lllyasviel
GitHub
parent
1ff382c8ef
commit
158afe088d
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@ -1,2 +1 @@
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version = '1.0.17'
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version = '1.0.19'
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@ -29,6 +29,14 @@ class StableDiffusionModel:
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self.clip = clip
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self.clip_vision = clip_vision
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def to_meta(self):
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if self.unet is not None:
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self.unet.model.to('meta')
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if self.clip is not None:
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self.clip.cond_stage_model.to('meta')
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if self.vae is not None:
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self.vae.first_stage_model.to('meta')
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@torch.no_grad()
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def load_model(ckpt_filename):
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@ -42,8 +50,8 @@ def load_lora(model, lora_filename, strength_model=1.0, strength_clip=1.0):
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return model
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lora = comfy.utils.load_torch_file(lora_filename, safe_load=True)
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model.unet, model.clip = comfy.sd.load_lora_for_models(model.unet, model.clip, lora, strength_model, strength_clip)
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return model
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unet, clip = comfy.sd.load_lora_for_models(model.unet, model.clip, lora, strength_model, strength_clip)
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return StableDiffusionModel(unet=unet, clip=clip, vae=model.vae, clip_vision=model.clip_vision)
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@torch.no_grad()
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@ -92,7 +100,7 @@ def get_previewer(device, latent_format):
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@torch.no_grad()
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def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sampler_name='dpmpp_2m_sde_gpu',
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scheduler='karras', denoise=1.0, disable_noise=False, start_step=None, last_step=None,
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force_full_denoise=False):
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force_full_denoise=False, callback_function=None):
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# SCHEDULERS = ["normal", "karras", "exponential", "simple", "ddim_uniform"]
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# SAMPLERS = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral",
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# "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
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@ -118,6 +126,8 @@ def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sa
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pbar = comfy.utils.ProgressBar(steps)
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def callback(step, x0, x, total_steps):
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if callback_function is not None:
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callback_function(step, x0, x, total_steps)
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if previewer and step % 3 == 0:
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previewer.preview(x0, step, total_steps)
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pbar.update_absolute(step + 1, total_steps, None)
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@ -1,46 +1,146 @@
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import modules.core as core
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import os
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import torch
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import modules.path
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from modules.path import modelfile_path, lorafile_path
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from comfy.model_base import SDXL, SDXLRefiner
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xl_base_filename = os.path.join(modelfile_path, 'sd_xl_base_1.0_0.9vae.safetensors')
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xl_refiner_filename = os.path.join(modelfile_path, 'sd_xl_refiner_1.0_0.9vae.safetensors')
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xl_base_offset_lora_filename = os.path.join(lorafile_path, 'sd_xl_offset_example-lora_1.0.safetensors')
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xl_base: core.StableDiffusionModel = None
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xl_base_hash = ''
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xl_base = core.load_model(xl_base_filename)
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xl_base = core.load_lora(xl_base, xl_base_offset_lora_filename, strength_model=0.5, strength_clip=0.0)
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del xl_base.vae
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xl_refiner: core.StableDiffusionModel = None
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xl_refiner_hash = ''
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xl_refiner = core.load_model(xl_refiner_filename)
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xl_base_patched: core.StableDiffusionModel = None
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xl_base_patched_hash = ''
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def refresh_base_model(name):
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global xl_base, xl_base_hash, xl_base_patched, xl_base_patched_hash
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if xl_base_hash == str(name):
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return
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filename = os.path.join(modules.path.modelfile_path, name)
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if xl_base is not None:
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xl_base.to_meta()
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xl_base = None
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xl_base = core.load_model(filename)
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if not isinstance(xl_base.unet.model, SDXL):
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print('Model not supported. Fooocus only support SDXL model as the base model.')
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xl_base = None
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xl_base_hash = ''
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refresh_base_model(modules.path.default_base_model_name)
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xl_base_hash = name
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xl_base_patched = xl_base
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xl_base_patched_hash = ''
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return
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xl_base_hash = name
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xl_base_patched = xl_base
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xl_base_patched_hash = ''
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print(f'Base model loaded: {xl_base_hash}')
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return
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def refresh_refiner_model(name):
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global xl_refiner, xl_refiner_hash
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if xl_refiner_hash == str(name):
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return
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if name == 'None':
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xl_refiner = None
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xl_refiner_hash = ''
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print(f'Refiner unloaded.')
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return
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filename = os.path.join(modules.path.modelfile_path, name)
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if xl_refiner is not None:
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xl_refiner.to_meta()
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xl_refiner = None
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xl_refiner = core.load_model(filename)
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if not isinstance(xl_refiner.unet.model, SDXLRefiner):
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print('Model not supported. Fooocus only support SDXL refiner as the refiner.')
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xl_refiner = None
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xl_refiner_hash = ''
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print(f'Refiner unloaded.')
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return
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xl_refiner_hash = name
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print(f'Refiner model loaded: {xl_refiner_hash}')
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xl_refiner.vae.first_stage_model.to('meta')
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xl_refiner.vae = None
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return
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def refresh_loras(loras):
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global xl_base, xl_base_patched, xl_base_patched_hash
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if xl_base_patched_hash == str(loras):
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return
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model = xl_base
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for name, weight in loras:
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if name == 'None':
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continue
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filename = os.path.join(modules.path.lorafile_path, name)
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model = core.load_lora(model, filename, strength_model=weight, strength_clip=weight)
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xl_base_patched = model
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xl_base_patched_hash = str(loras)
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print(f'LoRAs loaded: {xl_base_patched_hash}')
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return
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refresh_base_model(modules.path.default_base_model_name)
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refresh_refiner_model(modules.path.default_refiner_model_name)
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refresh_loras([(modules.path.default_lora_name, 0.5), ('None', 0.5), ('None', 0.5), ('None', 0.5), ('None', 0.5)])
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@torch.no_grad()
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def process(positive_prompt, negative_prompt, steps, switch, width, height, image_seed, callback):
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positive_conditions = core.encode_prompt_condition(clip=xl_base.clip, prompt=positive_prompt)
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negative_conditions = core.encode_prompt_condition(clip=xl_base.clip, prompt=negative_prompt)
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positive_conditions_refiner = core.encode_prompt_condition(clip=xl_refiner.clip, prompt=positive_prompt)
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negative_conditions_refiner = core.encode_prompt_condition(clip=xl_refiner.clip, prompt=negative_prompt)
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positive_conditions = core.encode_prompt_condition(clip=xl_base_patched.clip, prompt=positive_prompt)
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negative_conditions = core.encode_prompt_condition(clip=xl_base_patched.clip, prompt=negative_prompt)
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empty_latent = core.generate_empty_latent(width=width, height=height, batch_size=1)
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sampled_latent = core.ksampler_with_refiner(
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model=xl_base.unet,
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positive=positive_conditions,
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negative=negative_conditions,
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refiner=xl_refiner.unet,
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refiner_positive=positive_conditions_refiner,
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refiner_negative=negative_conditions_refiner,
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refiner_switch_step=switch,
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latent=empty_latent,
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steps=steps, start_step=0, last_step=steps, disable_noise=False, force_full_denoise=True,
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seed=image_seed,
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callback_function=callback
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)
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if xl_refiner is not None:
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decoded_latent = core.decode_vae(vae=xl_refiner.vae, latent_image=sampled_latent)
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positive_conditions_refiner = core.encode_prompt_condition(clip=xl_refiner.clip, prompt=positive_prompt)
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negative_conditions_refiner = core.encode_prompt_condition(clip=xl_refiner.clip, prompt=negative_prompt)
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sampled_latent = core.ksampler_with_refiner(
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model=xl_base_patched.unet,
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positive=positive_conditions,
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negative=negative_conditions,
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refiner=xl_refiner.unet,
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refiner_positive=positive_conditions_refiner,
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refiner_negative=negative_conditions_refiner,
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refiner_switch_step=switch,
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latent=empty_latent,
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steps=steps, start_step=0, last_step=steps, disable_noise=False, force_full_denoise=True,
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seed=image_seed,
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callback_function=callback
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)
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else:
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sampled_latent = core.ksampler(
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model=xl_base_patched.unet,
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positive=positive_conditions,
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negative=negative_conditions,
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latent=empty_latent,
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steps=steps, start_step=0, last_step=steps, disable_noise=False, force_full_denoise=True,
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seed=image_seed,
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callback_function=callback
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)
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decoded_latent = core.decode_vae(vae=xl_base_patched.vae, latent_image=sampled_latent)
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images = core.image_to_numpy(decoded_latent)
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@ -5,3 +5,35 @@ lorafile_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../mode
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temp_outputs_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../outputs/'))
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os.makedirs(temp_outputs_path, exist_ok=True)
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default_base_model_name = 'sd_xl_base_1.0_0.9vae.safetensors'
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default_refiner_model_name = 'sd_xl_refiner_1.0_0.9vae.safetensors'
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default_lora_name = 'sd_xl_offset_example-lora_1.0.safetensors'
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default_lora_weight = 0.5
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model_filenames = []
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lora_filenames = []
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def get_model_filenames(folder_path):
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if not os.path.isdir(folder_path):
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raise ValueError("Folder path is not a valid directory.")
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filenames = []
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for filename in os.listdir(folder_path):
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if os.path.isfile(os.path.join(folder_path, filename)):
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_, file_extension = os.path.splitext(filename)
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if file_extension.lower() in ['.pth', '.ckpt', '.bin', '.safetensors']:
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filenames.append(filename)
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return filenames
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def update_all_model_names():
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global model_filenames, lora_filenames
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model_filenames = get_model_filenames(modelfile_path)
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lora_filenames = get_model_filenames(lorafile_path)
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return
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update_all_model_names()
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@ -2,7 +2,7 @@
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styles = [
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{
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"name": "sai-base",
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"name": "None",
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"prompt": "{prompt}",
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"negative_prompt": ""
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},
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@ -529,7 +529,7 @@ styles = [
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]
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styles = {k['name']: (k['prompt'], k['negative_prompt']) for k in styles}
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default_style = styles['sai-base']
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default_style = styles['None']
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style_keys = list(styles.keys())
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@ -1,3 +1,7 @@
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### 1.0.19
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* Unlock to allow changing model.
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### 1.0.17
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* Change default model to SDXL-1.0-vae-0.9. (This means the models will be downloaded again, but we should do it as early as possible so that all new users only need to download once. Really sorry for day-0 users. But frankly this is not too late considering that the project is just publicly available in less than 24 hours - if it has been a week then we will prefer more lightweight tricks to update.)
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45
webui.py
45
webui.py
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@ -1,16 +1,23 @@
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import gradio as gr
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import modules.path
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import random
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import fooocus_version
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import modules.default_pipeline as pipeline
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from modules.sdxl_styles import apply_style, style_keys, aspect_ratios
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from modules.default_pipeline import process
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from modules.cv2win32 import close_all_preview, save_image
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from modules.util import generate_temp_filename
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from modules.path import temp_outputs_path
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def generate_clicked(prompt, negative_prompt, style_selction, performance_selction,
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aspect_ratios_selction, image_number, image_seed, progress=gr.Progress()):
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aspect_ratios_selction, image_number, image_seed, base_model_name, refiner_model_name,
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l1, w1, l2, w2, l3, w3, l4, w4, l5, w5, progress=gr.Progress()):
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loras = [(l1, w1), (l2, w2), (l3, w3), (l4, w4), (l5, w5)]
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pipeline.refresh_base_model(base_model_name)
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pipeline.refresh_refiner_model(refiner_model_name)
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pipeline.refresh_loras(loras)
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p_txt, n_txt = apply_style(style_selction, prompt, negative_prompt)
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@ -35,10 +42,10 @@ def generate_clicked(prompt, negative_prompt, style_selction, performance_selcti
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progress(float(done_steps) / float(all_steps), f'Step {step}/{total_steps} in the {i}-th Sampling')
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for i in range(image_number):
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imgs = process(p_txt, n_txt, steps, switch, width, height, seed, callback=callback)
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imgs = pipeline.process(p_txt, n_txt, steps, switch, width, height, seed, callback=callback)
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for x in imgs:
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local_temp_filename = generate_temp_filename(folder=temp_outputs_path, extension='png')
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local_temp_filename = generate_temp_filename(folder=modules.path.temp_outputs_path, extension='png')
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save_image(local_temp_filename, x)
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seed += 1
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@ -61,21 +68,45 @@ with block:
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with gr.Row():
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advanced_checkbox = gr.Checkbox(label='Advanced', value=False, container=False)
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with gr.Column(scale=0.5, visible=False) as right_col:
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with gr.Tab(label='Generator Setting'):
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with gr.Tab(label='Setting'):
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performance_selction = gr.Radio(label='Performance', choices=['Speed', 'Quality'], value='Speed')
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aspect_ratios_selction = gr.Radio(label='Aspect Ratios (width × height)', choices=list(aspect_ratios.keys()),
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value='1152×896')
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image_number = gr.Slider(label='Image Number', minimum=1, maximum=32, step=1, value=2)
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image_seed = gr.Number(label='Random Seed', value=-1, precision=0)
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negative_prompt = gr.Textbox(label='Negative Prompt', show_label=True, placeholder="Type prompt here.")
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with gr.Tab(label='Image Style'):
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with gr.Tab(label='Style'):
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style_selction = gr.Radio(show_label=False, container=True,
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choices=style_keys, value='cinematic-default')
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with gr.Tab(label='Advanced'):
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with gr.Row():
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base_model = gr.Dropdown(label='SDXL Base Model', choices=modules.path.model_filenames, value=modules.path.default_base_model_name, show_label=True)
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refiner_model = gr.Dropdown(label='SDXL Refiner', choices=['None'] + modules.path.model_filenames, value=modules.path.default_refiner_model_name, show_label=True)
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with gr.Accordion(label='LoRAs', open=True):
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lora_ctrls = []
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for i in range(5):
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with gr.Row():
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lora_model = gr.Dropdown(label=f'SDXL LoRA {i+1}', choices=['None'] + modules.path.lora_filenames, value=modules.path.default_lora_name if i == 0 else 'None')
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lora_weight = gr.Slider(label='Weight', minimum=-2, maximum=2, step=0.01, value=modules.path.default_lora_weight)
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lora_ctrls += [lora_model, lora_weight]
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model_refresh = gr.Button(label='Refresh', value='Refresh All Files', variant='secondary')
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def model_refresh_clicked():
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modules.path.update_all_model_names()
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results = []
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results += [gr.update(choices=modules.path.model_filenames), gr.update(choices=['None'] + modules.path.model_filenames)]
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for i in range(5):
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results += [gr.update(choices=['None'] + modules.path.lora_filenames), gr.update()]
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return results
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model_refresh.click(model_refresh_clicked, [], [base_model, refiner_model] + lora_ctrls)
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advanced_checkbox.change(lambda x: gr.update(visible=x), advanced_checkbox, right_col)
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ctrls = [
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prompt, negative_prompt, style_selction,
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performance_selction, aspect_ratios_selction, image_number, image_seed
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]
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ctrls += [base_model, refiner_model] + lora_ctrls
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run_button.click(fn=generate_clicked, inputs=ctrls, outputs=[gallery])
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block.launch(inbrowser=True)
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