diff --git a/readme.md b/readme.md index ffdb186..4e3e3cd 100644 --- a/readme.md +++ b/readme.md @@ -179,6 +179,42 @@ PYTHONPATH=. python scepter/tools/webui.py --cfg scepter/methods/studio/scepter_ We deploy a work studio on Modelscope that includes only the inference tab, please refer to [ms_scepter_studio](https://www.modelscope.cn/studios/damo/scepter_studio/summary) +## 🖼️ Gallery + +### Dragon Year Special: Dragon Tuner + + + + + + + + + + + + + + +
Gold Dragon TunerSloppy Dragon TunerRed Dragon Tuner
+ Papercraft Mantra		Azure Dragon Tuner
+ Pose Control
+ +### Text Effect Image + + + + + + + + + + + + + + +
Conditional ImageMidas Control
"Race track, top view"		Midas Control
+ Watercolor Mantra
"white lilies"		Midas Control
+ Dragon Tuner
"Spring Festival, Chinese dragon"
+ ## ✨ Features ### Text-to-Image Generation @@ -204,9 +240,9 @@ We deploy a work studio on Modelscope that includes only the inference tab, plea - 🪄 denotes that the model has been published. - More models will be released in the future. -| Model | URL | -|--------|------------------------------------------------------------------------------------------------------------------------------------------------| -| SCEdit | [ModelScope](https://modelscope.cn/models/damo/scepter_scedit/summary) [HuggingFace](https://huggingface.co/scepter-studio/scepter_scedit) | +| Model | URL | +|--------|-------------------------------------------------------------------------------------| +| SCEdit | [ModelCard](https://modelscope.cn/models/damo/scepter_scedit/summary) | PS: Scripts running within the SCEPTER framework will automatically fetch and load models based on the required dependency files, eliminating the need for manual downloads. diff --git a/scepter/modules/model/backbone/unet/unet_module.py b/scepter/modules/model/backbone/unet/unet_module.py index 7900196..6aef1fe 100644 --- a/scepter/modules/model/backbone/unet/unet_module.py +++ b/scepter/modules/model/backbone/unet/unet_module.py @@ -492,13 +492,20 @@ class DiffusionUNet(BaseModel): hs.append(h) h = self.middle_block(h, emb, context) for m_id, module in enumerate(self.output_blocks): - h = torch.cat([h, self.lsc_identity[m_id](hs.pop())], dim=1) + skip_h = hs.pop() + if 'tuner_scale' in kwargs and kwargs[ + 'tuner_scale'] is not None and kwargs['tuner_scale'] < 1.0: + tuner_scale = kwargs['tuner_scale'] + tuner_h = self.lsc_identity[m_id](skip_h) - skip_h + h = torch.cat([h, skip_h + tuner_scale * tuner_h], dim=1) + else: + h = torch.cat([h, self.lsc_identity[m_id](skip_h)], dim=1) target_size = hs[-1].shape[-2:] if len(hs) > 0 else None h = module(h, emb, context, target_size) out = self.out(h) return out - def _forward_control(self, x, emb, context, hint, alpha=0.5, **kwargs): + def _forward_control(self, x, emb, context, hint, **kwargs): control_scale = kwargs.pop('control_scale', 1.0) multi_csc_tuners = self.control_blocks # hints @@ -535,8 +542,8 @@ class DiffusionUNet(BaseModel): skip_h_new = skip_h + control_scale * multi_control_h else: # csc-tuner + sc-tuner - skip_h_new = skip_h + alpha * multi_control_h + ( - 1 - alpha) * tuner_h + tuner_scale = kwargs['tuner_scale'] + skip_h_new = skip_h + control_scale * multi_control_h + tuner_scale * tuner_h h = torch.cat([h, skip_h_new], dim=1) target_size = hs[-1].shape[-2:] if len(hs) > 0 else None h = module(h, emb, context, target_size) @@ -834,13 +841,20 @@ class DiffusionUNetXL(DiffusionUNet): hs.append(h) h = self.middle_block(h, emb, context) for m_id, module in enumerate(self.output_blocks): - h = torch.cat([h, self.lsc_identity[m_id](hs.pop())], dim=1) + skip_h = hs.pop() + if 'tuner_scale' in kwargs and kwargs[ + 'tuner_scale'] is not None and kwargs['tuner_scale'] < 1.0: + tuner_scale = kwargs['tuner_scale'] + tuner_h = self.lsc_identity[m_id](skip_h) - skip_h + h = torch.cat([h, skip_h + tuner_scale * tuner_h], dim=1) + else: + h = torch.cat([h, self.lsc_identity[m_id](skip_h)], dim=1) target_size = hs[-1].shape[-2:] if len(hs) > 0 else None h = module(h, emb, context, target_size) out = self.out(h) return out - def _forward_control(self, x, emb, context, hint, alpha=0.5, **kwargs): + def _forward_control(self, x, emb, context, hint, **kwargs): control_scale = kwargs.pop('control_scale', 1.0) multi_csc_tuners = self.control_blocks # hints @@ -877,8 +891,8 @@ class DiffusionUNetXL(DiffusionUNet): skip_h_new = skip_h + control_scale * multi_control_h else: # csc-tuner + sc-tuner - skip_h_new = skip_h + alpha * multi_control_h + ( - 1 - alpha) * tuner_h + tuner_scale = kwargs['tuner_scale'] + skip_h_new = skip_h + control_scale * multi_control_h + tuner_scale * tuner_h h = torch.cat([h, skip_h_new], dim=1) target_size = hs[-1].shape[-2:] if len(hs) > 0 else None h = module(h, emb, context, target_size) diff --git a/scepter/modules/model/network/diffusion/diffusion.py b/scepter/modules/model/network/diffusion/diffusion.py index 5a23c56..4f8080a 100644 --- a/scepter/modules/model/network/diffusion/diffusion.py +++ b/scepter/modules/model/network/diffusion/diffusion.py @@ -54,7 +54,8 @@ class GaussianDiffusion(object): guide_rescale=None, clamp=None, percentile=None, - cat_uc=False): + cat_uc=False, + **kwargs): """ Apply one step of denoising from the posterior distribution q(x_s | x_t, x0). Since x0 is not available, estimate the denoising results using the learned @@ -79,7 +80,7 @@ class GaussianDiffusion(object): # prediction if guide_scale is None: assert isinstance(model_kwargs, dict) - out = model(xt, t=t, **model_kwargs) + out = model(xt, t=t, **model_kwargs, **kwargs) else: # classifier-free guidance (arXiv:2207.12598) # model_kwargs[0]: conditional kwargs @@ -87,7 +88,7 @@ class GaussianDiffusion(object): assert isinstance(model_kwargs, list) and len(model_kwargs) == 2 if guide_scale == 1.: - out = model(xt, t=t, **model_kwargs[0]) + out = model(xt, t=t, **model_kwargs[0], **kwargs) else: if cat_uc: @@ -111,11 +112,12 @@ class GaussianDiffusion(object): all_model_kwargs[key], value) all_out = model(xt.repeat(2, 1, 1, 1), t=t.repeat(2), - **all_model_kwargs) + **all_model_kwargs, + **kwargs) y_out, u_out = all_out.chunk(2) else: - y_out = model(xt, t=t, **model_kwargs[0]) - u_out = model(xt, t=t, **model_kwargs[1]) + y_out = model(xt, t=t, **model_kwargs[0], **kwargs) + u_out = model(xt, t=t, **model_kwargs[1], **kwargs) out = u_out + guide_scale * (y_out - u_out) # rescale the output according to arXiv:2305.08891 @@ -262,7 +264,8 @@ class GaussianDiffusion(object): guide_rescale, clamp, percentile, - cat_uc=cat_uc)[-2] + cat_uc=cat_uc, + **kwargs)[-2] # collect intermediate outputs if return_intermediate == 'xt': @@ -467,8 +470,8 @@ class GaussianDiffusion(object): t = self._sigma_to_t(sigma).repeat(len(xt)).round().long() x0 = self.denoise(xt * c_in, t, None, model, model_kwargs, - guide_scale, guide_rescale, clamp, - percentile)[-2] + guide_scale, guide_rescale, clamp, percentile, + **kwargs)[-2] # collect intermediate outputs if return_intermediate == 'xt': intermediates.append(xt) diff --git a/scepter/studio/inference/inference_ui/component_names.py b/scepter/studio/inference/inference_ui/component_names.py index 1fb1de2..d25853f 100644 --- a/scepter/studio/inference/inference_ui/component_names.py +++ b/scepter/studio/inference/inference_ui/component_names.py @@ -83,7 +83,6 @@ class DiffusionUIName(): self.image_number = 'Images Number' self.resolutions_height = 'Output Height' self.resolutions_width = 'Output Width' - self.negative_prompt = 'Negative Prompt' self.negative_prompt_placeholder = 'Type Prompt Here' self.negative_prompt_description = 'Describing what you do not want to see.' @@ -93,6 +92,12 @@ class DiffusionUIName(): self.discretization = 'Discretization' self.random_seed = 'Use Random Seed' self.seed = 'Used Seed' + self.example_block_name = 'Prompt Examples' + self.examples = [ + 'dream dandelion', 'Mount Everest', 'a boy wearing a jacket', + 'Spring, Birds, Cawing, Branches', + 'Cyberpunk, Maiden, Heavy Machinery', 'A Frog' + ] elif language == 'zh': @@ -110,6 +115,12 @@ class DiffusionUIName(): self.discretization = '离散化' self.random_seed = '使用随机种子' self.seed = '使用的种子' + self.example_block_name = '提示词样例' + self.examples = [ + 'dream dandelion', 'Mount Everest', 'a boy wearing a jacket', + 'Spring, Birds, Cawing, Branches', + 'Cyberpunk, Maiden, Heavy Machinery', 'A Frog' + ] class MantraUIName(): @@ -127,6 +138,13 @@ class MantraUIName(): self.style_negative_template = 'Mantra Negative Prompt Template' self.style_example = 'Mantra Results Example' self.style_example_prompt = 'Mantra Example Prompt' + self.example_block_name = 'Examples' + self.examples = [ + [['Adorable 3D Character'], 'a girl'], + [['Watercolor 2'], 'a single flower'], + [['Action Figure'], + 'a close up of a small rabbit wearing a hat and scarf'] + ] elif language == 'zh': self.mantra_styles = '咒语风格' @@ -140,6 +158,12 @@ class MantraUIName(): self.style_negative_template = '咒语负向提示模板' self.style_example = '咒语示例图' self.style_example_prompt = '咒语示例提示词' + self.example_block_name = '样例' + self.examples = [ + [['可爱的3D角色'], 'a girl'], [['水彩'], 'a single flower'], + [['动作人偶'], + 'a close up of a small rabbit wearing a hat and scarf'] + ] class RefinerUIName(): @@ -179,6 +203,11 @@ class TunerUIName(): self.tuner_prompt_example = 'Prompt Example' self.base_model = 'Base Model Name' self.custom_tuner_model = 'Customized Model' + self.advance_block_name = 'Advance Setting' + self.tuner_scale = 'Tuner Scale' + self.example_block_name = 'Examples' + self.examples = [[['Pencil Sketch Drawing'], 'a girl in a jacket'], + [['Flat 2D Art'], 'a cat']] elif language == 'zh': self.tuner_model = '微调模型' @@ -189,6 +218,11 @@ class TunerUIName(): self.tuner_prompt_example = '示例提示词' self.base_model = '基础模型' self.custom_tuner_model = '自定义模型' + self.advance_block_name = '高级设置' + self.tuner_scale = '微调强度' + self.example_block_name = '样例' + self.examples = [[['铅笔素描'], 'a girl in a jacket'], + [['扁平2D艺术'], 'a cat']] class ControlUIName(): diff --git a/scepter/studio/inference/inference_ui/diffusion_ui.py b/scepter/studio/inference/inference_ui/diffusion_ui.py index b1eb8dc..e4f0f34 100644 --- a/scepter/studio/inference/inference_ui/diffusion_ui.py +++ b/scepter/studio/inference/inference_ui/diffusion_ui.py @@ -68,6 +68,7 @@ class DiffusionUI(UIBase): def create_ui(self, *args, **kwargs): self.cur_paras = self.get_default(self.diffusion_paras, self.default_input) + self.example_block = gr.Row(equal_height=True, visible=True) with gr.Row(equal_height=True): self.negative_prompt = gr.Textbox( label=self.component_names.negative_prompt, @@ -154,6 +155,12 @@ class DiffusionUI(UIBase): self.refresh_seed = gr.Button(value=refresh_symbol) def set_callbacks(self, model_manage_ui, **kwargs): + gallery_ui = kwargs.pop('gallery_ui') + with self.example_block: + gr.Examples(label=self.component_names.example_block_name, + examples=self.component_names.examples, + inputs=gallery_ui.prompt) + def random_checked(r): value = -1 return (gr.Row(visible=not r), gr.Textbox(value=value)) diff --git a/scepter/studio/inference/inference_ui/gallery_ui.py b/scepter/studio/inference/inference_ui/gallery_ui.py index 9460a9e..4313091 100644 --- a/scepter/studio/inference/inference_ui/gallery_ui.py +++ b/scepter/studio/inference/inference_ui/gallery_ui.py @@ -51,189 +51,203 @@ class GalleryUI(UIBase): elem_id='generate_button', visible=True) + def generate_gallery(self, + prompt, + mantra_state, + tuner_state, + control_state, + refine_state, + diffusion_model, + first_stage_model, + cond_stage_model, + refiner_cond_model, + refiner_diffusion_model, + tuner_model, + tuner_scale, + custom_tuner_model, + control_model, + control_scale, + crop_type, + control_cond_image, + negative_prompt, + prompt_prefix, + sample, + discretization, + output_height, + output_width, + image_number, + sample_steps, + guide_scale, + guide_rescale, + refine_strength, + refine_sampler, + refine_discretization, + refine_guide_scale, + refine_guide_rescale, + style_template, + style_negative_template, + image_seed, + show_jpeg_image=True): + current_pipeline = self.pipe_manager.get_pipeline_given_modules({ + 'diffusion_model': + diffusion_model, + 'first_stage_model': + first_stage_model, + 'cond_stage_model': + cond_stage_model, + 'refiner_cond_model': + refiner_cond_model, + 'refiner_diffusion_model': + refiner_diffusion_model + }) + now_pipeline = self.pipe_manager.model_level_info[diffusion_model][ + 'pipeline'][0] + used_tuner_model = [] + if not isinstance(tuner_model, list): + tuner_model = [tuner_model] + for tuner_m in tuner_model: + if tuner_m is None or tuner_m == '': + continue + if (now_pipeline in self.pipe_manager.model_level_info['tuners'] + and tuner_m in self.pipe_manager.model_level_info['tuners'] + [now_pipeline]): + tuner_m = self.pipe_manager.model_level_info['tuners'][ + now_pipeline][tuner_m]['model_info'] + used_tuner_model.append(tuner_m) + used_custom_tuner_model = [] + if not isinstance(custom_tuner_model, list): + custom_tuner_model = [custom_tuner_model] + for tuner_m in custom_tuner_model: + if tuner_m is None or tuner_m == '': + continue + if (now_pipeline + in self.pipe_manager.model_level_info['customized_tuners'] + and tuner_m in self.pipe_manager. + model_level_info['customized_tuners'][now_pipeline]): + tuner_m = self.pipe_manager.model_level_info[ + 'customized_tuners'][now_pipeline][tuner_m]['model_info'] + used_custom_tuner_model.append(tuner_m) + + if (now_pipeline in self.pipe_manager.model_level_info['controllers'] + and control_model in self.pipe_manager. + model_level_info['controllers'][now_pipeline]): + control_model = self.pipe_manager.model_level_info['controllers'][ + now_pipeline][control_model]['model_info'] + + prompt_rephrased = style_template.replace( + '{prompt}', + prompt) if not style_template == '' and mantra_state else prompt + prompt_rephrased = f'{prompt_prefix}{prompt_rephrased}' if not prompt_prefix == '' else prompt_rephrased + negative_prompt_rephrased = negative_prompt + style_negative_template if mantra_state else negative_prompt + pipeline_input = { + 'prompt': prompt_rephrased, + 'negative_prompt': negative_prompt_rephrased, + 'sample': sample, + 'sample_steps': sample_steps, + 'discretization': discretization, + 'original_size_as_tuple': [int(output_height), + int(output_width)], + 'target_size_as_tuple': [int(output_height), + int(output_width)], + 'crop_coords_top_left': [0, 0], + 'guide_scale': guide_scale, + 'guide_rescale': guide_rescale, + } + if refine_state: + pipeline_input['refine_sampler'] = refine_sampler + pipeline_input['refine_discretization'] = refine_discretization + pipeline_input['refine_guide_scale'] = refine_guide_scale + pipeline_input['refine_guide_rescale'] = refine_guide_rescale + else: + refine_strength = 0 + results = current_pipeline( + pipeline_input, + num_samples=image_number, + intermediate_callback=None, + refine_strength=refine_strength, + img_to_img_strength=0, + tuner_model=used_tuner_model + + used_custom_tuner_model if tuner_state else None, + tuner_scale=tuner_scale if tuner_state or control_state else None, + control_model=control_model if control_state else None, + control_scale=control_scale + if tuner_state or control_state else None, + control_cond_image=control_cond_image if control_state else None, + crop_type=crop_type if control_state else None, + seed=int(image_seed)) + images = [] + before_images = [] + if 'images' in results: + images_tensor = results['images'] * 255 + images = [ + Image.fromarray(images_tensor[idx].permute( + 1, 2, 0).cpu().numpy().astype(np.uint8)) + for idx in range(images_tensor.shape[0]) + ] + if 'before_refine_images' in results and results[ + 'before_refine_images'] is not None: + before_refine_images_tensor = results['before_refine_images'] * 255 + before_images = [ + Image.fromarray(before_refine_images_tensor[idx].permute( + 1, 2, 0).cpu().numpy().astype(np.uint8)) + for idx in range(before_refine_images_tensor.shape[0]) + ] + if 'seed' in results: + print(results['seed']) + print(images, before_images) + if show_jpeg_image: + save_list = [] + for i, img in enumerate(images): + save_image = os.path.join(self.cfg.WORK_DIR, + f'cur_gallery_{i}.jpg') + img.save(save_image) + save_list.append(save_image) + images = save_list + return ( + gr.Column(visible=len(before_images) > 0), + before_images, + images, + ) + + def generate_image(self, *args, **kwargs): + gallery_result = self.generate_gallery(*args, **kwargs) + before_refine_panel, before_refine_gallery, output_gallery = gallery_result + return (before_refine_panel, before_refine_gallery, output_gallery[0]) + def set_callbacks(self, inference_ui, model_manage_ui, diffusion_ui, mantra_ui, tuner_ui, refiner_ui, control_ui, **kwargs): - def generate_image(mantra_state, - tuner_state, - control_state, - refine_state, - diffusion_model, - first_stage_model, - cond_stage_model, - refiner_cond_model, - refiner_diffusion_model, - tuner_model, - custom_tuner_model, - control_model, - crop_type, - control_cond_image, - prompt, - negative_prompt, - prompt_prefix, - sample, - discretization, - output_height, - output_width, - image_number, - sample_steps, - guide_scale, - guide_rescale, - refine_strength, - refine_sampler, - refine_discretization, - refine_guide_scale, - refine_guide_rescale, - style_template, - style_negative_template, - image_seed, - show_jpeg_image=True): - current_pipeline = self.pipe_manager.get_pipeline_given_modules({ - 'diffusion_model': - diffusion_model, - 'first_stage_model': - first_stage_model, - 'cond_stage_model': - cond_stage_model, - 'refiner_cond_model': - refiner_cond_model, - 'refiner_diffusion_model': - refiner_diffusion_model - }) - now_pipeline = self.pipe_manager.model_level_info[diffusion_model][ - 'pipeline'][0] - used_tuner_model = [] - if not isinstance(tuner_model, list): - tuner_model = [tuner_model] - for tuner_m in tuner_model: - if tuner_m is None or tuner_m == '': - continue - if (now_pipeline - in self.pipe_manager.model_level_info['tuners'] - and tuner_m in self.pipe_manager. - model_level_info['tuners'][now_pipeline]): - tuner_m = self.pipe_manager.model_level_info['tuners'][ - now_pipeline][tuner_m]['model_info'] - used_tuner_model.append(tuner_m) - used_custom_tuner_model = [] - if not isinstance(custom_tuner_model, list): - custom_tuner_model = [custom_tuner_model] - for tuner_m in custom_tuner_model: - if tuner_m is None or tuner_m == '': - continue - if (now_pipeline in - self.pipe_manager.model_level_info['customized_tuners'] - and tuner_m in self.pipe_manager. - model_level_info['customized_tuners'][now_pipeline]): - tuner_m = self.pipe_manager.model_level_info[ - 'customized_tuners'][now_pipeline][tuner_m][ - 'model_info'] - used_custom_tuner_model.append(tuner_m) - if (now_pipeline - in self.pipe_manager.model_level_info['controllers'] - and control_model in self.pipe_manager. - model_level_info['controllers'][now_pipeline]): - control_model = self.pipe_manager.model_level_info[ - 'controllers'][now_pipeline][control_model]['model_info'] + self.gen_inputs = [ + self.prompt, mantra_ui.state, tuner_ui.state, control_ui.state, + refiner_ui.state, model_manage_ui.diffusion_model, + model_manage_ui.first_stage_model, + model_manage_ui.cond_stage_model, refiner_ui.refiner_cond_model, + refiner_ui.refiner_diffusion_model, tuner_ui.tuner_model, + tuner_ui.tuner_scale, tuner_ui.custom_tuner_model, + control_ui.control_model, control_ui.control_scale, + control_ui.crop_type, control_ui.cond_image, + diffusion_ui.negative_prompt, diffusion_ui.prompt_prefix, + diffusion_ui.sampler, diffusion_ui.discretization, + diffusion_ui.output_height, diffusion_ui.output_width, + diffusion_ui.image_number, diffusion_ui.sample_steps, + diffusion_ui.guide_scale, diffusion_ui.guide_rescale, + refiner_ui.refine_strength, refiner_ui.refine_sampler, + refiner_ui.refine_discretization, refiner_ui.refine_guide_scale, + refiner_ui.refine_guide_rescale, mantra_ui.style_template, + mantra_ui.style_negative_template, diffusion_ui.image_seed + ] - prompt_rephrased = style_template.replace( - '{prompt}', prompt - ) if not style_template == '' and mantra_state else prompt - prompt_rephrased = f'{prompt_prefix}{prompt_rephrased}' if not prompt_prefix == '' else prompt_rephrased - negative_prompt_rephrased = negative_prompt + style_negative_template if mantra_state else negative_prompt - pipeline_input = { - 'prompt': prompt_rephrased, - 'negative_prompt': negative_prompt_rephrased, - 'sample': sample, - 'sample_steps': sample_steps, - 'discretization': discretization, - 'original_size_as_tuple': - [int(output_height), int(output_width)], - 'target_size_as_tuple': - [int(output_height), int(output_width)], - 'crop_coords_top_left': [0, 0], - 'guide_scale': guide_scale, - 'guide_rescale': guide_rescale, - } - if refine_state: - pipeline_input['refine_sampler'] = refine_sampler - pipeline_input['refine_discretization'] = refine_discretization - pipeline_input['refine_guide_scale'] = refine_guide_scale - pipeline_input['refine_guide_rescale'] = refine_guide_rescale - else: - refine_strength = 0 - results = current_pipeline( - pipeline_input, - num_samples=image_number, - intermediate_callback=None, - refine_strength=refine_strength, - img_to_img_strength=0, - tuner_model=used_tuner_model + - used_custom_tuner_model if tuner_state else None, - control_model=control_model if control_state else None, - control_cond_image=control_cond_image - if control_state else None, - crop_type=crop_type if control_state else None, - seed=int(image_seed)) - images = [] - before_images = [] - if 'images' in results: - images_tensor = results['images'] * 255 - images = [ - Image.fromarray(images_tensor[idx].permute( - 1, 2, 0).cpu().numpy().astype(np.uint8)) - for idx in range(images_tensor.shape[0]) - ] - if 'before_refine_images' in results and results[ - 'before_refine_images'] is not None: - before_refine_images_tensor = results[ - 'before_refine_images'] * 255 - before_images = [ - Image.fromarray(before_refine_images_tensor[idx].permute( - 1, 2, 0).cpu().numpy().astype(np.uint8)) - for idx in range(before_refine_images_tensor.shape[0]) - ] - if 'seed' in results: - print(results['seed']) - print(images, before_images) - if show_jpeg_image: - save_list = [] - for i, img in enumerate(images): - save_image = os.path.join(self.cfg.WORK_DIR, - f'cur_gallery_{i}.jpg') - img.save(save_image) - save_list.append(save_image) - images = save_list - return ( - gr.Column(visible=len(before_images) > 0), - before_images, - images, - ) + self.gen_outputs = [ + self.before_refine_panel, self.before_refine_gallery, + self.output_gallery + ] - self.generate_button.click( - generate_image, - inputs=[ - mantra_ui.state, tuner_ui.state, control_ui.state, - refiner_ui.state, model_manage_ui.diffusion_model, - model_manage_ui.first_stage_model, - model_manage_ui.cond_stage_model, - refiner_ui.refiner_cond_model, - refiner_ui.refiner_diffusion_model, tuner_ui.tuner_model, - tuner_ui.custom_tuner_model, control_ui.control_model, - control_ui.crop_type, control_ui.cond_image, self.prompt, - diffusion_ui.negative_prompt, diffusion_ui.prompt_prefix, - diffusion_ui.sampler, diffusion_ui.discretization, - diffusion_ui.output_height, diffusion_ui.output_width, - diffusion_ui.image_number, diffusion_ui.sample_steps, - diffusion_ui.guide_scale, diffusion_ui.guide_rescale, - refiner_ui.refine_strength, refiner_ui.refine_sampler, - refiner_ui.refine_discretization, - refiner_ui.refine_guide_scale, refiner_ui.refine_guide_rescale, - mantra_ui.style_template, mantra_ui.style_negative_template, - diffusion_ui.image_seed - ], - outputs=[ - self.before_refine_panel, self.before_refine_gallery, - self.output_gallery - ], - queue=True) + self.generate_button.click(self.generate_gallery, + inputs=self.gen_inputs, + outputs=self.gen_outputs, + queue=True) + + self.prompt.submit(self.generate_gallery, + inputs=self.gen_inputs, + outputs=self.gen_outputs, + queue=True) diff --git a/scepter/studio/inference/inference_ui/mantra_ui.py b/scepter/studio/inference/inference_ui/mantra_ui.py index 22143f0..1be6c42 100644 --- a/scepter/studio/inference/inference_ui/mantra_ui.py +++ b/scepter/studio/inference/inference_ui/mantra_ui.py @@ -47,64 +47,75 @@ class MantraUI(UIBase): def create_ui(self, *args, **kwargs): self.state = gr.State(value=False) - with gr.Row(equal_height=True, visible=False) as self.tab: - with gr.Column(scale=1): - with gr.Group(visible=True): - with gr.Row(equal_height=True): - self.style = gr.Dropdown( - label=self.component_names.mantra_styles, - choices=self.all_styles[self.default_pipeline], - value=None, - multiselect=True, - interactive=True) - with gr.Row(equal_height=True): - with gr.Column(scale=1): - self.style_name = gr.Text( + with gr.Column(visible=False) as self.tab: + with gr.Row(): + with gr.Column(scale=1): + with gr.Group(visible=True): + with gr.Row(equal_height=True): + self.style = gr.Dropdown( + label=self.component_names.mantra_styles, + choices=self.all_styles[self.default_pipeline], + value=None, + multiselect=True, + interactive=True) + with gr.Row(equal_height=True): + with gr.Column(scale=1): + self.style_name = gr.Text( + value='', + label=self.component_names.style_name) + with gr.Column(scale=1): + self.style_source = gr.Text( + value='', + label=self.component_names.style_source) + with gr.Column(scale=1): + self.style_desc = gr.Text( + value='', + label=self.component_names.style_desc) + with gr.Row(equal_height=True): + self.style_prompt = gr.Text( value='', - label=self.component_names.style_name) - with gr.Column(scale=1): - self.style_source = gr.Text( + label=self.component_names.style_prompt, + lines=4) + with gr.Row(equal_height=True): + self.style_negative_prompt = gr.Text( value='', - label=self.component_names.style_source) - with gr.Column(scale=1): - self.style_desc = gr.Text( + label=self.component_names. + style_negative_prompt, + lines=4) + with gr.Column(scale=1): + with gr.Group(visible=True): + with gr.Row(equal_height=True): + self.style_template = gr.Text( value='', - label=self.component_names.style_desc) - with gr.Row(equal_height=True): - self.style_prompt = gr.Text( - value='', - label=self.component_names.style_prompt, - lines=4) - with gr.Row(equal_height=True): - self.style_negative_prompt = gr.Text( - value='', - label=self.component_names.style_negative_prompt, - lines=4) - with gr.Column(scale=1): - with gr.Group(visible=True): - with gr.Row(equal_height=True): - self.style_template = gr.Text( - value='', - label=self.component_names.style_template, - lines=2) - with gr.Row(equal_height=True): - self.style_negative_template = gr.Text( - value='', - label=self.component_names.style_negative_template, - lines=2) - with gr.Row(equal_height=True): - self.style_example = gr.Image( - label=self.component_names.style_example, - source='upload', - value=None, - interactive=False) - with gr.Row(equal_height=True): - self.style_example_prompt = gr.Text( - value='', - label=self.component_names.style_example_prompt, - lines=2) + label=self.component_names.style_template, + lines=2) + with gr.Row(equal_height=True): + self.style_negative_template = gr.Text( + value='', + label=self.component_names. + style_negative_template, + lines=2) + with gr.Row(equal_height=True): + self.style_example = gr.Image( + label=self.component_names.style_example, + source='upload', + value=None, + interactive=False) + with gr.Row(equal_height=True): + self.style_example_prompt = gr.Text( + value='', + label=self.component_names. + style_example_prompt, + lines=2) + self.example_block = gr.Accordion( + label=self.component_names.example_block_name, open=True) def set_callbacks(self, model_manage_ui, **kwargs): + gallery_ui = kwargs.pop('gallery_ui') + with self.example_block: + gr.Examples(examples=self.component_names.examples, + inputs=[self.style, gallery_ui.prompt]) + def change_style(style, diffusion_model): style_template = '' style_negative_template = [] diff --git a/scepter/studio/inference/inference_ui/tuner_ui.py b/scepter/studio/inference/inference_ui/tuner_ui.py index 4e73f93..4ca4f06 100644 --- a/scepter/studio/inference/inference_ui/tuner_ui.py +++ b/scepter/studio/inference/inference_ui/tuner_ui.py @@ -47,53 +47,74 @@ class TunerUI(UIBase): def create_ui(self, *args, **kwargs): self.state = gr.State(value=False) - with gr.Row(equal_height=True, visible=False) as self.tab: - with gr.Column(variant='panel', scale=1, min_width=0): - with gr.Group(visible=True): - with gr.Row(equal_height=True): - with gr.Column(scale=1): - self.tuner_model = gr.Dropdown( - label=self.component_names.tuner_model, - choices=self.tunner_choices, + with gr.Column(visible=False) as self.tab: + with gr.Row(): + with gr.Column(variant='panel', scale=1, min_width=0): + with gr.Group(visible=True): + with gr.Row(equal_height=True): + with gr.Column(scale=1): + self.tuner_model = gr.Dropdown( + label=self.component_names.tuner_model, + choices=self.tunner_choices, + value=None, + multiselect=True, + interactive=True) + with gr.Column(scale=1): + self.custom_tuner_model = gr.Dropdown( + label=self.component_names. + custom_tuner_model, + choices=[], + value=None, + multiselect=True, + interactive=True) + with gr.Row(equal_height=True): + with gr.Column(scale=1): + self.tuner_type = gr.Text( + value='', + label=self.component_names.tuner_type) + with gr.Column(scale=1): + self.base_model = gr.Text( + value='', + label=self.component_names.base_model) + with gr.Column(scale=1): + self.tuner_desc = gr.Text( + value='', + label=self.component_names.tuner_desc, + lines=4) + with gr.Column(variant='panel', scale=1, min_width=0): + with gr.Group(visible=True): + with gr.Row(equal_height=True): + self.tuner_example = gr.Image( + label=self.component_names.tuner_example, + source='upload', value=None, - multiselect=True, - interactive=True) - with gr.Column(scale=1): - self.custom_tuner_model = gr.Dropdown( - label=self.component_names.custom_tuner_model, - choices=[], - value=None, - multiselect=True, - interactive=True) - with gr.Row(equal_height=True): - with gr.Column(scale=1): - self.tuner_type = gr.Text( + interactive=False) + with gr.Row(equal_height=True): + self.tuner_prompt_example = gr.Text( value='', - label=self.component_names.tuner_type) - with gr.Column(scale=1): - self.base_model = gr.Text( - value='', - label=self.component_names.base_model) - with gr.Column(scale=1): - self.tuner_desc = gr.Text( - value='', - label=self.component_names.tuner_desc, - lines=4) - with gr.Column(variant='panel', scale=1, min_width=0): - with gr.Group(visible=True): - with gr.Row(equal_height=True): - self.tuner_example = gr.Image( - label=self.component_names.tuner_example, - source='upload', - value=None, - interactive=False) - with gr.Row(equal_height=True): - self.tuner_prompt_example = gr.Text( - value='', - label=self.component_names.tuner_prompt_example, - lines=2) + label=self.component_names. + tuner_prompt_example, + lines=2) + + with gr.Accordion(label=self.component_names.advance_block_name, + open=False): + self.tuner_scale = gr.Slider( + label=self.component_names.tuner_scale, + minimum=0.0, + maximum=1.0, + step=0.05, + value=1.0, + interactive=True) + + self.example_block = gr.Accordion( + label=self.component_names.example_block_name, open=True) def set_callbacks(self, model_manage_ui, **kwargs): + gallery_ui = kwargs.pop('gallery_ui') + with self.example_block: + gr.Examples(examples=self.component_names.examples, + inputs=[self.tuner_model, gallery_ui.prompt]) + def tuner_model_change(tuner_model, diffusion_model): diffusion_model_info = self.pipe_manager.model_level_info[ diffusion_model] diff --git a/scepter/studio/preprocess/caption_editor_ui/dataset_gallery_ui.py b/scepter/studio/preprocess/caption_editor_ui/dataset_gallery_ui.py index 01f83ea..df8a730 100644 --- a/scepter/studio/preprocess/caption_editor_ui/dataset_gallery_ui.py +++ b/scepter/studio/preprocess/caption_editor_ui/dataset_gallery_ui.py @@ -5,8 +5,8 @@ from __future__ import annotations import os.path import gradio as gr - import imagehash + from scepter.modules.utils.file_system import FS from scepter.studio.preprocess.caption_editor_ui.component_names import \ DatasetGalleryUIName