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| import torch | |
| import gradio as gr | |
| from diffusers import StableDiffusionPipeline | |
| from diffusers import DDIMScheduler,EulerDiscreteScheduler,EulerAncestralDiscreteScheduler,UniPCMultistepScheduler | |
| from diffusers import KDPM2DiscreteScheduler,KDPM2AncestralDiscreteScheduler,PNDMScheduler,StableDiffusionPipeline | |
| from diffusers import DPMSolverMultistepScheduler | |
| import random | |
| def set_pipeline(model_id_repo,scheduler): | |
| model_ids_dict = { | |
| "pokemon": "yashAI007/pokemon" | |
| } | |
| model_id = model_id_repo | |
| model_repo = model_ids_dict.get(model_id) | |
| print("model_repo :",model_repo) | |
| pipe = StableDiffusionPipeline.from_pretrained( | |
| model_repo, | |
| # torch_dtype=torch.float16, # to run on cpu | |
| use_safetensors=True, | |
| ).to("cpu") | |
| # pipe = StableDiffusionPipeline.from_pretrained( | |
| # model_repo, | |
| # torch_dtype=torch.float16, # to run on Gpu | |
| # use_safetensors=True, | |
| # ).to("cuda") | |
| scheduler_classes = { | |
| "DDIM": DDIMScheduler, | |
| "Euler": EulerDiscreteScheduler, | |
| "Euler a": EulerAncestralDiscreteScheduler, | |
| "UniPC": UniPCMultistepScheduler, | |
| "DPM2 Karras": KDPM2DiscreteScheduler, | |
| "DPM2 a Karras": KDPM2AncestralDiscreteScheduler, | |
| "PNDM": PNDMScheduler, | |
| "DPM++ 2M Karras": DPMSolverMultistepScheduler, | |
| "DPM++ 2M SDE Karras": DPMSolverMultistepScheduler, | |
| } | |
| sampler_name = scheduler # Example sampler name, replace with the actual value | |
| scheduler_class = scheduler_classes.get(sampler_name) | |
| if scheduler_class is not None: | |
| print("sampler_name:",sampler_name) | |
| pipe.scheduler = scheduler_class.from_config(pipe.scheduler.config) | |
| else: | |
| pass | |
| return pipe | |
| def img_args( | |
| prompt, | |
| negative_prompt, | |
| model_id_repo = "pokemon", | |
| scheduler= "Euler a", | |
| height=896, | |
| width=896, | |
| num_inference_steps = 30, | |
| guidance_scale = 7.5, | |
| num_images_per_prompt = 1, | |
| seed = 0 | |
| ): | |
| print(model_id_repo) | |
| print(scheduler) | |
| print(prompt,"&&&&&&&&&&&&&&&&") | |
| pipe = set_pipeline(model_id_repo,scheduler) | |
| if seed == 0: | |
| seed = random.randint(0,25647981548564) | |
| print(f"random seed :{seed}") | |
| generator = torch.manual_seed(seed) | |
| else: | |
| generator = torch.manual_seed(seed) | |
| print(f"manual seed :{seed}") | |
| image = pipe(prompt=prompt, | |
| negative_prompt = negative_prompt, | |
| height = height, | |
| width = width, | |
| num_inference_steps = num_inference_steps, | |
| guidance_scale = guidance_scale, | |
| num_images_per_prompt = num_images_per_prompt, # default 1 | |
| generator = generator, | |
| ).images | |
| return image | |
| block = gr.Blocks().queue() | |
| block.title = "Inpaint Anything" | |
| with block as image_gen: | |
| with gr.Column(): | |
| with gr.Row(): | |
| gr.Markdown("## Pokemon Image Generation") | |
| with gr.Row(): | |
| with gr.Column(): | |
| prompt = gr.Textbox(placeholder="what you want to generate",label="Positive Prompt") | |
| negative_prompt = gr.Textbox(placeholder="what you don't want to generate",label="Negative prompt") | |
| run_btn = gr.Button("image generation", elem_id="select_btn", variant="primary") | |
| with gr.Accordion(label="Advance Options",open=False): | |
| model_selection = gr.Dropdown(choices=["pokemon"],value="pokemon",label="Models") | |
| schduler_selection = gr.Dropdown(choices=["DDIM","Euler","Euler a","UniPC","DPM2 Karras","DPM2 a Karras","PNDM","DPM++ 2M Karras","DPM++ 2M SDE Karras"],value="Euler a",label="Scheduler") | |
| guidance_scale_slider = gr.Slider(label="guidance_scale", minimum=0, maximum=15, value=7.5, step=0.5) | |
| num_images_per_prompt_slider = gr.Slider(label="num_images_per_prompt", minimum=0, maximum=5, value=1, step=1) | |
| height_slider = gr.Slider(label="height", minimum=0, maximum=1024, value=512, step=1) | |
| width_slider = gr.Slider(label="width", minimum=0, maximum=1024, value=512, step=1) | |
| num_inference_steps_slider = gr.Slider(label="num_inference_steps", minimum=0, maximum=150, value=30, step=1) | |
| seed_slider = gr.Slider(label="Seed Slider", minimum=0, maximum=256479815, value=0, step=1) | |
| with gr.Column(): | |
| out_img = gr.Gallery(label='Output', show_label=False, elem_id="gallery", preview=True) | |
| run_btn.click(fn=img_args,inputs=[prompt,negative_prompt,model_selection,schduler_selection,height_slider,width_slider,num_inference_steps_slider,guidance_scale_slider,num_images_per_prompt_slider,seed_slider],outputs=[out_img]) | |
| image_gen.launch() |