231,6 +279,9 @@ with gr.Blocks(css=css, theme=gr.themes.Ocean(), title="MoD ControlNet Tile Upsc
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with gr.Sidebar(label="Parameters", open=True):
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with gr.Row(elem_id="parameters_row"):
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gr.Markdown("### General parameters")
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tile_weighting_method = gr.Dropdown(
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label="Tile Weighting Method", choices=["Cosine", "Gaussian"], value="Cosine"
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)
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@@ -260,7 +311,7 @@ with gr.Blocks(css=css, theme=gr.themes.Ocean(), title="MoD ControlNet Tile Upsc
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negative_prompt.value,
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4096,
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0.0,
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-
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0.35,
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1.0,
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0.3,
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@@ -282,7 +333,21 @@ with gr.Blocks(css=css, theme=gr.themes.Ocean(), title="MoD ControlNet Tile Upsc
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4,
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1024,
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"Cosine"
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],
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[ "./examples/2.jpg",
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prompt.value,
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negative_prompt.value,
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@@ -297,6 +362,20 @@ with gr.Blocks(css=css, theme=gr.themes.Ocean(), title="MoD ControlNet Tile Upsc
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1024,
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"Cosine"
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],
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[ "./examples/3.jpg",
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prompt.value,
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negative_prompt.value,
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@@ -311,6 +390,20 @@ with gr.Blocks(css=css, theme=gr.themes.Ocean(), title="MoD ControlNet Tile Upsc
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1280,
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"Gaussian"
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],
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[ "./examples/4.jpg",
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prompt.value,
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negative_prompt.value,
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@@ -325,6 +418,20 @@ with gr.Blocks(css=css, theme=gr.themes.Ocean(), title="MoD ControlNet Tile Upsc
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1024,
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"Gaussian"
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],
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[ "./examples/5.jpg",
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prompt.value,
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negative_prompt.value,
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@@ -338,7 +445,7 @@ with gr.Blocks(css=css, theme=gr.themes.Ocean(), title="MoD ControlNet Tile Upsc
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4,
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1024,
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"Cosine"
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-
]
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],
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inputs=[
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input_image,
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@@ -355,7 +462,7 @@ with gr.Blocks(css=css, theme=gr.themes.Ocean(), title="MoD ControlNet Tile Upsc
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max_tile_size,
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tile_weighting_method,
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],
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fn=
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outputs=result,
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cache_examples=False,
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)
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@@ -369,6 +476,7 @@ with gr.Blocks(css=css, theme=gr.themes.Ocean(), title="MoD ControlNet Tile Upsc
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).then(
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fn=predict,
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inputs=[
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input_image,
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prompt,
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negative_prompt,
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create_hdr_effect,
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progressive_upscale,
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select_scheduler,
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torch_gc,
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)
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device = "cuda"
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pipe = None
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last_loaded_model = None
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# Initialize the models and pipeline
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controlnet = ControlNetUnionModel.from_pretrained(
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).to(device=device)
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vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16).to(device=device)
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def load_model(model_id):
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global pipe, last_loaded_model
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if model_id != last_loaded_model:
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pipe = None
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torch_gc()
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pipe = StableDiffusionXLControlNetTileSRPipeline.from_pretrained(
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model_id, controlnet=controlnet, vae=vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
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).to(device)
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#pipe.enable_model_cpu_offload() # << Enable this if you have limited VRAM
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pipe.enable_vae_tiling() # << Enable this if you have limited VRAM
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pipe.enable_vae_slicing() # << Enable this if you have limited VRAM
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last_loaded_model = model_id
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load_model("SG161222/RealVisXL_V5.0_Lightning")
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# region functions
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@spaces.GPU(duration=120)
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def predict(
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model_id,
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image,
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prompt,
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negative_prompt,
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):
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global pipe
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# Load model if changed
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load_model(model_id)
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# Set selected scheduler
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print(f"Using scheduler: {scheduler}...")
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pipe.scheduler = select_scheduler(pipe, scheduler)
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return image
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def clear_result():
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return gr.update(value=None)
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def select_tile_weighting_method(tile_weighting_method):
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return gr.update(visible=True if tile_weighting_method=="Gaussian" else False)
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@spaces.GPU(duration=120)
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def run_for_examples(image,
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prompt,
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negative_prompt,
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resolution,
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hdr,
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num_inference_steps,
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denoising_strenght,
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controlnet_strength,
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tile_gaussian_sigma,
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scheduler,
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guidance_scale,
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max_tile_size,
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tile_weighting_method):
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predict(
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model.value,
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image,
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prompt,
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negative_prompt,
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resolution,
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hdr,
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num_inference_steps,
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denoising_strenght,
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controlnet_strength,
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tile_gaussian_sigma,
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scheduler,
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guidance_scale,
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max_tile_size,
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tile_weighting_method)
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# endregion
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css = """
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result = gr.Image(
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label="Generated Image", show_label=True, format="png", interactive=False, scale=1, height=500, min_width=670
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)
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with gr.Row():
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gr.HTML("<div style='color: red;'>If you are not a Pro account, run the LCM sampler examples on the RealVisXL_V5.0_Lightning model. For best results use the UniPC sampler and RealVisXL_V5.0 model examples.</div>")
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with gr.Row():
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with gr.Accordion("Input Prompt", open=False):
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with gr.Column():
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with gr.Sidebar(label="Parameters", open=True):
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with gr.Row(elem_id="parameters_row"):
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gr.Markdown("### General parameters")
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model = gr.Dropdown(
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label="Model", choices=["SG161222/RealVisXL_V5.0_Lightning", "SG161222/RealVisXL_V5.0"], value="SG161222/RealVisXL_V5.0_Lightning"
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)
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tile_weighting_method = gr.Dropdown(
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label="Tile Weighting Method", choices=["Cosine", "Gaussian"], value="Cosine"
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)
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negative_prompt.value,
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4096,
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0.0,
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25,
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0.35,
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1.0,
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0.3,
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4,
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1024,
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"Cosine"
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],
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[ "./examples/2.jpg",
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prompt.value,
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negative_prompt.value,
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4096,
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0.5,
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25,
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0.35,
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1.0,
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0.3,
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"LCM",
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4,
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1024,
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"Cosine"
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],
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[ "./examples/2.jpg",
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prompt.value,
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negative_prompt.value,
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1024,
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"Cosine"
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],
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[ "./examples/3.jpg",
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prompt.value,
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negative_prompt.value,
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5120,
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0.5,
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25,
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0.35,
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1.0,
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0.3,
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"LCM",
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4,
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1280,
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"Gaussian"
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],
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[ "./examples/3.jpg",
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prompt.value,
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negative_prompt.value,
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1280,
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"Gaussian"
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],
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[ "./examples/4.jpg",
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prompt.value,
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negative_prompt.value,
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8192,
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0.1,
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25,
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0.35,
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1.0,
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0.3,
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"LCM",
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4,
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1024,
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"Gaussian"
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],
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[ "./examples/4.jpg",
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prompt.value,
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negative_prompt.value,
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1024,
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"Gaussian"
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],
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[ "./examples/5.jpg",
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prompt.value,
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negative_prompt.value,
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8192,
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0.3,
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25,
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0.35,
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1.0,
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0.3,
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"LCM",
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4,
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1024,
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"Cosine"
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],
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[ "./examples/5.jpg",
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prompt.value,
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negative_prompt.value,
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4,
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1024,
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"Cosine"
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]
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],
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inputs=[
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input_image,
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max_tile_size,
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tile_weighting_method,
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],
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fn=run_for_examples,
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outputs=result,
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cache_examples=False,
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)
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).then(
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fn=predict,
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inputs=[
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model,
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input_image,
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prompt,
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negative_prompt,
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