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Runtime error
Runtime error
update ifx
Browse files- sam2edit_demo.py +2 -4
- sam2edit_lora.py +6 -3
sam2edit_demo.py
CHANGED
@@ -72,8 +72,6 @@ def create_demo_template(process, process_image_click=None, examples=None,
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label="Image Resolution", minimum=256, maximum=768, value=512, step=64)
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refine_image_resolution = gr.Slider(
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label="Image Resolution", minimum=256, maximum=8192, value=1024, step=64)
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-
strength = gr.Slider(
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label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
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guess_mode = gr.Checkbox(
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label='Guess Mode', value=False)
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detect_resolution = gr.Slider(
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@@ -93,12 +91,12 @@ def create_demo_template(process, process_image_click=None, examples=None,
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result_text = gr.Text(label='BLIP2+Human Prompt Text')
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ips = [source_image_brush, enable_all_generate, mask_image, control_scale, enable_auto_prompt, a_prompt, n_prompt, num_samples, image_resolution,
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detect_resolution, ddim_steps, guess_mode,
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run_button.click(fn=process, inputs=ips, outputs=[
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result_gallery_refine, result_gallery_init, result_gallery_ref, result_text])
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ip_click = [origin_image, enable_all_generate, click_mask, control_scale, enable_auto_prompt, a_prompt, n_prompt, num_samples, image_resolution,
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detect_resolution, ddim_steps, guess_mode,
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run_button_click.click(fn=process,
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inputs=ip_click,
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label="Image Resolution", minimum=256, maximum=768, value=512, step=64)
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refine_image_resolution = gr.Slider(
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label="Image Resolution", minimum=256, maximum=8192, value=1024, step=64)
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guess_mode = gr.Checkbox(
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label='Guess Mode', value=False)
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detect_resolution = gr.Slider(
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result_text = gr.Text(label='BLIP2+Human Prompt Text')
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ips = [source_image_brush, enable_all_generate, mask_image, control_scale, enable_auto_prompt, a_prompt, n_prompt, num_samples, image_resolution,
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detect_resolution, ddim_steps, guess_mode, scale, seed, eta, enable_tile, refine_alignment_ratio, refine_image_resolution]
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run_button.click(fn=process, inputs=ips, outputs=[
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result_gallery_refine, result_gallery_init, result_gallery_ref, result_text])
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ip_click = [origin_image, enable_all_generate, click_mask, control_scale, enable_auto_prompt, a_prompt, n_prompt, num_samples, image_resolution,
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detect_resolution, ddim_steps, guess_mode, scale, seed, eta, enable_tile, refine_alignment_ratio, refine_image_resolution]
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run_button_click.click(fn=process,
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inputs=ip_click,
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sam2edit_lora.py
CHANGED
@@ -256,7 +256,7 @@ def obtain_generation_model(base_model_path, lora_model_path, controlnet_path, g
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'lllyasviel/control_v11p_sd15_inpaint', torch_dtype=torch.float16) # inpainting controlnet
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)
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if generation_only:
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pipe = StableDiffusionControlNetPipeline.from_pretrained(
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base_model_path, controlnet=controlnet, torch_dtype=torch.float16, safety_checker=None
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)
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@@ -466,7 +466,7 @@ class EditAnythingLoraModel:
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control_scale,
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enable_auto_prompt, a_prompt, n_prompt,
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num_samples, image_resolution, detect_resolution,
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ddim_steps, guess_mode,
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enable_tile=True, refine_alignment_ratio=None, refine_image_resolution=None, condition_model=None):
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if condition_model is None:
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@@ -544,7 +544,7 @@ class EditAnythingLoraModel:
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prompt_embeds = torch.cat([prompt_embeds] * num_samples, dim=0)
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negative_prompt_embeds = torch.cat(
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[negative_prompt_embeds] * num_samples, dim=0)
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if enable_all_generate and
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self.pipe.safety_checker = lambda images, clip_input: (
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images, False)
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x_samples = self.pipe(
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@@ -556,6 +556,7 @@ class EditAnythingLoraModel:
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width=W,
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image=[control.type(torch.float16)],
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controlnet_conditioning_scale=[float(control_scale)],
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).images
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else:
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multi_condition_image = []
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@@ -579,6 +580,7 @@ class EditAnythingLoraModel:
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height=H,
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width=W,
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controlnet_conditioning_scale=multi_condition_scale,
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).images
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results = [x_samples[i] for i in range(num_samples)]
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@@ -605,6 +607,7 @@ class EditAnythingLoraModel:
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width=img_tile.size[0],
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controlnet_conditioning_scale=1.0,
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alignment_ratio=refine_alignment_ratio,
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).images
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results_tile += x_samples_tile
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'lllyasviel/control_v11p_sd15_inpaint', torch_dtype=torch.float16) # inpainting controlnet
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)
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+
if generation_only and extra_inpaint:
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pipe = StableDiffusionControlNetPipeline.from_pretrained(
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base_model_path, controlnet=controlnet, torch_dtype=torch.float16, safety_checker=None
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)
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control_scale,
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enable_auto_prompt, a_prompt, n_prompt,
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num_samples, image_resolution, detect_resolution,
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ddim_steps, guess_mode, scale, seed, eta,
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enable_tile=True, refine_alignment_ratio=None, refine_image_resolution=None, condition_model=None):
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if condition_model is None:
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prompt_embeds = torch.cat([prompt_embeds] * num_samples, dim=0)
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negative_prompt_embeds = torch.cat(
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[negative_prompt_embeds] * num_samples, dim=0)
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+
if enable_all_generate and self.extra_inpaint:
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self.pipe.safety_checker = lambda images, clip_input: (
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images, False)
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x_samples = self.pipe(
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width=W,
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image=[control.type(torch.float16)],
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controlnet_conditioning_scale=[float(control_scale)],
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guidance_scale=scale,
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).images
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else:
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multi_condition_image = []
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height=H,
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width=W,
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controlnet_conditioning_scale=multi_condition_scale,
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guidance_scale=scale,
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).images
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results = [x_samples[i] for i in range(num_samples)]
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width=img_tile.size[0],
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controlnet_conditioning_scale=1.0,
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alignment_ratio=refine_alignment_ratio,
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guidance_scale=scale,
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).images
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results_tile += x_samples_tile
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